Tootfinder

Exercising muscles pumps out substances that can suppress the growth of breast cancer cells,
according to an important new study of exercise and cancer.

The study, published last month, involved 32 women who’d survived breast cancer.
After a single session of interval training or weightlifting, their blood contained higher levels of certain molecules,
and those factors helped put the brakes on laboratory-grown breast cancer cells

A single exercise session may slow cancer cell growth, new study shows
Exercise may not only help prevent cancer but also could fight it at a cellular level.

World’s premier cancer institute faces crippling cuts and chaos
https://www.dailykos.com/stories/2025/7/11/2332701/-World-s-premier-cancer-institute-faces-crippling-cuts-and-chaos

Don't miss today's Metacurity for ongoing reports from hacker summer camp and all the other critical infosec developments you should know, including
--US feds clawed back $1 million from BlackSuit and Royal gangs,
--US seeks to recoup $1m from DPRK hackers,
--US charges four Ghanian nationals for romance scams and BECs,
--Personal data of 500K cervical cancer victims stolen,
--FCC issues new rules for subsea data cable security,
--Erlang/OTP vulnerab…

US feds clawed back $1 million from BlackSuit and Royal gangs
US seeks to recoup $1m from DPRK hackers, US charges four Ghanian nationals for romance scams and BECs, Personal data of 500K cervical cancer victims stolen, FCC issues new rules for subsea data cable security, Erlang/OTP vulnerability exploited in the wild against OT networks, much more

Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
Mobarak Abumohsen, Enrique Costa-Montenegro, Silvia Garc\'ia-M\'endez, Amani Yousef Owda, Majdi Owda
https://arxiv.org/abs/2508.06287

Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing…

As US regulators restrict Covid mRNA vaccines
and as independent vaccine advisers re-examine the shots, scientists fear that an unlikely target could be next: 👉cancer research.
Messenger RNA, or mRNA, vaccines have shown promise in treating and preventing cancers that have often been difficult to address -- such as pancreatic cancer, brain tumors and others.
🆘 But groundbreaking research could stall as federal and state officials target mRNA shots,
including ending fe…

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
Alyssa Unell, Noel C. F. Codella, Sam Preston, Peniel Argaw, Wen-wai Yim, Zelalem Gero, Cliff Wong, Rajesh Jena, Eric Horvitz, Amanda K. Hall, Ruican Rachel Zhong, Jiachen Li, Shrey Jain, Mu Wei, Matthew Lungren, Hoifung Poon
https://arxiv.org/abs/2509.07325…

CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatment recommendations is time-intensive, requires specialized expertise, and is prone to error. Advances in large language model (LLM) capabilities promise to reduce the time required to generate treatment recommendations and improve accuracy. We present an LLM agent-based approach to automatically generate guideline-con…

5-year-old boy had his cancer treatments interrupted with his deportation as his family files suit against ICE | The Independent
https://www.independent.co.uk/news/world/americas/us-politics/boy-cancer-treatment-deportation-ice-b2806522.html

Eating Plant-Based Can Reduce Cancer And Cardiometabolic Disease Risk, Says Study https://plantbasednews.org/lifestyle/health/eating-plant-based-reduce-disease-risk/

Eating Plant-Based Can Reduce Cancer And Cardiometabolic Disease Risk, Says Study
A study has found that following a healthy plant-based diet could reduce the risk of cancer and cardiometabolic diseases

In an internal Meta essay, an outgoing AI researcher says a "culture of fear", with performance reviews and layoffs, is spreading like "a metastatic cancer" (Kalley Huang/The Information)
https://www.theinformation.com/articles/me…

Meta AI Researcher Warns of ‘Metastatic Cancer’ Afflicting Company Culture
Over the past month, Meta Platforms has overhauled its stumbling efforts in artificial intelligence by making a head-spinning series of hires of leaders and researchers from outside the company. But a key question has lingered: Why has Meta struggled so badly in AI that it’s injecting so much ...

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Mohammad Abbadi, Yassine Himeur, Shadi Atalla, Wathiq Mansoor
https://arxiv.org/abs/2509.05004

Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study
Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classica…

Optical discrimination of live single cancer cells using reflection-based nanohole array sensor
Alfredo Franco, Izan Calder\'on, Dolores Ortiz, Jos\'e L. Fern\'andez-Luna, Fernando Moreno
https://arxiv.org/abs/2508.08205

Optical discrimination of live single cancer cells using reflection-based nanohole array sensor
In this research, a reflection-based nanohole array sensor system is presented for discriminating between migration-competent cancer cells that maintain the integrity of the actin cortex and those cells lacking the actin cortex and thus unable to migrate. Unlike previous transmission-based approaches, this configuration allows for more practical integration into in situ diagnostic tools. For the first time, the system performance is analyzed by studying the spectral features of the reflected li…

Series D, Episode 07 - Assassin
CANCER: You could give that little bitch a message for me. You can tell her I'll be saving her to the last. [Tarrant leaves]
[Control room]
AVON: Come on, woman, come on!
https://blake.torpidity.net/m/407/307 B7B2

"Compost Bacteria for Cancer Treatment: Scientists Develop Breakthrough Drug Delivery System"
#Vaccines #Health #Compost

Compost Bacteria for Cancer Treatment: Scientists Develop Breakthrough Drug Delivery System | Happy Eco News
Compost bacteria for cancer treatment represents a breakthrough discovery by Australian scientists who developed protein cages that deliver chemotherapy drugs directly to tumors. This targeted approach could eliminate devastating side effects like hair loss and nausea that plague current cancer patients, revolutionizing how doctors treat various cancers worldwide.

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
Changchun Yang, Haoyang Li, Yushuai Wu, Yilan Zhang, Yifeng Jiao, Yu Zhang, Rihan Huang, Yuan Cheng, Yuan Qi, Xin Guo, Xin Gao
https://arxiv.org/abs/2507.06418

PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
While pathology foundation models have transformed cancer image analysis, they often lack integration with molecular data at single-cell resolution, limiting their utility for precision oncology. Here, we present PAST, a pan-cancer single-cell foundation model trained on 20 million paired histopathology images and single-cell transcriptomes spanning multiple tumor types and tissue contexts. By jointly encoding cellular morphology and gene expression, PAST learns unified cross-modal representati…

Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Ojonugwa Oluwafemi Ejiga Peter, Daniel Emakporuena, Bamidele Dayo Tunde, Maryam Abdulkarim, Abdullahi Bn Umar
https://arxiv.org/abs/2508.06137

Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potential of CNNs for medical image analysis faces two limitations: they fail to process both local information and wide contextual data adequately, and do not provide explainable AI (XAI) operations that doctors need to accept them in clinics. The researcher developed the MammoFormer…

Circulating tumor cell detection in cancer patients using in-flow deep learning holography
Kevin Mallery, Nathaniel R. Bristow, Nicholas Heller, Yash Travadi, Ali Arafa, Kaylee Kamalanathan, Catalina Galeano-Garces, Mahdi Ahmadi, Grant Schaap, Alexa Hesch, Olivia Hedeen, Zikora Izuora, Joel Hapke, Jeffrey Miller, Arjun Viswanathan, Ivo Babris, Songyi Bae, Tuan Le, Tony Clacko, Emmanuel S. Antonarakis, Badrinath R. Konety, Justin M. Drake, Jiarong Hong

Circulating tumor cell detection in cancer patients using in-flow deep learning holography
Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow d…

Fears of brain drain grow as Trump cuts take a toll on UCLA
Research that was years in the making
— into diabetes, cancer, heart disease, strokes, PTSD and fighting antibiotic-resistant infections
— is on pause at UCLA, nearly a month after UCLA saw around $500 million in federal grants suspended.
While a federal judge ordered the restorationof 114 National Science Foundation grants in early August, grants from the National Institutes of Health remain suspended.
S…

Fears of brain drain grow as Trump cuts take a toll on UCLA
Several UCLA scientists tell LAist that their experiments on cancer, heart disease and other ailments are imperiled, but they’re more worried that young academics will abandon the field and that humanity will be worse off for it.

Somehow I only just found out that Jolly Ranchers were "banned" in the UK a month ago.
(They're not banned as such, but most shops won't sell products when informed they're not compliant with food standards. London's thousand "American candy" shops will still have them if you want the forbidden hydrocarbons.)

Several US Jolly rancher sweets unsafe to eat, FSA says
The FSA says chemical compounds in the sweets can increase the risk of cancer and damage DNA.

Yay cancer? https://defenseofliberty.social/@MalcolmNance/115174522856775509

Malcolm Nance (@MalcolmNance@defenseofliberty.social)
Attached: 1 image #russian #propagandist Simonyan diagnosed with cancer, — rosZMI. What do you say?

Ex-Texans, Eagles LB Braman in cancer treatment https://www.espn.com/nfl/story/_/id/45675050/former-texans-eagles-lb-bryan-braman-rare-form-cancer

Tahoe Therapeutics, which is building AI models of living cells, raised $30M led by Amplify Partners at a $120M valuation, taking its total funding to $42M (Alex Knapp/Forbes)
https://www.forbes.com/sites/alexknapp/202

Biotech Startup Tahoe Therapeutics Raised $30 Million To Build AI Models Of Living Cells
The Palo Alto, California-based company, now valued at $120 million, has developed a scalable way to quickly generate crucial biological data needed for AI models–and use them to find new cures for cancer.

Rock legend Bobby Whitlock dies after hidden struggle
#TDIM #RIP
https://rollingout.com/2025/08/10/bobb

Rock legend Bobby Whitlock dies after hidden struggle
Bobby Whitlock, co-founder of Derek and the Dominos and collaborator on Eric Clapton's "Layla," died at 77 after brief cancer battle at his Texas home.

Optimizing MV CBCT Imaging Protocols Using NTCP and Secondary Cancer Risk: A Multi-Site Study in Breast, Pelvic, and Head & Neck Radiotherapy
Thanh Tai Duong, Tien Phat Luong, Trung Kien Tran, Tuan Linh Duong, Ngoc Anh Nguyen, Quang Hung Nguyen, Peter Sandwall, Parham Alaei, David Bradley, James C. L. Chow
https://arxiv.org/abs/2508.05…

Optimizing MV CBCT Imaging Protocols Using NTCP and Secondary Cancer Risk: A Multi-Site Study in Breast, Pelvic, and Head & Neck Radiotherapy
Purpose: To evaluate the cumulative radiobiological impact of daily Megavoltage Cone-Beam Computed Tomography (MV-CBCT) imaging dose based on Normal Tissue Complication Probability (NTCP) and Excess Absolute Risk (EAR) of secondary malignancies among radiotherapy patients treated for breast, pelvic, and head and neck cancers. This study investigated whether MV-CBCT imaging dose warrants protocol personalization according to patient age, anatomical treatment site, and organ-specific radiosensiti…

Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials
James P. Long, Abhishikta Roy, Ehsan Irajizad, Kim-Anh Do, Yu Shen
https://arxiv.org/abs/2508.04633

Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials
In meta-analytic modeling, the functional relationship between a primary and surrogate endpoint is estimated using summary data from a set of completed clinical trials. Parameters in the meta-analytic model are used to assess the quality of the proposed surrogate. Recently, meta-analytic models have been employed to evaluate whether late-stage cancer incidence can serve as a surrogate for cancer mortality in cancer screening trials. A major challenge in meta-analytic models is that uncertainty …

Artificial sweeteners _may_ cause cancer.
Artificial Intelligence _may_ cause stupidity

Biden had procedure to remove skin cancer from forehead (Brett Samuels/The Hill)
https://thehill.com/policy/healthcare/5487236-biden-procedure-skin-cancer-forehead/
http://www.memeorandum.com/250904/p152#a250904p152

Differentiation of Wild-Type and CRISPR-Modified Colon Cancer Cells Using Brillouin Microscopy
Mykyta Kizilov, Vsevolod Cheburkanov, Vladislav V. Yakovlev
https://arxiv.org/abs/2507.05329

Differentiation of Wild-Type and CRISPR-Modified Colon Cancer Cells Using Brillouin Microscopy
This study investigates the mechanical properties of colon cancer cells through Brillouin microscopy, focusing on the differentiation between wild-type (WT) and CRISPR-modified cells. Brillouin microspectroscopy, a non-invasive technique, was employed to measure Brillouin shifts and full width at half maximum (FWHM) values of the cells in vitro. Using a custom-built confocal Brillouin microspectrometer, both WT and CRISPR-modified cells exhibited distinct mechanical responses. Statistical analy…

A Machine Learning Framework for Breast Cancer Treatment Classification Using a Novel Dataset
Md Nahid Hasan, Md Monzur Murshed, Md Mahadi Hasan, Faysal A. Chowdhury
https://arxiv.org/abs/2507.06243

A Machine Learning Framework for Breast Cancer Treatment Classification Using a Novel Dataset
Breast cancer (BC) remains a significant global health challenge, with personalized treatment selection complicated by the disease's molecular and clinical heterogeneity. BC treatment decisions rely on various patient-specific clinical factors, and machine learning (ML) offers a powerful approach to predicting treatment outcomes. This study utilizes The Cancer Genome Atlas (TCGA) breast cancer clinical dataset to develop ML models for predicting the likelihood of undergoing chemotherapy or horm…

Product idea: condensed cancer cells in inhaler. They let you achieve the final goal much faster, without having to poison yourself and everyone around you for years.

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

Estimating breast cancer recurrence in a population-based registry in Georgia, US
Chrystelle Kiang, Micah Streiff, Rebecca Nash, Robert H. Lyles, Deirdre Cronin-Fenton, Anke Huels, Timothy L. Lash, Kevin C. Ward
https://arxiv.org/abs/2508.04649

Estimating breast cancer recurrence in a population-based registry in Georgia, US
Although the descriptive epidemiology of primary breast cancer is well characterized in the US, breast cancer recurrence rates have not been measured in an unselected population. The number of breast cancer survivors at risk for recurrence is growing each year, so recurrence surveillance is a pressing need. We used missing data methods to impute breast cancer recurrence and estimate the risk of recurrence in the Cancer Recurrence Information and Surveillance Program (CRISP) cohort in the Georgi…

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
Xinglong Liang, Jiaju Huang, Luyi Han, Tianyu Zhang, Xin Wang, Yuan Gao, Chunyao Lu, Lishan Cai, Tao Tan, Ritse Mann
https://arxiv.org/abs/2507.07126

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morphology, and interference from physiological high-metabolic signals. Current mainstream approaches follow the practice of one network solving the segmentation of multiple cancer lesions by treating all cancers as a single task. However, this overlooks the unique characteristics of different cancer types. Considering the specificity and similarity of different cancers in terms of metastatic patterns,…

Quantifying the Impact of Epigallocatechin Gallate and Piperine on D. tigrina Regeneration
Vinil Polepalli
https://arxiv.org/abs/2509.06972 https://arxiv.o…

Quantifying the Impact of Epigallocatechin Gallate and Piperine on D. tigrina Regeneration
The increasing global cancer burden necessitates exploration of effective and affordable treatments. Epigallocatechin gallate (EGCG), a green tea catechin, and piperine, a black pepper alkaloid, have demonstrated promising anti-cancer properties. Leveraging the regenerative capacity of Dugesia tigrina (planaria) and their neoblast stem cells as a model for cancer growth, this study investigated the combined effects of EGCG and piperine on cell proliferation. Planaria were exposed to varying con…

@… My mother died of pancreatic cancer, too, as did her sister. I agree with everything you've posted.

Phase Transitions in a Particle Model for the Self-Adaptive Response to Cancer Dynamics
Christian Kuehn, Quirin Vogel
https://arxiv.org/abs/2508.03577 https://

Phase Transitions in a Particle Model for the Self-Adaptive Response to Cancer Dynamics
In this paper, we present a probabilistic analysis of a dynamical particle model for the self-adaptive immune response to cancer, as proposed by the first author in a previous work. The model is motivated by the interplay between immune surveillance and cancer evolution. We rigorously confirm the sharp phase transition in immune system learning predicted in the original work. Additionally, we compute the expected amount of information acquired by the immune system about cancer cells over time. …

🦀 Cancer risk doubles for Iowa, NC pesticide applicators who used carbaryl, study finds
#poison

Cancer risk doubles for Iowa, NC pesticide applicators who used carbaryl, study finds
By Brian Bienkowski Pesticide applicators in North Carolina and Iowa who sprayed the insecticide carbaryl often during their career have a higher risk of getting stomach, esophageal, tongue and prostate cancers, according to new research led by the National Cancer Institute (NCI). 

Little update: the meds they gave him made him feel _much_ better and he started to eat well again so we should be able to get his weight up (he got really skinny).
We won't know if it's cancer or not for a while, but if it is we will definitely not try to draw it out with any painful and unlikely to work therapy for it and instead make sure he feels well.

Did you know that there is a statistically significant correlation between not getting shot, and dying of cancer?
I guess gun control must cause cancer huh.

Supreme Court ruling puts some vaccines at risk
Public health advocates won a big case in the Supreme Court on the last day of this year’s term
-- but the victory came with an asterisk.
The decision ended one threat to the no-cost preventive services
— from cancer and diabetes screenings to statin drugs and vaccines
— used by more than 150 million Americans who have health insurance.
⚠️ But it did so by empowering the nation’s foremost vaccine skeptic: …

Randy Moss completes cancer treatment, details conversation with Deion Sanders amid coach's battle

https://www.cbssports.com/nfl/news/randy-m

"The Neuroscience of Cancer
Scientists are making surprising discoveries about the connections between cancer and the nervous system"
https://magazine.hms.harvard.edu/articles/neuroscience-cancer

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Vinil Polepalli
https://arxiv.org/abs/2508.02743 https://arxiv.org/pdf…

A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification
Pan-cancer classification using transcriptomic (RNA-Seq) data can inform tumor subtyping and therapy selection, but is challenging due to extremely high dimensionality and limited sample sizes. In this study, we propose a novel deep learning framework that uses a class-conditional variational autoencoder (cVAE) to augment training data for pan-cancer gene expression classification. Using 801 tumor RNA-Seq samples spanning 5 cancer types from The Cancer Genome Atlas (TCGA), we first perform feat…

Just made aware #DavidRoach, the singer of #Junkyard, has left the building after a long, tough battle with cancer #RIP.
On their first couple releases, his band whose guitar flank had members of the Big Boys and Minor Threat …

And just like in humans, long-term sun damage can lead to skin cancer.
"Animals can also get skin cancer. Cancer in general among animals is quite common," says Mejdell.
https://www.sciencenorway.no/can-animals-also-get-sunburned/2533345

Can animals also get sunburned?
Sometimes animals also need to be protected with sunscreen.

Another case of death by alternative medicine. Anyone who recommends the Gerson method should be in the dock, IMO.
https://

My sister died after refusing cancer treatment. Now I want to get justice for her – and hold my mum to account
Gabriel Shemirani blames his mother’s conspiracy theories for the death of his twin Paloma at age 23. As the inquest nears a verdict, he talks to Marianna Spring about his campaign against health misinformation

Survival Analysis with Discrete Biomarkers Under a Semiparametric Bayesian Conditional Poisson Model
Aijun Yang, Phineas T. Hamilton, Brad H. Nelson, Julian J. Lum, Mary Lesperance, Farouk S. Nathoo
https://arxiv.org/abs/2509.08162

Survival Analysis with Discrete Biomarkers Under a Semiparametric Bayesian Conditional Poisson Model
Discrete biomarkers derived as cell densities or counts from tissue microarrays and immunostaining are widely used to study immune signatures in relation to survival outcomes in cancer. Although routinely collected, these signatures are not measured with exact precision because the sampling mechanism involves examination of small tissue cores from a larger section of interest. We model these error-prone biomarkers as Poisson processes with latent rates, inducing heteroscedasticity in their cond…

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
Dongchen Li (College of Computer Science and Engineering, Northeastern University, Shenyang, China), Jitao Liang (College of Computer Science and Engineering, Northeastern University, Shenyang, China), Wei Li (College of Computer Science and Engineering, Northeastern University, Shenyang, China), Xiaoyu Wang (Liaoning Cancer Hospital and Institute, She…

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records
Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and multilingual nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techni…

Cowboys legend Deion Sanders reveals cancer diagnosis, removal of bladder over offseason https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/07/28/cowboys-deion-sanders-cancer-diagnosis-bladder-remo…

Coal is linked to cancer
#CoalKills https://cleantechnica.com/2025/08/18/coal-is-linked-to-cancer-not-to-wind-power/

It feels as if the scammers don't even try anymore:
Hello, my dearest
My name is Mrs. Theresa Brown. I am a dying woman, and I have decided to donate what I have to a reliable individual who will act as I was told.
I am 69 years old and was diagnosed with cancer about 4 years ago. God has touched me to donate what I have inherited from my late father to you for the good work of God rather than allow my uncle to use my father's hard-earned funds ungodly.
I will …

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Emerson P. Grabke, Babak Taati, Masoom A. Haider
https://arxiv.org/abs/2507.06384

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation. The recent CCELLA LDM improved prostate cancer detection performance using synthetic MRI for classifier training but was limited to the axial T2-weighted (AxT2) sequence, did not investigate inter-institutional domain shift, and prioritized radiology over histopathology outcomes. We propose CCELLA++ to address these limitations and improve cl…

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
Yinghan Li, Yilin Yao, Junghua Lin, Nanxi Wang
https://arxiv.org/abs/2506.21927

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The data…

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Trixia Simangan, Ahmed Nadeem Abbasi, Yipeng Hu, Shaheer U. Saeed
https://arxiv.org/abs/2509.04886

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its success depends on accurate preoperative planning of cryoprobe placements to fully cover the tumour and avoid critical anatomy. This planning is currently manual, expertise-dependent, and time-consuming, leading to variability in treatment quality and limited scalability. In this work, we introduce Cryo-RL, a reinforcem…

Targeting Melanoma-Specific Tyrosinase: Cyclic Peptide Disrupts Actin Dynamics for Precision Apoptosis Induction
Ruoyang Zhao, Xiaowei Wang, Jiajia Hu, Qingqing Sun, Xinmin Zhao, Jun Guo, Feng Zhang, Min Wu
https://arxiv.org/abs/2507.06534

Targeting Melanoma-Specific Tyrosinase: Cyclic Peptide Disrupts Actin Dynamics for Precision Apoptosis Induction
Melanoma is an aggressive and highly metastatic cancer that exhibits stubborn resistance to conventional therapies, highlighting the need for novel treatments. Existing therapeutic strategies often suffer from systemic toxicity, poor efficacy and fast-gained drug resistance. In this study, we designed a cyclic peptide system (c-RGDKYQ) that takes the advantage of the overexpression of tyrosinase in melanoma cells to trigger enzyme-mediated oxidation and self-assembly. The assembled peptide nano…

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Noorul Wahab, Ethar Alzaid, Jiaqi Lv, Adam Shephard, Shan E Ahmed Raza
https://arxiv.org/abs/2509.05037

MultiSurv: A Multimodal Deep Survival Framework for Prostrate and Bladder Cancer
Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present MultiSurv, a multimodal deep survival model utilising DeepHit with a projection layer and inter-modality cross-attention, which integrates heterogeneous patient data, including clinical, MRI, RNA-seq and whole-slide pathology features. The model is designed to capture complementary prognostic signals across modali…

Please consider supporting Sofia's Camera Club ride for pancreatic cancer up Cypress https://cypresschallenge.ca/team/scc This will be our 8th year. @… join…

Walter Scott Jr. of The Whispers Dead at 81 After Battle With Cancer
#RIP #TDIM
https://…

Walter Scott Jr. of The Whispers Dead at 81 After Battle With Cancer
Walter Scott's death has been announced. The Whispers member is dead after a cancer battle.

Joe Biden undergoes surgery to remove skin cancer on forehead (Chris Megerian/Associated Press)
https://apnews.com/article/joe-biden-skin-cancer-surgery-mohs-forehead-a9a10a03c713d817b85e28b6132ae86a
http://www.memeorandum.com/250904/p139#a250904p139

RFK Advisor Claims "COVID Jab" Gave UK Royals Cancer - Joe.My.God.
https://www.joemygod.com/2025/09/rfk-advisor-claims-covid-jab-gave-uk-royals-cancer/

Invisible invaders:
unveiling the carcinogenic threat of microplastics and nanoplastics in colorectal cancer
Key findings reveal that MicroPlastics/NanoPlastics,
via ingestion, inhalation, and dermal contact,
translocate across biological barriers to induce DNA damage, oxidative stress, and intestinal barrier disruption by reducing tight junction proteins,
while triggering chronic inflammation through pro-inflammatory cytokines and gut microbiota dysbiosis.

Frontiers | Invisible invaders: unveiling the carcinogenic threat of microplastics and nanoplastics in colorectal cancer-a systematic review
ObjectiveMicroplastics (MPs, 0.1–5000 μm) and nanoplastics (NPs, 0.001–0.1 μm) are ubiquitous environmental pollutants with strong persistence and bioaccumul...

Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
Rebeka Sultana, Hibiki Horibe, Tomoaki Murakami, Ikuko Shimizu
https://arxiv.org/abs/2507.03325

Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
Hematoxylin and Eosin (H&E)-stained images are commonly used to detect nuclear or cancerous regions in cells from images captured by a microscope. Identifying cancer cytoplasm is crucial for determining the type of cancer; hence, obtaining accurate cancer cytoplasm regions in cell images is important. While CMOS images often lack detailed information necessary for diagnosis, hyperspectral images provide more comprehensive cell information. Using a deep learning model, we propose a method for de…

Harnessing Topological Valley-Hall States in Photonic Crystals for Robust Cancer Cell Detection
Junayet Hossain, Sajid Muhaimin Choudhury, Mohammed Imamul Hassan Bhuiyan
https://arxiv.org/abs/2509.05690

Harnessing Topological Valley-Hall States in Photonic Crystals for Robust Cancer Cell Detection
Topological photonic crystals (TPhCs) are the photonic analogs of topological insulators, inspired by the quantum Hall effect and its variants, including the integer, spin, and valley Hall effects. Among these, valley-hall photonic crystals (VPhCs) achieve topological protection by breaking inversion symmetry, making them closely related to valleytronics. Like other TPhCs, VPhCs enable robust, backscattering-free light propagation, even in the presence of structural imperfections, sharp bends, …

Series D, Episode 07 - Assassin
VILA: Auction! You're joking.
DAYNA: All right, so Domo is a planet, the ninth is a date, but who or what is Cancer?
SOOLIN: I don't know.
TARRANT: He knows, though, [looking at Avon] don't you? You know about Cancer.
https://blake.torpidity.net/m/407/13

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Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Former Eagles' Super Bowl champion battling rare form of cancer: Bryan Braman is 'in the fight of his life'

https://www.cbssports.com/…

Send your good thoughts to Watson, who is now an older cat and has pancreatitis and possibly cancer. Here’s a photo of when we got him long ago.

An interview with Dr. Raj Jena, the UK's first clinical professor of AI in radiation oncology, on how AI is improving cancer care, quality control, and more (Sarah Neville/Financial Times)
https://www.ft.com/content/fcc33754-d583-4302-aaff-de9c09783706

"Immunologists at UNIGE have revealed a previously unknown anti-tumor role of CD4 T lymphocytes, marking a breakthrough that could pave the way for a new generation of immunotherapies."
Scientists Turn Forgotten Cells Into Powerful Cancer Killers
https://scitechdaily.com/scientists-turn-forgotten-cells-into-powerful-cancer-killers/

Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
Hao Peng, Yuanyuan Zhang, Steve Jiang, Robert Timmerman, John Minna
https://arxiv.org/abs/2508.08030

Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
Radiation response in cancer is shaped by complex, patient specific biology, yet current treatment strategies often rely on uniform dose prescriptions without accounting for tumor heterogeneity. In this study, we introduce a meta learning framework for one-shot prediction of radiosensitivity measured by SF2 using cell line level gene expression data. Unlike the widely used Radiosensitivity Index RSI a rank-based linear model trained on a fixed 10-gene signature, our proposed meta-learned model …

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Garazi Retegui, Jaione Etxeberria, Mar\'ia Dolores Ugarte
https://arxiv.org/abs/2507.21714

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly inte…

On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Jacqueline J. Vallon, William Overman, Wanqiao Xu, Neil Panjwani, Xi Ling, Sushmita Vij, Hilary P. Bagshaw, John T. Leppert, Sumit Shah, Geoffrey Sonn, Sandy Srinivas, Erqi Pollom, Mark K. Buyyounouski, Mohsen Bayati
https://arxiv.org/abs/2509.0…

On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture patterns the end user requires. For example, a model may predict a non-monotonically decreasing relationship between cancer stage and survival, keeping all other features fixed. In this paper, we present a reproducible framework for investigating this misalignment between model behavior and clinical experiential learning…

Most Americans still don’t know that alcohol can cause cancer — and the alcohol industry is working hard to make sure it stays that way.
For the past three years, the industry,
aided by its allies in Congress and later the Trump administration,
has sought to discredit and eventually bury a major analysis
that offers new evidence of the link between drinking alcohol and getting sick and dying from various causes, including cancer.
It appears their campaign has succ…

Exclusive: RFK Jr. and the White House buried a major study on alcohol and cancer. Here’s what it shows.
Scientists provided the Trump administration with evidence of alcohol’s harms. Here’s why you still haven’t seen this new study.

Coach Deion Sanders' return to Colorado lifts team spirits after his battle with bladder cancer https://www.foxsports.com/articles/cfb/coach-deion-sanders-return-to-colorado-lifts-team-spirits-after-his-battl…

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients
Qilong Xing, Zikai Song, Bingxin Gong, Lian Yang, Junqing Yu, Wei Yang
https://arxiv.org/abs/2507.06994

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients
Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these challenges, we present a large-scale dataset and introduce a novel framework for multi-modal feature fu…

Please watch this excellent TV documentary. The anti-medical lunatics are now in charge in the USA -RFK Jr is not just an ill-educated crank. He's killing people.
https://www.bbc.co.uk/iplayer/episode/m002f2c4/panoram…

Panorama - Cancer Conspiracy Theories: Why Did Our Sister Die?
Marianna Spring investigates the rise of health conspiracy theories. Are social media companies’ algorithms pushing potentially harmful content at vulnerable patients?

Wrist bone segmentation in X-ray images using CT-based simulations
Youssef ElTantawy, Alexia Karantana, Xin Chen
https://arxiv.org/abs/2507.07131 https://

Wrist bone segmentation in X-ray images using CT-based simulations
Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains challenging. Deep-learning-based methods have achieved superior performance in medical image segmentation tasks but often require a large amount of high-quality annotated data for model training. Providing such an annotated dataset is not only time-consuming …

GloBIAS: strengthening the foundations of BioImage Analysis
A. A. Corbat (BioImage Informatics Unit, Science for Life Laboratory and Department of Information Technology, Uppsala University, Sweden), C. G. Walther (German BioImaging, Gesellschaft f\"ur Mikroskopie und Bildanalyse e.V., Konstanz, Germany, University of Vienna, Vienna, Austria), L. R. de la Ballina (Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebe…

GloBIAS: strengthening the foundations of BioImage Analysis
There is a global need for BioImage Analysis (BIA) as advances in life sciences increasingly rely on cutting-edge imaging systems that have dramatically expanded the complexity and dimensionality of biological images. Turning these data into scientific discoveries requires people with effective data management skills and knowledge of state-of-the-art image processing and data analysis, in other words, BioImage Analysts. The Global BioImage Analysts' Society (GloBIAS) aims to enhance the profile…

Emerging AI Approaches for Cancer Spatial Omics
Javad Noorbakhsh, Ali Foroughi pour, Jeffrey Chuang
https://arxiv.org/abs/2506.23857 https://

Emerging AI Approaches for Cancer Spatial Omics
Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity which demands not only improved data integration, but also n…

MAGA Radio Host's Cancer-Stricken Wife Can't Get COVID Shot Because of 'Kook' RFK Jr. (Jack Silvers/The Daily Beast)
https://www.thedailybeast.com/maga-radio-host-erick-ericksons-cancer-stricken-wife-cant-get-covid-shot-because-of-kook-rfk-jr/
http://www.memeorandum.com/250830/p38#a250830p38

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
S M Taslim Uddin Raju, Md. Milon Islam, Md Rezwanul Haque, Hamdi Altaheri, Fakhri Karray
https://arxiv.org/abs/2507.07006

GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial tasks in computer-aided pathology. However, microscopic WSI face challenges such as redundant patches and unknown patch positions due to subjective pathologist captures. Moreover, generating automatic pathology captions remains a significant challenge. To address these issues, we introduce a novel GNN-ViTCap framework for…

Love how no one is wearing masks, not even in medical offices https://www.usatoday.com/story/life/health-wellness/2025/07/30/flu-covid-sleeping-cancer-cells/85418692007/

Hall of Famer Randy Moss set to return to NFL television gig after missing time due to cancer treatment

https://www.cbssports.com/nfl/news/hall-of…

UChicago researchers have developed a new liquid biopsy test that uses RNA modifications to detect early-stage colorectal cancer with 95% accuracy with a simple blood draw
https://biologicalsciences.uchicago.edu/news/liquid-biopsy-rna-cancer

Innovative liquid biopsy test uses RNA to detect early-stage cancer
UChicago researchers have developed a new liquid biopsy test that uses RNA modifications to detect early-stage colorectal cancer with 95% accuracy.

Kennedy considering firing members of preventive services task force (Berkeley Lovelace Jr/NBC News)
https://www.nbcnews.com/health/cancer/rfk-jr-considering-firing-task-force-cancer-screening-rcna221188
http://www.memeorandum.com/250726/p4#a250726p4

Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland, Melisa S. Guelen, Jay Patel, Dania Daye, Jean-Philippe Thiran, Albert E. Kim, Christopher P. Bridge
https://arxiv.org/abs/2509.00900

Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest performance and do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011. To address this unmet need, we present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer ri…

Model Compression Engine for Wearable Devices Skin Cancer Diagnosis
Jacob M. Delgado-L\'opez, Andrea P. Seda-Hernandez, Juan D. Guadalupe-Rosado, Luis E. Fernandez Ramirez, Miguel Giboyeaux-Camilo, Wilfredo E. Lugo-Beauchamp
https://arxiv.org/abs/2507.17125

Model Compression Engine for Wearable Devices Skin Cancer Diagnosis
Skin cancer is one of the most prevalent and preventable types of cancer, yet its early detection remains a challenge, particularly in resource-limited settings where access to specialized healthcare is scarce. This study proposes an AI-driven diagnostic tool optimized for embedded systems to address this gap. Using transfer learning with the MobileNetV2 architecture, the model was adapted for binary classification of skin lesions into "Skin Cancer" and "Other." The TensorRT framework was emplo…

Pediatric Brain Cancer Trial Group to Be Phased Out (Nina Agrawal/New York Times)
https://www.nytimes.com/2025/08/28/well/pediatric-brain-cancer-trial-group.html?unlocked_article_code=1.h08.elWM.RbvuJbwbKWb2&smid=url-share
http://www.memeorandum.com/250828/p134#a250828p134

Jaguars' Hines-Allen: Son nearly cancer-free https://www.espn.com/nfl/story/_/id/45824114/jaguars-josh-hines-allen-son-doing-well-cancer-foundation-set-up

Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Population-Based Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
Anindo Saha (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Joeran S. Bosma (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Jasper J. Twilt (on behalf of the PI-CAI, ProCAncer-I, COMFORT, STHLM3-MRI,PRIME consortia), Alexander B. C. D. Ng (on behalf of …

Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Population-Based Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
In this intercontinental, confirmatory study, we include a retrospective cohort of 22,481 MRI examinations (21,288 patients; 46 cities in 22 countries) to train and externally validate the PI-CAI-2B model, i.e., an efficient, next-generation iteration of the state-of-the-art AI system that was developed for detecting Gleason grade group $\geq$2 prostate cancer on MRI during the PI-CAI study. Of these examinations, 20,471 cases (19,278 patients; 26 cities in 14 countries) from two EU Horizon pro…

A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs
Niccol\`o McConnell, Pardeep Vasudev, Daisuke Yamada, Daryl Cheng, Mehran Azimbagirad, John McCabe, Shahab Aslani, Ahmed H. Shahin, Yukun Zhou, The SUMMIT Consortium, Andre Altmann, Yipeng Hu, Paul Taylor, Sam M. Janes, Daniel C. Alexander, Joseph Jacob

A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs
Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility …

"When is cancer political?" Medical researchers, patients decry Trump admin's layoffs, budget cuts - CBS News
https://www.cbsnews.com/news/when-is-cancer-political-medical-researchers-patients-decry-trump-admins-layoffs-budget-cuts/

Countries on track to eliminate cervical cancer:
-Australia by 2028
-Sweden by 2027
-Rwanda by 2030
-UK by 2040
-Indonesia by 2030
-Bhutan 2030
-El Salvador by 2030
-Canada by 2040
How? HPV vaccine. Same one RFK Jr is trying to ban.
You will note USA is not on this list.
https://…

Hybrid-View Attention for csPCa Classification in TRUS
Zetian Feng, Juan Fu, Xuebin Zou, Hongsheng Ye, Hong Wu, Jianhua Zhou, Yi Wang
https://arxiv.org/abs/2507.03421

Hybrid-View Attention for csPCa Classification in TRUS
Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, and accurate identification of clinically significant PCa (csPCa) is critical for timely intervention. Transrectal ultrasound (TRUS) is widely used for prostate biopsy; however, its low contrast and anisotropic spatial resolution pose diagnostic challenges. To address these limitations, we propose a novel hybrid-view attention (HVA) network for csPCa classification in 3D TRUS that leverages complementary information fr…

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldshtein
https://arxiv.org/abs/2506.12190

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized…

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Peyman Sharifian, Xiaotong Hong, Alireza Karimian, Mehdi Amini, Hossein Arabi
https://arxiv.org/abs/2507.06410

Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Breast density assessment is a crucial component of mammographic interpretation, with high breast density (BI-RADS categories C and D) representing both a significant risk factor for developing breast cancer and a technical challenge for tumor detection. This study proposes an automated deep learning system for robust binary classification of breast density (low: A/B vs. high: C/D) using the VinDr-Mammo dataset. We implemented and compared four advanced convolutional neural networks: ResNet18, …

Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder
Biagio Brattoli, Jack Shi, Jongchan Park, Taebum Lee, Donggeun Yoo, Sergio Pereira
https://arxiv.org/abs/2508.02431

Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder
Identifying actionable driver mutations in non-small cell lung cancer (NSCLC) can impact treatment decisions and significantly improve patient outcomes. Despite guideline recommendations, broader adoption of genetic testing remains challenging due to limited availability and lengthy turnaround times. Machine Learning (ML) methods for Computational Pathology (CPath) offer a potential solution; however, research often focuses on only one or two common mutations, limiting the clinical value of the…

Classification based deep learning models for lung cancer and disease using medical images
Ahmad Chaddad, Jihao Peng, Yihang Wu
https://arxiv.org/abs/2507.01279

Classification based deep learning models for lung cancer and disease using medical images
The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the established ResNet framework. This model is specifically designed to improve the prediction of lung cancer and diseases using the images. To address the challenge of missing feature information that occurs during the downsampling process in CNNs, we integrate …

Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
Soumen Ghosh, Christine Jestin Hannan, Rajat Vashistha, Parveen Kundu, Sandra Brosda, Lauren G. Aoude, James Lonie, Andrew Nathanson, Jessica Ng, Andrew P. Barbour, Viktor Vegh
https://

Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
Robust generalization is essential for deploying deep learning based tumor segmentation in clinical PET-CT workflows, where anatomical sites, scanners, and patient populations vary widely. This study presents the first cross cancer evaluation of nnU-Net on PET-CT, introducing two novel, expert-annotated whole-body datasets. 279 patients with oesophageal cancer (Australian cohort) and 54 with lung cancer (Indian cohort). These cohorts complement the public AutoPET dataset and enable systematic s…

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
Souradeep Chakraborty, Ruoyu Xue, Rajarsi Gupta, Oksana Yaskiv, Constantin Friedman, Natallia Sheuka, Dana Perez, Paul Friedman, Won-Tak Choi, Waqas Mahmud, Beatrice Knudsen, Gregory Zelinsky, Joel Saltz, Dimitris Samaras
https://

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
The ability to predict the attention of expert pathologists could lead to decision support systems for better pathology training. We developed methods to predict the spatio-temporal (where and when) movements of pathologists' attention as they grade whole slide images (WSIs) of prostate cancer. We characterize a pathologist's attention trajectory by their x, y, and m (magnification) movements of a viewport as they navigate WSIs using a digital microscope. This information was obtained from 43 p…

Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
Zhuoyan Shen, Esther B\"ar, Maria Hawkins, Konstantin Br\"autigam, Charles-Antoine Collins-Fekete
https://arxiv.org/abs/2509.02585

Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
This report details our submission to the Mitotic Domain Generalization (MIDOG) 2025 challenge, which addresses the critical task of mitotic figure detection in histopathology for cancer prognostication. Following the "Bitter Lesson"\cite{sutton2019bitterlesson} principle that emphasizes data scale over algorithmic novelty, we have publicly released two new datasets to bolster training data for both conventional \cite{Shen2024framework} and atypical mitoses \cite{shen_2025_16780587}. Besides, w…

Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning
Hongyi Pan, Gorkem Durak, Elif Keles, Deniz Seyithanoglu, Zheyuan Zhang, Alpay Medetalibeyoglu, Halil Ertugrul Aktas, Andrea Mia Bejar, Ziliang Hong, Yavuz Taktak, Gulbiz Dagoglu Kartal, Mehmet Sukru Erturk, Timurhan Cebeci, Maria Jaramillo Gonzalez, Yury Velichko, Lili Zhao, Emil Agarunov, Federica Proietto Salanitri, Concetto Spampinato, Pallavi Tiwari, Ziyue Xu, Sachin Jam…

Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning
Pancreatic cancer is projected to become the second-deadliest malignancy in Western countries by 2030, highlighting the urgent need for better early detection. Intraductal papillary mucinous neoplasms (IPMNs), key precursors to pancreatic cancer, are challenging to assess with current guidelines, often leading to unnecessary surgeries or missed malignancies. We present Cyst-X, an AI framework that predicts IPMN malignancy using multicenter MRI data, leveraging MRI's superior soft tissue contras…