Multimodal Treatment of Recurrence and Distant Metastases of Colorectal Cancer

Background: The objective of this study is to evaluate the efficacy and safety of different third-line treatment regimens for metastatic colorectal cancer (mCRC) through a comprehensive analysis and network meta-analysis (NMA). Additionally, the study aims to provide guidance on selecting appropriate third-line systemic treatment regimens for patients with mCRC.

Methods: We conducted a search of the PubMed, Embase, Web of Science, and Cochrane Central Register of Controlled Trials databases from January 1, 2005, to May 20, 2023, to include phase II/III randomized clinical trials (RCTs) of third-line treatments for mCRC. The primary outcome assessed in the NMA was median overall survival (mOS), and other outcomes included median progression-free survival (mPFS), disease control rate (DCR), and grade 3 or higher adverse events (≥3AEs).

Results: Ultimately, nine phase II/III RCTs involving five treatment regimens were included in this study. Trifluridine/tipiracil (TAS-102) plus bevacizumab (hazard ratio [HR] 0.41, 95% credible interval [CrI] 0.32-0.52) was found to be the most effective treatment for mOS compared to best supportive care (BSC). TAS-102 plus bevacizumab also significantly improved mPFS compared to BSC (HR 0.20, 95% CrI 0.16-0.25). In terms of adverse events (AEs), TAS-102 (RR 0.52, 95% CrI 0.35-0.74) had a lower incidence of ≥3AEs compared to fruquintinib, but fruquintinib (RR 1.79, 95% CrI 1.10-3.11) showed better improvement in DCR than TAS-102. Subgroup analysis using the Bayesian surface under the cumulative ranking curve (SUCRA) ranked the regimens based on the OS benefit. The results indicated that TAS-102 plus bevacizumab ranked first across age, gender, Eastern Cooperative Oncology Group performance status (ECOG PS), and time from initial diagnosis of metastatic disease to randomization.

Conclusion: TAS-102, fruquintinib, TAS-102 plus bevacizumab, the regorafenib standard dose regimen (regorafenib), and the regorafenib dose-escalation regimen (regorafenib 80+) all demonstrated improved OS and PFS compared to BSC in mCRC patients. However, TAS-102 plus bevacizumab may be the optimal choice for third-line treatment in mCRC patients.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php, CRD42023434929.

Background: Distant metastasis from rectal cancer usually results in poorer survival and quality of life, so early identification of patients at high risk of distant metastasis from rectal cancer is essential.

Method: The study used eight machine-learning algorithms to construct a machine-learning model for the risk of distant metastasis from rectal cancer. We developed the models using 23867 patients with rectal cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Meanwhile, 1178 rectal cancer patients from Chinese hospitals were selected to validate the model performance and extrapolation. We tuned the hyperparameters by random search and tenfold cross-validation to construct the machine-learning models. We evaluated the models using the area under the receiver operating characteristic curves (AUC), the area under the precision-recall curve (AUPRC), decision curve analysis, calibration curves, and the precision and accuracy of the internal test set and external validation cohorts. In addition, Shapley’s Additive explanations (SHAP) were used to interpret the machine-learning models. Finally, the best model was applied to develop a web calculator for predicting the risk of distant metastasis in rectal cancer.

Result: The study included 23,867 rectal cancer patients and 2,840 patients with distant metastasis. Multiple logistic regression analysis showed that age, differentiation grade, T-stage, N-stage, preoperative carcinoembryonic antigen (CEA), tumor deposits, perineural invasion, tumor size, radiation, and chemotherapy were-independent risk factors for distant metastasis in rectal cancer. The mean AUC value of the extreme gradient boosting (XGB) model in ten-fold cross-validation in the training set was 0.859. The XGB model performed best in the internal test set and external validation set. The XGB model in the internal test set had an AUC was 0.855, AUPRC was 0.510, accuracy was 0.900, and precision was 0.880. The metric AUC for the external validation set of the XGB model was 0.814, AUPRC was 0.609, accuracy was 0.800, and precision was 0.810. Finally, we constructed a web calculator using the XGB model for distant metastasis of rectal cancer.

Conclusion: The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation