Prognostic factors and therapeutic effects of different treatment modalities for colorectal cancer liver metastases

Nomograms for predicting overall survival in colorectal cancer patients with metastasis to the liver, lung, bone, and brain

BACKGROUND

The aim of this study was to identify the heterogeneous and homogeneous prognostic factors associated with distant metastasis to the liver, lung, bone, and brain in colorectal cancer (CRC) patients and then construct nomograms to predict the prognosis.

METHODS

CRC patients registered in the surveillance, epidemiology, and end results database between 2010 and 2017 were included. A Cox regression model was used to analyse homogeneous and heterogeneous prognostic factors, and Kaplan‒Meier analysis was performed to estimate overall survival (OS). Predictive nomograms were constructed, and their performance was evaluated with C-indexes, calibration curves and the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

A total of 37,641 patients with distant metastasis to the liver, lung, bone, and brain were included. The median survival times of patients with liver metastasis, lung metastasis, bone metastasis, and brain metastasis were 12.00 months (95% CI 11.73-12.27 months), 10.00 months (95% CI 9.60-10.41 months), 5.00 months (95% CI 4.52-5.48 months), and 3.00 months (95% CI 2.28-3.72 months), respectively. An older age, higher N stage, elevated carcinoembryonic antigen level, no surgery at the primary site and no/unknown treatment with chemotherapy were identified as homogeneous prognostic factors for the four types of metastases. The calibration curves, C-indexes and AUCs exhibited good performance for predicting the OS of patients with distant metastases to the liver, lung, bone, and brain.

CONCLUSIONS

CRC patients with distant metastasis to the liver, lung, bone, and brain exhibited homogeneous and heterogeneous prognostic factors, all of which were associated with shorter survival. The nomograms showed good accuracy and may be used as tools for clinicians to predict the prognosis of CRC patients with distant metastasis.

Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study

Simple Summary

The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity.

Abstract

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.