Laparoscopic surgery on broken points for uterine sarcoma in the early stage decrease prognosis

Lymphadenectomy in the treatment of sarcomas - indications and technique

Sarcomas are a rare type of malignancy with limited treatment options so far. This analysis aimed to describe the impact of lymphadenectomy on treating sarcoma patients. Sarcomas characterized by lymphatic spread are rare. For this reason, lymphadenectomy is not a procedure that is performed frequently. However, there are histological subtypes that spread more frequently through lymphatic vessels, such as rhabdomyosarcoma (RMS), epithelioid sarcoma (ES), clear cell sarcoma (CCS), and angiosarcoma. On the other hand, synovial sarcoma (SS) is not characterized by an increased tendency to lymphogenous metastases. In our study, we focus on these subtypes of sarcomas. The relationship between lymphadenectomy results and the subsequent prognosis of the patients was investigated. Metastases in the lymph nodes are diagnosed synchronously with distant metastases or when the primary tumor is detected. At the same time, despite lymphadenectomy, sarcoma patients developed further distant metastases. Currently, lymphadenectomy is not a routinely recommended method of treatment for patients with sarcomas. Most often, its potential use is indicated in the case of epithelioid sarcoma, clear cell sarcoma, and rhabdomyosarcoma after a previous positive sentinel lymph node biopsy (SLNB) result. Multicenter randomized prospective clinical trials on the role of lymphadenectomy in the treatment of sarcomas are needed.

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma

OBJECTIVE

Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources.

METHODS

The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas.

RESULTS

Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity.

CONCLUSION

Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases

Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists' diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.

Uterine carcinosarcoma: An overview

Uterine carcinosarcoma (UCS), also known as malignant mixed Müllerian tumor, is a rare gynecological malignancy characterized by poor prognosis. This "biphasic" neoplasm presents an admixture of epithelial and mesenchymal/sarcomatoid tumor cells which partially share their molecular signature and exhibit a typical epithelial-to-mesenchymal transition gene expression profile. Due to the rarity of this cancer, at present there is a scarcity of specific treatment guidelines. Surgical resection remains the best curative option for localized disease, whereas the addition of peri-operative radiotherapy, chemotherapy and chemoradiation has been shown to further improve disease outcomes. In the metastatic setting, palliative chemotherapy is currently the treatment of choice, although no consensus exists about the best regimen to be delivered. Besides standard treatment options for the advanced disease, mechanistic insights into UCS pathogenesis and identification of its histopathological and molecular features boosted the development of novel, and potentially more effective, therapeutic agents, that will be here discussed.