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Purpose: To explore the diagnostic value of intralesional and perilesional radiomics based on multimodal ultrasound (US) images in predicting the malignant ACR TIRADS 4 thyroid nodules (TNs). Methods: A total of 297 cases of TNs in patients who underwent preoperative thyroid grayscale US and shear wave elastography (STE) were enrolled (training cohort: n = 150, internal validation cohort: n = 77, external validation cohort: n = 70). Regions of interests (ROIs) were delineated on grayscale US images and STE images, and then an isotropic expansion of 1.0, 1.5, 2.0, 2.5, and 3.0 mm was applied. Predictive models were established using recursive feature elimination‐support vector machines (RFE‐SVM) based on radiomics features calculated by random forest. Results: The perilesional ROI1.5mm expansion achieved the highest area under curve (AUC) (AUC: 0.753 for grayscale US, 0.728 for STE; 95% confidence interval (CI): 0.664–0.743, 0.684–0.739, respectively). The joint model had the highest AUC values of 0.936 in the training dataset, 0.926 in internal dataset, and 0.893 in external dataset. The calibration curve showed good consistency and the decision curve indicated a greater clinical net benefit of the joint model. Conclusion: Joint model containing perilesional radiomics (1.5 mm) had significant value in predicting the malignant ACR TIRADS 4 TNs. [ABSTRACT FROM AUTHOR]

Purpose: To explore the optimal peri‐tumoral regions on ultrasound (US) images and investigate the performance of multimodal radiomics for predicting axillary lymph node metastasis (ALNM). Methods: This retrospective study included 326 patients (training cohort: n = 162, internal validation cohort: n = 74, external validation cohort: n = 90). Intra‐tumoral region of interests (ROIs) were delineated on US and digital mammography (DM) images. Peri‐tumoral ROI (PTR) on US images were gained by dilating actual 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5 mm radius surrounding the tumor. Support vector machine (SVM) method was used to calculate the importance of radiomics features and to pick the 10 most important. Recursive feature elimination‐SVM was used to evaluate the efficacy of models with different feature numbers used. Results: The PTR0.5mm yielded a maximum AUC of 0.802 (95% confidence interval (CI): 0.676–0.901) within the validation cohort using SVM classifier. The multimodal radiomics (intra‐tumoral US and DM and US‐based PTR0.5mm radiomics model) achieved the highest predictive ability (AUC = 0.888/0.844/0.835 and 95% CI = 0.829–0.936/0.741–0.929/0.752–0.896 for training/internal validation/external validation cohort, respectively). Conclusion: The PTR0.5mm could be the optimal area for predicting ALNM. A favorable predictive accuracy for predicting ALNM was achieved using multimodal radiomics and its based nomogram. [ABSTRACT FROM AUTHOR]

The rising incidence of ectopic pregnancy (EP) in China underscores the critical need for early and accurate diagnosis to achieve optimal patient outcomes. Colour Doppler ultrasound, β-hCG, and progesterone testing are essential tools in the detection of EP, with transvaginal ultrasound offering precise visualization of EP lesions. Lower levels of β-hCG and progesterone in EP cases compared to normal pregnancies serve as key diagnostic markers. Awareness of atypical symptoms is crucial to prevent missed or misdiagnosed cases, underscoring the importance of timely intervention to mitigate severe complications.To investigate the differences in colour Doppler ultrasound findings, serum β-hCG levels, and progesterone levels between normal and ectopic pregnancies, in order to provide insights into the clinical diagnosis and treatment of EP.Sixty patients who showed no gestational sac in the uterus during early pregnancy and were either highly suspected or pathologically confirmed to have EP were selected for this study. As controls, fifty women with normal pregnancies during the same period were also included. Colour Doppler ultrasound findings (endometrial thickness, pelvic effusion, uterine effusion, and adnexal mass), serum β-hCG levels, and progesterone levels were recorded and compared between the two groups.Colour Doppler ultrasound showed that the EP group had significantly greater incidences of pelvic effusion, uterine effusion, and an adnexal mass than did the control group. Moreover, the EP patients had significantly thinner endometria (8.3 ± 3.37 mm vs. 16.12 ± 3.09 mm; P < 0.05), lower serum β-hCG levels (846.18-1444.09 vs. 2429.24–3020.56; P < 0.05), and lower progesterone levels (9.1 ± 7.19 vs. 17.66 ± 5.63; P < 0.05) than did the controls. Receiver operating characteristic (ROC) analysis revealed that endometrial thickness, serum β-hCG concentration, and progesterone concentration had considerably high area under the curve (AUC), sensitivity, and specificity for the diagnosis of EP.Colour Doppler ultrasound, serum β-hCG concentration, and progesterone concentration have high diagnostic value for EP. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases., Patients and Methods: 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA)., Results: The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training ( P < 0.001, P = 0.002, respectively) and validation sets ( P < 0.001, P = 0.034, respectively)., Conclusion: Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses., Competing Interests: The authors declare that they have no conflict of interest., (© 2023 Ma et al.)