With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

Objective: To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. Methods: We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. Results: Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. Conclusions: In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

Background and Objectives: Effective management of gastrointestinal stromal tumors (GISTs) requires accurate risk assessment due to their variable carcinogenic potential. This study aimed to improve GIST classification using a novel deep learning–based GIST risk prediction model based on EUS imaging modalities. Methods: We retrospectively analyzed EUS images of 341 patients diagnosed with GIST at a tertiary medical center between January 2016 and March 2022. Patients were selected based on specific criteria, including pathologic validation of surgical outcomes. The dataset allowed for the development and validation of a deep learning risk prediction model (DLRPM), a traditional risk prediction model (TRPM), and a combined risk prediction model (CRPM). Model performance was evaluated using sensitivity, specificity, positive and negative predictive values, accuracy, and statistical analysis. Results: The efficacy of the 3 prognostic models (TRPM, DLRPM, and CRPM) for GIST classification was evaluated using a dataset consisting of 1019 EUS images from 341 patients. These models were developed using a training subset of 310 patients and subsequently validated in a defined group of 31 consecutive patients. Using multivariate logistic regression, TRPM showed an accuracy rate of 71.10%. Using a DenseNet-121 framework developed specifically for medical imaging, the DLRPM demonstrated superior predictive capabilities with an accuracy rate of 92.65%. The CRPM achieved a prediction accuracy of 90.32%. In addition, receiver operating characteristic curve analysis revealed area under the curve values of 0.909, 0.932, and 0.843 for CRPM, DLRPM, and TRPM, respectively. However, comparative statistical evaluation between these models showed no significant differences in area under the curve. Conclusions: The novel DLRPM improved the accuracy of GIST risk stratification by EUS. CRPM is a promising method to integrate deep learning with traditional statistical methods, potentially optimizing clinical decision-making and patient outcomes in gastroenterological oncology.

Background. Granuloma annulare (GA) and sarcoidosis are granulomatous inflammatory diseases that share similarities. Objective. To identify the histological and immunohistochemical (IHC) features of GA and sarcoidosis. Methods. A retrospective review of 36 patients with GA and 26 with sarcoidosis was performed. Results from hematoxylin and eosin (H&E) staining and IHC staining of MMP-9 and pSTAT1 within the skin lesions of GA and sarcoidosis were analyzed, and random forest was applied for developing a predictive model. Results. Significantly greater expressions of MMP-9 (especially in elastic fibers, EFs, ) and pSTAT1 ( ) were observed in lesion samples of GA versus sarcoidosis patients. In GA patients, MMP-9 was significantly upregulated in the interstitial type ( ), while staining of pSTAT1 was positively correlated with the area of mucinous collagen in palisading GA ( , ). In sarcoidosis patients, MMP-9 ( , ) and pSTAT1 ( , ) were found to show stronger expressions in lesions with less lymphocyte infiltration. The predictive model demonstrated an AUC of 0.9675. Conclusion. These results indicate that MMP-9 and pSTAT1 might exert roles in granulomatous inflammation in different modes, and the presence of more robust MMP-9 staining in EFs appears to be more suggestive of GA.