{Reference Type}: Journal Article
{Title}: Application of novel algorithm on a retrospective series to implement the molecular classification for endometrial cancer.
{Author}: Arcieri M;Vizzielli G;Occhiali T;Giorgiutti C;Tius V;Pregnolato S;Mariuzzi L;Orsaria M;Tulisso A;Damante G;D'Elia AV;Cucinella G;Chiantera V;Fanfani F;Ercoli A;Driul L;Scambia G;Restaino S;
{Journal}: Eur J Surg Oncol
{Volume}: 50
{Issue}: 7
{Year}: 2024 Jul 23
{Factor}: 4.037
{DOI}: 10.1016/j.ejso.2024.108436
{Abstract}: <B>BACKGROUND: </B>The study aimed to validate the Betella algorithm, focusing on molecular analyses exclusively for endometrial cancer patients, where molecular classification alters risk assessment based on ESGO/ESTRO/ESP 2020 guidelines.<BR><B>METHODS: </B>Conducted between March 2021 and March 2023, the retrospective research involved endometrial cancer patients undergoing surgery and comprehensive molecular analyses. These included p53 and mismatch repair proteins immunohistochemistry, as well as DNA sequencing for POLE exonuclease domain. We applied the Betella algorithm to our population and evaluated the proportion of patients in which the molecular analysis changed the risk class attribution.<BR><B>RESULTS: </B>Out of 102 patients, 97 % obtained complete molecular analyses. The cohort exhibited varying molecular classifications: 10.1 % as POLE ultra-mutated, 30.3 % as mismatch repair deficient, 11.1 % as p53 abnormal, and 48.5 % as non-specified molecular classification. Multiple classifiers were present in 3 % of cases. Integrating molecular classification into risk group calculation led to risk group migration in 11.1 % of patients: 7 moved to lower risk classes due to POLE mutations, while 4 shifted to higher risk due to p53 alterations. Applying the Betella algorithm, we can spare the POLE sequencing in 65 cases (65.7 %) and p53 immunochemistry in 17 cases (17.2 %).<BR><B>CONCLUSIONS: </B>In conclusion, we externally validated the Betella algorithm in our population. The application of this new proposed algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, allowing for the rationalization of the resources that can be allocated otherwise, not only for the benefit of settings with low resources, but of all settings in general.