ArticleRadiation oncology (London, England)2021
Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Trial
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Optimal timing of the cumulative dose and factors related to acute urinary toxicity in prostate cancer treated using magnetic resonance linear accelerator.Radiological physics and technology · 2026Article
- Assessing Postpartum Levator Ani Muscle Recovery: A Feasibility Study on Automated Texture Analysis in Transvaginal Ultrasound.Ultrasound in medicine & biology · 2025Article
- Radiomic analysis of the deltoid and scapula: identification of computed tomography-image based measurements predictive of pain, motion, and function before and after shoulder arthroplasty.JSES international · 2025Article
- RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.Journal of medical systems · 2025Article
- Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.Biomolecules · 2025Review
- Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment.Quantitative imaging in medicine and surgery · 2024Review
- A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.Scientific reports · 2024Article
- Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies.Scientific reports · 2023Article
- Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer.Journal of digital imaging · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Risk analysis of grade ≥ 2 radiation pneumonitis based on radiotherapy timeline in stage III/IV non-small cell lung cancer treated with volumetric modulated arc therapy: a retrospective study.BMC pulmonary medicine · 2022Article
- Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population.EJNMMI physics · 2022Article
- Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.Scientific reports · 2022Article
- Article
- Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer.Cancers · 2021Article
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Abstract
backgroundRadiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics.
methodsA total of 304 NSCLC (Stages I-IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test-retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan-Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability.
resultsIn the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCC
conclusionsOur results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.
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