Objective:To explore the predictive value of 18F-FDG PET/CT in molecular subtyping of triple-negative breast cancer. Methods:A retrospective analysis was performed on the clinical and imaging data of 227 breast cancer patients who underwent 18F-FDG PET/CT examination in the Tianjin Medical University Cancer Institute & Hospital from January 1, 2010 to December 31, 2022. Based on the expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) in the primary breast cancer, the patients were categorized into two groups: triple-negative breast cancer (TNBC) and non-TNBC. Radiomic features were extracted from images of both groups, and a radiomic model was constructed to predict the molecular subtype of the TNBC groups. In addition, the clinical data, CT morphological features, and PET metabolic parameters of both groups were compared to determine the indicators with statistically significant differences and develop a comprehensive radiomic model combined with clinical characteristics. Results:Compared to the non-TNBC group, the TNBC groups exhibited more significant invasiveness in terms of tumor diameter, margins, ipsilateral axillary lymph node metastasis, invasion of neighboring skin or papillae, and PET metabolic parameters ( t = -3.19; χ2 = 7.30, 8.10, 5.34; t = 3.80, 3.30, 3.42, P < 0.05). The constructed 18F-FDG PET/CT radiomic model proved effective in predicting the molecular subtype of the TNBC group, and the receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.83 (95% CI 0.78-0.88), an accuracy of 75.9%, a sensitivity of 74.5%, and a specificity of 77.2%. In contrast, the constructed comprehensive radiomic model displayed an AUC of 0.86 (95% CI 0.81-0.90), an accuracy of 77.2%, a sensitivity of 78.6%, and a specificity of 75.9%. Conclusions:18F-FDG PET/CT plays an important role in predicting molecular subtypes of TNBC. The constructed radiomic model and comprehensive radiomic model can further enhance the prediction efficacy of PET metabolic parameters and accelerate the development of accurate treatment protocols in clinical practice, thus improving the prognosis of breast cancer.

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
Algorithms ; Arrhythmias, Cardiac ; Databases, Factual ; Electrocardiography/methods* ; Humans ; Support Vector Machine