Machine Learning Models in Prediction of Treatment Response After Chemoembolization with MRI Clinicoradiomics Features

Okan İnce, Hakan Önder, Mehmet Gençtürk, Hakan Cebeci, Jafar Golzarian, Shamar J Young

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: To evaluate machine learning models, created with radiomics and clinicoradiomics features, ability to predict local response after TACE. Materials and Methods: 188 treatment-naïve patients (150 responders, 38 non-responders) with HCC who underwent TACE were included in this retrospective study. Laboratory, clinical and procedural information were recorded. Local response was evaluated by European Association for the Study of the Liver criteria at 3-months. Radiomics features were extracted from pretreatment pre-contrast enhanced T1 (T1WI) and late arterial-phase contrast-enhanced T1 (CE-T1) MRI images. After data augmentation, data were split into training and test sets (70/30). Intra-class correlations, Pearson’s correlation coefficients were analyzed and followed by a sequential-feature-selection (SFS) algorithm for feature selection. Support-vector-machine (SVM) models were trained with radiomics and clinicoradiomics features of T1WI, CE-T1 and the combination of both datasets, respectively. Performance metrics were calculated with the test sets. Models’ performances were compared with Delong’s test. Results: 1128 features were extracted. In feature selection, SFS algorithm selected 18, 12, 24 and 8 features in T1WI, CE-T1, combined datasets and clinical features, respectively. The SVM models area-under-curve was 0.86 and 0.88 in T1WI; 0.76, 0.71 in CE-T1 and 0.82, 0.91 in the combined dataset, with and without clinical features, respectively. The only significant change was observed after inclusion of clinical features in the combined dataset (p = 0.001). Higher WBC and neutrophil levels were significantly associated with lower treatment response in univariant analysis (p = 0.02, for both). Conclusion: Machine learning models created with clinical and MRI radiomics features, may have promise in predicting local response after TACE. Level of Evidence: Level 4, Case–control study. Graphical Abstract: [Figure not available: see fulltext.]

Original languageEnglish (US)
Pages (from-to)1732-1742
Number of pages11
JournalCardiovascular and Interventional Radiology
Volume46
Issue number12
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).

Keywords

  • Chemoembolization
  • Hepatocellular carcinoma
  • Machine learning

PubMed: MeSH publication types

  • Journal Article

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