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

  • Clinical Investigation
  • Imaging
  • Published:
CardioVascular and Interventional Radiology Aims and scope Submit manuscript

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

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Data Availability

The computer code is shared at the corresponding author’s github page available at https://github.com/okanince/TACE_Study. The web site is blinded in the manuscript for peer-review process.

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Authors and Affiliations

  1. Department of Radiology, Medical School, University of Minnesota, 420 Delaware Street S.E, Minneapolis, MN, 55455, USA

    Okan İnce, Mehmet Gençtürk, Hakan Cebeci & Jafar Golzarian

  2. Department of Radiology, Health Sciences University, Prof. Dr. Cemil TASCIOGLU City Hospital, Istanbul, Turkey

    Hakan Önder

  3. Department of Radiology, College of Medicine, University of Arizona, 1501 N. Campbell Avenue, Tucson, AZ, 85724, USA

    Shamar Young

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  1. Okan İnce
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Correspondence to Okan İnce.

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Conflict of interest

Jafar Golzarian is a consultant for SIRTEX Medical and Shamar Young is a consultant for Boston Scientific. The other authors declare that they have no conflict of interest.

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İnce, O., Önder, H., Gençtürk, M. et al. Machine Learning Models in Prediction of Treatment Response After Chemoembolization with MRI Clinicoradiomics Features. Cardiovasc Intervent Radiol 46, 1732–1742 (2023). https://doi.org/10.1007/s00270-023-03574-z

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