AI and Cancer Research ── 2026-10-28
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.
Read by cancer type: Cancer biology（1） · Breast（0） · Lung（0） · Pancreatic（0） · Prostate（0） · ACC（0） · Brain（0） · Ovarian（1） · Endometrial（0） · Gastric（0） · Liver（1） · Kidney（0） · Bladder（0）
Cancer molecular biology and AI
- Abstract
Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inhibition of the poly(ADP-ribose) polymerase 1 (PARP1) cancer target. Using our recently developed methods based on atomistic simulation and machine learning (ML), we screened a set of 5 million hypothetical photoactive ligands. Our workflow used protein-ligand docking to identify candidates with differential PARP1 binding under light and dark conditions; ML force fields and quantum chemistry calculations to predict pKa, absorption spectra, and thermal half-lives; graph-based surrogate models to screen additional compounds; excited-state nonadiabatic dynamics with ML force fields to estimate quantum yields; and free energy perturbation (FEP) to refine binding predictions. From these predictions, we prioritized a small set of synthetically feasible candidates expected to have red-shifted absorption spectra, thermal half-lives on the order of seconds to minutes, and isomer-dependent PARP1 binding under visible-light control. We synthesized 10 candidates and experimentally characterized their photobehavior and PARP1 inhibition constants. Among the validated compounds, 1 showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 ± 28.3 μM vs 14.4 ± 1.9 μM). These results validate the computation-guided screening strategy for identifying red-shifted PARP1 photoinhibitors, while also underscoring current limitations such as rapid thermal relaxation in aqueous media.
Journal IF-equivalent: 17.8 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: Axelrod S, Kašpar M, Jelínková K, Šmídková M, Bartůňková E, Štěpánová S, et al. Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors. Journal of the American Chemical Society. 2026 Sep 14;148(37):39657-69. doi:10.1021/jacs.6c08644.Checked: Abstract only
See all 1 Cancer biology papers →
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
No new qualifying paper for this issue.
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
No new qualifying paper for this issue.
Ovarian cancer: treatment, trials and AI
- Abstract
This study evaluated the feasibility of predicting pathogenic BRCA1/2 variants in ovarian cancer using radiogenomic analysis of contrast-enhanced MRI. Twenty six patients were analyzed, including 14 with pathogenic BRCA1/2 variants and 12 without. Contrast enhanced MRI was used to generate two dimensional (2D) and three dimensional (3D) tumor segmentations. Radiomic features extracted with PyRadiomics were reduced using least absolute shrinkage and selection operator (LASSO) regression, yielding two selected features. Linear discriminant analysis (LDA) models were trained on 18 patients and tested on 8, with performance assessed using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve. The DeLong test compared AUCs between segmentation methods. Automated machine learning (AutoML) was performed with a training set of 20 patients and a test set of 6, with multiple performance metrics calculated. LDA achieved approximately 60% accuracy in both 2D and 3D test datasets. AUCs were 0.80 (3D) and 0.77 (2D), with no significant difference (p=0.85). AutoML identified CatBoost_BAG_L2_FULL as the optimal model, achieving accuracy above 80%. MRI-based radiogenomic analysis demonstrated moderate classification performance for identifying pathogenic BRCA1/2 variants in ovarian cancer. These preliminary findings suggest a potential role for noninvasive prediction, warranting further validation in larger cohorts.
Journal IF-equivalent: 0.0 (OpenAlex 2-year mean citedness, value as of 2026-10-26, retrieved 2026-10-28; not the official Clarivate IF)Reference: [No authors listed]. Radiogenomic Analysis of Contrast-Enhanced MRI for Predicting Pathogenic BRCA Variants in Ovarian Cancer. Acta medica Okayama. [Epub ahead of print]. doi:10.18926/AMO/71341.Checked: Abstract only
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
Vessels encapsulating tumor clusters (VETC) are significantly associated with poor prognosis in hepatocellular carcinoma (HCC), with implications for patient survival and clinical management. This study aimed to evaluate a deep learning (DL) radiomics model based on Gd-EOB-DTPA-enhanced MRI for preoperative prediction of the VETC pattern in HCC. Patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between January 2016 and August 2024 were retrospectively enrolled. Visually assessed radiological features were collected to construct a clinical-radiological (CR) model. Deep learning and handcrafted radiomics features were extracted from arterial, portal venous, and hepatobiliary phases. Feature selection and DL radiomics model construction were performed using the least absolute shrinkage and selection operator (LASSO). Multivariable logistic regression was applied to develop a hybrid model and corresponding nomogram. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUC). Cox regression analysis was performed to identify predictors of recurrence-free survival (RFS). In the training cohort, the hybrid DL-radiomics nomogram yielded the highest AUC (0.92), compared with 0.87 for the DL model, 0.80 for the radiomics model, and 0.74 for the CR model. However, standalone DL model showed better discriminative performance in the internal validation cohort, with an AUC of 0.87, sensitivity of 0.80, and specificity of 0.87. VETC status, AST level, and intratumoral necrosis were identified as independent predictors of RFS and incorporated into the VAI prognostic model. The VAI model achieved an AUC of 0.75 (95% CI, 0.68-0.80), which was significantly higher than those of three conventional prognostic systems (range, 0.52 [95% CI, 0.46-0.59] to 0.58 [95% CI, 0.51-0.64]). The hybrid DL-radiomics model based on Gd-EOB-DTPA-enhanced MRI showed favorable discriminative performance for VETC prediction in the training cohort, while the standalone DL model demonstrated better performance in the internal validation cohort. The VAI model enables individualized prediction of postoperative RFS for patients undergoing hepatectomy.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-13, retrieved 2026-10-15; not the official Clarivate IF)Reference: Wang M, Wang Y, Cao L, Shen Y, Xi D, Yan R, et al. Deep Learning Radiomics to Predict Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma Based on Gd-EOB-DTPA-Enhanced MRI. J Digit Imaging Inform med. 2026 Oct 26 [Epub ahead of print]. doi:10.1007/s10278-026-02456-3.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
No new qualifying paper for this issue.