Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.

PURPOSE: To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. MATERIALS AND METHODS: This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. RESULTS: There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. CONCLUSION: The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

Age-related metabolic changes in the corpus callosum: assessment with MR spectroscopy.

PURPOSE: The aim of this study was to examine metabolite changes in different parts of the corpus callosum (CC), and to relate these changes to different age groups using magnetic resonance spectroscopy (MRS). MATERIALS AND METHODS: A total of 76 healthy subjects participated in the study with MRS analyses (39 females and 37 males). Subjects were grouped by age into four groups, in increasing order: Groups 1, 2, 3, and 4. Single-section 2D multivoxel spectroscopy was performed using chemical-shift imaging techniques. The voxels were placed on the rostrum, genu, corpus, and splenium of the CC. Peak metabolite ratios of N-acetylaspartate (NAA)/choline (Cho), NAA/creatine (Cr), and Cho/Cr were calculated from the rostrum, genu, body, and splenium. One way analysis of variance test was performed for the detection of changes in different age groups. Pearson correlation test was performed for correlation of metabolite ratio related to age. RESULTS: Statistically significant differences were found for NAA/Cho ratios for the rostrum, corpus, and splenium, and NAA/Cr ratios for the corpus and splenium between Groups 1 and 2, Groups 1 and 3, and Groups 1 and 4. Metabolite ratios of the corpus and splenium were similar. This similarity was also valid for parts of the rostrum and genu. CONCLUSION: Metabolite ratios in the CC are influenced by age. Age-related changes and regional metabolite levels may cause these alterations. Analyses of the CC may be informative for the evaluation of white matter. MRS may help to demonstrate metabolite levels and ratios of the CC.