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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.

Oya Altinok1,2,3, Wai Lone J Ho4, Lary Robinson5

  • 1Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Medrxiv : the Preprint Server for Health Sciences
|May 1, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a CT-based radiomics classifier to predict non-small cell lung cancer (NSCLC) recurrence. Habitat-based radiomics showed superior performance in identifying high-risk patients for recurrence-free survival.

Keywords:
habitat imagingimage biomarkersradiomicstumor heterogeneity

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Area of Science:

  • Radiomics and Medical Imaging
  • Oncology
  • Biomarker Discovery

Background:

  • Patient outcomes after non-small cell lung cancer (NSCLC) surgery vary despite similar staging.
  • Identifying reliable biomarkers for predicting recurrence is crucial for personalized treatment strategies.

Purpose of the Study:

  • To develop and validate a pre-surgical CT-based radiomics classifier for predicting recurrence risk in NSCLC patients.
  • To compare the predictive performance of intratumoral radiomics, habitat-based radiomics, and a combined model.

Main Methods:

  • A cohort of 293 surgically resected NSCLC patients was divided into training and testing sets.
  • Tumor habitats were identified using unsupervised clustering on pre-surgical CT images.
  • Radiomic features were extracted from intratumoral and habitat-defined regions to build logistic regression classifiers.

Main Results:

  • The combined radiomics classifier achieved the highest AUC (0.82), outperforming intratumoral (0.75) and habitat (0.81) models.
  • Habitat-based radiomics significantly stratified patients for recurrence-free survival (HR=5.41), with habitat-derived information being the strongest predictor.
  • High-risk patients identified by the combined model showed the largest risk estimate (HR=8.43).

Conclusions:

  • Habitat-based radiomics offers superior predictive performance for NSCLC recurrence compared to intratumoral radiomics.
  • CT-based radiomics classifiers can effectively stratify patients based on recurrence risk, aiding in treatment decisions.