AI and Cancer Research ── 2026-11-08
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（2）
Hematologic malignancies: AML（0） · CML（0） · ALL（0） · CLL（0） · Lymphoma（0） · Myeloma（0）
Solid tumors: Breast（2） · Lung（2） · Colorectal（0） · Pancreatic（0） · Prostate（2） · Brain（2） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（0） · Kidney（0） · Bladder（1）
Rare cancers: ACC（0）
Cancer molecular biology and AI
- Abstract
BACKGROUNDEsophagogastroduodenoscopy is operator-dependent, and early upper gastrointestinal neoplasms are frequently missed.Although randomized trials and a previous metaanalysis have reported that real-time artificial intelligence (AI) improves neoplasm detection, the first large multicenter trial with centralized pathology adjudication found no significant benefit for gastric neoplasms.Furthermore, the pooled evidence has not been reassessed following publication of this trial. AIMTo reappraise whether AI-assisted endoscopy increases per-patient neoplasm detection overall and by anatomical site. METHODSFollowing a registered protocol (PROSPERO CRD420261424675), we searched PubMed, EMBASE, and Cochrane Library through June 2026 and two Chinese-language databases through September 2026 for randomized trials comparing conventional and real-time AI-assisted endoscopy in adults.Risk of bias (RoB) and certainty of evidence were assessed using the RoB 2 tool and GRADE rating.Effects were pooled in Python using restricted maximum-likelihood random-effects models with Hartung-Knapp-Sidik-Jonkman confidence intervals (CIs).Prediction intervals were calculated, with sensitivity analyses by anatomical target and system functionality. RESULTSFourteen randomized trials (90828 participants) were included.AI increased per-patient detection of upper gastrointestinal neoplasms [10 trials; risk ratio (RR): 1.47, 95%CI:1.25-1.73;I 2 = 40%; prediction interval 1.04-2.09;moderate certainty], with similar 4/51 esophageal (RR: 1.61, 95%CI: 1.27-2.03;I 2 = 0%; moderate certainty) and gastric (RR:1.45, 95%CI: 1.11-1.89;I 2 = 60%; low certainty; subgroup P = 0.50) estimates and no variation by system functionality.Patient-level premalignant lesion detection increased (4 trials; RR: 1.80, 95%CI: 1.09-2.96),early cancer detection was imprecise (6 trials; RR:1.28, 95%CI: 0.86-1.91),and the lesion-level miss rate was lower with AI (RR: 0.21, 95%CI: 0.09-0.49).Adverse events did not differ; heterogeneous procedural outcomes were synthesized narratively.The largest, centrally adjudicated trial drove the gastric heterogeneity. CONCLUSION AI-assisted endoscopy increases upper gastrointestinal neoplasm detection in Asiantrial settings, although the evidence for gastric neoplasia remains heterogeneous and of low certainty.Further confirmatory trials with patient-important endpoints are warranted.
Journal IF-equivalent: 4.7 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Choi Sj, Chae G. Artificial intelligence-assisted upper gastrointestinal endoscopy for neoplasm detection: Systematic review and meta-analysis of randomized controlled trials. World J Gastroenterol. 2026 Nov 4 [Epub ahead of print]. doi:10.3748/wjg.127326.Checked: Abstract only - Abstract
BACKGROUND: Estimating tumor fraction from whole-genome cell-free DNA sequencing is critical for liquid biopsy, but is hampered by weak signals and baseline noise at low tumor fractions. Existing computational methods often require matched controls or large labeled datasets for training and lack uncertainty quantification. To address these gaps, we developed purNPE, a Bayesian deep-learning framework trained without labeled cancer cell-free DNA samples. Specifically, purNPE leverages a two-part generative model: one component simulates diverse tumor copy-number profiles based on evolutionary genealogies, while a second, data-driven component learns and replicates realistic sequencing background patterns from cancer-free cell-free DNA. By training a Neural Posterior Estimator on synthetic tumor profiles augmented with learned noise, purNPE performs amortized inference in milliseconds without needing a reference sample set at inference. RESULTS: In a real-world pan-cancer cohort, purNPE achieved comparable performance with existing methods against an orthogonal mutant-allele-fraction proxy (MAE [Formula: see text]). In semi-synthetic and in silico experiments, purNPE showed calibrated uncertainty estimates and a dose-response across low tumor-fraction spike-ins, with separation of approximately 1% TF spike-ins from blanks under the evaluated conditions. CONCLUSIONS: This work provides a proof-of-concept framework for using simulation-based inference to derive calibrated, uncertainty-aware TF estimates. Larger prospective cohorts with stronger orthogonal ground truth are required before analytical sensitivity or clinical utility can be established.
Journal IF-equivalent: 7.7 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Volkov H, Raitses-Gurevich M, Grad M, Shlayem R, Danilevsky A, Rubinek T, et al. Simulation-based Bayesian deep learning enables uncertainty-aware tumor fraction estimation in cell-free DNA. BioData Mining. 2026 Aug 6;19(1):84. doi:10.1186/s13040-026-00595-5.Checked: Abstract only
See all 2 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
- Abstract
The integration of artificial intelligence (AI) into digital pathology has revolutionised breast cancer diagnostics, offering transformative opportunities to automate and refine molecular profiling, which is essential to precision oncology. Accurate determination of receptor status-estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)-remains pivotal in guiding personalised treatment planning and prognostic assessment. However, traditional immunohistochemistry (IHC) and molecular assays are limited by resource intensity, lengthy turnaround times, and inter-observer variability, underscoring the need for scalable computational alternatives. This study proposes a novel integrative AI-driven framework for predicting breast cancer receptor profiles directly from hematoxylin and eosin (H&E)-stained histopathology images. The framework employs a hybrid vision transformer-convolutional neural network (ViT-CNN) architecture with a cross-attention fusion mechanism to capture both global contextual information and fine-grained local features. Experiments show AUCs of 0.967, 0.952, and 0.941 for ER, PR, and HER2 prediction, respectively, representing relative improvements of 12.4%, 9.8%, and 10.8% over the best-performing prior method (DCS-ST), re-evaluated under our identical protocol (absolute AUC differences of 0.107, 0.085 and 0.092). Furthermore, the framework enables automated molecular subtyping into luminal A, luminal B, HER2-enriched, and triple-negative categories with a macro-averaged F1-score of 95.7%. A comprehensive multi-institutional validation was performed on 2,847 external whole-slide images from three partner institutions (not including the primary BINO Hospital test set), showing consistent performance (a mean AUC reduction of 0.83 percentage points relative to the internal test set) across the staining protocols and scanners represented. Integrated explainability modules, including attention visualisations and Grad-CAM, enhance interpretability, supporting pathologist review. In a single-institution retrospective workflow audit, AI assistance was associated with an estimated 72.7% reduction in diagnostic turnaround time (from a conventional mean of 72.4 hours to 19.8 hours; Section 6.10, where approximately 65% of the AI-assisted time is un-itemised queueing and logistics) and, in a 120-WSI reader study, with higher inter-observer agreement (kappa 0.847 to 0.967). These clinical-impact estimates are hypothesis-generating and require prospective validation. The findings position the framework as a promising, explainable approach to AI-assisted digital pathology that warrants prospective, multi-centre clinical evaluation.
Journal IF-equivalent: 2.8 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Albalawi ES, Qayyum J, Qayyum J. Explainable hybrid vision transformer-convolutional neural network with cross-attention fusion for multi-institutional breast cancer receptor status prediction from H&E histopathology images. Science Progress. 2026 Nov 5;109(4):00368504261504501. doi:10.1177/00368504261504501.Checked: Abstract only - Abstract
Breast cancer is a highly prevalent and molecularly heterogeneous malignancy, where therapeutic resistance and off-target toxicity remain major challenges. Here, we introduce BreastCAR, a multi-target quantitative structure-activity relationship framework trained on ChEMBL bioactivity data covering 53 cancer-relevant targets and 233975 compounds. The model integrates 2048-bit Morgan fingerprints with physicochemical descriptors and applies random forest, XGBoost, LightGBM (LGB), support vector machine with RBF kernel (SVM-RBF) and multilayer perceptron (MLP) for both regression (pIC 50 ) and three-class activity classification. The framework achieves a mean R 2 of 0.723 and root mean squared error of 0.661, with a peak R 2 of 0.894 for NOTC1. Classification performance shows a mean F1 score of 0.826, area under the curve-receiver operating characteristic of 0.919, Matthews correlation coefficient of 0.679 and balanced accuracy of 0.759. Y-randomisation confirmed non-trivial model learning (permuted R 2 = -0.167 vs. real 0.723; permuted F1 = 0.467 vs. real 0.826). Applicability domain analysis (Tanimoto k-NN, 95th-percentile threshold) demonstrated that 94.8% of test compounds fall within the training AD. To experimentally validate the top-ranked BreastCAR predictions, a panel of 13 benzylidene-scaffold compounds (R26-01 to R26-13) was evaluated in vitro against MCF-7 human breast cancer cells using the MTT antiproliferative assay (n = 3, IC 50 determined by non-linear regression). Two compounds-R26-06 (4-bromobenzylidene, IC 50 = 5.84 ± 0.24 μM, SI = 9.2) and R26-07 (3-chlorobenzylidene, IC 50 = 6.72 ± 0.28 μM, SI = 8.4)-surpassed tamoxifen (IC 50 = 14.23 ± 0.58 μM) with statistical significance (p < 0.001), demonstrating that BreastCAR-guided prioritisation translates computational predictions into experimentally confirmed antiproliferative activity. A FastAPI-based web interface enables real-time SMILES input, structural visualisation and activity prediction. The complete code and models are provided to ensure reproducibility.
Journal IF-equivalent: 3.5 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Arulsamy S, Ramesh D, Kumar V, Kumar S, Arora P. BreastCAR: A Machine Learning Framework for Breast Cancer Drug Activity Prediction. Molecular Informatics. 2026 Nov 5;45(11):e70062. doi:10.1002/minf.70062.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
Lung cancer clinical decision support demands precise reasoning across complex, multi-stage oncological workflows. Existing multimodal large language models (MLLMs) fail to handle guideline-constrained staging and treatment reasoning. We formalize three oncological precision treatment (OPT) tasks for lung cancer, spanning TNM staging, treatment recommendation, and end-to-end clinical decision support. We introduce LungCURE, the first standardized multimodal benchmark built from 1,000 real-world, clinician-labeled cases across more than 10 hospitals. We further propose LCAgent, a multi-agent framework that ensures guideline-compliant lung cancer clinical decision-making by suppressing cascading reasoning errors across the clinical pathway. Experiments reveal large differences across various large language models (LLMs) in their capabilities for complex medical reasoning, when given precise treatment requirements. We further verify that LCAgent, as a simple yet effective plugin, enhances the reasoning performance of LLMs in real-world medical scenarios.
Journal IF: unknown (could not be matched)Reference: Hao F, Yang J, Zhu Y, Yu Z, Wu Q, Wang Y, et al. LungCURE: Benchmarking Multimodal Real-World Clinical Reasoning for Precision Lung Cancer Diagnosis and Treatment. Proceedings of the 34th ACM International Conference on Multimedia. 2026 Nov 9 [Epub ahead of print]. doi:10.1145/3767308.3838653.Checked: Abstract only - Abstract
Imaging-defined high-risk phenotypes may capture biologically aggressive disease states, but translating them into therapeutic hypotheses remains challenging. We developed ParetoRx, a radiogenomic imaging-informatics framework integrating computed tomography radiomics, pathway-level transcriptomic analysis, multiobjective Pareto optimization, and model-free Q-learning for non-small cell lung cancer (NSCLC). Pretreatment CT data from 402 patients were used for model development and internal validation, with 110 patients forming a held-out external cohort. The final radiomics model achieved an external C-index of 0.707 (95% CI 0.627-0.783); high-risk patients had worse overall survival (HR 3.461, 95% CI 1.674-7.153), with minimal change after adjustment for histology (HR 3.444, 95% CI 1.662-7.137). Gene set enrichment and single-sample pathway analyses identified exploratory associations involving E2F targets, MYC targets V1, inflammatory response, and hypoxia, although no pathway met FDR < 0.05. LINCS L1000 perturbational profiles were evaluated using transcriptomic reversal, pathway specificity, and a feasibility-related proxy, with 4188 compounds retained in the common complete-case analysis. Multiobjective Pareto optimization was combined with three-split consensus ranking and sequential policy evaluation. TWS-119 showed consistent prioritization across all three splits (ranks 1, 3, and 4) and was Pareto-optimal in each split. In simulation-based evaluation, model-free Q-learning achieved higher terminal-goal attainment than myopic and static policies across all three splits, with improvements of approximately 6.2-13.3 percentage points over the myopic comparator. These findings support ParetoRx as an exploratory framework for biologically informed compound prioritization and computational sequential decision-making, while independent drug response and clinical validation remain necessary.
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: Kawahara D, Kishi M, Kadooka Y. ParetoRx: A Multiobjective Radiogenomic Framework Integrating Pareto Optimization and Q-Learning for Exploratory Compound Prioritization in Non-Small Cell Lung Cancer. J Digit Imaging Inform med. 2026 Nov 5 [Epub ahead of print]. doi:10.1007/s10278-026-02451-8.Checked: Abstract only
Colorectal cancer: screening, 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
The objective was to evaluate the clinical context-dependent value of biparametric MRI (bpMRI)-derived imaging representations for prostate cancer (PCa) risk stratification, with emphasis on whether imaging features provide complementary information in patients within the prostate-specific antigen (PSA) gray zone. This dual-campus retrospective study included 536 patients with pathologically confirmed PCa who underwent pretreatment bpMRI. Patients were classified as low/intermediate and high-risk according to NCCN criteria. Patients from Campus 1 were randomly divided into training and internal test cohorts, and Campus 2 served as an independent test cohort. Radiomics, vision transformer, and contrastive language-image pretraining (CLIP) features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. Clinical and combined clinical-imaging models were developed. Performance was assessed using ROC analysis, calibration, decision curve analysis, and SHAP interpretation, with predefined evaluation in the PSA gray zone. In the independent test cohort, the combined model achieved the highest overall discrimination, with an AUC of 0.921, followed closely by the clinical baseline model (AUC, 0.907). Among imaging-only models, the radiomics model showed the highest overall AUC (0.844). SHAP identified PSA as the dominant predictor, indicating clinically driven overall performance. In the PSA gray zone, clinical and combined model performance declined (AUCs, 0.381 and 0.600), whereas CLIP achieved the highest AUC (0.785) and favorable net benefit. The value of bpMRI-derived imaging representations for PCa risk stratification is context-dependent. CLIP-derived representations may provide complementary discrimination when PSA-based information is less informative, supporting context-aware evaluation of imaging AI models.
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: Zou F, Xiong H, Chen J, Qiao X, Ai G, Wu Y, et al. Clinical Context-Dependent Value of bpMRI-Derived Imaging Representations for Prostate Cancer Risk Stratification. J Digit Imaging Inform med. 2026 Nov 5 [Epub ahead of print]. doi:10.1007/s10278-026-02490-1.Checked: Abstract only - Abstract
This systematic review and meta-analysis evaluates the performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) models for predicting biochemical recurrence (BCR) in patients with prostate cancer treated with definitive external beam radiation therapy (EBRT), with or without concomitant hormone therapy. The review focuses on machine-learning, deep-learning, and radiomics-based prediction models that use MRI-derived information, either alone or in combination with clinical, pathological, or laboratory variables. The primary objective is to synthesize model performance using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), while examining between-study heterogeneity and the robustness of pooled estimates. The review follows PRISMA 2020 and PRISMA-DTA reporting guidance, with data extraction informed by the CHARMS checklist for prediction-model studies.
Journal IF: unknown (could not be matched)Reference: [No authors listed]. Artificial Intelligence–Assisted Magnetic Resonance Imaging for Predicting Biochemical Recurrence After External Beam Radiation Therapy in Prostate Cancer: A Systematic Review and Meta-Analysis. Open Science Framework. [Epub ahead of print]. doi:10.17605/osf.io/kmh3s.Checked: Abstract only
Brain tumours: treatment, trials and AI
- Abstract
Preoperative differentiation of glioma from meningioma on MRI is important for treatment planning but depends on expert interpretation. A 3D ResNet-34 with CBAM attention was trained on four-modality MRI from 2251 BraTS 2023 patients (1801 for four-fold cross-validation; 450 held out for testing) and achieved 96% accuracy, an AUC of 0.993, and an expected calibration error of 0.0165 on the test set. For layer-wise explanation, Grad-CAM and SmoothGrad were applied at the four residual stages, and a reconstruction decoder was added to obtain full-resolution, decoder-mediated attribution at the 4 × 4 × 4 bottleneck, where standard Grad-CAM is too coarse. Four quantitative metrics gave complementary results: the decoder-assisted attribution had the highest pointing-game accuracy (80.2%) and the lowest deletion AUC, whereas Layer 1 had the highest insertion AUC and Layer 2 the highest mean faithfulness delta, with no significant faithfulness difference among Layers 1, 2, and 4. Glioma was localized by most layers, while meningioma was localized mainly through the decoder-assisted pathway. A preliminary clinical review and a normalization analysis are also reported. The decoder offers an annotation-free way to obtain full-resolution attribution from deep layers of volumetric models.
Journal IF-equivalent: 6.6 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Pagonis N, Plomaritis K, Kogias E, Foroglou N, Chouvarda I. From Classification to Clinical Trust: 3D Deep Learning and Layer-Wise Explainability for Brain Tumor MRI. Sensors. 2026 Nov 5;26(21):6993. doi:10.3390/s26216993.Checked: Abstract only - Abstract
BACKGROUND: Accurate classification of Isocitrate Dehydrogenase (IDH) mutation status is crucial for the diagnosis and treatment of adult-type diffuse gliomas as defined by the 2021 World Health Organization (WHO) Classification. However, conventional histological and molecular diagnostics are time-consuming, labor-intensive, costly, and often limited in accessibility. Recent advances in artificial intelligence have enabled automated and unbiased glioma classification directly from histopathological images, but challenges remain in capturing complex pathological and molecular features effectively. METHODS: In this study, we propose FOCUS (Feature Optimization and Cascaded Unified Screening), an integrated framework for IDH mutation status classification in gliomas using glioma pathology slides. The histopathological images are divided into patches, from which a Vision Transformer (ViT) extracts interpretable features related to nuclear morphology and tumor progression-associated protein expression. A Patch-based Graph Convolutional Network (PatchGCN) further aggregates spatial context to capture both local morphology and global tumor heterogeneity. A cascaded feature selection strategy is employed to refine biomarker identification, followed by a machine learning classifier for final classification. RESULTS: Using the proposed framework, the Support Vector Machine (SVM) demonstrated promising performance on a dataset of 73 patients (accuracy = 0.902, AUC = 0.909). CONCLUSIONS: These results highlight the potential of the FOCUS framework as a robust and interpretable tool for IDH mutation status classification, providing a computational approach to distinguish IDH-mutant (astrocytoma and oligodendroglioma) from IDH-wildtype (glioblastoma) tumors.
Journal IF-equivalent: 4.2 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Su Y, Li F, Yang H, Bai J, Lu J. FOCUS: a feature optimization and cascaded unified screening strategy for computer-aided pathology-based glioma molecular subtype classification. Diagn Pathol. 2026 Aug 8;21(1):84. doi:10.1186/s13000-026-01836-1.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
No new qualifying paper for this issue.
Kidney cancer: treatment, trials and AI
No new qualifying paper for this issue.
Bladder cancer: treatment, trials and AI
- Abstract
INTRODUCTION: Bladder cancer is the most prevalent malignancy of the urinary tract. This study aimed to identify candidate biomarkers for bladder cancer through an integrated multi-omics analysis. METHODS: Single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), and proteomic datasets were analyzed to characterize cellular populations and identify differentially expressed genes (DEGs) and proteins (DEPs). Candidate genes were prioritized using four machinelearning algorithms, with model interpretation performed using SHapley Additive exPlanations (SHAP). Gene set enrichment analysis (GSEA) was conducted to explore associated pathways, whereas Enrichr/DSigDB enrichment and molecular docking were performed to investigate potential therapeutic compounds and compound-gene interactions. A five-gene SVM model was externally validated in the independent bulk transcriptomic cohort GSE13507. The expression levels of the candidate genes were further evaluated in bladder cancer cell lines. RESULTS: Fifteen major cell populations were identified. Exploratory analyses highlighted endothelial perivascular stromal cells and inflammatory cancer-associated fibroblasts as cell populations of interest, with predicted intercellular communication patterns suggesting potential roles in the bladder cancer microenvironment. Moreover, 7 genes/proteins showed concordant upregulation and 122 downregulation across all three datasets, and these candidates were subsequently subjected to machine- learning analysis. CFD, WNT2B, MAPK13, ABLIM1, and PRKAR2B were prioritized as five candidate genes for bladder cancer, and their expression patterns and potentially associated pathways were further evaluated in bladder cancer samples and cell lines. Among the four evaluated algorithms, SVM demonstrated the highest reported internal holdout-set AUC of 1.000 (95% CI: 1.000-1.000), and was selected for SHAP interpretation. In the independent GSE13507 cohort, the five-gene SVM model achieved an AUC of 0.815 (95% CI: 0.752-0.877). Molecular docking suggested potential interaction poses between Rifaximin and CFD, and between MAPK13 and NG- 25/XMD13-2. DISCUSSION: The integration of multi-omics data with an interpretable SHAP-based model provides an exploratory framework for prioritizing candidate bladder cancer biomarkers and generating hypotheses for subsequent biological and clinical validation. CONCLUSION: This multi-omics study identified candidate biomarkers and exploratory compoundgene associations for bladder cancer. These findings require validation in additional independent multicenter cohorts and functional studies before clinical application.
Journal IF-equivalent: 4.6 (OpenAlex 2-year mean citedness, value as of 2026-11-06, retrieved 2026-11-07; not the official Clarivate IF)Reference: Guan H, Liu X, Hong T, Li H, Liu G, Zhang Y, et al. Integrative Multi-Omics Analysis with Interpretable Machine Learning Prioritizes Candidate Biomarkers for Bladder Cancer. CGT. 2026 Oct 27;26. doi:10.2174/0115665232538656261006070001.Checked: Abstract only
Rare cancers
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.