AI and Cancer Research ── 2026-10-31
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） · Pancreatic（0） · Prostate（0） · Brain（2） · Ovarian（0） · Endometrial（0） · Gastric（0） · Liver（0） · Kidney（0） · Bladder（1）
Rare cancers: ACC（0）
Cancer molecular biology and AI
- Abstract
Background: Metabolic dysfunction-associated steatohepatitis (MASH) increases the risk of cirrhosis and liver cancer, with liver fibrosis being the primary determinant of adverse outcomes. However, the molecular mechanisms driving fibrosis progression in MASH remains incompletely understood.
Methods: We integrated four bulk RNA-seq datasets into a merged cohort (MergeCohort) and analyzed one single-nucleus RNA-seq (snRNA-seq) dataset. Robust Rank Aggregation (RRA), WGCNA and machine learning were combined to identify core genes associated with MASH fibrosis. Additionally, we established MASH mice model induced by methionine-choline-deficient (MCD) diet, and further check the expression of hub genes through quantitative real-time PCR (qRT-PCR). Single-nucleus transcriptomic profiling of liver tissues from MASH patients across distinct fibrosis stages was investigated to delineate cellular heterogeneity, reconstruct intercellular communication networks, and characterize key transcription factors (TFs) as well as receptor-ligand interactions involved in MASH-associated fibrosis.
Results: Based on the MergeCohort, we detected two key gene network modules associated with fibrosis progression in MASH through WGCNA. By integrating machine learning algorithms, we discovered seven core genes (CAPN6, FAP, KRT23, SOX4, STMN2, TAGLN and VWF) showing favorable discriminatory capability for fibrosis stage, achieving an area under the curve (AUC) of 0.81 (95% CI 0.69–0.93) in a distinct external cohort. Moreover, CAPN6, FAP and KRT23 showed directionally consistent upregulation in the liver tissues of MASH mouse model induced by MCD diet. Subsequently, applying cell-type deconvolution analysis, we assigned the predominant cell types (hepatic stellate cells (HSCs), cholangiocytes, hepatocytes and macrophages) to the key WGCNA modules. At the single-nucleus level, we identified FGF13 + cholangiocytes and SAAs + hepatocytes subpopulations. Transcription factor regulon analysis identified key TFs (CREB5 and STAT3), which may play potential regulatory roles in FGF13 + cholangiocytes and SAAs + hepatocytes. Cell-cell communication analysis further revealed crucial ligand-receptor pairs between HSCs and FGF13 + cholangiocytes (VEGFA_NRP1 and APP_CD74 axes), SAAs + hepatocytes and HSCs (RSPO3_LGR4 and IGF1_IGF1R axes), SAAs + hepatocytes and macrophages (TNFSF14_TNFRSF14 and SELE_CD44 axes) associated with the progression of liver fibrosis in MASH.
Conclusion: This combined approach defines molecular signatures related to MASH fibrosis that may inform the development of novel therapeutic strategies and serve as a tissue signature associated with advanced fibrosis in MASH.
Journal IF-equivalent: 4.8 (OpenAlex 2-year mean citedness, value as of 2026-10-15, retrieved 2026-10-16; not the official Clarivate IF)Reference: Zhang J, Wang J, Shen Y, Zhang Y, Luo Z, Kong X, et al. Integrative machine learning analysis of bulk and single-nucleus RNA sequencing reveals key cell types and biomarkers driving MASH-associated fibrosis. Front Cell Dev Biol. 2026 Oct 29;14:1961567. doi:10.3389/fcell.2026.1961567.Checked: Abstract only - Abstract
BACKGROUND: Recent investigations have demonstrated the relevance in cancer of a variety of small RNAs, beyond microRNAs (miRNAs). However, the comprehensive application of these RNAs to cancer detection in dogs remains unexplored. HYPOTHESIS/OBJECTIVES: We investigated whether combining miRNAs and other small RNAs using a custom database improves the accuracy of machine learning cancer screening in dogs compared with models using only miRNAs. ANIMALS: A total of 389 dogs were enrolled: 85 with cancer, 77 with nonmalignant diseases, and 227 healthy controls. METHODS: Small RNAs in blood samples were sequenced, and miRNAs (via miRBase) and other small RNAs (via a custom database) were profiled. Based on these profiles, 2 datasets were prepared: one with only miRNAs and another combining miRNAs with other small RNAs. For each dataset, 12 classification models were constructed using identical methods to enable paired comparisons of performance, with 5-fold cross-validation and area under the receiver operating characteristic curve (AUC) as the primary metrics. RESULTS: The model that integrated both miRNAs and other small RNAs yielded a significantly higher AUC compared with the miRNA-only model (median AUC: 0.926 [95% CI, 0.911-0.944] vs 0.881 [0.860-0.897]; Wilcoxon signed-rank test, P < .001), indicating enhanced capability in discriminating malignant tumors in dogs. CONCLUSIONS AND CLINICAL IMPORTANCE: We investigated the comprehensive integration of diverse small RNA species, beyond miRNAs, using a machine learning approach for cancer screening in dogs. Although further prospective validation in independent cohorts is needed, our results indicate that this strategy improves the performance of cancer classification models in dogs.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Harako S, Yuki M, Noguchi S, Ohno T, Nozaki N, Nishida R, et al. Novel liquid biopsy based on comprehensive small RNA profiling for screening of cancer in dogs utilizing machine learning. Journal of Veterinary Internal Medicine. 2026 Oct 30;40(5):aalag270. doi:10.1093/jvimsj/aalag270.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
Background/Objectives: Peritumoral ultrasound may capture tissue characteristics relevant to treatment sensitivity. In this study, we examined whether malignancy-related prediction scores (PS) were associated with pathological response to neoadjuvant therapy in breast cancer.
Methods: Support vector machine classifiers were developed for 1, 3, 5, and 7 mm peritumoral regions using 1716 pathologically confirmed lesions. We assessed PS against postoperative Miller–Payne (MP) grade in 132 patients who completed neoadjuvant therapy and surgery. Ordinal logistic regression examined overall associations, interactions with hormone receptor (HR) status and Ki67, and treatment-stratified associations adjusted for both. Holm correction addressed multiple testing. The 7 mm classifier was refitted using 1584 patients outside the response cohort while retaining the originally selected features.
Results: Higher 7 mm PS was associated with lower MP grade overall (odds ratio [OR] per 0.1-unit increase, 0.79; 95% confidence interval [CI], 0.66–0.95; Holm-adjusted p = 0.048). HR and Ki67 interactions remained significant at 1 mm (adjusted p = 0.049 and 0.036, respectively). In 60 complete cases receiving chemotherapy plus anti-HER2 therapy, the original 7 mm PS was associated with MP grade after adjustment (OR, 0.60; 95% CI, 0.42–0.85; adjusted p = 0.037). The refitted PS showed an attenuated association in these patients (OR, 0.709; 95% CI, 0.526–0.955; raw p = 0.0239; Holm-adjusted p = 0.0478). Associations in the original classifier test subsets (n = 22–26) were not significant after correction.
Conclusions: The 7 mm score may complement HR and Ki67 for response assessment during chemotherapy plus anti-HER2 therapy. Interactions at 1 mm suggest variation with molecular characteristics. These findings warrant independent validation.
Journal IF-equivalent: 4.0 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Wang Y, Shi A, Lian J, Liang R, Wang J, Jiang J. Multiscale Peritumoral Ultrasound Machine Learning Scores and Pathological Response to Neoadjuvant Therapy in Breast Cancer. Diagnostics. 2026 Oct 29;16(21):3580. doi:10.3390/diagnostics16213580.Checked: Abstract only - Abstract
Grade III/IV chemotherapy-induced neutropenia (CIN) is a serious complication of breast cancer chemotherapy. Existing prediction models often rely on post-chemotherapy laboratory values, limiting their utility at the point of prescribing. We aimed to develop machine learning (ML) models predicting Grade III/IV CIN using exclusively pre-chemotherapy data, and to evaluate cross-cycle stability within the same cohort. We retrospectively analysed breast cancer patients who received chemotherapy from September 2011 to January 2025. Only pre-chemotherapy variables were used as inputs. Cycle 1 records were split into training (80%) and internal validation (20%) sets. Cycles 2-8 records formed the cross-cycle test set. Nine candidate ML classifiers were evaluated. Hyperparameter tuning, Youden-index threshold optimisation, calibration, decision curve analysis, SHAP-based feature attribution, and per-cycle AUC were assessed. On the Cycle 1 validation set, the tuned Logistic L1 and Extra Trees models achieved AUCs of 0.760 (95% CI 0.703-0.812) and 0.763 (95% CI 0.707-0.815), respectively. On the pooled Cycles 2-8 cross-cycle test set, the corresponding AUCs were 0.746 (95% CI 0.727-0.764) and 0.729 (95% CI 0.709-0.748). Logistic L1 maintained an AUC above 0.70 across all cycles. SHAP attribution identified docetaxel and epirubicin as dominant predictors. Baseline neutrophil count, total bilirubin, and albumin were the leading non-drug clinical features, particularly in Logistic L1. Models trained exclusively on pre-chemotherapy data retained broadly similar discrimination when applied to subsequent cycles within the same cohort. Logistic L1 showed more consistent cross-cycle discrimination, which supports prospective, cycle-by-cycle risk stratification supporting G-CSF prophylaxis and dose-modification decisions.
Journal IF-equivalent: 2.0 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Lin M, Weng Y, Chen W, Cai C, Wu Y, Guo J, et al. Pre-chemotherapy machine learning models for predicting grade III/IV chemotherapy-induced neutropenia in breast cancer: training on cycle 1 and cross-cycle evaluation. Support Care Cancer. 2026 Oct 29;34(11):1173. doi:10.1007/s00520-026-11402-6. PMID: 42955369.Checked: Abstract only
Lung cancer: treatment, trials and AI
- Abstract
PURPOSE: To develop and externally test a multiparametric MRI habitat-based framework integrating intratumoral heterogeneity (ITH), peritumoral heterogeneity (PTH), and clinical characteristics for predicting intracranial progression in patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases receiving first-line third-generation (3rd-gen) EGFR-TKI monotherapy. MATERIALS AND METHODS: This retrospective multicenter study included patients with EGFR-mutant lung adenocarcinoma and de novo brain metastases who received first-line 3rd-gen EGFR-TKI monotherapy. Patients were assigned to an internal training cohort (n = 90) and an external testing cohort (n = 51). The binary prediction outcome was intracranial progression within 20 months after treatment initiation. Pretreatment contrast-enhanced T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were analyzed. The index brain metastasis and a 6-mm peritumoral region were segmented, followed by voxel-wise habitat clustering. Habitat-specific radiomic features were extracted and selected to construct ITH, PTH, and combined intratumoral-peritumoral habitat (PITH) signatures. Clinical predictors were evaluated using univariable and multivariable logistic regression. Support vector machine models were evaluated using receiver operating characteristic, decision curve, calibration, and Kaplan-Meier analyses. RESULTS: A total of 141 patients were analyzed, including 75 with progressive disease (PD) and 66 without PD (non-PD). Multivariable logistic regression identified EGFR mutation subtype (odds ratio [OR] = 2.390, 95% confidence interval [CI]: 1.187-4.811, P = 0.015) and the maximum diameter of the index brain metastasis as independent predictors (OR = 2.048, 95% CI: 1.204-3.483, P = 0.008). Models based on imaging heterogeneity features outperformed the clinical model overall. The clinical model achieved an AUC of 0.662, while the ITH, PTH, and PITH models achieved AUCs of 0.715, 0.724, and 0.758, respectively. The combined model showed favorable performance (AUC 0.795, accuracy 76.5%, sensitivity 70.4%, and specificity 83.3%). Patients classified by the combined model as high risk had significantly shorter intracranial progression-free survival (iPFS), than those classified as low risk (log-rank P = 0.019). CONCLUSION: The proposed framework integrating ITH, PTH, and clinical features may provide a noninvasive approach for predicting intracranial progression risk and stratifying prognosis in patients with EGFR-mutant lung adenocarcinoma with newly diagnosed BMs receiving first-line 3rd-gen EGFR-TKI monotherapy.
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: Deng G, Li H, Dai Z, Fan J, Song X, Pang G, et al. MRI-based intratumoral and peritumoral heterogeneity for predicting intracranial progression in EGFR-mutant lung adenocarcinoma patients with treatment-naive brain metastases receiving first-line third-generation EGFR-TKIs. J Transl Med. 2026 Oct 17;24(1):1361. doi:10.1186/s12967-026-09109-7. PMID: 42958288.Checked: Abstract only - Abstract
Lung adenocarcinoma (LUAD) is a highly invasive malignancy associated with poor prognosis. However, reliable molecular subtypes and prognostic signatures for LUAD, particularly their associations with tumor immunity and treatment response, remain poorly characterized. This study aimed to identify distinct molecular subtypes and establish a robust prognostic signature for LUAD, while exploring potential therapeutic targets associated with cisplatin resistance. In this study, we integrated ten clustering algorithms with 101 machine learning methods to identify two distinct molecular subtypes of LUAD and established a robust consensus machine learning-derived signature (CMLS). Patients belonging to the Cluster A subtype exhibited a more favorable prognosis and an enhanced immune response. Furthermore, a CMLS model constructed from 19 genes exhibited strong predictive power. Individuals with high CMLS scores displayed reduced CD4 + T-cell infiltration, poorer survival outcomes, and diminished responses to immunotherapy, whereas those with the low CMLS scores showed improved clinical outcomes, better immunotherapy responsiveness, and features of an immunologically "hot" tumor phenotype. Among the identified genes, ribonucleotide reductase subunit M2 (RRM2) emerged as a core regulator associated with LUAD prognosis. RRM2 was markedly upregulated in lung cancer tissues and the LUAD cell lines and was associated with the cisplatin (DDP) resistance. Suppression of RRM2 expression significantly enhanced the cisplatin sensitivity of A549/DDP cells. In summary, our findings establish a molecular classifier that stratifies LUAD patients into distinct risk categories and highlight RRM2 as a promising biomarker and therapeutic target associated with cisplatin resistance.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Huang H, Tao T, Li Z, Zhai K, Cui B, Feng H, et al. Integrating Machine Learning to Identify Prognostic Subtypes and the Cisplatin Resistance Gene RRM2 in Lung Adenocarcinoma. Thorac Cancer. 2026 Oct 30;17(21):e70407. doi:10.1111/1759-7714.70407. PMID: 42958928.Checked: Abstract only
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
- Abstract
Introduction: Craniopharyngiomas are rare sellar/suprasellar tumors with long-term postoperative morbidity, including permanent diabetes insipidus (DI) and tumor recurrence. Existing clinical tools offer limited individualized prognostication. We introduce a pilot artificial intelligence (AI) platform to hypothesize postoperative outcomes in craniopharyngioma surgery.
Methods: Patients with histologically confirmed craniopharyngioma who underwent surgical resection between 2008 and 2023 at a single institution were retrospectively analyzed. Clinical, radiographic, and intraoperative features (e.g., tumor dimensions, volume, intraventricular extension, surgical approach, and extent of resection) were modeled using machine learning algorithms with a unified ensemble pipeline to predict permanent DI and tumor recurrence. Explainable AI interpretation using Shapley Additive Explanations (SHAP) quantified individual feature-level contributions, which were integrated into a FastAPI-powered web application for interactive prediction.
Results: 59 patients were used in a stratified cross-validation model training, of whom permanent DI occurred in 65.5% and tumor recurrence was recorded in 28.8%. The blended Ridge-Catboost ensemble achieved the highest performance for permanent DI prediction (AUC: 0.77±0.05, F1: 0.81±0.04), and a blended Naïve Bayes-Catboost ensemble was chosen for recurrence prediction (AUC: 0.78±0.12, F1: 0.67±0.15). SHAP-based feature ranking revealed that pre-operative BMI, age, and endocrine or visual dysfunction were the strongest predictors of DI and recurrence. Surgical approach and postoperative BMI changes further modulated DI risk. The app can be accessed at tinyurl.com/craniopharyngiomas.
Conclusion: In this small, single-institution cohort, ensemble learning and model tuning helped stabilize model accuracy and discriminative power. This exploratory AI prototype for prognostication might eventually support data-driven risk assessment in skull base oncology.
Journal IF-equivalent: 0.1 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Babel S, Patel A, Parasher A, Tabor M, Agazzi S, van Loveren H, et al. Craniopharyngioma.AI: An Explainable Ensemble Learning Framework for Personalized Craniopharyngioma Outcome Prediction. J Neurol Surg B Skull Base. 2026 Oct 29 [Epub ahead of print]. doi:10.1055/a-2992-8671.Checked: Abstract only - Abstract
PURPOSE: This study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers. METHODS: A retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training (n = 280) and test (n = 120) cohorts. RESULTS: Spatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively). CONCLUSIONS: Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.
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: Xie H, Peng X, Chen X, Yu Y, Yi Z, Meng W, et al. Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study. BMC Med Imaging. 2026 Aug 3;26(1):562. doi:10.1186/s12880-026-02632-5. PMID: 42958086.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
Muscle-invasive bladder cancer (MIBC) frequently develops resistance to platinum-based chemotherapy, and tools for characterizing treatment-associated tumor microenvironmental features remain limited. We developed an artificial intelligence (AI)-based framework integrating pathological imaging, multiomics data, single-cell RNA sequencing, and ex vivo drug-sensitivity testing. Three convolutional neural network models trained using hematoxylin-and-eosin-stained whole-slide images from The Cancer Genome Atlas (TCGA) classified tumor, necrosis, and lymphocyte-enriched regions and were evaluated in an independent tissue microarray cohort. Patches negative for all three classifiers were designated AI-defined "Other" regions; this residual category was not trained as a cancer-associated fibroblast (CAF) classifier. Higher Other% was associated with shorter overall survival in univariable analyses, but the association was not significant after multivariable adjustment in TCGA. Multiomics and tissue-origin analyses linked Other% to stromal content. Scissor analysis of eight single-cell RNA-sequencing datasets identified Other-associated Scissor+ cells across epithelial and fibroblast compartments, while ACTA2 expression and extracellular matrix-remodeling pathways were enriched. In 48 MIBC specimens assessed by the hydrogel-embedded histoculture drug sensitivity test, Other% was higher in the gemcitabine-cisplatin-resistant group. Multiplex immunofluorescence further showed enrichment of alpha-smooth muscle actin signal in AI-defined Other regions. Erdafitinib showed ex vivo activity in a subset of specimens, but FGFR alteration status was unavailable and clinical efficacy was not evaluated. These findings support an association between AI-defined Other regions, CAF-related stromal features, and chemoresistance, while prospective validation is required before clinical use.
Journal IF-equivalent: 3.7 (OpenAlex 2-year mean citedness, value as of 2026-10-29, retrieved 2026-10-31; not the official Clarivate IF)Reference: Hu C, Wang F, Zhang Q, Xu H, Huang J, Xiong X, et al. Deep learning-based multiomics integration identifies AI-defined tissue regions associated with cancer-associated fibroblast enrichment and chemoresistance in muscle-invasive bladder cancer. Funct Integr Genomics. 2026 Oct 29;26(1):308. doi:10.1007/s10142-026-02086-6. PMID: 42955415.Checked: Abstract only
Rare cancers
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.