New Aspects of Sarcomas of Uterine Corpus-A Brief Narrative Review.
Stoyan KostovYavor KornovskiVesela IvanovaDeyan L DzhenkovDimitar MetodievRafal WatrowskiYonka IvanovaStanislav SlavchevDimitar MitevAngel YordanovPublished in: Clinics and practice (2021)
Sarcomas of the uterine corpus are rare malignant neoplasms, which are further classified into mesenchymal tumors, and mixed (epithelial plus mesenchymal) tumors. The main issues concerning these neoplasms are the small number of clinical trials, insufficient data from evidence-based medicine, insignificant interest from the pharmaceutical industry, all of which close a vicious circle. The low frequency of these malignancies implies insufficient experience in the diagnosis, hence incomplete surgical and complex treatment. Additionally, the rarity of these sarcomas makes it very difficult to develop clinical practice guidelines. Preoperative diagnosis, neoadjuvant and adjuvant chemoradiation, target and hormone therapies still raise many controversies. Disagreements about the role and type of surgical treatment are also often observed in medical literature. There are still insufficient data about the role of pelvic lymph node dissection and fertility-sparing surgery. Pathologists' experience is of paramount importance for an accurate diagnosis. Additionally, genetics examinations become part of diagnosis in some sarcomas of the uterine corpus. Some gene mutations observed in uterine sarcomas are associated with different outcomes. Therefore, a development of molecular classification of uterine sarcomas should be considered in the future. In this review, we focus on the epidemiology, pathogenesis, pathology, diagnosis and treatment of the following sarcomas of the uterine corpus: leiomyosarcoma, low- and high-grade endometrial stromal sarcomas, undifferentiated sarcoma and adenosarcoma. Uterine carcinosarcomas are excluded as they represent an epithelial tumor rather than a true sarcoma.
Keyphrases
- high grade
- low grade
- rectal cancer
- clinical trial
- bone marrow
- stem cells
- systematic review
- prostate cancer
- lymph node
- type diabetes
- squamous cell carcinoma
- electronic health record
- early stage
- big data
- metabolic syndrome
- deep learning
- risk factors
- machine learning
- adipose tissue
- atrial fibrillation
- robot assisted
- radiation therapy
- single molecule
- radical prostatectomy
- study protocol
- data analysis