RESUMEN
BACKGROUND: Breast cancer is the most common malignant tumor in the world. Intraoperative frozen section of sentinel lymph nodes is an important basis for determining whether axillary lymph node dissection is required for breast cancer surgery. We propose an RRCART model based on a deep-learning network to identify metastases in 2362 frozen sections and count the wrongly identified sections and the associated reasons. The purpose is to summarize the factors that affect the accuracy of the artificial intelligence model and propose corresponding solutions. METHODS: We took the pathological diagnosis of senior pathologists as the gold standard and identified errors. The pathologists and artificial intelligence engineers jointly read the images and heatmaps to determine the locations of the identified errors on sections, and the pathologists found the reasons (false reasons) for the errors. Through NVivo 12 Plus, qualitative analysis of word frequency analysis and nodal analysis was performed on the error reasons, and the top-down error reason framework of "artificial intelligence RRCART model to identify frozen sections of breast cancer lymph nodes" was constructed based on the importance of false reasons. RESULTS: There were 101 incorrectly identified sections in 2362 slides, including 42 false negatives and 59 false positives. Through NVivo 12 Plus software, the error causes were node-coded, and finally, 2 parent nodes (high-frequency error, low-frequency error) and 5 child nodes (section quality, normal lymph node structure, secondary reaction of lymph nodes, micrometastasis, and special growth pattern of tumor) were obtained; among them, the error of highest frequency was that caused by normal lymph node structure, with a total of 45 cases (44.55%), followed by micrometastasis, which occurred in 30 cases (29.70%). CONCLUSIONS: The causes of identification errors in examination of sentinel lymph node frozen sections by artificial intelligence are, in descending order of influence, normal lymph node structure, micrometastases, section quality, special tumor growth patterns and secondary lymph node reactions. In this study, by constructing an artificial intelligence model to identify the error causes of frozen sections of lymph nodes in breast cancer and by analyzing the model in detail, we found that poor quality of slices was the preproblem of many identification errors, which can lead to other errors, such as unclear recognition of lymph node structure by computer. Therefore, we believe that the process of artificial intelligence pathological diagnosis should be optimized, and the quality control of the pathological sections included in the artificial intelligence reading should be carried out first to exclude the influence of poor section quality on the computer model. For cases of micrometastasis, we suggest that by differentiating slices into high- and low-confidence groups, low-confidence micrometastatic slices can be separated for manual identification. The normal lymph node structure can be improved by adding samples and training the model in a targeted manner.
Asunto(s)
Neoplasias de la Mama , Secciones por Congelación , Niño , Humanos , Femenino , Inteligencia Artificial , Neoplasias de la Mama/diagnóstico , Micrometástasis de Neoplasia/diagnóstico , Ganglios LinfáticosRESUMEN
BACKGROUND: Thymic clear cell carcinoma is a rare mediastinal neoplasm, with only 25 reported cases to date. We report a case of a 45-year-old man with thymic clear cell carcinoma. We think imaging and laboratory tests may be helpful for differential diagnosis. CASE PRESENTATION: A 45-year-old male was admitted to a local hospital for chest distress with cardiopalmus. CT showed a mediastinal mass. Laboratory examination results were all in the normal range. Histologically, the tumor cells had a clear cytoplasm, and immunohistochemically, the tumor cells were positive for epithelial markers. We performed abdominal and pelvic CT and further examined serum levels of thyroxine, parathyroid hormone and AFP postoperatively, which were normal. The patient received postoperative radiotherapy, and CT showed left adrenal metastasis at 20 months after surgery. CONCLUSION: Thymic clear cell carcinoma is a rare malignant neoplasm. Adrenal metastasis can occur. Patients undergo thymectomy with chemotherapy or with radiotherapy have better outcoming. Metastasis, direct invasion of parathyroid carcinoma and other primary tumors in the mediastinum should be excluded. Immunohistochemical markers, imaging and laboratory examination can help to exclude metastasis.
Asunto(s)
Carcinoma , Neoplasias del Mediastino , Timoma , Neoplasias del Timo , Masculino , Humanos , Persona de Mediana Edad , Neoplasias del Timo/cirugía , Timoma/patología , Neoplasias del Mediastino/diagnóstico , Mediastino/patología , Carcinoma/patologíaRESUMEN
The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists' misdiagnosis rate (p < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists' performance was not significant (p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.