AI and Cancer

A deep learning system trained on whole-slide images classifies nine renal tumor subtypes with high accuracy and outperforms traditional grading in predicting patient outcomes.

Insilico Medicine ("Insilico"), a clinical-stage generative artificial intelligence (AI)-driven biotechnology company, today announced the publication of a collaborative study in the journal npj Precision Oncology.

Researchers at the Johns Hopkins Kimmel Cancer Center validated an artificial intelligence (AI)-powered blood test that accurately detected liver cancer in people from two geographically and biologically distinct populations and also uncovered the underlying biological signals that make the test effective.

Researchers have developed a new artificial intelligence-based method that could significantly improve the detection and classification of brain tumors from MRI scans, achieving higher accuracy than conventional diagnostic systems.

Researchers developed Oncoformer, a multimodal transformer that analyzes longitudinal health records and chest X-rays to classify cancer, predict diagnoses up to one year in advance, infer tumor stage, and forecast treatment response and recurrence risk. Validation across large independent cohorts showed strong performance, although prospective trials in diverse, average-risk populations are needed before clinical use.

A new artificial intelligence (AI) tool developed by La Trobe University researchers could help predict which stage-two bowel cancer patients are at risk of relapse, in an advance that could ultimately lead to thousands getting life-saving treatment earlier.

Artificial intelligence developed by researchers at UCL has revealed a promising combination of cancer treatments that could help patients with locally advanced rectal cancer live longer.

From a routine pathology slide, CANVAS can predict immunotherapy resistance better than current clinical methods, offering a potential

The future of federally funded research at Harvard Medical School — supported by taxpayers and done in service

Researchers presented a groundbreaking artificial intelligence (AI)-guided technique today at the Society of NeuroInterventional Surgery's (SNIS) 23rd Annual Meeting that may improve the accuracy of delivering experimental therapies directly to malignant brain tumors.

A powerful new AI tool has uncovered what could be one of the biggest integrity problems in modern science. After analyzing 2.6 million cancer research papers published between 1999 and 2024, researchers identified more than 250,000 studies with writing patterns resembling papers suspected of being produced by fraudulent "paper mills."

Using computational tools, researchers from the Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine have developed a method to predict which patients with a primary liver cancer called hepatocellular carcinoma (HCC) would most benefit from combination treatment using immunotherapy and a targeted therapy that blocks the growth signals that the tumor depends on.

The tool, based on histology only, may help identify patients most likely to benefit from triplet therapy.

Endoscopy-based deep learning algorithms demonstrate higher sensitivity and specificity than endoscopists overall in detecting early esophageal squamous cell carcinoma.

Cancer immunotherapy drugs known as immune checkpoint inhibitors (ICIs) can be miracle drugs for cancer patients, curing some and turning deadly disease into a manageable chronic condition in others.

Quibim, a global leader in precision imaging and artificial intelligence (AI) solutions, today announces the European and UK launch of QP-Breast®, its CE and UKCA-marked AI tool - the first in Europe and the United Kingdom to detect breast cancer using magnetic resonance imaging - which automatically identifies suspected lesions on breast MRI.

A machine learning algorithm predicts early-onset liver cancer in young adults better than traditional models.

Digestive system cancers, including hepatobiliary and gastrointestinal malignancies, remain a major global oncological burden. Although immune checkpoint inhibitors have changed the treatment landscape for some patients, their overall efficacy remains limited, with only about 15% to 30% of patients achieving durable clinical benefit.

Researchers from The University of Texas MD Anderson Cancer Center demonstrated that an artificial intelligence (AI)-based analysis of tumor biopsies can predict responses to immunotherapy in a study of patients with rare cancers, published in the Journal for ImmunoTherapy of Cancer.

Over the past two decades, high‑throughput sequencing has dramatically expanded our understanding of the gut microbiome's composition.

A human‑in‑the‑loop AI framework rapidly nominates CAR T targets, leading to a GPNMB‑directed CAR T cell with activity across melanoma, leukemia, and colorectal cancer in preclinical studies. The post AI Framework Surfaces New CAR T Target with Multi‑Cancer Potential appeared first on GEN - Genetic Engineering and Biotechnology News.

Using artificial intelligence (AI), researchers found that image-based risk scores for breast cancer derived from screening mammograms evolve over time and differ between women who develop cancer and those who do not, opening the door to a new era of dynamic breast cancer risk assessment.

Researchers at the UCLA Health Jonsson Comprehensive Cancer Center have developed a new platform that combines 3D bioprinting, advanced imaging and artificial intelligence to better monitor how cancer responds to treatment.

Tiny algae-based robots guided by magnets could improve bladder cancer treatment by boosting delivery of chemotherapy drugs into tumours, researchers say.

Endometrial cancer is the most common gynecologic cancer, with more than 69,000 cases diagnosed in the U.S. in 2025 and increasing up to 3% annually.

A multimodal AI model identified a subset of men with very high-risk localized prostate cancer who derived substantial benefit from adding abiraterone to standard therapy.

Physicians and researchers at the Netherlands Cancer Institute have developed an AI model that outperforms physicians at evaluating treatment response in pleural mesothelioma.

Cancer care is becoming too complex for any one clinician to track alone, making AI an increasingly valuable tool in modern oncology.

Experts in Heidelberg have developed an AI system that can classify brain tumors with unprecedented accuracy using standard microscopic tissue sections.

A new AI model reliably identifies hidden pancreatic ductal adenocarcinoma on routine CT scans months before the clinical diagnosis of the disease.

Three commercially available radiology AI systems have shown the potential to flag early signs of breast cancer up to 6 years before a diagnosis, according to a study published in Radiology, the flagship journal of the Radiological Society of North America (RSNA).

Women with abnormal mammograms often have to wait for weeks to find out whether they have breast cancer.

Online information about artificial intelligence (AI) and its impact on cancer research and treatment for both the patient and general-public audiences is limited, and the available webpages and videos are largely of low quality, difficult to read, and frequently omit risks of AI use, according to new research presented today at the at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting (Abstract 9000) and led by researchers from the Abramson Cancer Center (ACC) of the University of Pennsylvania and Penn's Perelman School of Medicine.

Researchers developed a spatial atlas of tertiary lymphoid structures across multiple cancer types, revealing how key features vary across tumor types and may influence patient outcomes. The post AI-Powered Pan-Cancer Map Reveals Tertiary Lymphoid Structures appeared first on GEN - Genetic Engineering and Biotechnology News.

In a new study published today in Science, researchers from The University of Texas MD Anderson Cancer Center developed a spatial atlas of specialized immune structures, called tertiary lymphoid structures (TLSs), across multiple cancer types.

Researchers at the Faculty of Information Technology at the University of Jyväskylä have used artificial intelligence to speed up the analysis of colorectal cancer samples and predict the functioning of the cells' DNA repair mechanism.

Researchers at University of California San Diego have developed a new artificial intelligence (AI) model that can translate a tumor's complex genetic profile into predictions about how that cancer may respond to treatment.

The Oncodarwinian Hypothesis (OdH) proposes a paradigm shift: cancer is not merely a disease but a potential macro‑immunoadaptive response – a self‑replicating algorithm that can be reprogrammed via AI‑based 3D printed p53 superproteins.

AI-driven genomic analysis could help researchers identify existing drugs that may be repurposed for subtype-specific breast cancer treatment. The review proposes an interpretability-driven framework that links multi-omics data, mechanistic validation, and clinical translation to make AI predictions more transparent and clinically useful.

Cancer patients who interact with an artificial intelligence (AI) avatar doctor before they meet their real-life consultant feel more knowledgeable and less stressed, according to research presented at the Congress of the European Society for Radiotherapy and Oncology (ESTRO 2026)

An AI technology is effective at planning the delivery of life-saving radiotherapy for cervical cancer and prostate cancer, according to results from a large international trial led by researchers at University College London (UCL) and the London School of Hygiene & Tropical Medicine (LSHTM).

Researchers at Rice University and the University of Texas MD Anderson Cancer Center have developed a compact, artificial

Researchers at Rice University and The University of Texas MD Anderson Cancer Center have developed a compact, artificial intelligence-powered imaging device that could transform how clinicians detect cancer.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, making early and accurate diagnosis essential for improving patient outcomes.

A team led by Cedars-Sinai Health Sciences University investigators has created a faster, cheaper way to determine the genes expressed in cancerous tumors.

While development is often described as a series of static snapshots of cell states, RegVelo models how these fate decisions are encoded in gene regulatory networks over time and space, and what drives cell state transitions. The post RegVelo AI Model Predicts Cell Fate, Tackles Developmental Disorders and Cancer appeared first on GEN - Genetic Engineering and Biotechnology News.

A new AI tool finds early hints of pancreatic cancer in CT scans that doctors would otherwise miss, an early test found.

Mayo Clinic's AI model identifies invisible signs of pancreatic cancer on routine CT scans, potentially transforming early detection and survival rates.

A new trial will test whether a tool that harnesses Apple Watch health data and artificial intelligence can help protect children undergoing cancer treatment from infections.

Artificial intelligence tools like ChatGPT are increasingly being explored in cancer care, but they can sometimes produce outdated or incorrect information. In medicine, where accuracy is critical, that risk is a serious concern.

An AI model (REDMOD) can pick up the very early subtle tissue changes of pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, which conventional imaging and the human eye find difficult to detect, finds research published online in the journal Gut.

The Mass General Brigham research team behind FaceAge, an artificial intelligence (AI) tool that can estimate a person's biological age from a single photo, is reporting in a new study that estimating biological age from multiple photos taken over time can provide even more information about how well a person with cancer will do with treatment.

A clinical trial designed to test an AI-based skin cancer detection app has some surprising results.

In a National Institutes of Health (NIH)-funded study, researchers developed a cancer assessment tool that can identify high-risk patients and the tumor

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method

Researchers at UC Berkeley and City of Hope, a cancer research and treatment organization, have created a novel

Researchers created a novel microfluidic platform, AI tool, and risk score that can assess women’s breast cancer risk at the cellular level, based on the how how breast cancer cells respond to being squeezed, and their “mechanical age.” The post AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure appeared first on GEN - Genetic Engineering and Biotechnology News.

From organoid models to pathology, AI is increasingly embedded across cancer research areas. Fay Lin, PhD, and Jonathan D. Grinstein, PhD, discuss how challenges, such as adoption and trust continue to hinder AI's impact on patient outcomes. The post AACR 2026 Video Update: Cancer Research Edges Toward an AI-Driven Era appeared first on GEN - Genetic Engineering and Biotechnology News.

In a National Institutes of Health (NIH)-funded study, researchers developed a cancer assessment tool that can identify high-risk patients and the tumor cells linked to that risk.

Path-IO accurately stratified immunotherapy outcomes for patients with metastatic non-small cell lung cancer (NSCLC). The model is validated across international real-world cohorts and a Phase III randomized clinical trial. The post AACR 2026: Lung Cancer Immunotherapy Response Predicted by Pathomics AI Model appeared first on GEN - Genetic Engineering and Biotechnology News.

Breast cancer is one of the most common malignancies worldwide, and mutations in the PI3K/AKT/mTOR (PAM) signaling pathway are prevalent in its development.

Breast cancer is the most common cancer among women, with over 2.3 million new cases diagnosed each year. Traditional diagnostic methods rely heavily on human judgment, which can lead to inconsistent outcomes.

A biology-guided artificial intelligence model applied to routine pathology slides accurately predicted outcomes and response to immunotherapy in patients with metastatic non-small cell lung cancer (NSCLC), according to a study presented at the American Association for Cancer Research (AACR) Annual Meeting 2026, held April 17-22.

By analyzing CpG-based DNA methylation, researchers from Kindai University in Japan have developed a machine learning model that accurately predicts the origin of diverse tumor types in patients with cancers of unknown primary (CUP). The post AACR 2026: Cancers of Unknown Primary Identified by DNA Methylation AI Model appeared first on GEN - Genetic Engineering and Biotechnology News.

A Cell Perspective argues that generative AI models could help tackle cancer’s multiscale, multimodal complexity by complementing the Hallmarks of Cancer framework. It proposes that models capable of complex pattern recognition, multimodal fusion, and contextual reasoning could improve cancer detection, biological discovery, and precision oncology, while still requiring rigorous validation and human oversight.

This Perspective reviews how AI is moving drug discovery from long, costly experimental pipelines toward earlier clinical translation, with an AI-designed TNIK inhibitor trial serving as an important proof-of-feasibility reference point. It argues that precision oncology could benefit from AI-driven integration of multi-omics, federated learning, and adaptive trial design, but only if validation, fairness, interpretability, and regulatory alignment improve.

A massive Swedish study shows that AI can spot people at higher risk of melanoma using routine health data. Advanced models significantly outperformed basic methods, identifying high-risk groups with striking accuracy. Some individuals flagged by the system had up to a 33% chance of developing melanoma within five years. This approach could pave the way for smarter, more targeted screening.

Healthcare registry data can show early risk patterns for melanoma skin cancer, according to a study from the University of Gothenburg.

A new randomized clinical trial found that men with localized, intermediate‑risk prostate cancer recovered faster and experienced less short‑term impact on their daily lives when treated with MRI‑guided, transurethral ultrasound ablation (TULSA) compared with robotic prostate surgery.

A team of Weill Cornell Medicine investigators is working to cross-train the next generation of cancer researchers in cancer biology and the use of artificial intelligence tools for research.

Recently, the Insilico team's research entitled "An Internal Sulfur-Lone Pair Interaction Enabled the Discovery of Potential and Sub-Family Selective PKMYT1 Inhibitors" was invited for publication as a cover Feature in ChemMedChem.

The NR3C1 (nuclear receptor subfamily 3 group C member 1) gene encodes the glucocorticoid receptor (GR), a nuclear receptor vital for maintaining physiological homeostasis, and inhibition of NR3C1 could increase the sensitivity of cancer cells to standard chemotherapy, particularly in ovarian cancer.

Lung cancer remains the leading cause of cancer-related deaths worldwide, accounting for nearly one in five cancer deaths - around 1.8 million lives lost each year.

Artificial intelligence (AI) could help physicians determine if survivors of childhood cancer need extra support - and the more information included in AI prompting, the better its performance.

Integrating LLMs in brain tumor care could enhance patient understanding, but requires strict oversight to manage risks and ensure reliable information.

Researchers have discovered that cancer spread isn’t random—it follows a kind of biological “program.” By studying colon tumor cells, they identified gene patterns that signal whether a cancer is likely to metastasize. Their AI model, MangroveGS, can predict this risk with about 80% accuracy and even works across multiple cancer types. This could transform how doctors decide who needs aggressive treatment and who doesn’t.

Increasing use of blood tests to detect prostate cancer is leading to overworked doctors. NTNU has now created an AI diagnostic tool that can help lighten the burden.

Cancer often begins when the genetic instructions that guide our cells become scrambled, allowing cells to grow uncontrollably. Now, scientists at EMBL have developed an AI-powered system called MAGIC that can automatically spot and tag cells showing early signs of chromosomal trouble—tiny DNA-filled structures known as micronuclei that are linked to future cancer development.

Researchers evaluated four deep learning models using over 112,000 negative screening mammograms from the UK NHS to determine how well artificial intelligence could predict future breast cancers missed during routine screening. The MIT-developed Mirai model performed best, identifying about 27.5% of interval cancers within the top 4% of women flagged as highest risk.

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New research warns that popular deep learning systems trained for cancer pathology may be relying on hidden shortcuts rather than genuine biological signals.

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Using machine learning, an electronic nose can "smell" early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in the scientific journal Advanced intelligent systems.

An international research team, with significant involvement from the Medical University of Vienna, has developed a new AI-based analysis method that can accurately classify brain tumors using genetic material from cerebrospinal fluid (CSF) and monitor the progression of the disease.

People with ulcerative colitis (UC), a chronic inflammatory bowel disease, are up to four times more likely to develop colorectal cancer than the general population.

Today, many men must choose between life-altering prostate removal or careful long-term monitoring. The EU-funded ROBIOSPY Project offers an intermediate option: precise needle-based diagnostics and targeted therapy powered by AI-driven robotics.

Eyonis LCS aids in both detecting and characterizing nodules, distinguishing it from its many competitors.

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A clinical trial shows that AI-assisted mammography can detect more cases of dangerous cancer and reduce missed diagnoses.

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The NHS has launched an AI and robot pilot to help spot lung cancer sooner, as well as a screening program that aims to help tackle inequalities in cancer.

University researchers have pioneered a new tool to determine the risk of secondary heart attacks in cancer patients using Artificial Intelligence.

Jan. 31, 2026: Our weekly roundup of the latest science in the news, as well as a few fascinating articles to keep you entertained over the weekend.

Cancer patients who suffer a heart attack face a dangerous mix of risks, which makes their clinical treatment particularly challenging.

FRIDAY, Jan. 30, 2026 — Artificial intelligence (AI) can help reduce the number of breast cancers found between mammogram screenings, clinical trial results show.There was a 12% reduction in cancer diagnoses in the years following AI-supported b...

Artificial intelligence (AI)-supported mammography identifies more cancers during screening and reduces the rate of breast cancer diagnosis by 12% in the years following, finds the first randomised controlled trial of its kind involving over 100,000 Swedish women published in The Lancet journal.

Interval cancers are aggressive tumours that grow during the interval after someone has been screened for cancer and before they are screened again, and AI seems to be able to identify them at an early stage

England's National Health Service trials of a new artificial intelligence and robotics technology for lung cancer detection is set to go underway.