Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.

PURPOSE: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model. METHODS: One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Six radiomic features were selected with LASSO. In the clinical model, six features (age, gender, thickness, fat stranding, symmetry, and lymph node) were included. Six radiomic and six clinical features were used in the combined model. Classification was done using two machine learning algorithms: Support Vector Machine (SVM) and Logistic Regression (LR). The data sets were divided into 80% training set and 20% test set. Then, training took place with all three datasets. The model's success was tested with the test set consisting of features not used during training. RESULTS: In the training set, the combined model had the best performance with the area under the curve (AUC) value of 0.99 for SVM and 0.95 for LR. In the radiomic-derived model, the AUC value is 0.87 in SVM and 0.79 in LR. In the clinical model, SVM made this distinction with 0.95 AUC and LR with 0.92 AUC value. In the test set, the classifier with the highest success distinguishing malignant wall thickening is SVM in the radiomic-derived model with an AUC value of 0.90. In other models, the AUC value is in the range of 0.75-0.86, and the accuracy values are in the range of 0.72-0.84. CONCLUSION: In conclusion, radiomic-based machine learning has shown high success in distinguishing malignant and benign BWT and may improve diagnostic accuracy compared to clinical features only. The results of our study may help ensure early diagnosis and treatment of colorectal cancers by facilitating the recognition of malignant BWT.

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.

Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes.

BACKGROUND: Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues. PURPOSE: To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes. MATERIAL AND METHODS: Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined. RESULTS: The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT. CONCLUSION: Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.

The correlations between dynamic contrast enhanced magnetic resonance imaging and immunohistochemical data in head and neck squamous cell carcinomas.

BACKGROUND: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can in vivo characterize tumor microvascular environment. The aim of the present study was to reveal the DCE-MRI findings and to determine the correlation between these findings and immunohistochemical data in head and neck squamous cell carcinoma (HNSCC). METHODS: Thirty-three patients diagnosed with primary HNSCC were evaluated retrospectively. DCE-MRI was conducted in all cases. CD34, CD105, and ki-67 expressions were analyzed with immunohistochemistry in tissue sections to determine micro-vessel density and proliferative activity. RESULTS: The DCE-MRI is a successful technique in distinguishing tumor tissue from normal tissue. It was determined that Ve, Ktrans, and ki-67 values were significantly higher in high-stage tumors and there were positive correlations between the Ktrans value (by standard ROI) and CD34 MVDmax and CD34 MVDmean values. No statistically significant correlation was determined between other parameters in DCE-MRI and immunohistochemical data, and T stage. DISCUSSION: DCE-MRI could successfully differentiate tumor tissue in HNSCC. Furthermore, it was observed that DCE-MRI had the potential to reveal certain immunohistochemical information in vivo.

Relationships Between DCE-MRI, DWI, and 18F-FDG PET/CT Parameters with Tumor Grade and Stage in Patients with Head and Neck Squamous Cell Carcinoma

Objectives: Properties of head and neck squamous cell carcinoma (HNSCC) such as cellularity, vascularity, and glucose metabolism interact with each other. This study aimed to investigate the associations between diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and positron emission tomography/computed tomography (PET/CT) in patients with HNSCC. Methods: Fourteen patients who were diagnosed with HNSCC were investigated using DCE-MRI, DCE, and 18fluoride-fluorodeoxyglucose PET/CT and evaluated retrospectively. Ktrans, Kep, Ve, and initial area under the curve (iAUC) parameters from DCE-MRI, ADCmax, ADCmean, and ADCmin parameters from DWI, and maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) parameters from PET were obtained. Spearman's correlation coefficient was used to analyze associations between these parameters. In addition, these parameters were grouped according to tumor grade and T and N stages, and the difference between the groups was evaluated using the Mann-Whitney U test. Results: Correlations at varying degrees were observed in the parameters investigated. ADCmean moderately correlated with Ve (p=0.035; r=0.566). Ktrans inversely correlated with SUVmax (p=0.017; r=-0.626). iAUC inversely correlated with SUVmax, SUVmean, TLG, and MTV (p<0.05, r≤-0.700). MTV (40% threshold) was significantly higher in T4 tumors than in T1-3 tumors (p=0.020). No significant difference was found in the grouping made according to tumor grade and N stage in terms of these parameters. Conclusion: Tumor cellularity, vascular permeability, and glucose metabolism had significant correlations at different degrees. Furthermore, MTV may be useful in predicting T4 tumors.

Adult Spinal Primary Leptomeningeal Medulloblastoma Presenting as Pseudotumour Cerebri Syndrome.

A previously well 34-year-old man presented with severe pseudotumour cerebri. Imaging showed that he had a cauda equina tumour which proved to be a medulloblastoma. There was no tumour mass in the posterior fossa so we assume that this was a primary leptomeningeal medulloblastoma. In patients with somewhat atypical pseudotumour, spinal imaging should always be considered.