Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer.

Purpose: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results: The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). Conclusions: The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis.

BACKGROUND: Development of a radiomics model for predicting lymph node metastasis status in rectal cancer patients based on 3-dimensional endoanal rectal ultrasound images. METHODS: This study retrospectively included 79 patients (41 with lymph node metastasis positive and 38 with lymph node metastasis negative) diagnosed with rectal cancer in our hospital from January 2018 to February 2022. The tumor's region of interest is first delineated by radiologists, from which radiomics features are extracted. Radiomics features were then selected by independent samples t-test, correlation coefficient analysis between features, and least absolute shrinkage and regression with selection operator. Finally, a multilayer neural network model is developed using the selected radiomics features, and nested cross-validation is performed on it. These models were validated by assessing their diagnostic performance and comparing the areas under the curve and recall rate curve in the test set. RESULTS: The areas under the curve of radiologist was 0.662 and the F1 score was 0.632. Thirty-four radiomics features were significantly associated with lymph node metastasis (P < .05), and 10 features were finally selected for developing multilayer neural network models. The areas under the curve of the multilayer neural network models were 0.787, 0.761, 0.853, and the mean areas under the curve was 0.800. The F1 scores of the multilayer neural network models were 0.738, 0.740, and 0.818, and the mean F1 score was 0.771. CONCLUSIONS: Radiomics models based on 3-dimensional endoanal rectal ultrasound can be used to identify lymph node metastasis status in rectal cancer patient with good diagnostic performance.

Diagnostic Accuracy of Three-Dimensional Endoanal Ultrasound for Anal Fistula: A Systematic Review and Meta-analysis.

BACKGROUND: Anal fistula is a relatively common anorectal disease. An accurate assessment of the main anal fistula type and the anatomy of the internal opening before surgery is necessary to obtain the best surgical results. Whether three-dimensional endoanal ultrasound (3D-EAUS) should be used as the first-line diagnostic tool for anal fistula is still controversial. The purpose of this study is to conduct a meta-analysis of the published literature on 3D-EAUS and anal fistula, and compare the results of 3D-EAUS and surgery to evaluate the diagnostic value of 3D-EAUS for anal fistula. METHODS: An online search of databases in English included PubMed, Embase, and Cochrane Library. After the diagnostic accuracy of 3D-EAUS of all anal fistula types was integrated, a single-group rate meta-analysis was performed; we analyzed 3D-EAUS separately for the diagnosis of different anal fistula types, and conducted a meta-analysis of test accuracy. The analysis combined sensitivity, specificity, and the respective 95% CI, to draw a summary receiver operating characteristic curve (SROC), and estimate the area under curve (AUC). RESULTS: Based on the inclusion criteria, we selected 8 studies covering 1057 cases of anal fistula and 548 cases of internal opening. The meta-analysis data show that 3D-EAUS has a total accuracy rate of 91% (95% CI, 88-94%). It has high sensitivity and specificity for different anal fistula classifications. The SROC curves for anal fistula internal openings were plotted, and the AUC was calculated to be 0.86 (95% CI, 0.83-0.89). CONCLUSIONS: 3D-EAUS can be used as the first-line diagnostic tool for anal fistula, because it has a high diagnostic accuracy for most anal fistulas. However, due to the insufficient diagnostic accuracy of 3D-EAUS for complex fistulas, 3D-EAUS combined with MRI examination can be used to more accurately detect the secondary extension of complex fistulas, so as to describe the complete anatomy of the fistula in more detail.