AI and Cancer Research ── 2026-11-02
New papers on cancer and AI, collected from journal feeds, PubMed (searched across all journals) and OpenAlex (a public index used instead of Google Scholar), judged by Jev (an AI that makes language judgements): original research, AI central, which of 13 themes, treatment or trials, and then ranked by importance up to a daily limit. Journals are not filtered by impact factor; each paper shows its journal's IF-equivalent (OpenAlex two-year mean citedness) and the date of that value. Each entry shows the full original abstract.
All cancers: Cancer biology（1）
Hematologic malignancies: AML（0） · CML（0） · ALL（0） · CLL（0） · Lymphoma（0） · Myeloma（0）
Solid tumors: Breast（0） · Lung（0） · Pancreatic（0） · Prostate（2） · Brain（2） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（2） · Kidney（0） · Bladder（1）
Rare cancers: ACC（0）
Cancer molecular biology and AI
- Abstract
Medical image fusion is a crucial technique that has been extensively employed in clinical diagnosis and treatment planning, driven by rapid advances in imaging techniques. Though several methods have been introduced in the past for effective fusion of source modalities, there is still room to improve fusion accuracy. In this work, a novel method of image decomposition is introduced using the mean of ordered pixel values filter, and is applied for multi-scale decomposition of input images for fusion. Further, a weight computation mechanism based on singular values of the Singular Value Decomposition (SVD) method is introduced to fuse coarse layers of source images, while the detail layers are fused at multiple levels with the help of Parameter-Adaptive Dual-Channel Weighted Pulse Coupled Neural Networks (PADCWPCNN). The fusion performance of the proposed method is evaluated on 125 slices of diverse brain diseases using both subjective and objective criteria. Experimental results demonstrated the efficacy of the proposed method, as it improved Average Gradient (AG), Standard Deviation (SD), Entropy (H), Spatial Frequency (SF), Edge Strength (QABF), and Mutual Information (MI) by 57%, 3%, 14.7%, 17.9%, 46%, and 14%, respectively, making it an appealing option for fusing medical images to effectively diagnose tumors and plan treatment accordingly.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-11-01, retrieved 2026-11-02; not the official Clarivate IF)Reference: Chinta RR, Kolluri R. A Novel Multi-Level Medical Image Fusion Using Ordered Pixel Mean Filter and Parameter Adaptive Dual Channel Weighted Pulse Coupled Neural Networks. Iraqi Journal of Science. 2026 Oct 30 [Epub ahead of print]. doi:10.24996/ijs.2026.67.10.26.Checked: Abstract only
See all 1 Cancer biology papers →
Hematologic malignancies
Acute myeloid leukemia (AML): treatment, trials and AI
No new qualifying paper for this issue.
Chronic myeloid leukemia (CML): treatment, trials and AI
No new qualifying paper for this issue.
Acute lymphoblastic leukemia (ALL): treatment, trials and AI
No new qualifying paper for this issue.
Chronic lymphocytic leukemia (CLL): treatment, trials and AI
No new qualifying paper for this issue.
Lymphoma: treatment, trials and AI
No new qualifying paper for this issue.
Multiple myeloma: treatment, trials and AI
No new qualifying paper for this issue.
Solid tumors
Breast cancer: treatment, trials and AI
No new qualifying paper for this issue.
Lung cancer: treatment, trials and AI
No new qualifying paper for this issue.
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
- Abstract
Background: Whole-gland habitat analysis may characterize intraprostatic heterogeneity without requiring index-lesion delineation. We developed and externally evaluated a lesion-independent biparametric MRI (bpMRI)-based whole-gland habitat framework for pretreatment identification of pathologically high-risk prostate cancer (PCa) and assessed a prespecified capsule-proximal periprostatic adipose tissue (PPAT) extension for incremental predictive value.
Methods: This retrospective two-center study included 301 patients with pathologically confirmed PCa: 241 for model development and 60 for independent external validation. Pathologically high-risk PCa was defined as Gleason score ≥ 4+3 or International Society of Urological Pathology grade group ≥ 3. Patient-specific K = 2 whole-gland habitats were generated by joint k-means clustering of T2-weighted images and apparent diffusion coefficient maps, with the lower-ADC cluster designated H1. Habitat-derived signatures and a PPAT 0–3 mm radiomics signature were combined with log-transformed prostate-specific antigen density (log-PSAD) in score-level fusion models. Development performance was estimated by stratified five-fold cross-validation using out-of-fold predictions; fitted models were applied unchanged to the external validation cohort.
Results: The external validation cohort included 47 patients with pathologically high-risk and 13 with lower-risk PCa. Conventional whole-gland radiomics combined with log-PSAD achieved an external area under the receiver operating characteristic curve (AUC) of 0.791 (95% CI, 0.664–0.899), and the standalone PPAT signature achieved an AUC of 0.702 (95% CI, 0.540–0.845). H1-derived gland fusion achieved an AUC of 0.836 (95% CI, 0.715–0.936), compared with 0.840 (95% CI, 0.710–0.941) for the H1-derived PPAT-extended fusion model. Adding PPAT changed the AUC by 0.003 (95% CI, −0.046 to 0.048; bootstrap p = 0.881). H1-derived gland fusion also had a lower Brier score (0.146 vs. 0.175), lower expected calibration error (0.153 vs. 0.219), and higher average net benefit over both prespecified threshold-probability ranges.
Conclusions: In a small independent external validation cohort ( n = 60), the lesion-independent whole-gland habitat framework showed discrimination between pathologically high-risk and lower-risk PCa. Adding capsule-proximal PPAT produced only a minimal increase in AUC and did not reduce prediction or calibration error or increase net benefit. Larger multicenter studies are needed to confirm the external findings and determine whether PPAT provides incremental predictive value beyond H1-derived gland fusion.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Huang J, Gong J, Zhang Y, Yang O, Li J, Chen J, et al. Biparametric MRI–based whole-gland habitat analysis for pretreatment identification of pathologically high-risk prostate cancer: a two-center study with external validation. Cancer Imaging. 2026 Oct 31 [Epub ahead of print]. doi:10.1186/s40644-026-01139-0.Checked: Abstract only - Abstract
We established and validated perineural invasion density (PNID) as a superior prognostic marker and developed an MRI-based AI model for its preoperative prediction. In this multicenter study of 1091 PCa patients undergoing radical prostatectomy, we evaluated PNID's association with biochemical recurrence (BCR) and radiographic progression-free survival (rPFS). We also analyzed concurrent β-blocker or antimuscarinic use with adjuvant ADT. A deep learning model was developed in a primary cohort (n = 523) and validated in two external cohorts (n = 317, 251). High-PNID was an independent prognostic factor for BCR (HR 4.06, 95% CI: 2.75-5.97, P < 0.001) and rPFS (HR 4.61, 95% CI: 2.97-7.16, P < 0.001). Notably, concurrent β-blocker use was significantly associated with favorable BCR outcomes exclusively in the High-PNID subgroup (HR 0.32, 95% CI: 0.12-0.84, P = 0.021), with no benefit in the Non-High PNID population or with antimuscarinics. The AI model showed excellent performance predicting High-PNID, with AUCs of 0.917 (training), 0.842 (internal validation), 0.889 and 0.915 (external validations). Model-identified High-PNID patients had significantly worse outcomes. PNID surpasses traditional PNI in prognostic accuracy and identifies a high-risk phenotype. High-PNID may define a subgroup benefiting from adjuvant β-blocker therapy, providing an exploratory clinical rationale for targeting the nerve-tumor microenvironment in stratified subpopulations. Hence, this model may be a reliable, non-invasive tool for preoperative identification of this high-risk subgroup, aiding clinicians in personalized risk stratification and treatment planning.
Journal IF-equivalent: 9.5 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Liu B, Zhang L, Zhu W, Yang M, Liu X, Li X, et al. Non‐Invasive Assessment of Perineural Invasion Density: A Novel Marker for Prognosis and Treatment Stratification in Prostate Cancer. Advanced Science. 2026 Oct 31 [Epub ahead of print]. doi:10.1002/advs.78460.Checked: Abstract only
Brain tumours: treatment, trials and AI
- Abstract
ABSTRACT Background Quantitative MRI may enable noninvasive glioma characterization, but optimal analytic strategies and prognostic utility remain uncertain.
Purpose: To compare volume‐of‐interest (VOI)‐mean, radiomics, and 2D, 2.5D, and 3D deep learning (DL) for preoperative molecular classification and assess the prognostic value of a 2.5D‐DL–derived isocitrate dehydrogenase wild‐type score (2.5D‐IDHwt score) in a postoperative clinicopathologic survival model. Study Type Retrospective multicenter study. Population One thousand four hundred and eight adults with diffuse glioma: training ( n = 409), temporal validation ( n = 300), and three external test cohorts ( n = 119, 310, and 270). Field Strength/Sequence 3 T; spin‐echo echo‐planar diffusion‐weighted imaging, 3D pseudo‐continuous arterial spin labeling, and multidynamic multiecho synthetic MRI. Assessment Apparent diffusion coefficient, cerebral blood flow, T1, T2, proton‐density, and contrast‐enhanced T1 maps were generated. Two neuroradiologists (5 and 20 years' experience) delineated whole‐tumor VOIs. Six map‐specific models were stacked separately for VOI‐mean, radiomics, 2D‐DL, 2.5D‐DL, and 3D‐DL. 2.5D‐qMR‐Clin combined the 2.5D‐IDHwt score with age, O 6 ‐methylguanine‐DNA methyltransferase promoter methylation, and extent of resection and was compared with PathoClin incorporating pathological IDH status. Statistical Tests Area under the curve (AUC), DeLong tests, Harrell C‐index, bootstrap resampling, calibration, decision‐curve analysis, Kaplan–Meier analysis, and Cox regression. p < 0.05 indicated significance.
Results: The 2.5D‐DL stacking model achieved the highest external AUCs for IDH mutation (0.89–0.93) and 1p/19q codeletion (0.92–0.95), significantly outperforming the other four stacking models. Reverse Kaplan–Meier median follow‐up was 1344 days (95% CI, 1316–1383); 27.5% died. External C‐indices for 2.5D‐qMR‐Clin were 0.750, 0.778, and 0.762 versus 0.704, 0.722, and 0.723 for PathoClin, with significant differences in test cohorts 2 and 3. Fixed‐cutoff risk stratification was significant across all external cohorts (hazard ratios, 5.30–5.80). A web‐based 2.5D‐qMR‐Clin explorer provided 1‐, 2‐, and 3‐year OS estimates. Data Conclusion Among evaluated approaches, 2.5D‐DL stacking performed best for molecular classification, and the 2.5D‐IDHwt score improved OS assessment in the postoperative clinicopathologic model. Evidence Level 3. Technical Efficacy Stage 2.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Ge X, Shen Y, Han Y, Li S, Feng X, Wang C, et al. Multiparametric Quantitative MRI With 2. 5D Deep Learning for Molecular Classification and Overall Survival Assessment in Adult‐Type Diffuse Gliomas. Magnetic Resonance Imaging. 2026 Oct 31 [Epub ahead of print]. doi:10.1002/jmri.70599.Checked: Abstract only - Abstract
A brain tumor (BT) arises due to the abnormal growth of cells. It ranks among the primary fatal malignancies for adults globally. Tumor treatment relies on early tumor recognition. The diagnosis is usually performed using medical imaging techniques, which assist in the accurate evaluation of the patient's brain. Therefore, timely disease detection and rapid treatment result in improved quality of life and increased life expectancy in such patients. Recently, deep learning (DL) models have led to considerable advancements in medical imaging. In this study, an automated brain tumor classification and severity prediction using image segmentation and a convolutional deep learning (ABTCSP-ISCDL) model is proposed. This study aims to develop an intelligent system for precise BT detection and severity classification to help with timely diagnosis and effective treatment planning. Besides, image segmentation is performed using a Transformer-based U-Net (TransUNet) method, which accurately isolates the tumor region. Furthermore, the ABTCSP-ISCDL technique performs feature extraction using the RegNet002 model to capture local and global features of the BT. Finally, the root mean square propagation (RMSProp) optimizer is used with a convolutional long short-term memory (ConvLSTM) network to perform tumor classification and severity prediction, improving both accuracy and overall performance. A comprehensive set of experiments on the ABTCSP-ISCDL model is performed using the Figshare BT dataset. The experimental analysis indicated that the proposed framework achieved an accuracy of 96.25% compared with conventional approaches.
Journal IF-equivalent: 1.4 (OpenAlex 2-year mean citedness, value as of 2026-11-01, retrieved 2026-11-02; not the official Clarivate IF)Reference: Chanemougavel V, Jayanthi K. Brain tumor segmentation, classification, and severity prediction using deep transfer learning and convolutional long short-term memory. IJATEE. 2026 Oct 31;13(143):854-75. doi:10.19101/ijatee.2025.121221250.Checked: Abstract only
Ovarian cancer: treatment, trials and AI
No new qualifying paper for this issue.
Endometrial cancer: treatment, trials and AI
No new qualifying paper for this issue.
Gastric cancer: treatment, trials and AI
No new qualifying paper for this issue.
Liver cancer: treatment, trials and AI
- Abstract
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with high recurrence rates following curative resection. Accurate non-invasive assessment of tumor aggressiveness and recurrence risk is critical for surgical decision-making and postoperative surveillance, yet remains challenging with conventional imaging modalities. Here, we developed a CT lesion foundation model (LFM), trained via self-supervised learning on 32,120 generic lesion and 6,387 HCC-specific CT patches to capture transferable intra-lesional imaging phenotypes. Across one internal and two external validation cohorts, the sequentially pretrained LFM (LFM-Seq) achieved area under the curve values of 0.916, 0.846, and 0.920 for microvascular invasion (MVI) assessment, and 0.833, 0.885 and 0.795 for pathological grade assessment. The model remained robust under scenarios of limited training data and tumor localization perturbations. Furthermore, LFM-Seq-derived risk scores stratified recurrence-free survival (RFS), yielding an externally validated C-index of 0.701, and remained independently associated with RFS after adjusting for clinicopathological factors. Exploratory biological interpretability analyses showed that LFM-Seq-derived risk phenotypes were associated with suppressed lymphocyte-related immune programs. These results suggest that CT lesion foundation models can support non-invasive and robust characterization of HCC aggressiveness, recurrence risk stratification and discovery of interpretable prognostic imaging biomarkers.
Journal IF-equivalent: 9.5 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Xia W, Shen Z, Li M, Lu J, Xia J, Cheng Y, et al. A CT Lesion Foundation Model for Non‐Invasive Assessment of Hepatocellular Carcinoma Aggressiveness and Prediction of Recurrence Risk. Advanced Science. 2026 Nov 1 [Epub ahead of print]. doi:10.1002/advs.78587.Checked: Abstract only - Abstract
Introduction: We aimed to evaluate the possible association between TNFRSF4 and the prognosis of hepatocellular carcinoma (HCC), and particularly to develop CT-based radiomics models to predict TNFRSF4 levels before surgery with either tumor per se or peritumoral changes.
Methods: 286 HCC patients' genomic data from the Cancer Genome Atlas (TCGA) and 35 individuals' arterial phase enhanced computed tomography (CT) data from the Cancer Imaging Archive (TCIA) were used for the prognostic analysis and radiomics feature extraction. Accordingly, the machine-learning algorithms, including maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE), were employed to construct the whole-tumor and whole-tumor-peritumoral (whole tumor and a 3 mm peritumor area) models with the receiver operating characteristic curve evaluating the accuracy. Results & Conclusions High TNFRSF4 expression was associated with shorter overall survival in this cohort. For the whole tumor model, 20 optimal features were extracted with mRMR, followed by screening out 3 representative subsets with RFE. When combined with the logistic regression model, the area under the receiver operating characteristic curve (AUC) for the training and verification sets equaled 0.813 and 0.759, respectively. Subsequently, the whole-tumor-peritumoral model was established, as well as 20 features derived from the mRMR algorithm and 4 typical subsets from RFE, and it was found that the AUC value was 0.871 in the training set and 0.803 in the verification set. In conclusion, the radiomics model based on the tumor per se and peritumoral information predicted the TNFRSF4 expression and its association with overall survival in HCC patients.
Journal IF-equivalent: 9.5 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Ye R, Wang S, Li Y, He Q. The prognostic value of TNFRSF4 in hepatocellular carcinoma using a two-zone CT imaging model. Heliyon. 2026 Nov;12(16):e45637. doi:10.1016/j.heliyon.2026.e45637.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Early detection of individuals at high risk for lung metastasis (LM) from upper tract urothelial carcinoma (UTUC) is important for postoperative risk stratification and surveillance planning. This retrospective study used five machine learning techniques to predict LM risk in UTUC patients. Data from the SEER database (2000–2017) were analyzed. The SEER cohort was randomly divided into a training set and an internal test set in a 7:3 ratio for model development and internal validation. No synthetic resampling technique was used in the final analysis. The Boruta algorithm and multivariable logistic regression identified key independent variables. The model’s performance was validated with data from 569 UTUC patients at the First Hospital of Jilin University. Evaluation included AUC, AUPRC, decision curve analysis, and SHAP interpretation. An online tool was developed for clinical use. Among the five machine learning models, RF showed strong apparent performance in some metrics, whereas LR demonstrated greater stability and generalizability across the internal test and external validation sets. Therefore, an LM-specific UTUC risk calculator was developed using LR for clinical application. This study developed a clinical prediction model for assessing LM risk in UTUC patients, offering potential support for clinical decision-making.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Wu H, Xu T, Cao H, Li Y, Zhou H, Wang L, et al. Machine learning predicts postoperative lung metastasis in upper tract urothelial carcinoma. Sci Rep. 2026 Oct 31 [Epub ahead of print]. doi:10.1038/s41598-026-77312-z.Checked: Abstract only
Rare cancers
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.