AI and Cancer Research ── 2026-10-22
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（4） · Breast（3） · Lung（2） · Pancreatic（1） · Prostate（2） · ACC（0） · Brain（1） · Ovarian（0） · Endometrial（0） · Gastric（2） · Liver（1） · Kidney（0） · Bladder（1）
Cancer molecular biology and AI
- Abstract
PURPOSE: In urological practice, the decision to resect or preserve a kidney with obstruction, tumor or atrophy is based on split renal function, although SPECT renal dynamic scintigraphy as the reference standard is resource-intensive. We developed and externally validated a non-contrast CT radiomics model to identify nonfunctioning kidneys and support urological decision-making in this setting. METHODS: In this three-center retrospective study (TRIPOD+AI), adults underwent non-contrast CT and SPECT renal dynamic scintigraphy within 72 h. IBSI-compliant radiomics features were extracted with PyRadiomics, and between-kidney asymmetry features were constructed. After three-stage selection (Spearman, recursive feature elimination, and LASSO), 12 machine learning models were developed and externally validated in 2 independent cohorts. A Rad-score and clinical nomogram were also derived. RESULTS: Among 1029 patients, the internal cohort included 418 patients. Twenty-three of features were retained. The random forest model yielded an internal test AUC of 0.9426, sensitivity of 85.5%, specificity of 88.3%, and negative predictive value of 95.6%. Its external AUCs were 0.875 and 0.913. The nomogram attained external AUCs of 0.920 and 0.881. At the thresholds fixed in the training data, the random forest achieved external sensitivity of 66.0 and 76.4% with negative predictive values of 94.8 and 93.3%, and the nomogram 92.5 and 93.7% with 98.3 and 97.2%. Both radiomics models outperformed logistic-regression baselines built from clinical variables and simple CT size and asymmetry measurements, although the margin over the combined baseline was not statistically significant in one external cohort. The Rad-score was correlated with single-kidney GFR (ρ = -0.738, P < 0.001) and facilitated risk stratification. CONCLUSION: Non-contrast CT radiomics identified nonfunctioning kidneys without contrast agents or radiotracers. Because non-contrast CT is already part of routine urological workup, this externally validated rule-out tool may reduce unnecessary SPECT examinations and support decisions between nephrectomy and nephron-sparing management.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Jiang Y, Zheng Y, Feng Z, Bao L, Wei Q. Non-contrast CT radiomics for identifying nonfunctioning kidneys: a multicenter development and external validation study. Int Urol Nephrol. 2026 Oct 20 [Epub ahead of print]. doi:10.1007/s11255-026-05451-3. PMID: 42902104.Checked: Abstract only - Abstract
Conventional radiomics is hindered by manual feature engineering and poor cross-tumor generalization. We introduce SAFARI (Self-supervised Tumor-Agnostic Framework for Radiomics Representation Learning), a novel, self-supervised model for radiomics that learns disease agnostic tumor characteristics. Built on a transformer architecture with a dual-objective self-supervised strategy, combining masked feature reconstruction and contrastive learning across tumor subvolumes, SAFARI was trained on 1,218 radiomic features from 27,144 tumor subvolumes across 1,086 patients with six major cancer types (lung, liver, colorectal, kidney, pancreatic, and colorectal liver metastases). In transfer learning experiments on independent datasets comprising 762 patients, fixed SAFARI embeddings outperformed our matched conventional radiomics baselines across multiple downstream tasks. Performance gains were substantial for predicting HER2 status in gallbladder cancer (AUC: 0.758 vs. 0.610), NSCLC histopathology (accuracy: 71.3% vs. 62.1%), and EGFR mutation status (AUC: 0.805 vs. 0.690). By demonstrating that a single pretrained model outperforms matched task-specific baselines on the majority of endpoints, SAFARI establishes a new, more robust paradigm for radiomic analysis in oncology.
Journal IF-equivalent: 3.2 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Gupta P, Bose K, Dutta N, Mudgil P, Arora C. SAFARI: A Self-supervised Tumor-Agnostic Framework for Radiomics Representation Learning in Oncology Practice. ACM Trans Comput Healthcare. 2026 Oct 19 [Epub ahead of print]. doi:10.1145/3859479.Checked: Abstract only
See all 4 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
BACKGROUND: Breast-conserving surgery (BCS) is the standard care for early breast cancer. However, positive surgical margins lead to high recurrence and require reoperation, remaining a clinical challenge. This study aims to develop an ensemble model to preoperatively predict positive margins using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: A total of 887 patients from three Chinese medical centers were included. Patients from the Yunnan (581 patients) and Fujian (51 patients) provinces were used for model development, with external testing on 188 patients from Guangdong and prospective testing on 67 patients from Yunnan. Three base models were developed: the clinical model based on 32 features, the deep learning (DL) model using ResNet-34, and the radiomics model utilizing radiomics features. The ensemble model combined predictions from these three base models using weighted voting. Performance metrics (accuracy, AUC, sensitivity, specificity) were computed, and prognostic analyses were conducted with a public dataset. RESULTS: The ensemble model outperformed each of the three base models, with the AUC of 0.931, accuracy of 0.872, sensitivity of 0.871, and specificity of 0.880 in the validation set. Robustness was further confirmed in independent test cohorts, yielding AUCs of 0.762 and 0.861. Furthermore, patients identified at high-risk by the model exhibited poor recurrence-free survival (RFS, HR = 8.117, p < 0.0001) and overall survival (OS, HR = 5.748, p < 0.0001), underscoring its prognostic value. CONCLUSIONS: The ensemble model based on DCE-MRI effectively predicts positive margins and prognosis for BCS patients, facilitating the clinical decision-making on the surgery. Trial registration This study was approved by the Ethics Committee of the Yunnan Cancer Hospital (KYLX2023-134), Xiang'an Hospital of Xiamen University (XDYX202305K26), and Cancer Hospital of Shantou University Medical College (2024059). The study was also registered with the Chinese Clinical Trial Registry (ChiCTR2400083298, Registered 19 April 2024, https://www.chictr.org.cn/showproj.html?%20proj=207948 ).
Journal IF-equivalent: 5.8 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Zhao X, Bai JW, Dong LA, Li ZH, He JZ, Chen YX, et al. Preoperative prediction of positive margins in breast-conserving surgery using an ensemble model based on DCE-MRI: a multi-center retrospective and prospective study. Breast Cancer Res. 2026 Jul 25;28(1):181. doi:10.1186/s13058-026-02354-0. PMID: 42902311.Checked: Abstract only - Abstract
BACKGROUND: Brain metastasis (BM) is a devastating complication of breast cancer (BC) with a poor prognosis. Early identification of high-risk patients is essential but currently lacks accurate predictive tools. This study aimed to develop a stable machine learning model for predicting BM in a large, diverse patient population. METHODS: We conducted a retrospective cohort study of 186,351 primary BC patients from the SEER program, the National Cancer Database, and a multi-center Chinese cohort. Patients were split into training and validation sets. Candidate predictors included demographic, clinicopathological, biomarker, and treatment-related variables, including surgery, chemotherapy, endocrine therapy, neoadjuvant therapy, and radiotherapy. LASSO regression was used for feature selection. Nine machine learning models were developed and compared using area under the curve (AUC) and decision curve analysis. The optimal model was deployed as an online tool. RESULTS: Among 186,351 patients, 2,982 (1.6%) developed BM. In baseline comparisons, patients who developed BM more frequently exhibited advanced disease characteristics and different treatment patterns, likely reflecting more aggressive disease presentation. Key predictors retained in the final parsimonious model included AJCC N/M/T stage, age, and surgery status. The LASSO-regularized Logistic Regression model achieved superior discrimination in the validation set, with an AUC of 0.907 (95% CI: 0.857-0.958). The model demonstrated good calibration, clinical net benefit, and interpretability, with SHAP analysis confirming the contribution of each retained predictor to individual risk estimates. Survival analysis confirmed the poor prognosis associated with BM (p < 0.001). An interactive online tool was developed for real-time risk estimation. CONCLUSION: We developed and internally validated a accurate, clinically translatable model for predicting BM in BC using over 186,000 patients from North American and Asian cohorts. This web-based tool can assist clinicians in identifying high-risk individuals, enabling personalized management and potentially improving patient outcomes.
Journal IF-equivalent: 8.3 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Yuan Q, Sha Y, Yu R, Yu H, Ye R, Du Y, et al. Interpretable machine learning model for brain metastasis in breast cancer: a large-scale, multi-center study. J Transl Med. 2026 Jul 24;24(1):1326. doi:10.1186/s12967-026-08675-0. PMID: 42902382.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
Tumor invasion and distant metastasis remain major causes of treatment failure in lung adenocarcinoma (LUAD), thereby elucidating the molecular mechanisms is critical for precise treatment of LUAD. Using bulk RNA-Seq and single cell transcriptome data, combining with multi-machine learning methods, we identified GOLM1 as a core gene associated with poor prognosis in LUAD, which is highly expressed in malignant epithelial cells characterized by increased extracellular matrix (ECM)-integrin signaling, glycosylation remodelling and invasion-related transcriptional programs. Mechanistically, we validated that STAT1 is an upstream transcription activator of GOLM1 that forms a STAT1-GOLM1 axis that triggers LUAD invasion. Candidate compounds were prioritized by computational molecular docking against STAT1 and subsequently evaluated in A549 cells. Experimental validation revealed that Compound 1 exhibited the strongest inhibitory effect on LUAD cells by regulating PI3K/AKT signaling, cell-cycle and apoptosis. Collectively, our results elucidated the significant role of STAT1-GOLM1 axis in LUAD and provided a promising AI-directed inhibitor for LUAD.
Journal IF-equivalent: 8.9 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Yang Q, Zeng Y, Ba L, Zhu Z, Bie Y, Zhao N, et al. Integrative transcriptomic and machine-learning analysis of the STAT1–GOLM1 axis in lung adenocarcinoma and small-molecule inhibitor discovery. npj Precis Onc. 2026 Oct 20 [Epub ahead of print]. doi:10.1038/s41698-026-01735-0.Checked: Abstract only - Abstract
Epidermal growth factor receptor (EGFR) mutations play a critical role in guiding targeted therapies for non-small cell lung cancer (NSCLC), yet current diagnostic methods rely heavily on molecular testing. This study proposes a novel patholomics-based approach to predict EGFR mutation status using histopathological (H&E) image features and machine learning.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Cheung E, Li A, Mok F, Kwong V, Cheung A, Tsang E, et al. MO27-6 Prediction of EGFR mutations in non-small cell lung cancer: a patholomics random forest approach. Annals of Oncology. 2026 Nov;37:S299. doi:10.1016/j.annonc.2026.07.158.Checked: Abstract only
Pancreatic cancer: treatment, trials and AI
- Abstract
Introduction: Perineural invasion (PNI) occurs in the majority of pancreatic ductal adenocarcinoma (PDAC) cases and is strongly associated with local recurrence, neuropathic pain, and poor survival, yet no reliable method exists for its preoperative identification. Here, we developed and externally validated a multi-sequence MRI-based intratumoral heterogeneity (ITH) scoring framework integrated with machine learning for the non-invasive prediction of PNI in PDAC.
Methods: Using preoperative T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI from 205 surgically resected patients across four medical centers (100 training, 43 internal test, and 28 and 34 external validation patients), we extracted two-dimensional and three-dimensional ITH features via K-means clustering-based subregion partitioning and incorporated independent clinical predictors tumor location and clinical T-stage to construct combined predictive models. Eleven machine learning algorithms were systematically evaluated.
Results: The combined clinical-ITH model achieved an area under the curve (AUC) of 0.907 in the internal test set and AUCs of 0.860 and 0.869 in the two external validation cohorts. Corresponding AUCs were 0.881, 0.860, and 0.850 for the ITH model and 0.758, 0.342, and 0.700 for the clinical logistic regression model. Discussion SHAP analysis ranked three T2WI-derived features as the largest contributors to the combined model output, feature effects varied in direction and were not uniformly monotonic. These findings demonstrate that multi-sequence MRI-based ITH quantification provides an interpretable approach to preoperative PNI assessment with consistent discrimination across the two external surgical cohorts, offering a potential tool to guide surgical planning and adjuvant therapy selection in pancreatic cancer.
Journal IF-equivalent: 6.1 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Ouyang Z, Song Z, Li Z, Cui S, Liu X, Shen W, et al. Machine learning models integrating 2D and 3D intratumoral heterogeneity features from multi-sequence MRI: accurate prediction of perineural invasion in pancreatic ductal adenocarcinoma. Front Artif Intell. 2026 Oct 20;9:1975752. doi:10.3389/frai.2026.1975752.Checked: Abstract only
Prostate cancer: treatment, trials and AI
- Abstract
This retrospective study developed and validated a T2-weighted imaging (T2WI)-based radiomics model for preoperative prediction of apical positive surgical margins (PSMs) in patients undergoing radical prostatectomy for prostate cancer. Overall, 235 patients (74 with apical PSMs and 161 without apical PSMs) were randomly divided into a training set (n = 164; 69.8%) and an independent test set (n = 71; 30.2%). Eligible patients had preoperative multiparametric magnetic resonance imaging within 6 weeks before surgery, an index lesion involving or abutting the prostatic apex, definitive apical margin status, and standard radical prostatectomy without nerve-sparing. Patients with inadequate imaging for apical assessment or region-of-interest delineation, prior transurethral resection of the prostate, or neoadjuvant androgen-deprivation therapy were excluded. A total of 1409 radiomic features were extracted from T2WI. After normalization and removal of highly correlated features (|r| > 0.99), six optimal features were selected by recursive feature elimination. A gradient boosting machine model was constructed, and performance was assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis. The apical PSM rate was 31.5%. In the test set, the model achieved an AUC of 0.874, Brier score of 0.120, accuracy of 87.3%, sensitivity of 81.8%, and specificity of 89.8%. Skewness was the most important feature, contributing 34.8% to the model. Apical PSMs were significantly associated with extraprostatic extension (P = 0.001), and the tumor-margin model provided the greatest net clinical benefit. T2WI-based radiomics may support individualized preoperative risk assessment and surgical planning for prostate cancer.
Journal IF-equivalent: 2.8 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Liu Y, Zhao YZ, Liu S, Gao XS, Ma MW. Preoperative apical margin risk estimation from T2-weighted MRI radiomics in prostate cancer with MRI-visible apical tumors: toward multidisciplinary decision support. Asian J Androl. 2026 Oct 20 [Epub ahead of print]. doi:10.4103/aja202625. PMID: 42902531.Checked: Abstract only - Abstract
Prostate cancer is one of the most common malignancies among men, and accurate segmentation of Gleason grades is crucial for effective diagnosis and treatment planning. Current deep learning methods often struggle with fusing complementary features from CNNs and Transformers, primarily relying on naive feature fusion techniques such as concatenation or summation. These methods fail to leverage the complementary strengths of texture and global context, limiting segmentation performance. To address this challenge, we propose TexTNet, a novel texture-guided hierarchical architecture that integrates Local Binary Pattern (LBP) features into the query projection of the Swin Transformer, creating a Texture-Guided Attention mechanism. This approach directs the Transformer’s attention to texture-rich regions such as glandular borders and nuclear clusters, improving segmentation accuracy. We further enhance feature fusion with the Dual Path Squeeze-Fusion-Excitation (DP-SFE) module, which mitigates boundary blurring, and introduce Asymmetric Convolution Blocks (ACB) for efficient feature extraction. Additionally, the Cross-Directional Feature Aggregator (CDFA) ensures precise positional reconstruction and noise suppression during decoder fusion. We evaluate TexTNet on the SICAPv2, PANDA, ProstateX, and HuBMAP datasets, demonstrating superior performance over state-of-the-art methods in prostate cancer segmentation. TexTNet shows promise as a clinical decision-support tool, demonstrating robust performance on complex, real-world histopathological images and the potential to improve diagnostic reliability for prostate cancer grading.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-08, retrieved 2026-10-09; not the official Clarivate IF)Reference: Arshad M, Chengliang W, Wajeeh Us Sima M, Shaikh JA, Alkhalaf S, Alturise F. TexTNet: a texture-guided hierarchical architecture for prostate cancer Gleason segmentation in whole slide images. Front Med. 2026 Oct 19;13:1815031. doi:10.3389/fmed.2026.1815031.Checked: Abstract only
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
BACKGROUND: Differentiating true progression (TP) from pseudoprogression (PsP) after chemoradiation for glioblastoma remains clinically difficult. We evaluated externally validated diagnostic performance of magnetic resonance imaging (MRI)-based machine learning models and appraised study quality and transparency. METHODS: PubMed, Embase, MEDLINE, and Cochrane Library were searched through October 1, 2025 (PROSPERO registration CRD420251144894). Eligible adult glioblastoma studies used MRI-based machine learning to distinguish TP from PsP against histopathology and/or clinicoradiologic follow-up. The primary meta-analysis included external test cohorts with extractable 2 × 2 data; internal and combined internal and external test analyses were secondary. Diagnostic accuracy was synthesized with bivariate random-effects models. RESULTS: Thirty-one studies including 2,344 patients undergoing imaging assessment were included. Seven external test cohorts, including 219 patient observations, entered the primary meta-analysis. Pooled sensitivity was 0.78 (95% confidence interval [CI], 0.63-0.88), specificity was 0.75 (95% CI, 0.63-0.84), and summary area under the receiver operating characteristic curve (AUC) was 0.81 (bootstrap 95% CI, 0.69-0.91). Internal test cohorts showed higher apparent performance than external cohorts (AUC, 0.91 vs 0.81), but the difference was not statistically significant (difference, -0.09; 95% CI, -0.22 to 0.02; P = 0.12). Risk of bias was frequently high or unclear, and reporting transparency was heterogeneous. CONCLUSIONS: MRI-based machine learning showed moderate-to-good external validation performance for TP-versus-PsP discrimination, but evidence remains limited by small external test cohorts, bias, and incomplete reporting. Larger transparent multicenter validations are needed before routine clinical implementation. Following chemoradiation for glioblastoma, new or worsening MRI abnormalities may represent either true tumor progression or pseudoprogression. This review evaluated machine learning studies for distinguishing true progression from pseudoprogression, emphasizing models tested at external centers. External validation showed moderate-to-good diagnostic performance, but the evidence was limited by small test cohorts, inconsistent reference standards, and incomplete reporting. Larger multicenter studies using current WHO 2021 molecular definitions, standardized diagnostic criteria, and complete reporting are needed before these models can be used routinely in clinical practice.
Journal IF-equivalent: 1.2 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Zhang X, Guo X, Wang Y, Wagstyl K, Booth TC. MRI-Based Machine Learning to Differentiate True Progression from Pseudoprogression in Glioblastoma: An Updated Systematic Review and Meta-Analysis. Neuro-Oncology. 2026 Oct 19 [Epub ahead of print]. doi:10.1093/neuonc/noag259.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
- Abstract
This dataset contains supporting data and analysis materials for the study entitled “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer.” The deposited materials include: (1) source numerical data underlying the experimental analyses presented in Figures 7 and 8; (2) Supplementary Tables S1-S7; (3) uncropped western blot images; and (4) analysis code used for public-dataset preprocessing, machine-learning analyses, statistical analyses, and visualization. Public transcriptomic datasets analyzed in the study were obtained from GEO, TCGA-STAD, and TISCH2. GEO datasets GSE118916, GSE54129, GSE63089, and GSE65801 were used as the training/discovery cohorts; GSE146996, GSE51575, and GSE66229 were used for external validation; TCGA-STAD was used for survival and clinicopathological analyses; and GSE134520 and GSE167297 were used for single-cell transcriptomic contextualization.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Supporting data for “Multi-cohort machine learning and experimental validation identify THY1 as an extracellular matrix remodeling-associated biomarker in gastric cancer”. Figshare. [Epub ahead of print]. doi:10.6084/m9.figshare.34715742.Checked: Abstract only - Abstract
At Maharat Nakhon Ratchasima Hospital, patients with advanced gastric cancer (AGC) often have very short overall survival (OS), typically only 2-3 months. Many discontinue treatment due to rapid physical decline. Early identification of patients at risk of OS < 90 days could support timely interventions and tailored treatment strategies.
Journal IF-equivalent: 1.7 (OpenAlex 2-year mean citedness, value as of 2026-10-20, retrieved 2026-10-21; not the official Clarivate IF)Reference: Thanestada J, Sihaban K. MO40-5 PREDICT-GC/ShortOS: machine learning and regression models for predicting very short survival in advanced gastric cancer. Annals of Oncology. 2026 Nov;37:S307. doi:10.1016/j.annonc.2026.07.186.Checked: Abstract only
Liver cancer: treatment, trials and AI
- Abstract
Esophageal variceal (EV) bleeding is a lethal complication in patients with hepatocellular carcinoma (HCC). Current risk stratification relies on invasive endoscopy or systemic surrogates of liver cirrhosis. This study aimed to develop a machine learning model utilizing EV-specific CT radiomics to identify high-risk esophageal varices, as defined by endoscopic criteria, in patients with HCC. 151 HCC patients were enrolled and divided into a training cohort (n = 101; 75 high-risk; January 2017 - December 2022) and an independent temporal validation cohort (n = 50; 29 high-risk; January 2023 - December 2024). Endoscopic stigmata were used as an endpoint, including active bleeding or varices with high-risk features. Radiomic features were extracted from contrast-enhanced CT images acquired within six months prior to endoscopy. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), Brier score, calibration curves, and decision curve analysis (DCA). In the training cohort, the varix-specific radiomics signature developed via the least absolute shrinkage and selection operator (LASSO) demonstrated excellent discriminative power (cross-validated AUC: 0.956; 95% CI 0.872-1.000). In the temporal validation cohort, this model maintained high generalizability, yielding an AUC of 0.926 (95% CI 0.846-0.984, sensitivity: 93.1%, specificity: 71.4%) as well as a Brier score of 0.1186 and good calibration (intercept = -0.157, slope = 1.354). Surface-to-Volume Ratio (SVR) and NGTDM Coarseness are feasible radiomics. The varix-specific radiomics model provides highly sensitive and mathematically stable non-invasive risk stratification for EV bleeding in patients with HCC.
Journal IF-equivalent: 2.6 (OpenAlex 2-year mean citedness, value as of 2026-10-21, retrieved 2026-10-22; not the official Clarivate IF)Reference: Li PC, Ma CY, Chang SA, Chen CC. Machine learning-based CT radiomics for identifying high risk esophageal varices of bleeding in hepatocellular carcinoma. Abdom Radiol (NY). 2026 Oct 20 [Epub ahead of print]. doi:10.1007/s00261-026-05808-3. PMID: 42900382.Checked: Abstract only
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
Intravesical Bacillus Calmette-Guérin (BCG) is the standard adjuvant therapy for high-risk non–muscle-invasive bladder cancer (NMIBC), yet outcomes vary, with high-grade recurrences (HGR) occurring in up to 40% of patients at 5 yr. Persistent global BCG shortages, treatment intolerance, and the emergence of alternative therapies have intensified the need for novel biomarkers to guide management [1].
Journal IF-equivalent: 1.5 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: [No authors listed]. Validation of a Computational Histology Artificial Intelligence–based Biomarker for BCG Treatment in Non–muscle-invasive Bladder Cancer in the ANZUP 1301 Randomized Phase 3 Trial. European Urology. [Epub ahead of print]. doi:10.1016/j.eururo.2026.10.004.Checked: Abstract only