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Oei RW, Lyu Y, Ye L, Kong F, Du C, Zhai R, Xu T, Shen C, He X, Kong L, Hu C, Ying H. Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics. J Pers Med 2021; 11:jpm11080787. [PMID: 34442430 PMCID: PMC8398698 DOI: 10.3390/jpm11080787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/08/2021] [Accepted: 08/10/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC. Methods: From January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results. Results: DeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test p = 0.02) achieved the best performance compared to CSF (c-index = 0.63, Brier score = 0.14, log-rank test p = 0.38) and CPH (c-index = 0.57, Brier score = 0.15, log-rank test p = 0.81). Conclusions: Both CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.
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Affiliation(s)
- Ronald Wihal Oei
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yingchen Lyu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lulu Ye
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Fangfang Kong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chengrun Du
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ruiping Zhai
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Tingting Xu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chunying Shen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xiayun He
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lin Kong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chaosu Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Hongmei Ying
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; (R.W.O.); (Y.L.); (L.Y.); (F.K.); (C.D.); (R.Z.); (T.X.); (C.S.); (X.H.); (L.K.); (C.H.)
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Correspondence: ; Tel.: +86-21-64175590; Fax: +86-21-6417477
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