ABSTRACT
RATIONALE: Ichthyosis uteri is a rare pathological condition characterized by the replacement of the endometrial lining by stratified squamous epithelium. Yet its occurrence with endometrial adenocarcinoma is very rare. PATIENT CONCERNS: A 68-year-old woman has been experiencing sporadic, minor vaginal hemorrhages for a few months. The gynecological evaluation revealed a uterine enlargement and imaging demonstrated an irregular mass within the uterus. DIAGNOSIS: Endometrial adenocarcinoma with transitional cell differentiation; ichthyosis uteri with dysplasia. INTERVENTIONS: Radical hysterectomy with pelvic lymphadenectomy was performed followed by postoperative radiotherapy. OUTCOMES: Postoperative follow-up at 8 months showed a favorable outcome without signs of recurrence and metastasis. LESSONS: Adequate pathological sampling is crucial to identifying the accompanying lesions of ichthyosis uteri. Finding molecular alterations in various pathological morphologies is important to understand the evolution of disease.
Subject(s)
Adenocarcinoma , Endometrial Neoplasms , Hysterectomy , Ichthyosis , Humans , Female , Aged , Endometrial Neoplasms/pathology , Endometrial Neoplasms/complications , Endometrial Neoplasms/surgery , Adenocarcinoma/pathology , Adenocarcinoma/complications , Adenocarcinoma/surgery , Ichthyosis/pathology , Ichthyosis/complications , Uterus/pathologyABSTRACT
BACKGROUND: In China, rapid intraoperative diagnosis of frozen sections of thyroid nodules is used to guide surgery. However, the lack of subspecialty pathologists and delayed diagnoses are challenges in clinical treatment. This study aimed to develop novel diagnostic approaches to increase diagnostic effectiveness. METHODS: Artificial intelligence and machine learning techniques were used to automatically diagnose histopathological slides. AI-based models were trained with annotations and selected as efficientnetV2-b0 from multi-set experiments. RESULTS: On 191 test slides, the proposed method predicted benign and malignant categories with a sensitivity of 72.65%, specificity of 100.0%, and AUC of 86.32%. For the subtype diagnosis, the best AUC was 99.46% for medullary thyroid cancer with an average of 237.6 s per slide. CONCLUSIONS: Within our testing dataset, the proposed method accurately diagnosed the thyroid nodules during surgery.