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Cai S, Li W, Deng C, Tang Q, Zhou Z. Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study. J Cancer Res Clin Oncol 2023; 149:17103-17113. [PMID: 37755576 DOI: 10.1007/s00432-023-05421-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND Cutaneous malignant melanoma (CMM) has the worst prognosis among skin cancers, especially metastatic CMM. Predicting its prognosis accurately could direct clinical decisions. METHODS The Surveillance, Epidemiology, and End Results database was screened to collect CMM patients' data. According to diagnosed time, patients were subdivided into three cohorts, train cohort (diagnosed between 2010 and 2013), validation cohort (diagnosed in 2014), and test cohort (diagnosed in 2015). Train cohort was used to train deep learning survival model for cutaneous malignant melanoma (DeepCMM). DeepCMM was then evaluated in train cohort and validation cohort internally, and validated in test cohort externally. RESULTS DeepCMM showed 0.8270 (95% CI, confidence interval, CI 0.8260-0.8280) as area under the receiver operating characteristic curve (AUC) in train cohort, 0.8274 (95% CI 0.8286-0.8298) AUC in validation cohort, and 0.8303 (95% CI 0.8289-0.8316) AUC in test cohort. Then DeepCMM was packaged into a Windows 64-bit software for doctors to use. CONCLUSION Deep learning survival model for cutaneous malignant melanoma (DeepCMM) can offer a reliable prediction on cutaneous malignant melanoma patients' overall survival.
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Affiliation(s)
- Siyu Cai
- Dermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China
| | - Wei Li
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing, 101149, China
| | - Cong Deng
- Department of Respiratory and Critical Care Medicine, General Hospital of Western Theater Command, No. 270 Rongdu Avenue, Chengdu, 610083, Sichuan, China
| | - Qiao Tang
- Dermatology Department, Medical Center Hospital of Qionglai City, No. 172 Xinglin Road, Qionglai City, Chengdu, 611500, Sichuan, China
| | - Zhou Zhou
- Dermatology Department, General Hospital of Western Theater Command PLA, No. 270, Rongdu Avenue, Chengdu, 610083, Sichuan, China.
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Chen S, Guo T, Zhang E, Wang T, Jiang G, Wu Y, Wang X, Na R, Zhang N. Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma. Heliyon 2022; 8:e10578. [PMID: 36158103 PMCID: PMC9489730 DOI: 10.1016/j.heliyon.2022.e10578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/14/2022] [Accepted: 09/05/2022] [Indexed: 11/03/2022] Open
Abstract
The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC.
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Affiliation(s)
- Siteng Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tuanjie Guo
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Encheng Zhang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guangliang Jiang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yishuo Wu
- Department of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiang Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rong Na
- Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China
| | - Ning Zhang
- Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Yu H, Ma SJ, Farrugia M, Iovoli AJ, Wooten KE, Gupta V, McSpadden RP, Kuriakose MA, Markiewicz MR, Chan JM, Hicks WL, Platek ME, Singh AK. Machine Learning Incorporating Host Factors for Predicting Survival in Head and Neck Squamous Cell Carcinoma Patients. Cancers (Basel) 2021; 13:cancers13184559. [PMID: 34572786 PMCID: PMC8467754 DOI: 10.3390/cancers13184559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Prognostication for cancer patients is integral for patient counseling and treatment planning, yet providing accurate prediction can be challenging using existing patient-specific clinical indicators and host factors. In this work, we evaluated common machine learning models in predicting head and neck squamous cell carcinoma (HNSCC) patients' overall survival based on demographic, clinical features and host factors. We found random survival forest had best performance among the models evaluated, which achieved a C-index of 0.729 and AUROC of 0.792 in predicting two-year overall survival. In addition, we verified that host factors are independently predictive of HNSCC overall survival, which improved the C-index by a margin of 0.026 and the AUROC by 0.034. Due to the strong correlation among host factors, we showed that proper dimension reduction is an important step before their incorporation into the machine learning models, which provides a host factor score reflecting the patients' nutrition and inflammation status. The score by itself showed excellent discriminating capacity with the high-risk group having a hazard ratio of 3.76 (1.93-7.32, p < 0.0001) over the low-risk group. The hazard ratios were further improved to 7.41 (3.66-14.98, p < 0.0001) by the random survival forest model after including demographic and clinical features.
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Affiliation(s)
- Han Yu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY 14263, USA;
| | - Sung Jun Ma
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (S.J.M.); (M.F.); (A.J.I.); (M.E.P.)
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, NY 14203, USA
| | - Mark Farrugia
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (S.J.M.); (M.F.); (A.J.I.); (M.E.P.)
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, NY 14203, USA
| | - Austin J. Iovoli
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (S.J.M.); (M.F.); (A.J.I.); (M.E.P.)
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, NY 14203, USA
| | - Kimberly E. Wooten
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Vishal Gupta
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Ryan P. McSpadden
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Moni A. Kuriakose
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Michael R. Markiewicz
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
- Department of Oral and Maxillofacial Surgery, School of Dental Medicine, University at Buffalo, The State University of New York, 3435 Main Street, Buffalo, NY 14214, USA
- Department of Neurosurgery, Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, NY 14203, USA
| | - Jon M. Chan
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Wesley L. Hicks
- Department of Head and Neck Surgery, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (K.E.W.); (V.G.); (R.P.M.); (M.A.K.); (M.R.M.); (J.M.C.); (W.L.H.J.)
| | - Mary E. Platek
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (S.J.M.); (M.F.); (A.J.I.); (M.E.P.)
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA
- Department of Dietetics, D’Youville College, 270 Porter Avenue, Buffalo, NY 14201, USA
| | - Anurag K. Singh
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, 665 Elm Street, Buffalo, NY 14203, USA; (S.J.M.); (M.F.); (A.J.I.); (M.E.P.)
- Correspondence: ; Tel.: +1-716-845-5715; Fax: +1-716-845-7616
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