RESUMO
Objective: To explore the application values of deep-learning based artificial intelligence (AI) automatic classification system, on the differential diagnosis of non-lactating mastitis (NLM) and malignant breast tumors, via its comparation with traditional ultrasound interpretations and the following interpretation conclusions made by the sonographers with various seniorities. Methods: A total of 707 patients suffering from breast lesions (475 malignant breast tumors and 232 NLM), were selected from the following three medical centers, including Zhejiang Cancer Hospital, Hebei Province Hospital of Traditional Chinese Medicine, and Yantai Affiliated Hospital of Binzhou Medical University, and the time period was set from April 2020 to September 2021. All selected cases firstly accepted the routine breast ultrasound diagnosis, followed by the interpretations from a senior sonographer with more than 15 years of work experience, and an intermediate-aged sonographer with more than 5 years of work experience, independently. Meanwhile, a third physician also interpreted the same ultrasound images by deep learning-based AI automatic classification system, independent of the interpretation results from the previous two physicians. The kappa test was performed to evaluate the consistency between the conventional ultrasound interpretation results and pathological results interpreted from physicians with different working experiences. Results: In total, 475 cases of malignant breast tumors (512 nodules) and 232 cases of NLM (255 nodules) were pathologically diagnosed. The accuracy, sensitivity, and specificity of conventional ultrasound interpretations vary from different sonographers with different working experiences. The accuracy, sensitivity, and specificity for intermediate-aged sonographers and senior sonographers were 76.92% (590/767), 84.71% (216/255), and 73.95% (374/512) and 87.35% (670/767), 86.27% (220/255), and 87.89% (450/512), respectively (P<0.001). In contrast, if the threshold was set as 0.5, the accuracy, sensitivity, and specificity from deep learning-based AI automatic classification system were 83.00%, 87.20%, and 85.33%, separately, and the area under the curve was 92.6. The results of the kappa consistency test indicated that the diagnosis results from the image interpretations by senior physicians and deep-learning based AI automatic classification system showed high consistency with postoperative pathological diagnosis results, and the kappa values are 0.72 and 0.71, respectively, with the P-value of less than 0.001. In contrast, the consistency between the image interpretation results from intermediate-aged physicians with less working experience, and postoperative pathological diagnosis results, seemed to be relatively lower, with a kappa value of only 0.53 and P-value of less than 0.001. Conclusions: The deep learning-based AI automatic classification system is expected to become a reliable auxiliary way to distinguish NLM and malignant breast tumors due to its high sensitivity, accuracy, and specificity.
RESUMO
Objective: To analyze the clinical features, ultrasonographic manifestations, pathological features, treatment and prognosis of primary thyroid squamous cell carcinoma (PSCTC) and summarize the experience in diagnosis and treatment of this condition. Methods: A retrospective analysis was conducted on patients who were admitted to Zhejiang Cancer Hospital from 2007 to 2021 due to thyroid nodules or thyroid malignant tumors that were ultimately confirmed by postoperative pathology as primary thyroid squamous cell carcinoma. We summarize the general situation, clinical information, laboratory examination, ultrasonic image characteristics, pathological examination, clinical treatment and prognosis of the patients. Results: PSCTC is most often seen in older men and progresses rapidly. In laboratory tests, some patients had elevated levels of tumor markers (CA199, squamous cell carcinoma antigen level), thyroglobulin levels and tumor-related substances, but all these indicators lacked specificity. The ultrasound features of PSCTC are mainly hypoechoic, hard, substantial nodules with gross borders and a grade 1-2 blood flow signal, sometimes with signs of necrosis and calcification. In terms of treatment, PSCTC is mainly surgically resected, though some patients in this study underwent iodine-131 radiation therapy, local radiotherapy, and chemotherapy with unclear results. None of the patients survived for very long after treatment, but the prognosis of patients with highly differentiated squamous carcinoma was significantly better than that of patients with poorly differentiated squamous carcinoma. Papillary thyroid carcinoma may be one of the causes of PSCTC. Conclusion: PSCTC is a malignant tumor with high malignancy and rapid clinical progression. Treatment options are mainly based on surgical resection and can be supplemented with radiotherapy and chemotherapy, but there is still a lack of a standardized treatment management system, and more cases and reports are needed to accumulate data.