+ EDUCATION Abstract This scoping review directs attention to artificial intelligence–mediated informal language learning…± HEALTH Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential t…± HEALTH Objective This article helps neurologists understand modern approaches to diagnosing obstructive sleep apnea,…± CLIMATE Antifouling paints often contain biocides designed to inhibit biological growth, and antifouling paint partic…+ EDUCATION Generative artificial intelligence (GenAI) tools are an increasingly common resource used in the classroom an…+ HEALTH Objective Compare the effectiveness of machine learning algorithms and traditional logistic regression in pre…± HEALTH Objective Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) ha…+ HEALTH Background Traumatic cervical spinal cord injury (TCSCI) often causes severe neurological dysfunction. Accura…
TruaceTracing the truth around AIWednesday, August 5, 2026
Subscribe
Health·G Space·Evidence-backed gain·Published 2026-08-02

Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Background/aim Predicting local recurrence remains challenging in carbon-ion radiotherapy (CIRT) for non-small cell lung cancer (NSCLC). In this study, we aimed to develop and validate a machine learning model to predict local recurrence after CIRT for early-stage peripheral NSCLC. Patients and methods We retrospectively analyzed patients treated with CIRT at our institution between 2010 and 2020. An Extreme Gradient Boosting classifier using clinical parameters was developed to predict local recurrence within 2…

TRV-2026-0622Peer-reviewedPermanent record — cite & verify
Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning

Metastatic prostatic adenocarcinoma - CT scan - Case 257 (8519047084) by Yale Rosen from USA. CC BY-SA 2.0 · https://creativecommons.org/licenses/by-sa/2.0

The quick read

Between 2010 and 2020, 124 patients with early-stage peripheral non-small cell lung cancer treated with carbon-ion radiotherapy at a single institution were analyzed retrospectively to develop a machine learning predictor of local recurrence within 24 months. An Extreme Gradient Boosting classifier trained on clinical parameters with nested threefold cross-validation achieved ROC-AUC 0.622 and PR-AUC 0.145, and separated patients into low-risk and high-risk groups.

The ability to flag a high-risk subgroup with 65.0% versus 94.0% 2-year local control could help prioritize closer surveillance or adjuvant strategies after CIRT, but the modest discrimination, low precision-recall performance, and small event count of 10 recurrences (8.1%) leave uncertainty about reliability and generalizability beyond this institution.

Main points
  • Retrospective analysis of 124 patients treated with CIRT between 2010 and 2020, with 10 (8.1%) experiencing local recurrence within two years.
  • Extreme Gradient Boosting classifier developed using nested threefold cross-validation optimizing ROC-AUC to predict recurrence within 24 months.
  • Model achieved ROC-AUC of 0.622 and PR-AUC of 0.145, with risk stratification showing 94.0% vs 65.0% 2-year local control.
  • Interpretability explored using SHapley Additive exPlanations (SHAP) and evaluation included survival analysis with log-rank test.
Gain

Clinical-parameter XGBoost model stratified patients into low-risk and high-risk groups with 94.0% vs 65.0% 2-year local control after carbon-ion radiotherapy for early-stage peripheral NSCLC.

The rundown

Researchers retrospectively analyzed 124 patients with early-stage peripheral NSCLC treated with carbon-ion radiotherapy between 2010 and 2020, with median follow-up of 44.9 months and 2-year local control rate of 91.0%. An Extreme Gradient Boosting classifier using clinical parameters was trained with nested threefold cross-validation optimizing ROC-AUC to predict recurrence within 24 months.

The model yielded ROC-AUC 0.622 and PR-AUC 0.145, but stratified patients into groups with 94.0% versus 65.0% 2-year local control. Authors used precision-recall evaluation, survival analysis, and SHAP for interpretability, concluding the clinical-parameter model may predict recurrence.

Sources

Reader signal

How should this claim be treated?

The debate