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Fanizzi A, Pomarico D, Rizzo A, Bove S, Comes MC, Didonna V, Giotta F, La Forgia D, Latorre A, Pastena MI, Petruzzellis N, Rinaldi L, Tamborra P, Zito A, Lorusso V, Massafra R. Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer. Sci Rep 2023; 13:8575. [PMID: 37237020 PMCID: PMC10220052 DOI: 10.1038/s41598-023-35344-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests.
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Affiliation(s)
- Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alessandro Rizzo
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy.
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Nicole Petruzzellis
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente Oncologico "Don Tonino Bello", I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori "Giovanni Paolo II", Viale Orazio Flacco 65, 70124, Bari, Italy
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Lou SJ, Hou MF, Chang HT, Lee HH, Chiu CC, Yeh SCJ, Shi HY. Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study. BIOLOGY 2021; 11:biology11010047. [PMID: 35053045 PMCID: PMC8773427 DOI: 10.3390/biology11010047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174-174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.
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Affiliation(s)
- Shi-Jer Lou
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
| | - Ming-Feng Hou
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Surgery, Division of Breast Oncology and Surgery, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Hong-Tai Chang
- Department of Surgery, Kaohsiung Municipal United Hospital, Kaohsiung 80457, Taiwan;
| | - Hao-Hsien Lee
- Department of General Surgery, Chi Mei Medical Center, Liouying 73658, Taiwan;
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung 82445, Taiwan;
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
| | - Shu-Chuan Jennifer Yeh
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80420, Taiwan
| | - Hon-Yi Shi
- Graduate Institute of Technological and Vocational Education, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan;
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 80708, Taiwan;
- Department of Business Management, National Sun Yat-sen University, Kaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
- Correspondence: ; Tel.: +886-7-3211101 (ext. 2648); Fax: +886-7-3137487
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