Image-Level Data Augmentation for Radiomics-Based Classification of Vital Versus Non-Vital Persistent Cervical Lymph Nodes After Chemoradiotherapy in HNSCC.
Simple Summary: After chemoradiotherapy for HNSCC, cervical lymph nodes persist despite treatment, yet only a subset harbor active tumors. Identifying vital from non-vital nodes on CT scans helps guide decisions. We developed a machine learning pipeline that extracts image features from CT scans and...
| Publicado en: | Cancers Vol. 18; no. 14; pp. 2293 - 2317 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195807583&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195807583 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jul2026 vid: 18 iid: 14 pid: 97109 pub: MDPI artinfo: ui: 195807583 195807583 195807583 10.3390/cancers18142293 195807583 ppf: 2293 ppct: 24 formats: tig: atl: Image-Level Data Augmentation for Radiomics-Based Classification of Vital Versus Non-Vital Persistent Cervical Lymph Nodes After Chemoradiotherapy in HNSCC. aug: au: Naccour, Sara Moawad, Assaad Santer, Matthias Dejaco, Daniel Widmann, Gerlig Kollotzek, Siegfried Freysinger, Wolfgang affil: Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria sug: subj: Squamous Cell Carcinoma of Head and Neck Therapy Lymph Nodes Pathology Lymph Nodes Classification Radiomics Chemoradiotherapy Machine Learning Tomography, X-Ray Computed Image Processing, Computer Assisted Image Interpretation, Computer Assisted Squamous Cell Carcinoma of Head and Neck Pathology Human Male Female Middle Age Aged Cancer Patients Funding Source Squamous Cell Carcinoma of Head and Neck Classification Descriptive Statistics Retrospective Design Record Review Otorhinolaryngology Care Academic Medical Centers Austria Data Analysis Software Classification Algorithms Confidence Intervals Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Simple Summary: After chemoradiotherapy for HNSCC, cervical lymph nodes persist despite treatment, yet only a subset harbor active tumors. Identifying vital from non-vital nodes on CT scans helps guide decisions. We developed a machine learning pipeline that extracts image features from CT scans and tested whether augmenting training data with realistic transformations improves classification. This strategy improved classification performance by up to 21.9%, although differences did not reach statistical significance in this limited sample. Background: Distinguishing vital from non-vital persistent cervical lymph nodes after chemoradiotherapy in HNSCC remains clinically challenging. We investigated whether image-level data augmentation improves CT-based radiomics classification for this task. Methods: We evaluated eight augmentation strategies and their 28 pairwise combinations in 55 patients, using Bayesian hyperparameter tuning with Optuna for parameter optimization. A radiomics pipeline comprising Radiomics features, five feature selectors, and seven classifiers was assessed using patient-level stratified 5-fold cross-validation. Configurations were ranked using a composite score defined as the mean of AUC, ACC and F1-score. Results: Feature selection improved the composite score from 0.659 to 0.742. The best augmented configuration, Window Contrast Variation, achieved a composite score of 0.803 and an AUC of 0.831, corresponding to an 8.2% relative point-estimate gain over feature selection alone and a 21.9% gain over the no-selection baseline when feature selection and augmentation were combined. Conclusions: These findings suggest that feature selection with optimized augmentation may enhance radiomics-based lymph node classification. However, individual augmentation-versus-baseline differences did not reach statistical significance in this limited sample, requiring confirmation in larger cohorts. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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