ObjectiveTo construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction using deep learning algorithms. MethodsA retrospective analysis was collected breast ultrasound images of 178 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to February 2024, which served as the training set. The deep learning algorithm was used to construct an intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction. In addition, a retrospective analysis was collected breast ultrasound images of 81 patients with thyroid dysfunction from the ultrasound database of the First Affiliated Hospital of Xinjiang Medical University from March 2024 to January 2025, which served as the validation set. The above system was used as validation set to diagnose whether patients with thyroid dysfunction had breast nodules, and the diagnostic efficacy of imaging physicians’ diagnosis and the intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction was analyzed. The consistency between the diagnosis of ultrasound physicians, intelligent ultrasound diagnosis system and the “gold standard” was tested by Kappa test. ResultsThere was no statistically significant difference in age, type of thyroid dysfunction, disease duration, number of breast nodules, and other clinical data between the training set and the validation set (P>0.05). The time required for the training set intelligent ultrasound diagnostic system to diagnose a single breast ultrasound image was (0.04±0.01) min, which was lower than that of an ultrasound specialist [(12.36±2.58) min], t=63.709, P＜0.001. The sensitivity, specificity, accuracy, and area under the curve (AUC) of detecting breast nodules in patients with thyroid dysfunction using an intelligent ultrasound diagnostic system were 97.87% (46/47), 100% (34/34), 98.77% (80/81), and 0.997 [95%CI: (0.951, 1.00)], respectively. The sensitivity, specificity, accuracy, and AUC of detecting breast nodules by ultrasound physicians were 89.36% (42/47), 91.18% (31/34), 90.12% (73/81), and 0.904 [95%CI: (0.818, 0.958)], respectively. The AUC of the intelligent ultrasound diagnosis system was higher than that of the ultrasound physician (Z=2.673, P=0.008). The detection results of breast nodules in patients with thyroid dysfunction diagnosed by ultrasound physicians were generally consistent with the “gold standard” (Kappa value=0.799, P＜0.001), while the intelligent ultrasound diagnosis system was in good agreement with the “gold standard” (Kappa value=0.975, P＜0.001). The confusion matrix results showed that the number of false positives was 3 and 0 for the ultrasound department physicians and the intelligent ultrasound diagnostic system, respectively, while the number of false negatives was 5 and 1. The calibration curve results indicated a high consistency between the diagnostic probability and the actual probability of the intelligent ultrasound diagnostic system, with the calibration curve fitting well with the ideal curve (Hosmer-Lemeshow test: χ2=1.246, P=0.997). ConclusionThe intelligent ultrasound diagnosis system for breast nodules in patients with thyroid dysfunction constructed by deep learning algorithm has good diagnostic efficacy, which can help ultrasound physicians improve screening efficiency and accuracy.
ObjectiveTo explore the value of magnetic resonance diffusion weighted imaging (DWI) in preoperative Bismuth-Corlette classification of hilar cholangiocarcinoma (HCCA). MethodsA total of 53 HCCA patients confirmed by postoperative pathology were retrospectively included. The accuracy of two sequence combinations, namely dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) + magnetic resonance cholangiopancreatography (MRCP) and DCE-MRI + MRCP + DWI, in evaluating the longitudinally involved bile duct segments and Bismuth-Corlette classification of HCCA was compared. Additionally, the correlation between apparent diffusion coefficient (ADC) values and tumor Bismuth-Corlette classification as well as degree of differentiation was analyzed. ResultsThere were 318 bile duct segments in 53 HCCA patients. The accuracy rate of DCE-MRI + MRCP was 93.7% (298/318), the sensitivity was 91.5% (161/176), and the specificity was 96.5% (137/142). The accuracy rate of DCE-MRI + MRCP + DWI was 96.5% (307/318), the sensitivity was 96.0% (169/176), and the specificity was 97.2% (138/142). Receiver operating characteristic (ROC) curve analysis showed that the area under the ROC curve (AUC) of DCE-MRI + MRCP + DWI was 0.966 [95%CI (0.940, 0.983), P＜0.001], and its diagnostic efficacy was superior to that of DCE-MRI + MRCP [AUC=0.940, 95%CI (0.908, 0.963), P＜0.001]. The DeLong test indicated a statistically significant difference in AUC between the two sequences (Z=2.633, P＜0.01). The accuracy rates of preoperative Bismuth-Corlette classification of HCCA evaluated by DCE-MRI + MRCP and DCE-MRI + MRCP + DWI were 86.8% (46/53) and 94.3% (50/53), respectively. After adding the DWI sequence, the consistency between Bismuth-Corlette classification results and surgical pathological classification results (Kappa=0.922, P<0.001) was higher than that of DCE-MRI + MRCP sequence (Kappa=0.820, P<0.001), with a statistically significant difference (χ2=160.370, P<0.001). In addition, the ADC value of HCCA was negatively correlated with tumordegree of differentiation (rs=–0.524, P<0.001), but had no significant correlation with its Bismuth-Corlette classification (rs=–0.058, P=0.682). ConclusionsDCE-MRI + MRCP + DWI sequence can effectively improve the accuracy in preoperative evaluation of the involvement of bile duct segments and Bismuth-Corlette classification of HCCA, which provides guidance for precise preoperative surgical planning in clinical practice. In addition, the ADC value can provide additional information required for non-invasive preoperative prediction of the prognosis of HCCA patients.