Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy Journal Article
| Authors: | Hu, R.; Chen, I.; Peoples, J.; Salameh, J. P.; Gönen, M.; Romesser, P. B.; Simpson, A. L.; Reyngold, M. |
| Article Title: | Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy |
| Abstract: | Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62–0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93–2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05–6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56–0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management. © 2022 The Author(s) |
| Keywords: | adult; cancer survival; controlled study; survival analysis; primary tumor; major clinical study; cancer growth; cancer radiotherapy; progression free survival; retrospective study; prediction; survival time; contrast enhancement; artificial intelligence; iohexol; tumor growth; liver parenchyma; computer vision; liver weight; gross tumor volume; feature extraction; machine learning; planning target volume; colorectal liver metastasis; human; article; prognostic assessment; feature selection; radiomics |
| Journal Title: | Physics and Imaging in Radiation Oncology |
| Volume: | 24 |
| ISSN: | 2405-6316 |
| Publisher: | Elsevier B.V. |
| Date Published: | 2022-10-01 |
| Start Page: | 36 |
| End Page: | 42 |
| Language: | English |
| DOI: | 10.1016/j.phro.2022.09.004 |
| PROVIDER: | scopus |
| PMCID: | PMC9485899 |
| PUBMED: | 36148155 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 3 October 2022 -- Source: Scopus |
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