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Salvador Comino MR, Youssef P, Heinzelmann A, Bernhardt F, Seifert C, Tewes M. Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer. JCO Clin Cancer Inform 2024; 8:e2400041. [PMID: 39197123 DOI: 10.1200/cci.24.00041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 06/25/2024] [Accepted: 07/15/2024] [Indexed: 08/30/2024] Open
Abstract
PURPOSE Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality. MATERIALS AND METHODS Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models. RESULTS The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]). CONCLUSION The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.
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Affiliation(s)
- Maria Rosa Salvador Comino
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Paul Youssef
- Institute for Artificial Intelligence in Medicine (IKIM), University of Duisburg-Essen, Essen, Germany
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Anna Heinzelmann
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Florian Bernhardt
- Department of Palliative Care, West German Cancer Center, University Hospital Muenster, University of Muenster, Muenster, Germany
| | - Christin Seifert
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Mitra Tewes
- Department of Palliative Medicine, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
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Cilla S, Rossi R, Habberstad R, Klepstad P, Dall'Agata M, Kaasa S, Valenti V, Donati CM, Maltoni M, Morganti AG. Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases. JCO Clin Cancer Inform 2024; 8:e2400027. [PMID: 38917384 DOI: 10.1200/cci.24.00027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/18/2024] [Accepted: 04/17/2024] [Indexed: 06/27/2024] Open
Abstract
PURPOSE The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial intelligence to predict 1-year survival after palliative radiotherapy (RT) for bone metastasis. MATERIALS AND METHODS Data collected in the multicentric PRAIS trial were extracted for 574 eligible adults diagnosed with metastatic cancer. The primary end point was the overall survival (OS) at 1 year (1-year OS) after the start of RT. Candidate covariate predictors consisted of 13 clinical and tumor-related pre-RT patient characteristics, seven dosimetric and treatment-related variables, and 45 pre-RT laboratory variables. ML models were developed and internally validated using the Python package. The effectiveness of each model was evaluated in terms of discrimination. A Shapley Additive Explanations (SHAP) explainability analysis to infer the global and local feature importance and to understand the reasons for correct and misclassified predictions was performed. RESULTS The best-performing model for the classification of 1-year OS was the extreme gradient boosting algorithm, with AUC and F1-score values equal to 0.805 and 0.802, respectively. The SHAP technique revealed that higher chance of 1-year survival is associated with low values of interleukin-8, higher values of hemoglobin and lymphocyte count, and the nonuse of steroids. CONCLUSION An explainable ML approach can provide a reliable prediction of 1-year survival after RT in patients with advanced cancer. The implementation of SHAP analysis provides an intelligible explanation of individualized risk prediction, enabling oncologists to identify the best strategy for patient stratification and treatment selection.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Responsible Research Hospital, Campobasso, Italy
| | - Romina Rossi
- Palliative Care Unit, IRCCS Istituto Romagnolo Studio Tumori "Dino Amadori", Meldola, Italy
| | - Ragnhild Habberstad
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Oncology, St Olavs University Hospital, Trondheim, Norway
| | - Pal Klepstad
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Anesthesiology and Intensive Care Medicine, St Olavs University Hospital, Trondheim, Norway
| | - Monia Dall'Agata
- Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Stein Kaasa
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Vanessa Valenti
- Palliative Care Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Costanza M Donati
- Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Marco Maltoni
- Medical Oncology Unit, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum-University of Bologna, Bologna, Italy
| | - Alessio G Morganti
- Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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McDonnell KJ. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise. J Clin Med 2023; 12:4830. [PMID: 37510945 PMCID: PMC10381436 DOI: 10.3390/jcm12144830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.
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Affiliation(s)
- Kevin J McDonnell
- Center for Precision Medicine, Department of Medical Oncology & Therapeutics Research, City of Hope Comprehensive Cancer Center, Duarte, CA 91010, USA
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Applications of Machine Learning in Palliative Care: A Systematic Review. Cancers (Basel) 2023; 15:cancers15051596. [PMID: 36900387 PMCID: PMC10001037 DOI: 10.3390/cancers15051596] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.
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Heinzen EP, Wilson PM, Storlie CB, Demuth GO, Asai SW, Schaeferle GM, Bartley MM, Havyer RD. Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial. BMC Palliat Care 2023; 22:9. [PMID: 36737744 PMCID: PMC9896817 DOI: 10.1186/s12904-022-01113-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 11/25/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND As primary care populations age, timely identification of palliative care need is becoming increasingly relevant. Previous studies have targeted particular patient populations with life-limiting disease, but few have focused on patients in a primary care setting. Toward this end, we propose a stepped-wedge pragmatic randomized trial whereby a machine learning algorithm identifies patients empaneled to primary care units at Mayo Clinic (Rochester, Minnesota, United States) with high likelihood of palliative care need. METHODS 42 care team units in 9 clusters were randomized to 7 wedges, each lasting 42 days. For care teams in treatment wedges, palliative care specialists review identified patients, making recommendations to primary care providers when appropriate. Care teams in control wedges receive palliative care under the standard of care. DISCUSSION This pragmatic trial therefore integrates machine learning into clinical decision making, instead of simply reporting theoretical predictive performance. Such integration has the possibility to decrease time to palliative care, improving patient quality of life and symptom burden. TRIAL REGISTRATION Clinicaltrials.gov NCT04604457 , restrospectively registered 10/26/2020. PROTOCOL v0.5, dated 9/23/2020.
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Affiliation(s)
- Ethan P. Heinzen
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Patrick M. Wilson
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Curtis B. Storlie
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Gabriel O. Demuth
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Shusaku W. Asai
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Gavin M. Schaeferle
- grid.66875.3a0000 0004 0459 167XRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN USA
| | - Mairead M. Bartley
- grid.66875.3a0000 0004 0459 167XCommunity Internal Medicine, Mayo Clinic, Rochester, MN USA
| | - Rachel D. Havyer
- grid.66875.3a0000 0004 0459 167XCommunity Internal Medicine, Mayo Clinic, Rochester, MN USA
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Battino M. Feature Paper Special Issue for Editorial Board Members (EBMs) of Diseases. Diseases 2022; 10:diseases10020018. [PMID: 35466188 PMCID: PMC9036201 DOI: 10.3390/diseases10020018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Maurizio Battino
- Department of Clinical Sciences, Polytechnic University of Marche, 60131 Ancona, Italy; ; Tel.: +39-071-220-4146
- International Joint Research Laboratory of Intelligent Agriculture and Agri-Product Processing, Jiangsu University, Zhenjiang 212013, China
- Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, 39011 Santander, Spain
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De Panfilis L, Peruselli C, Tanzi S, Botrugno C. AI-based clinical decision-making systems in palliative medicine: ethical challenges. BMJ Support Palliat Care 2021; 13:183-189. [PMID: 34257065 DOI: 10.1136/bmjspcare-2021-002948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 06/28/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness. METHODS We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning. RESULTS AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients' psychosocial and spiritual distress and their wishes to initiate EOL discussions CONCLUSIONS: Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.
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Affiliation(s)
- Ludovica De Panfilis
- Bioethics Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Carlo Peruselli
- Palliative Care Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Silvia Tanzi
- Palliative Care Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
| | - Carlo Botrugno
- Research Unit on Everyday Bioethics and Ethics of Science, Department of Legal Sciences, University of Florence, Firenze, Toscana, Italy
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