| Title: |
Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis |
| Authors: |
Montagnon, Emmanuel; Cerny, Milena; Hamilton, Vincent; Derennes, Thomas; Ilinca, André; Elforaici, Mohamed El Amine; Jabbour, Gilbert; Rafie, Edmond; Wu, Anni; Perdigon Romero, Francisco; Cadrin-Chênevert, Alexandre; Kadoury, Samuel; Turcotte, Simon; Tang, An |
| Contributors: |
Bennett, Sean; Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données; Université de Montréal Roger Des Groseillers Research Chair in Hepatopancreatobiliary Surgical Oncology |
| Source: |
PLOS ONE ; volume 19, issue 9, page e0307815 ; ISSN 1932-6203 |
| Publisher Information: |
Public Library of Science (PLoS) |
| Publication Year: |
2024 |
| Collection: |
PLOS Publications (via CrossRef) |
| Description: |
Objective The purpose of this study was to determine and compare the performance of pre-treatment clinical risk score (CRS), radiomics models based on computed (CT), and their combination for predicting time to recurrence (TTR) and disease-specific survival (DSS) in patients with colorectal cancer liver metastases. Methods We retrospectively analyzed a prospectively maintained registry of 241 patients treated with systemic chemotherapy and surgery for colorectal cancer liver metastases. Radiomics features were extracted from baseline, pre-treatment, contrast-enhanced CT images. Multiple aggregation strategies were investigated for cases with multiple metastases. Radiomics signatures were derived using feature selection methods. Random survival forests (RSF) and neural network survival models (DeepSurv) based on radiomics features, alone or combined with CRS, were developed to predict TTR and DSS. Leveraging survival models predictions, classification models were trained to predict TTR within 18 months and DSS within 3 years. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on the test set. Results For TTR prediction, the concordance index (95% confidence interval) was 0.57 (0.57–0.57) for CRS, 0.61 (0.60–0.61) for RSF in combination with CRS, and 0.70 (0.68–0.73) for DeepSurv in combination with CRS. For DSS prediction, the concordance index was 0.59 (0.59–0.59) for CRS, 0.57 (0.56–0.57) for RSF in combination with CRS, and 0.60 (0.58–0.61) for DeepSurv in combination with CRS. For TTR classification, the AUC was 0.33 (0.33–0.33) for CRS, 0.77 (0.75–0.78) for radiomics signature alone, and 0.58 (0.57–0.59) for DeepSurv score alone. For DSS classification, the AUC was 0.61 (0.61–0.61) for CRS, 0.57 (0.56–0.57) for radiomics signature, and 0.75 (0.74–0.76) for DeepSurv score alone. Conclusion Radiomics-based survival models outperformed CRS for TTR prediction. More accurate, noninvasive, and early prediction of patient outcome may help reduce exposure to ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1371/journal.pone.0307815 |
| Availability: |
https://doi.org/10.1371/journal.pone.0307815; https://dx.plos.org/10.1371/journal.pone.0307815 |
| Rights: |
http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.3E39C945 |
| Database: |
BASE |