Development of machine learning model algorithm for prediction of 5‐year soft tissue myxoid liposarcoma survival. Issue 7 (8th March 2021)

• Record Type: Journal Article
• Authors: Kamalapathy, Pramod N.; Ramkumar, Dipak B.; Karhade, Aditya V.; Kelly, Sean; Raskin, Kevin; Schwab, Joseph; Lozano‐Calderón, Santiago
• Is Part Of: Journal of surgical oncology. Volume 123:Issue 7(2021)
• Page Start: 1610
• Page End: 1617
• Publication Date: 2021-03-08
• Subjects: liposarcoma -- machine learning -- survival; Cancer -- Surgery -- Periodicals; Neoplasms -- Periodicals; 616
• DOI: 10.1002/jso.26398
• Languages: English
• ISSNs: 0022-4790

Abstract

Abstract: Background: Predicting survival in myxoid liposarcoma (MLS) patients is very challenging given its propensity to metastasize and the controversial role of adjuvant therapy. The purpose of this study was to develop a machine‐learning algorithm for the prediction of survival at five years for patients with MLS and externally validate it using our institutional cohort. Methods: Two databases, the surveillance, epidemiology, and end results program (SEER) database and an institutional database, were used in this study. Five machine learning models were created based on the SEER database and performance was rated using the TRIPOD criteria. The model that performed best on the SEER data was again tested on our institutional database. Results: The net‐elastic penalized logistic regression model was the best according to our performance indicators. This model had an area under the curve (AUC) of 0.85 when compared to the SEER testing data and an AUC of 0.76 when tested against institutional database. An application to use this calculator is available at https://sorg-apps.shinyapps.io/myxoid_liposarcoma/ . Conclusion: MLS is a soft‐tissue sarcoma with adjunct treatment options that are, in part, decided by prognostic survival. We developed the first machine‐learning predictive algorithm specifically for MLS using the SEER registry that retained performance during external validation with institutional data.