8 research outputs found
Leiomyoma with KAT6B-KANSL1 fusion: case report of a rapidly enlarging uterine mass in a postmenopausal woman
Abstract Background Uterine leiomyomas, in contrast to sarcomas, tend to cease growth following menopause. In the setting of a rapidly enlarging uterine mass in a postmenopausal patient, clinical distinction of uterine leiomyoma from sarcoma is difficult and requires pathologic examination. Case presentation A 74-year-old woman presented with postmenopausal bleeding and acute blood loss requiring transfusion. She was found to have a rapidly enlarging uterine mass clinically suspicious for sarcoma. An abdominal hysterectomy and bilateral salpingo-oophorectomy were performed. A 15.5 cm partially necrotic intramural mass was identified in the uterine corpus. The tumor was classified as a cellular leiomyoma. RNA sequencing identified a KAT6B-KANSL1 fusion that was confirmed by RT-PCR and Sanger sequencing. After 6 months of follow-up, the patient remains asymptomatic without evidence of disease. Conclusion Prior studies of uterine leiomyomas have identified KAT6B (previously MORF) rearrangements in uterine leiomyomas, but this case is the first to identify a KAT6B-KANSL1 gene fusion in a uterine leiomyoma. While alterations of MED12 and HMGA2 are most common in uterine leiomyomas, a range of other genetic pathways have been described. Our case contributes to the evolving molecular landscape of uterine leiomyomas
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Deep learning detects premalignant lesions in the Fallopian tube
Tubo-ovarian high-grade serous carcinoma is believed to originate in the fallopian tubes, arising from precursor lesions like serous tubal intraepithelial carcinoma (STIC) and serous tubal intraepithelial lesion (STIL). Adequate diagnosis of these precursors is important, but can be challenging for pathologists. Here we present a deep-learning algorithm that could assist pathologists in detecting STIC/STIL. A dataset of STIC/STIL (n = 323) and controls (n = 359) was collected and split into three groups; training (n = 169), internal test set (n = 327), and external test set (n = 186). A reference standard was set for the training and internal test sets, by a panel review amongst 15 gynecologic pathologists. The training set was used to train and validate a deep-learning algorithm (U-Net with resnet50 backbone) to differentiate STIC/STIL from benign tubal epithelium. The model’s performance was evaluated on the internal and external test sets by ROC curve analysis, achieving an AUROC of 0.98 (95% CI: 0.96–0.99) on the internal test set, and 0.95 (95% CI: 0.90–0.99) on the external test set. Visual inspection of all cases confirmed the accurate detection of STIC/STIL in relation to the morphology, immunohistochemistry, and the reference standard. This model’s output can aid pathologists in screening for STIC, and can contribute towards a more reliable and reproducible diagnosis