Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.

PURPOSE: Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. MATERIALS AND METHODS: Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). RESULTS: Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. CONCLUSIONS: ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.

Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment.

PURPOSE: The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period. MATERIALS AND METHODS: CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA). RESULTS: Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed. CONCLUSION: CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.

Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.

RATIONALE AND OBJECTIVES: This study aimed to investigate whether benign and malignant renal solid masses could be distinguished through machine learning (ML)-based computed tomography (CT) texture analysis. MATERIALS AND METHODS: Seventy-nine patients with 84 solid renal masses (21 benign; 63 malignant) from a single center were included in this retrospective study. Malignant masses included common renal cell carcinoma (RCC) subtypes: clear cell RCC, papillary cell RCC, and chromophobe RCC. Benign masses are represented by oncocytomas and fat-poor angiomyolipomas. Following preprocessing steps, a total of 271 texture features were extracted from unenhanced and contrast-enhanced CT images. Dimension reduction was done with a reliability analysis and then with a feature selection algorithm. A nested-approach was used for feature selection, model optimization, and validation. Eight ML algorithms were used for the classifications: decision tree, locally weighted learning, k-nearest neighbors, naive Bayes, logistic regression, support vector machine, neural network, and random forest. RESULTS: The number of features with good reproducibility was 198 for unenhanced CT and 244 for contrast-enhanced CT. Random forest algorithm demonstrated the best predictive performance using five selected contrast-enhanced CT texture features. The accuracy and area under the curve metrics were 90.5% and 0.915, respectively. Having eliminated the highly collinear features from the analysis, the accuracy and area under the curve values slightly increased to 91.7% and 0.916, respectively. CONCLUSION: ML-based contrast-enhanced CT texture analysis might be a potential method for distinguishing benign and malignant solid renal masses with satisfactory performance.

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.

OBJECTIVE: To evaluate the performance of quantitative computed tomography (CT) texture analysis using different machine learning (ML) classifiers for discriminating low and high nuclear grade clear cell renal cell carcinomas (cc-RCCs). MATERIALS AND METHODS: This retrospective study included 53 patients with pathologically proven 54 cc-RCCs (31 low-grade [grade 1 or 2]; 23 high-grade [grade 3 or 4]). In one patient, two synchronous cc-RCCs were included in the analysis. Mean age was 57.5 years. Thirty-four (64.1%) patients were male and 19 were female (35.9%). Mean tumour size based on the maximum diameter was 57.4 mm (range, 16-145 mm). Forty patients underwent radical nephrectomy and 13 underwent partial nephrectomy. Following pre-processing steps, two-dimensional CT texture features were extracted using portal-phase contrast-enhanced CT. Reproducibility of texture features was assessed with the intra-class correlation coefficient (ICC). Nested cross-validation with a wrapper-based algorithm was used in feature selection and model optimisation. The ML classifiers were support vector machine (SVM), multilayer perceptron (MLP, a sort of neural network), naïve Bayes, k-nearest neighbours, and random forest. The performance of the classifiers was compared by certain metrics. RESULTS: Among 279 texture features, 241 features with an ICC equal to or higher than 0.80 (excellent reproducibility) were included in the further feature selection process. The best model was created using SVM. The selected subset of features for SVM included five co-occurrence matrix (ICC range, 0.885-0.998), three run-length matrix (ICC range, 0.889-0.992), one gradient (ICC = 0.998), and four Haar wavelet features (ICC range, 0.941-0.997). The overall accuracy, sensitivity (for detecting high-grade cc-RCCs), specificity (for detecting high-grade cc-RCCs), and overall area under the curve of the best model were 85.1%, 91.3%, 80.6%, and 0.860, respectively. CONCLUSIONS: The ML-based CT texture analysis can be a useful and promising non-invasive method for prediction of low and high Fuhrman nuclear grade cc-RCCs. KEY POINTS: • Based on the percutaneous biopsy literature, ML-based CT texture analysis has a comparable predictive performance with percutaneous biopsy. • Highest predictive performance was obtained with use of the SVM. • SVM correctly classified 85.1% of cc-RCCs in terms of nuclear grade, with an AUC of 0.860.

Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.

OBJECTIVE: To develop externally validated, reproducible, and generalizable models for distinguishing three major subtypes of renal cell carcinomas (RCCs) using machine learning-based quantitative computed tomography (CT) texture analysis (qCT-TA). MATERIALS AND METHODS: Sixty-eight RCCs were included in this retrospective study for model development and internal validation. Another 26 RCCs were included from public databases (The Cancer Genome Atlas-TCGA) for independent external validation. Following image preparation steps (reconstruction, resampling, normalization, and discretization), 275 texture features were extracted from unenhanced and corticomedullary phase CT images. Feature selection was firstly done with reproducibility analysis by three radiologists, and; then, with a wrapper-based classifier-specific algorithm. A nested cross-validation was performed for feature selection and model optimization. Base classifiers were the artificial neural network (ANN) and support vector machine (SVM). Base classifiers were also combined with three additional algorithms to improve generalizability performance. Classifications were done with the following groups: (i), non-clear cell RCC (non-cc-RCC) versus clear cell RCC (cc-RCC) and (ii), cc-RCC versus papillary cell RCC (pc-RCC) versus chromophobe cell RCC (chc-RCC). Main performance metric for comparisons was the Matthews correlation coefficient (MCC). RESULTS: Number of the reproducible features is smaller for the unenhanced images (93 out of 275) compared to the corticomedullary phase images (232 out of 275). Overall performance metrics of the machine learning-based qCT-TA derived from corticomedullary phase images were better than those of unenhanced images. Using corticomedullary phase images, ANN with adaptive boosting algorithm performed best for discrimination of non-cc-RCCs from cc-RCCs (MCC = 0.728) with an external validation accuracy, sensitivity, and specificity of 84.6%, 69.2%, and 100%, respectively. On the other hand, the performance of the machine learning-based qCT-TA is rather poor for distinguishing three major subtypes. The SVM with bagging algorithm performed best for discrimination of pc-RCC from other RCC subtypes (MCC = 0.804) with an external validation accuracy, sensitivity, and specificity of 69.2%, 71.4%, and 100%, respectively. CONCLUSIONS: Machine learning-based qCT-TA can distinguish non-cc-RCCs from cc-RCCs with a satisfying performance. On the other hand, the performance of the method for distinguishing three major subtypes is rather poor. Corticomedullary phase CT images provide much more valuable texture parameters than unenhanced images.

Retrospective study of 24 cases of acute appendiceal diverticulitis: CT findings and pathological correlations.

PURPOSE: Appendiceal diverticulitis is relatively rare and is difficult to distinguish clinically and radiologically from acute appendicitis. The aim of this study was to describe the computed tomography (CT) findings of acute appendiceal diverticulitis. MATERIALS AND METHODS: Among the 1329 patients who underwent appendectomy at our institution between January 2010 and July 2015, 28 were diagnosed pathologically with appendiceal diverticulitis, including 24 patients who were evaluated by preoperative CT. The control group consisted of 38 patients without diverticulitis. Average age of patients, ratio of males to females, appendiceal diameter, presence of a diverticulum, diverticular enhancement, peri-appendiceal fat stranding, peri-appendiceal loculated fluid and perforation, and the presence of appendicolith were evaluated retrospectively. RESULTS: Peri-appendiceal fat stranding (p < 0.005), appendiceal diameter (p < 0.005), and peri-appendiceal loculated fluid differed significantly between the diverticulitis and non-diverticulitis groups (p < 0.005). CONCLUSION: Although relatively uncommon, appendiceal diverticulitis should be included in the differential diagnosis of acute appendicitis. It differs from typical acute appendicitis by the presence of an inflamed diverticulum, seen on CT. These patients are also more likely to have peri-appendiceal extra-luminal loculated fluid, peri-appendiceal fat stranding, and a larger diameter of the appendix. The latter finding is likely due to the increased intraluminal pressure.

Does the abdominal ultrasonography reliable in the diagnosis of postoperative pancreatic fistula after pancreaticoduodenectomy in the first postoperative week?

PURPOSE: We evaluated the efficacy of ultrasonography (US) in the early postoperative period after pancreaticoduodenectomy (PD) to diagnose postoperative-pancreatic-fistula (POPF). Early diagnosis is important to prevent POPF-dependent mortality after PD. The value of radiological modalities for early diagnosing POPF is not clear. METHODS: Forty-five patients who underwent transabdominal-US in the first postoperative week after PD were retrospectively evaluated. Two types of grouping methods were performed. Firstly, peripancreatic or perianastomotic fluid collections at least 2 cm in diameter were considered to be a primary positive result on US. Patients then divided into 2 groups: group 1, US-positive and group 2, US-negative. Secondly, to increase the power of US, in addition to primary positive results, the presence of fever, leukocytosis or hyperamylasemia was considered to be a secondary positive result (group 1S). The remaining patients were considered to have secondary negative results (group 2S). The sensitivity and specificity for both grouping methods were calculated for the diagnosis of PF and clinically important PF (ciPF), according to the International Study Group on Pancreatic Fistula criteria. RESULTS: For the first grouping method, the sensitivity was 36% and 28% and the specificity was 80% and 85% for PF and ciPF, respectively. For the second grouping method, the sensitivity was 36% and 29% and the spesificity was 74% and 81% for PF and ciPF, respectively. The unloculated fluid collections were not related to a significant increase in the risk of POPF (P = 0.694). CONCLUSION: Abdominal-US has low sensitivity and high specificity for the early diagnosis of POPF after PD.

Preoperative localization of distal colorectal tumours.

BACKGROUND: The detection of true localization of the tumour are crucial to driving the proper treatment algorithm in distally-located colorectal cancers (CRCs). The performance of four methods; colonoscopy, computed tomography (CT), magnetic resonance imaging (MRI), and fluoro-deoxy-glucose-positron emission tomography scan (FDG/PET-CT), were evaluated to identify the localizations of distal colorectal malignancies according to the rectum, sigmoid colon and recto- sigmoid junction (RSJ). MATERIALS AND METHODS: Medical records of patients who underwent colorectal surgery for tumours located on the sigmoid colon, RSJ, or rectum were reviewed retrospectively. METHODS: In total, 156 patients were included in the study. In terms of overall accuracy, colonoscopy, CT, MRI and FDG/PET-CT had similar accuracy rates, with 74%, 67%, 75%, and 74%, respectively. Colonoscopy was relatively less sensitive for rectosigmoid tumours (33%), while CT was less sensitive for rectal tumours (26%). MRI was less specific for tumours located on the rectum (33%). CONCLUSIONS: It is crucial to correctly identify the location of distal colorectal tumours in order to plan accurate treatment strategies. Preoperative modalities, including colonoscopy, CT, MRI, and FDG/PET-CT, do not provide excellent accuracy for tumours of the distal colorectal tumours. To increase the success of these modalities; combined use could be more successful. KEY WORDS: Colonoscopy, Computed tomography Distal colorectal cancer, Magnetic resonance imaging.