± HEALTH Objective To evaluate the accuracy, completeness, clarity, source transparency, and readability of leading AI…+ HEALTH Background Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimiz…+ HEALTH Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition a…+ SCIENCE Fire ants (Solenopsis spp. Westwood) pose a major ecological and economic threat, mainly due to the invasive…± HEALTH Objectives The current study aimed to quantify the diagnostic accuracy of commonly utilized chatbots includin…+ HEALTH Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools…± HEALTH Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operation…+ HEALTH Colorectal cancer (CRC) exhibits substantial metabolic heterogeneity. This study developed a robust prognosti…
TruaceTracing the truth around AIWednesday, August 5, 2026
Subscribe
TRV-2026-0622Certified recordPeer-reviewed

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…

Health · G Space — documented gain · certified 2026-08-02 · v1 · article view · machine-readable

Current reading — 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.

What this doesn’t fix

Retrospective single-institution study with only 124 patients and 10 recurrence events, and modest discriminative performance limits generalizability.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0622, v1: “Local Recurrence Prediction After Carbon-ion Radiotherapy for Early-stage Non-small Cell Lung Cancer Using Machine Learning.” Truvace, 2026-08-02. /record/TRV-2026-0622 (accessed at citation time). sha256 82418ef08b2d18ce…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv182418ef08b2d…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0622 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.