AI and Cancer Research ── Lung ── 2026-10-19
Lung cancer: treatment, trials and AI
- Abstract
We developed THINKERS-X: Lung, the eXtended multimodal evolution of THINKERS-Lung, to determine whether multisequence MRI radiomics provides incremental value for lesion-specific outcome prediction and exploratory dose-scenario evaluation after Gamma Knife radiosurgery (GKRS) for lung cancer brain metastases. We performed a retrospective single-center study of 767 patients with 3,728 lung cancer brain metastases treated with GKRS. THINKERS-X preserved the original mixture-of-experts and discrete-time survival architecture while adding a dedicated computational neuroradiology expert. Approximately 2,400 radiomic features per metastasis were extracted from pretreatment FSPGR/T1-weighted postcontrast, T2-weighted, and SWAN MRI. Validation used patient-grouped 5-fold cross-validation and a completely closed patient-grouped internal holdout cohort. In grouped cross-validation, THINKERS-X achieved a mean AUC 12 of 0.919 ± 0.021 versus 0.876 for THINKERS-Lung (paired difference, 0.043; 95% CI 0.026-0.061; p 12 improved from 0.863 to 0.929 (95% CI 0.895-0.958), with a paired difference of 0.066 (95% CI 0.031-0.101; p < 0.001). Mean absolute error decreased from 1.26 to 0.84 months. The calibrated Brier score was 0.039, calibration slope 0.94, and intercept 0.04. Radiomics altered model-derived dose-response profiles in 30.0% of metastases and the model-ranked candidate dose in 23.9%. THINKERS-X improved lesion-specific longitudinal prediction, temporal accuracy, and probabilistic calibration beyond THINKERS-Lung while materially altering exploratory dose-scenario evaluation in a subset of metastases. External multicenter and prospective validation are required before clinical implementation.
Journal IF-equivalent: 4.1 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: Reyes JS, Hadjipanayis CG, Niranjan A. THINKERS-X: multisequence MRI radiomics in a mixture-of-experts model for outcome prediction and exploratory dose-scenario evaluation in lung cancer brain metastases treated with radiosurgery. Clin Exp Metastasis. 2026 Oct 17;43(6):59. doi:10.1007/s10585-026-10443-9.Checked: Abstract only - Abstract
Effectiveness of anticancer drug varies among various types of cancers so understanding their response towards these medications is essential. The paper describes an algorithm to classify drug response patterns from the lung cancer subset of GDSC2 data set through machine learning techniques. Continuous LN_IC50 response values are classified as sensitive and resistant based on predetermined cut-off points for creating a balanced dataset for modeling purposes. Eight out of nine numbers are obtained by drugs, cells lines targets pathways related responses while selecting them via selectkbest method through ANOVA Ftest. Logistic Regression, Support Vector Machine, Random Forest and XGBoost models were trained and evaluated on a held-out test set of 6,404 samples from an unbalanced dataset containing 32,018 records. Test accuracy of these models was between 92.61% and 92.96%, whereas their ROC-AUC value ranged from 0.978 to 0.981. Integrated with a Django based research tool by means of an algorithm that combines predictions via vote for producing outputs from this system. This device is meant solely to serve as an educational tool; it cannot be used by patients for self-diagnosis, healthcare decisions, or prescription of medications.
Journal IF-equivalent: 1.0 (OpenAlex 2-year mean citedness, value as of 2026-10-18, retrieved 2026-10-19; not the official Clarivate IF)Reference: P KP, Mohanapriya D, Thara L. AI-Based Cancer Drug Response Prediction: A Machine Learning Ensemble for Lung Cancer Pharmacogenomic Classification. Int J Sci Res Comput Sci Eng Inf Technol. 2026 Oct 17;12(5):466-78. doi:10.32628/cseit2612553.Checked: Abstract only