Prognostic Impact of Budding Grade in Patients With Residual Liver Recurrence of Colorectal Cancer After Initial Hepatectomy

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Tumor budding and the prognosis of patients with metastatic colorectal cancer: a meta-analysis

PURPOSE

Tumor budding has been suggested to be associated with poor survival of patients with colorectal cancer (CRC). However, it is unclear whether the association remains in patients with metastatic CRC (mCRC). The aim of the systematic review and meta-analysis was to investigate the potential predictive role of tumor budding for the prognosis of patients with mCRC.

METHODS

PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant observational studies comparing the survival outcomes between mCRC patients with high versus low tumor budding. Data collection, literature searching, and statistical analysis were conducted independently by two authors. Using a heterogeneity-incorporating random-effects model, the results were pooled.

RESULTS

In this meta-analysis, 1503 patients from nine retrospective cohort studies were included. Pooled results showed that compared to those with low tumor budding, mCRC patients with high tumor budding were associated with a poor progression-free survival (hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.31 to 2.07, p < 0.001; I2 = 30%) and overall survival (HR, 1.60; 95% CI, 1.33 to 1.93; p < 0.001; I2 = 0%). Influencing analysis by excluding one study at a time showed consistent results (p all < 0.05). Subgroup analyses showed consistent results in tumor budding evaluated from the primary cancer and metastases, in studies with a high tumor budding defined as ≥ 10 or 15 and ≥ 5 buds/HPF and in studies analyzed with univariate and multivariate regression models (p for subgroup difference all > 0.05).

CONCLUSION

A high-degree tumor budding may be associated with poor prognosis in patients with mCRC.