AI and Cancer Research ── 2026-10-30
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（2） · Breast（3） · Lung（0） · Pancreatic（1） · Prostate（3） · ACC（0） · Brain（2） · Ovarian（1） · Endometrial（0） · Gastric（0） · Liver（2） · Kidney（0） · Bladder（2）
Cancer molecular biology and AI
- Abstract
Barrett's esophagus (BE) patients require improved cancer risk stratification to identify high-risk individuals while reducing over-treatment in low-risk populations. BE evolution towards adenocarcinoma is likely driven by a combination of genomic and microenvironmental factors, suggesting the potential of predictive models that integrate both. Here, we developed BEACON (Barrett’s Esophagus DNA content Abnormality and immune ecology for Cancer Outcome), a spatially aware framework integrating deep learning-based prediction of DNA content and immune architecture from routine histopathology. First, DACOR, a multi-instance learning model that predicts DNA content from histopathology, was trained using 777 BE biopsies with flow cytometry-based DNA content data scanned at two institutions, and it demonstrated strong performance across both validation and test datasets for DNA content abnormality prediction. Patients classified as DNA content abnormal by DACOR demonstrated increased cancer progression. Complementary models for cell classification and tissue segmentation enabled spatial immune ecology metric computations. Abnormal regions exhibited increased lymphoplasmacytic inflammation versus normal regions. Among patients with DNA content abnormality, cancer progressors exhibited increased plasma cell clustering adjacent to abnormal epithelium compared to non-progressors. Finally, a logistic regression model was developed that integrated molecular, immune ecological, and epithelial morphology features for cancer risk stratification. The integrated risk classification model stratified DNA content abnormal patients into high- and low-risk groups that corresponded with progression and patient survival. Overall, BEACON spatially integrates molecular abnormality with immune ecology to stratify BE patients by cancer progression risk using routine pathology images.
Journal IF-equivalent: 0.6 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Ercan C, Pan X, Paulson TG, Stachler MD, Göknur Akarca F, Grady WM, et al. Histopathology-Based Spatial Profiling of Immune and Molecular Features Predicts Cancer Risk in Barrett’s Esophagus Patients. Cancer Res. 2026 Oct 27 [Epub ahead of print]. doi:10.1158/0008-5472.can-26-1476. PMID: 42944677.Checked: Abstract only - Abstract
OBJECTIVE: To develop and validate a noninvasive model for classifying T stage in gastrointestinal malignancies using circulating tumor cells (CTCs) and serum tumor markers and to compare the predictive value of CTCs with that of conventional tumor markers. METHODS: This retrospective study included 328 patients with gastrointestinal malignancies from two hospitals in the same region. The internal modelling cohort comprised 214 patients (T1 + T2, n = 49; T3 + T4, n = 165), and the independent validation cohort comprised 114 patients (T1 + T2, n = 55; T3 + T4, n = 59). Univariable and multivariable logistic regression analyses were used to identify stage-related factors associated with CTCs. Three-dimensional-matched feature sets were constructed to minimize bias from unequal feature numbers. Repeated stratified fivefold cross-validation, the foldwise synthetic minority oversampling technique, and weighted cross-entropy loss were used to address severe class imbalance. Model performance was evaluated using AUC, balanced accuracy, F1 score, and calibration metrics rather than overall accuracy. Logistic regression, decision tree, support vector machine, k-nearest neighbors, and the newly developed CTC-AttnNet were compared. K‒M and Cox analyses were performed as exploratory recurrence analyses. RESULTS: CTC count alone moderately discriminated the overall TNM stage (AUC = 0.742) but poorly discriminated the T stage (AUC = 0.595); the AUCs of all four conventional tumor markers were < 0.60. Advanced overall TNM stage was independently associated with CTC positivity (OR = 1.844, 95% CI: 1.255-2.710; P = 0.002). Under dimension-matched conditions, the CTC-containing feature set achieved an external validation AUC of 0.76, outperforming the dimension-reduced conventional marker combination (AUC = 0.65). CTC-AttnNet achieved an external validation AUC of 0.82, a balanced accuracy of 0.78, and an F1 score of 0.76. Ablation analysis revealed that the attention module improved the recognition of minority class T1 + T2 cases. CTC positivity was also independently associated with recurrence (HR = 5.263, 95% CI 1.637-16.927; P = 0.005). CONCLUSION: CTCs were significantly associated with gastrointestinal tumor stage and postoperative recurrence. Multivariable CTC-based models discriminated T stage better than models based on four conventional serum tumor markers did, although the CTC count alone had limited discriminatory ability. CTC-AttnNet represents an exploratory noninvasive stratification tool for this retrospective regional cohort and provides a methodological framework for developing liquid biopsy prediction models.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Li L, Sun C, Wang S, Qiang H, Liu Z, Wang H, et al. CTC-AttnNet: development and validation of an attention-based neural network for staging gastrointestinal malignancies. World J Surg Oncol. 2026 Aug 1;24(1):409. doi:10.1186/s12957-026-04530-0. PMID: 42947149.Checked: Abstract only
See all 2 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
To develop and compare machine-learning (ML) models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics, clinical variables, and their combination for predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer, and to critically evaluate the stability of each model in an independent test cohort. This retrospective single-center study included 272 patients treated between May 2020 and May 2025 who were retrospectively analyzed. pCR was determined using the Miller-Payne grading system, corresponding to ypT0/Tis ypN0 (no residual tumor in sampled axillary nodes). In total, 1,834 features were extracted from pretreatment phase-3 DCE-MRI with PyRadiomics. All feature selection (Mann–Whitney U test, Spearman redundancy pruning, LASSO) and hyperparameter tuning were performed exclusively in the training cohort ( n = 217); the held-out test cohort ( n = 55) was used once for final evaluation. LR, a radial-basis-function (RBF) SVM, and an MLP were compared, together with a clinical signature (HER2 and axillary nodal status) and a radiomics–clinical nomogram; discrimination (AUC) and decision curve analysis (DCA) were assessed. Of 272 patients, 98 (36.03%) achieved pCR. Mann–Whitney screening and Spearman pruning retained 34 features, from which LASSO selected 17 with non-zero coefficients. The SVM-based radiomics model performed best (training AUC: 0.873; test AUC: 0.782). The clinical signature was the most stable model (training AUC 0.885; test AUC 0.806), whereas the nomogram, despite the highest training AUC (0.901), showed a marked decline in the test cohort (AUC 0.686, 95% CI 0.350–1.000), and its DCA advantage was not reproduced in the small test cohort. HER2 positivity and node-negative disease robustly identified patients likely to achieve pCR, and MRI radiomics provided moderate complementary discrimination; however, the incremental benefit of the combined nomogram was not confirmed in the independent test cohort, indicating possible overfitting. The radiomics-based models require stratified or external validation before clinical deployment.
Journal IF-equivalent: 3.8 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Li H, Zhang L, Zeng Y, Ye Z, Zhang Y. MRI-based radiomics analysis: comparative application of different machine learning models in predicting pathological complete response after neoadjuvant chemotherapy for breast cancer. BMC Med Imaging. 2026 Oct 28 [Epub ahead of print]. doi:10.1186/s12880-026-02917-9.Checked: Abstract only - Abstract
Background: Machine learning-based assessment of tumor-infiltrating lymphocytes (TILs) has been proposed to improve the reproducibility and clinical utility of immune-contexture evaluation in breast cancer histopathology. However, existing studies differ substantially in task definition, annotation strategy, model architecture, validation design, and clinical endpoint. This systematic review and evidence-gap analysis aimed to map the translational readiness of TIL-related machine learning studies in breast cancer histopathology and to identify cluster-specific methodological gaps.
Methods: A systematic search of PubMed, Web of Science, Scopus, ScienceDirect, and Google Scholar was conducted up to 20 April 2026, supplemented by backward citation tracing and a targeted Google search of the 30 most relevant additional results. Eligible studies included breast cancer histopathology-based machine learning, deep learning, artificial intelligence, or computational image-analysis studies evaluating TILs or TIL-related immune-cell features. Studies were grouped by principal application into detection/classification, outcome prognostication, and treatment-response prediction. Extracted variables included data source, specimen type, model family, annotation strategy, validation design, clinical comparator use, calibration, decision-curve analysis, incremental value, reproducibility, and methodological challenges. Because of heterogeneity in endpoints and performance metrics, synthesis was descriptive and evidence-map based.
Results: The primary evidence map included 51 histopathology-image studies: 19 detection/classification, 18 outcome prognostication, and 14 treatment-response prediction studies. External validation was reported in 31.6% of detection/classification studies, 61.1% of prognostication studies, and 57.1% of treatment-response prediction studies. Clinical comparator reporting increased across the same continuum, from 63.2% to 94.4% and 92.9%, respectively. However, calibration was reported in only one prognostication study (5.6%) and in no detection/classification or treatment-response prediction studies. Decision-curve analysis was similarly limited to one prognostication study. Class imbalance was near-universal (94.7–100% across clusters). Scanner/stain robustness was addressed in a majority of studies (63.2–83.3%), but fairness or subgroup analyses remained inconsistently reported (27.8–57.1%).
Conclusions: TIL machine learning in breast cancer is technically active but clinically under-validated. Future studies should move beyond model development toward externally validated, calibrated, clinically benchmarked, reproducible, and decision-relevant evidence, using cluster-specific planning standards for detection, prognostication, and treatment-response prediction. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024574566 , identifier CRD42024574566.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Dunenova G, Sarsembayev A, Oshibayeva A, Kyrykbayeva S, Abdrakhmanova Z, Omarova B, et al. Machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer histopathology: a systematic review and evidence-gap analysis of translational readiness. Front Oncol. 2026 Oct 26;16:1908349. doi:10.3389/fonc.2026.1908349.Checked: Abstract only
Lung cancer: treatment, trials and AI
No new qualifying paper for this issue.
Pancreatic cancer: treatment, trials and AI
- Abstract
Background and Objectives: This study evaluated whether preoperative neutrophil-to-lymphocyte ratio (NLR) improves prediction of clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreaticoduodenectomy. Materials and Methods: This single-center retrospective cohort included 120 of 158 patients operated on between January 2019 and March 2026; 38 lacked documented pancreatic duct diameter. CR-POPF followed the 2016 International Study Group on Pancreatic Surgery definition. A random forest incorporating duct diameter, estimated blood loss, and NLR was compared with a model omitting NLR and penalized logistic regression using 10-times-repeated stratified five-fold cross-validation. Post hoc clinicopathological sensitivity analyses added pancreatic texture and tumor origin, then tested inflammatory–nutritional markers individually. Discrimination, average precision, Brier score, calibration, and exploratory decision-curve analysis were assessed.
Results: CR-POPF occurred in 16 patients (13.3%). Biochemical leak occurred in 28 (23.3%). The NLR-integrated model achieved an area under the receiver operating characteristic curve (AUC) of 0.721 (95% confidence interval [CI], 0.576–0.850), versus 0.760 for the comparator and 0.736 for penalized logistic regression. Expanded baseline AUC was 0.743; adding NLR yielded 0.715. Monocyte-to-lymphocyte ratio increased AUC numerically to 0.779, but the paired difference remained uncertain (ΔAUC, 0.037; 95% CI, −0.017 to 0.096). Other evaluated markers showed no clear incremental benefit. Postpancreatectomy acute pancreatitis occurred in seven (5.8%).
Conclusions: NLR did not demonstrate incremental predictive value. The monocyte-to-lymphocyte ratio finding was hypothesis-generating. Small event numbers and predictor selection outside cross-validation limit interpretation; performance may remain optimistic. These findings do not support clinical implementation without independent external validation.
Journal IF-equivalent: 4.0 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-30; not the official Clarivate IF)Reference: Gunes G, Ozcan A, Unsal A. Internal Evaluation of a Machine Learning-Based Random Forest Model for Clinically Relevant Postoperative Pancreatic Fistula After Pancreaticoduodenectomy: Limited Incremental Value of Inflammatory–Nutritional Markers. J Clin Med. 2026 Oct 28;15(21):8379. doi:10.3390/jcm15218379.Checked: Abstract only
Prostate cancer: treatment, trials and AI
- Abstract
Prostate cancer (PCa) demonstrates complex associations with type 2 diabetes mellitus (T2DM) and ageing, though their interconnected molecular mechanisms remain poorly defined. This study aimed to identify and validate diagnostic biomarkers at the intersection of these pathways to enable early detection and risk stratification in high-risk populations. Bioinformatic analysis was performed on PCa and T2DM transcriptomic data downloaded from the GEO database, followed by differential expression analysis, weighted gene co-expression network analysis (WGCNA), and functional enrichment. Eight machine learning algorithms were applied to identify hub ageing-related genes (ARGs), with SHAP analysis interpreting feature importance. A nomogram model was constructed and validated across multiple cohorts. Immune infiltration, drug sensitivity, single-cell RNA sequencing, and spatial transcriptomics were used for mechanistic exploration. Three key ARGs (SOX4, PRNP, and FOXO4) were identified and used to develop a diagnostic model with high accuracy (AUCs: 0.726-0.905). SOX4 promoted immune evasion, PRNP exhibited context-dependent duality, and FOXO4 functioned as a tumor suppressor. Spatial and single-cell analyses revealed cell-type-specific expression and fibroblast-epithelial communication via MIF, LRP1, and FZD1. Drug sensitivity profiling identified abiraterone and docetaxel as potential therapeutics for high- and low-risk patients, respectively. In conclusion, SOX4, PRNP, and FOXO4 are key biomarkers linking PCa, T2DM, and ageing, and the diagnostic model shows robust performance and clinical utility, providing insights into microenvironment-driven mechanisms and personalized treatment strategies for PCa patients with T2DM.
Journal IF-equivalent: 5.1 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-30; not the official Clarivate IF)Reference: Zhang W, Zhang X, Zhang C, Shi X, Deng Y, Gao Y, et al. Identification of ageing-related genes in prostate cancer with type 2 Diabetes using machine learning: a multi-omics study focusing on SOX4, PRNP, and FOXO4. Artif Cells Nanomed Biotechnol. 2026 Oct 28;54(1):890-907. doi:10.1080/21691401.2026.2737622. PMID: 42952172.Checked: Abstract only - Abstract
Objective. Prediction of dose-averaged linear energy transfer (LETd) may facilitate LET-aware evaluation of proton therapy plans. This study developed a cascade deep learning framework for simultaneous three-dimensional prediction of dose and LETd distributions for conventional dose-based optimization (DoseOpt) and LETd-incorporated optimization (LETOpt) in prostate intensity-modulated proton therapy. Approach. A total of 150 patients with prostate cancer were included, each with DoseOpt and LETOpt plans. Planning CT images and anatomical structure masks were used as inputs. The cascade network transferred an intermediate latent representation from the dose prediction pathway to the LETd prediction pathway. Stepwise, multitask, and anatomy-only LETd (only-LET) networks served as comparators. Model performance was evaluated using five-fold cross-validation. Main results. All dose-predicting models showed high spatial agreement, with cascade Dice similarity coefficients of approximately 0.93, 0.96, and 0.95 for the 50%, 75%, and 95% prescription isodose volumes. No significant differences in structure-specific dose errors remained after false discovery rate correction. The cascade model achieved the lowest global LETd MAE for DoseOpt and LETOpt, 0.108 ± 0.025 and 0.111 ± 0.025 keV/μm, respectively, significantly lower than those of the multitask, stepwise, and only-LET models. Structure-specific LETd performance was endpoint dependent, with favorable cascade performance for several CTV and organ-at-risk endpoints. LETOpt primarily redistributed LETd while largely preserving dose, and the cascade model reproduced the principal plan-specific changes, including reductions in rectal and rectal-wall LETd. Mean inference time was 0.505 ± 0.020 s per patient. Significance. The proposed framework enables rapid simultaneous prediction of dose and LETd for predefined planning strategies from patient anatomy without requiring a precomputed dose distribution, supporting LET-aware plan comparison and providing a basis for future LETd-guided biological optimization.
Journal IF-equivalent: 2.9 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Liu Q, Wei S, Peng H, Tang Y, Chen X, Dai J, et al. A cascade deep learning network for simultaneous prediction of dose and linear energy transfer in proton therapy of prostate cancer. Phys Med Biol. 2026 Oct 27 [Epub ahead of print]. doi:10.1088/1361-6560/aeba33. PMID: 42945684.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
Introduction: Glioblastoma (GBM) commonly recurs close to the original gross tumor volume (GTV), suggesting treatment-resistant subregions may be present before radiologically visible progression. This study investigated whether deep learning on apparent diffusion coefficient (ADC) maps could generate pixel-wise probability maps of treatment-resistant regions within the GTV.
Methods: The training/validation set included images of 64 in-house patients after radiation fraction 10 (Fx10) and 40 external patients before treatment used as an auxiliary cross-domain training cohort; the held-out test set included 16 in-house Fx10 patients. Resistant regions were defined as the overlap between the GTV from scans acquired at radiation fraction 10 and subsequent recurrence after finishing treatment. Models were trained on 2D GTV-cropped slices with patient-level separation to prevent data leakage. Single- and multimodal MRI inputs were compared, and ADC-based models (including baseline UNet, pretrained ResNet34, BiomedCLIP-assisted UNet, and a BLIP image-text UNet) were later evaluated. Performance was evaluated using conventional metrics, including ROC AUC, PR AUC, F1-score, recall, specificity, and precision.
Results: ADC was the most informative single modality, achieving 69% and 80% for Receiver Operating Characteristic (ROC)- and Precision-Recall (PR)-Area Under the Curve (AUC), respectively. In the ADC-based architecture comparison, no model was consistently superior across all metrics. The baseline UNet achieved the highest accuracy, F1-score, and recall, whereas pretrained ResNet34 and BLIP-assisted models showed slightly higher ROC AUC than the baseline UNet (both 70%). Error analysis showed that performance decreased substantially for small or sparse resistant regions, and false-negative errors were enriched near resistant-region and GTV boundaries.
Conclusions: ADC-based deep learning showed feasibility for pixel-wise spatial mapping of treatment-resistant glioblastoma regions within the GTV. A UNet trained from scratch provided the strongest sensitivity/F1 trade-off, whereas pretrained and VLM-assisted models provided only metric-specific benefits. Model comparisons showed that BiomedCLIP-assisted and BLIP image-text UNet models did not clearly improve fine spatial segmentation performance over the baseline UNet.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-07, retrieved 2026-10-08; not the official Clarivate IF)Reference: Rezvanjou S, Osapoetra LO, Moore-Palhares D, Lawrence LSP, Lau AZ, Sahgal A, et al. Pixel-Wise Prediction of Glioblastoma Recurrence Using Mid-Treatment MRI and Deep Learning. Cancers. 2026 Oct 27;18(21):3465. doi:10.3390/cancers18213465.Checked: Abstract only - Abstract
Background/Objectives: Glioma diagnosis increasingly depends on the integration of magnetic resonance imaging (MRI), molecular characteristics, and clinical information. Foundation models may support AI-assisted diagnosis through transferable representation learning and multimodal integration, but evidence supporting clinical use remains fragmented. This systematic scoping review maps current applications of foundation models in glioma neuroimaging and evaluates their relevance to tumor delineation, molecular characterization, multimodal diagnostic support, and clinical translation. Following PRISMA-ScR principles, six bibliographic sources were searched through 26 July 2026. Eligible records were categorized by model family, pretraining and adaptation strategy, imaging application, multimodal integration, validation design, and clinical relevance. Among 3688 identified records, 2071 remained after deduplication, 525 underwent detailed eligibility assessment, and 125 were included. Evidence was concentrated in promptable segmentation (n = 47), reusable brain MRI representations (n = 26), vision language and language model applications (n = 22), molecular classification and prognosis prediction (n = 16), and translational or governance studies (n = 14). Foundation models showed potential for tumor delineation, reusable representation learning, molecular phenotype prediction, and multimodal diagnostic support. However, independent external validation, robustness to imaging heterogeneity and missing sequences, calibration, and prospective workflow evaluation remain limited. Foundation models offer promising capabilities for AI-assisted glioma diagnosis, particularly in segmentation, reusable MRI representation learning, molecular characterization, and multimodal support. Current evidence is nevertheless insufficient for routine clinical implementation. Multicenter validation, standardized evaluation, uncertainty assessment, and prospective studies of diagnostic and workflow utility should be priorities for future research.
Journal IF-equivalent: 4.3 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Yu Y, Zhang P, Xia C, Ma X, Xie F. Foundation Models for Glioma MRI: A Systematic Scoping Review of Clinical Applications and Translational Readiness. Brain Sci. 2026 Oct 26;16(11):1159. doi:10.3390/brainsci16111159.Checked: Abstract only
Ovarian cancer: treatment, trials and AI
- Abstract
138 PARP inhibitors (PARPi) have transformed the treatment of high-grade serous tubo-ovarian cancer 139 (HGSOC), but current predictive biomarkers lack accuracy, scalability, and availability. Here, we 140 present an AI framework that infers PARPi response directly from routine hematoxylin and eosin 141 (H&E)-stained slides. Given the limited availability of large PARPi-treated datasets, we use platinum– 142 taxane chemotherapy response as a surrogate endpoint, training on 519 platinum–taxane-treated patients 143 and validating in two independent cohorts comprising 3,905 patients. Across these cohorts, the model 144 stratifies patients into risk groups based on overall survival, despite the variabilities across centers. 145 Applied to an independent cohort of 118 BRCA-wildtype patients receiving first-line niraparib, the 146 model’s risk predictions are associated with progression-free and overall survival, independent of 147 clinical covariates. To our knowledge, this is the first demonstration that PARPi response in HGSOC 148 can be derived directly from routinely collected H&E slides, supporting more precise and accessible 149 patient selection for PARPi therapy. 150 151
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Deep learning-based analysis of routine histopathology slides predicts response to PARP inhibitors in high grade serous ovarian cancer. Apollo. [Epub ahead of print]. doi:10.17863/cam.136330.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
Background & Aims: Clinical responses to atezolizumab plus bevacizumab (AB) therapy can vary across hepatocellular carcinoma (HCC) nodules among individual patients. This study aimed to derive and externally validate a non-invasive radiomics signature (ABRad) translated from a transcriptome signature for nodule-level prediction of AB response (ABS).
Methods: We originally derived and validated ABS using 209 and 44 pre-treatment HCC samples from the IMbrave150 and GO30140 trials, respectively. ABS was then translated into ABRad using a machine-learning approach on 59 patients with paired genome-wide transcriptome and pre-resection computed tomography imaging from surgically resected HCC. Subsequently, ABRad was externally validated using 178 HCC nodules from 55 patients treated with AB therapy.
Results: The 148-gene ABS predicted therapeutic response and progression-free survival (PFS) in the clinical trial cohorts. The final ABRad model, constructed using a random forest algorithm, was heavily weighted toward contrast-enhanced features. In the external validation cohort, ABRad demonstrated a significant positive association with the degree of therapeutic response (p <0.001). The areas under receiver operating characteristics curve for predicting complete response (CR), objective response (OR), and disease control (DC) were 0.71 (95% confidence interval [CI], 0.61-0.80), 0.65 (95% CI, 0.56-0.73), and 0.69 (95% CI, 0.59-0.78), respectively. Nodule-level ABRad was independently associated with therapeutic response with adjusted odds ratios of 3.39 (95% CI, 1.40-8.21) for CR, 2.55 (95% CI, 1.19-5.67) for OR, and 2.60 (95% CI, 1.03-6.56) for DC. Patient-level ABRad was significantly associated with prolonged PFS (p = 0.005).
Conclusions: ABRad successfully predicted nodule-level response to AB therapy in HCC.
Journal IF-equivalent: 5.3 (OpenAlex 2-year mean citedness, value as of 2026-10-24, retrieved 2026-10-26; not the official Clarivate IF)Reference: Tanaka T, Fujiwara N, Fujii T, Kimura G, Izuoka K, Tempaku M, et al. A radio-transcriptome signature for nodule-level prediction of response to atezolizumab plus bevacizumab in hepatocellular carcinoma. Liver Cancer. 2026 Oct 27 [Epub ahead of print]. doi:10.1159/lic/admag026.Checked: Abstract only - Abstract
Background: Radioresistance limits durable local control in hepatocellular carcinoma (HCC), but the relationship between cuproptosis-related metabolism and radiation response remains incompletely characterized.
Methods: We integrated 26 cuproptosis- and radiation-related genes across LIRI-JP ( n = 231), GSE14520 ( n = 225), and TCGA-LIHC ( n = 325). Consensus clustering and 127 machine-learning configurations were used to develop and validate a prognostic score. GSE149614 single-cell data, scTenifoldKnk virtual knockout, an irradiated MHCC97H model, and postoperative immunohistochemistry were used for biological evaluation.
Results: A gradient boosting machine score consistently stratified overall survival across the three cohorts. In bulk cohorts, ENTPD5 expression was lower in the high-risk group and inversely correlated with the risk score. By contrast, ENTPD5 showed a weak positive association with the score in 6,033 malignant cells and was upregulated in radiation-selected MHCC97H cells ( p = 0.036; log2 fold change = 0.316). Virtual knockout predicted attenuation of unfolded-protein-response and radioresistance-associated programs. Among 42 patients with evaluable clinical outcomes, recurrence occurred in 11/39 ENTPD5-positive and 3/3 ENTPD5-negative cases (28.2% vs. 100%; Fisher’s exact p = 0.032). ENTPD5-positive cases had longer progression-free survival (log-rank p = 0.019; univariable Cox hazard ratio = 0.22, 95% confidence interval 0.05–0.89), although the ENTPD5-negative group was small.
Conclusions: The integrated score is prognostic in retrospective HCC cohorts, and ENTPD5 shows context-dependent associations across bulk tumors, malignant-cell states, radiation-selected cells, and postoperative tissues. These findings support ENTPD5 as a candidate marker for further study rather than establish a causal or treatment-predictive role.
Journal IF-equivalent: 3.9 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Wu QQ, Zhang XF, Zhang Y, Wang HY, Wang LF, Xu Y, et al. Deciphering ENTPD5-driven radioresistance in hepatocellular carcinoma via multi-algorithm machine learning and single-cell virtual knockout. BMC Cancer. 2026 Oct 26 [Epub ahead of print]. doi:10.1186/s12885-026-17093-w.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Objective: Artificial intelligence (AI) chatbots are increasingly used as accessible sources of medical information. However, concerns remain regarding the quality, reliability, readability, and clinical applicability of AI-generated health content. Intravesical Bacillus Calmette-Guérin (BCG) therapy is a standard adjuvant treatment for intermediate- and high-risk non-muscle invasive bladder cancer (NMIBC) and requires adequate patient understanding of treatment procedures and potential adverse effects. This study aimed to evaluate the quality, reliability, readability, and guideline adherence of information provided by commonly used AI chatbots regarding intravesical BCG therapy.
Methods: This cross-sectional methodological study evaluated responses generated by four AI chatbots (ChatGPT, Copilot, Gemini, and Perplexity) to a standardized question regarding intravesical BCG therapy. Responses were independently assessed by five urologists using validated tools including DISCERN, PEMAT-P, WRR, and the Coleman-Liau Index (CLI). Compliance with European Association of Urology (EAU) guidelines was evaluated using a 5-point Likert scale.
Results: Responses ranged from 454 to 550 words and required a university-level reading ability (CLI range: 16.02–19.35). DISCERN scores indicated moderate information quality across all chatbots, with Gemini demonstrating the numerically highest median score (51 [47–59]). PEMAT-P actionability scores were generally low (median 40%). Guideline adherence was moderate, with all chatbots receiving a median Likert score of 3.
Conclusion: AI chatbots provide moderate-quality information regarding intravesical BCG therapy but require high reading-grade levels and offer limited actionable guidance. AI-generated information may support patient education but should not replace physician counseling.
Journal IF-equivalent: 0.3 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-30; not the official Clarivate IF)Reference: Zengin S, Boyaci C, Olgun A, Kutluay O, Gul A. Evaluation of Patient Information Provided by Artificial Intelligence Chatbots on Intravesical BCG Therapy for Bladder Cancer: A Cross-Sectional Methodological Study. New J Urol. 2026 Oct 28;21(3):131-7. doi:10.33719/nju1929548.Checked: Abstract only - Abstract
Background: Bladder neuroendocrine carcinoma (BNEC) is a rare yet highly aggressive malignancy with an extremely poor prognosis. The current TNM staging system provides limited prognostic information and validated prognostic prediction tools are lacking. This study aimed to develop and validate a machine learning (ML)-based model for individualised survival prediction in BNEC patients using large-scale population data.
Methods: Data from pathologically confirmed BNEC patients from 2004 to 2021 were extracted from the US Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. On the basis of the independent prognostic factors, eight machine learning models, namely, random forest (RF), gradient boosting machine (GBM), LASSO-Cox, CoxBoost, support vector machine (SVM), XGBoost, SuperPC, and plsRcox, were constructed using the training set (70% of the total sample). Model performance was comprehensively evaluated on the validation set (30% of total samples) using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Results: A total of 2117 patients with BNEC were included in the final analysis. Multivariate analysis revealed age >74 years (HR = 1.51, p<0.001), male sex (HR = 1.17, p=0.038), advanced T stage (T2–T4), positive nodal status (N1–N2), distant metastasis (M1, HR = 1.99, p<0.001), surgery (TURBT: HR = 0.75, P = 0.017; RC: HR = 0.36, P<0.001), radiotherapy (HR = 0.79, p=0.002), and chemotherapy (HR = 0.43, p<0.001) as independent prognostic factors. Among the eight machine learning models, the GBM algorithm demonstrated the best performance in both the training and validation sets, with AUC values for 1-year, 3-year, and 5-year survival prediction being 0.846/0.737, 0.825/0.730, and 0.816/0.742, respectively. Decision curve analysis (DCA) demonstrated greater clinical net benefit across a broad range of threshold probabilities, while the calibration curves indicated high agreement between the predicted and observed outcomes. Risk stratification based on the GBM-derived risk score revealed significant differences in survival between the high-risk and low-risk groups (p < 0.0001).
Conclusion: The GBM-based machine learning prognostic model demonstrated comparatively favorable performance in predicting survival outcomes among patients with BNEC. This model may provide a useful tool for individualized prognostic assessment and risk stratification, although further validation in multicentre prospective cohorts is warranted.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Qi S, Dong W, Shi Z, Sun Y, Zhang D, Liu C, et al. Development and validation of a machine learning-based model for predicting survival in patients with bladder neuroendocrine carcinoma: a large retrospective study. Front Oncol. 2026 Oct 27;16:1956255. doi:10.3389/fonc.2026.1956255.Checked: Abstract only