A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data

Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes.

The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM.

This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance.

Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9.

Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.