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Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Thio, Quirina C. B. S., MD; Karhade, Aditya V., BE; Ogink, Paul T., MD; Raskin, Kevin A., MD; De Amorim Bernstein, Karen, MD; Lozano Calderon, Santiago A., MD, PhD; Schwab, Joseph H., MD, MS

Clinical Orthopaedics and Related Research®: October 2018 - Volume 476 - Issue 10 - p 2040–2048
doi: 10.1097/CORR.0000000000000433
CLINICAL RESEARCH
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Background Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time.

Questions/purposes The purposes of this study were (1) to develop machine-learning algorithms for the prediction of 5-year survival in patients with chondrosarcoma; and (2) to deploy the best algorithm as an accessible web-based app for clinical use.

Methods All patients with a microscopically confirmed diagnosis of conventional or dedifferentiated chondrosarcoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2010. SEER covers approximately 30% of the US population and consists of demographic, tumor characteristic, treatment, and outcome data. In total, 1554 patients met the inclusion criteria. Mean age at diagnosis was 52 years (SD 17), ranging from 7 to 102 years; 813 of the 1554 patients were men (55%); and mean tumor size was 8 cm (SD 6), ranging from 0.1 cm to 50 cm. Exact size was missing in 340 of 1544 patients (22%), grade in 88 of 1544 (6%), tumor extension in 41 of 1544 (3%), and race in 16 of 1544 (1%). Data for 1-, 3-, 5-, and 10-year overall survival were available for 1533 (99%), 1512 (98%), 1487 (96%), and 977 (63%) patients, respectively. One-year survival was 92%, 3-year survival was 82%, 5-year survival was 76%, and 10-year survival was 54%. Missing data were imputed using the nonparametric missForest method. Boosted decision tree, support vector machine, Bayes point machine, and neural network models were developed for 5-year survival. These models were chosen as a result of their capability of predicting two outcomes based on prior work on machine-learning models for binary classification. The models were assessed by discrimination, calibration, and overall performance. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0 with 1.0 being perfect discrimination and 0.5 that the model is no better than chance at making a prediction. The Brier score measures the squared difference between the predicted probability and the actual outcome. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. The Brier scores of the models are compared with the null model, which is calculated by assigning each patient a probability equal to the prevalence of the outcome.

Results Four models for 5-year survival were developed with c-statistics ranging from 0.846 to 0.868 and Brier scores ranging from 0.117 to 0.135 with a null model Brier score of 0.182. The Bayes point machine was incorporated into a freely available web-based application. This application can be accessed through https://sorg-apps.shinyapps.io/chondrosarcoma/.

Conclusions Although caution is warranted, because the prediction model has not been validated yet, healthcare providers could use the online prediction tool in daily practice when survival prediction of patients with chondrosarcoma is desired. Future studies should seek to validate the developed prediction model.

Level of Evidence Level III, prognostic study.

Q. C. B. S. Thio, A. V. Karhade, P. T. Ogink, K. Raskin, S. Lozano-Calderon, J. H. Schwab, Division of Orthopaedic Oncology, Department of Orthopaedics, Massachusetts General Hospital–Harvard Medical School, Boston, MA, USA

K. de Amorim Bernstein, Department of Radiation Oncology, Massachusetts General Hospital–Harvard Medical School, Boston, MA, USA

Q. C. B. S. Thio, Room 3.946, Yawkey Building, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA, email: quirina.thio@gmail.com

One of the authors (JHS) reports personal fees from Medtronic (Minneapolis, MN, USA), personal fees from AO Spine (Paoli, PA, USA), and personal fees from Stryker (Kalamazoo, MI, USA), outside the submitted work.

All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.

Clinical Orthopaedics and Related Research® neither advocates nor endorses the use of any treatment, drug, or device. Readers are encouraged to always seek additional information, including FDA approval status, of any drug or device before clinical use.

Each author certifies that his or her institution waived approval for the human protocol for this investigation and that all investigations were conducted in conformity with ethical principles of research.

Received April 23, 2018

Accepted July 16, 2018

© 2018 Lippincott Williams & Wilkins LWW
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