AI and Cancer Research ── 2026-10-13
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（1） · Lung（0） · Pancreatic（0） · Prostate（0） · ACC（0） · Brain（0） · Ovarian（0） · Endometrial（0） · Gastric（1） · Liver（0） · Kidney（0） · Bladder（0）
Cancer molecular biology and AI
- Abstract
Cancer classification based on molecular information is a more complex multiclass learning problem, as tumors from different tissues can present with different expression profiles and cancers with similar molecular characteristics can occur in different tissues. The Cancer Genome Atlas (TCGA) gene-expression data is used to create a practical pan-cancer classification framework that is described in this paper. The working dataset consisted of 11,062 labelled samples representing 33 cancer types, after preparation and labelling. Representation of the input was reduced to 500 gene-expression features using a feature-selection stage. A single XGBoost multiclass classifier was then trained to understand the relationship between the chosen expression profile and the 33 cancer classes. The implementation performed with an accuracy of 94.49%, precision of 94.66%, recall of 94.49%, and F1 score of 94.27% on held-out test data. To make the model easier to inspect, a feature-importance analysis based on SHAP was added. The trained model was deployed into a Streamlit dashboard, with a FastAPI backend to upload a gene-expression sample and display the predicted cancer type, probabilities of cancer classes and explanation information. The system is a research and educational prototype, not a clinically validated diagnostic system; an independent validation should be required before clinical interpretation.
Journal IF-equivalent: 0.5 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Lokesh N, Thara L, Kumuthaveni R. A Comparative Study of Machine Learning Models for Pan-Cancer Prediction and Explainable Feature Analysis. Int J Sci Res Sci Eng Technol. 2026 Oct 11;13(5):296-308. doi:10.32628/ijsrset2613529.Checked: Abstract only - Abstract
Histopathological growth patterns (HGPs) in colorectal liver metastases (CRLM) significantly influence patient prognosis and response to systemic therapy. While the desmoplastic HGP is associated with better outcomes, the replacement HGP often indicates a more aggressive phenotype. Current preoperative imaging cannot distinguish these patterns, and biopsies are often unreliable due to limited sampling of the tumor–liver interface. This systematic review and meta-analysis evaluates the diagnostic performance of radiomics/deep learning models for the non-invasive prediction of HGPs based on imaging data. This study was conducted following PRISMA guidelines, searching 4 databases up to August 30, 2026. Studies developing radiomics or deep learning models to predict HGPs in CRLM patients using preoperative CT or MRI were included. Methodological quality and risk of bias were assessed using the RQS 2 and QUADAS-2 tools. Pooled sensitivity and specificity were calculated using a random-effects model. Eight retrospective reports representing seven independent study populations were included in the systematic review; seven independent populations comprising 724 unique patients were included in the quantitative synthesis. The pooled sensitivity and specificity were 0.86 (95% CI: 0.81–0.90) and 0.77 (95% CI: 0.64–0.87), respectively. The SROC curve yielded an AUC of 0.845. Subgroup analysis did not reach statistical significance in most analyses, and its findings should be interpreted with caution given the limited number of studies available. The mean RQS 2.0 score was 17.1 (31%), indicating that while technical development is standardized, prospective validation and clinical application measures are currently lacking. Imaging-based AI models showed encouraging discriminatory performance for non-invasive HGP classification. However, the available evidence does not demonstrate clinical utility or improvement in patient management or outcomes. Prospective multicenter validation and decision-impact studies are required before clinical implementation can be considered.
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: Rahnama Y, Eghbali S, Pishraft-Sabet H, Eghbali S, Kolahi S, Ahmadi-Tafti SM, et al. Imaging radiomics for predicting histopathological growth patterns in colorectal liver metastases: a systematic review and diagnostic meta-analysis. World J Surg Onc. 2026 Oct 11 [Epub ahead of print]. doi:10.1186/s12957-026-04613-y.Checked: Abstract only
See all 2 Cancer biology papers →
Breast cancer: treatment, trials and AI
- Abstract
BACKGROUND: The heterogeneity of breast cancer is a key factor influencing treatment decisions and patient prognosis. As a crucial cell proliferation marker, the expression level of the Ki-67 protein is closely associated with tumor aggressiveness, recurrence risk, and prognosis. The goal of this study was to develop and validate an integrated radiomics model based on digital breast tomosynthesis (DBT) that incorporates both habitat subregions and peritumoral features to preoperatively predict Ki-67 expression status in breast cancer. METHODS: A total of 146 patients with invasive breast cancer who underwent preoperative DBT were retrospectively enrolled in the study and divided into training (n=102) and testing (n=44) cohorts. Intratumoral regions were manually segmented, whereas peritumoral regions (2, 4, and 6 mm) were automatically delineated using mask padding. Moran's index clustering was used to divide the tumors into four habitat subregions. Radiomics features were extracted from the intratumoral regions, habitat subregions, and peritumoral regions. Following feature selection, radiomics models were established using machine learning algorithms. A combined model was subsequently developed by integrating habitat subregions and peritumoral radiomics features with clinical factors. The area under the curve (AUC) was used to assess the performance of radiomics models. RESULTS: In predicting Ki-67 expression status, the rbf_support vector machine (SVM) model, constructed from habitat radiomics features, outperformed the other models, with AUC values of 0.841 and 0.820 in the training and testing cohorts, respectively. The combined model slightly increased the AUC values to 0.849 and 0.852, respectively. CONCLUSIONS: A combined model integrating habitat, peritumoral radiomics features, and clinical factors demonstrated optimal accuracy in predicting Ki-67 expression levels in breast cancer; this model may help clinicians develop personalized treatment plans for breast cancer patients.
Journal IF-equivalent: 1.9 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Gong J, Zhuang Z, Liu S, Xu L, Liu H, Zhu H, et al. Moran’s index habitat and peritumoral radiomics based on digital breast tomosynthesis for predicting Ki-67 status in breast cancer. Quant Imaging Med Surg. 2026 Oct;16(10):807-7. doi:10.21037/qims-2026-1-0047.Checked: Abstract only
Lung cancer: treatment, trials and AI
No new qualifying paper for this issue.
Pancreatic cancer: treatment, trials and AI
No new qualifying paper for this issue.
Prostate cancer: treatment, trials and AI
No new qualifying paper for this issue.
Adenoid cystic carcinoma: treatment, trials and AI
No new qualifying paper for this issue.
Brain tumours: treatment, trials and AI
No new qualifying paper for this issue.
Ovarian cancer: treatment, trials and AI
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
Preoperative anemia is common in gastric cancer and may adversely affect patient outcomes, particularly among women. This study aimed to evaluate the prognostic significance of anemia and to construct a machine learning model based on inflammation–nutrition-related indices for individualized survival prediction. A total of 1,178 female gastric cancer patients undergoing curative R0 resection were retrospectively analyzed. Kaplan–Meier and Cox regression analyses assessed the prognostic role of anemia. Inflammatory and nutritional indices were selected using recursive feature elimination, and multiple machine learning algorithms were developed to predict 5-year overall survival. Model performance and interpretability were evaluated using AUC metrics and SHAP analysis. Anemia was identified as an independent adverse prognostic factor. Distinct prognostic patterns of inflammation–nutrition indices were observed between anemic and non-anemic groups. The XGBoost model achieved the best predictive performance (AUC = 0.735 and 0.773, respectively) and provided interpretable risk stratification, with high-risk patients showing significantly poorer survival across subgroups. The XGBoost model based on routine hematologic indicators showed promising predictive performance in this single-center proof-of-concept study. With further external validation and prospective evaluation, it may serve as a simple and readily available tool for preliminary risk assessment. However, its current clinical utility remains to be confirmed in broader patient populations.
Journal IF-equivalent: 2.4 (OpenAlex 2-year mean citedness, value as of 2026-10-12, retrieved 2026-10-13; not the official Clarivate IF)Reference: Qi J, Sun W, Zhang L, Jin X. Distinct prognostic patterns of inflammation nutrition indices in female gastric cancer patients with different anemia statuses and construction of an XGBoost predictive model. Discov Onc. 2026 Oct 11 [Epub ahead of print]. doi:10.1007/s12672-026-06051-4.Checked: Abstract only
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
No new qualifying paper for this issue.