Department of Orthopedic Surgery, Musculoskeletal Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
J Surg Oncol. 2021 Jun;123(7):1610-1617. doi: 10.1002/jso.26398. Epub 2021 Mar 8.
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.
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.
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/.
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.
由于黏液样脂肪肉瘤（MLS）易发生转移，辅助治疗作用存在争议，因此预测 MLS 患者的生存情况极具挑战性。本研究旨在开发一种用于预测 MLS 患者五年生存率的机器学习算法，并使用我们的机构队列对其进行外部验证。
本研究使用了两个数据库，即监测、流行病学和最终结果计划（SEER）数据库和机构数据库。基于 SEER 数据库创建了五个机器学习模型，并根据 TRIPOD 标准对性能进行了评估。在 SEER 数据上表现最好的模型再次在我们的机构数据库上进行了测试。
根据我们的性能指标，净弹性罚 logistic 回归模型是最佳模型。与 SEER 测试数据相比，该模型的曲线下面积（AUC）为 0.85，与机构数据库相比，AUC 为 0.76。该计算器的应用程序可在 https://sorg-apps.shinyapps.io/myxoid_liposarcoma/ 上使用。
MLS 是一种软组织肉瘤，辅助治疗方案部分取决于预后生存情况。我们使用 SEER 登记处开发了第一个专门针对 MLS 的机器学习预测算法，在使用机构数据进行外部验证时保留了性能。
