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

Tumour Budding Is a Useful Predictor to Identify High-Risk Stage II Colon Cancer Patients After Curative Surgery

Aim. Although it is now accepted in the literature that tumour budding (TB) is a useful survival indicator in colon cancer (CC), there are still uncertainties about daily use. Here we methodologically examined the role of TB on survival in CC. Methods. In our study, we examined colon cancer patients who had surgery up to 15 years before presentation. TB was calculated separately using different comprehensive methodological methods. Results. We first investigated an optimal evaluation method. Relationship with prognostic factors was better (Venous invasion [p = .001], advanced pT [p = .003], perineural invasion [p = .040], MSS [p = .016], advanced size [p = .001], tumour obstruction [p = .005], margin involvement [p = .043], and nodal involvement [p = .028]) in Method-1. Similarly, with the same method, the success of the cut-off value, the correlation of TB data (r = .724), and the repeatability of the method (Κappa = .53-.75) were quite good (ROC = .816 [.707-.925]). Then, survival analysis was performed using the best three methods, including this method. In univariate analysis using Method-1, survival analyses were worse in high TB patients (RFS: 81%, p < .001; OS: 84%, p < .001). Multivariate analyses using the same method confirmed that high TB for RFS and OS was an independent poor prognostic parameter for survival (p = .002, Hazard ratio [HR]: 1.42 [1.13-1.80]) and OS (p = .014, HR: 1.38 [1.07-1.79]). Conclusions. With our study, we showed that tumour budding calculated by the standard method is a very valuable prognostic parameter in stage II CC and can contribute to the detection of patients with poor prognosis in stage II CC.

New Tumor Budding Evaluation in Head and Neck Squamous Cell Carcinomas

BACKGROUND

Tumor budding (TB) is a histomorphological characteristic of the tumor invasion front and it has an impact on the tumor outcome prediction for head and neck squamous cell carcinoma (HNSCC) aetiopathology.

PATIENTS AND METHODS

The average TB score (TB rel) of all tumor-positive marginal sections (n = 443) in the primary tumor was analyzed in the FFPE-fixed tumor slices of 66 patients with HNSCC, and they were compared with cryo-fixed sections.

RESULTS

TB rel correlates with tumor aggressiveness (i.e., lymph node metastasis quantity, lymph node ratio, extra capsular growth, Pn1, pV1, grading). The TB scores often vary between the different tumor margins of FFPE sections in the same patient, and in many cases, they differ depending on the fixation method.

CONCLUSION

Our data show that a randomly selected marginal cut cannot reliably mirror the TB score, and thus, they cannot predict the prognostic outcome. However, TB rel could be a tool that compensates for differences in TB score analysis. TB score determination in cryo sections seems to be inaccurate compared with TB determination in FFPE.

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.