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Deasy JO. Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making. Semin Radiat Oncol 2024; 34:379-394. [PMID: 39271273 DOI: 10.1016/j.semradonc.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.
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Affiliation(s)
- Joseph O Deasy
- Department of Medical Physics, Attending Physicist, Chief, Service for Predictive Informatics, Chair, Memorial Sloan Kettering Cancer Center, New York, NY..
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Levine MN, Kemppainen J, Rosenberg M, Pettengell C, Bogach J, Whelan T, Saha A, Ranisau J, Petch J. Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided. Learn Health Syst 2024; 8:e10409. [PMID: 39036532 PMCID: PMC11257056 DOI: 10.1002/lrh2.10409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 07/23/2024] Open
Abstract
Purpose In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients. Methods Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course. Oncologists were asked to identify examples where clinical practice guidelines (CPGs) or policy changes had varying impacts on practice. These constructs were evaluated by extracting the corresponding data. Results Breast cancer patients (5768) seen at the Juravinski Cancer Centre between January 2014 and June 2022 were included. The average age was 62.5 years. The commonest histology was invasive ductal carcinoma (74.6%); 77% were estrogen receptor-positive and 15.5% were HER2 Neu positive. Breast-conserving surgery (BCS) occurred in 56%. For the 4294 patients who received systemic therapy, the initial indications were adjuvant (3096), neoadjuvant (828) and palliative (370). Metastases occurred in 531 patients and 495 patients died. Lowest-income patients had a higher mortality rate. For the adoption of CPGs, the uptake for adjuvant bisphosphonate was very low, 8% as predicted, compared to 64% for pertuzumab, a HER2 targeted agent and 40.2% for CD4/6 inhibitors in metastases. During COVID-19, the provincial cancer agency issued a policy to shorten the duration of radiation after BCS. There was a significant reduction in the average number of fractions to the breast by five fractions. Conclusion Our platform characterized care and the clinical course of breast cancer patients. Practice changes in response to regulatory developments and policy changes were measured. Establishing a data platform is important for an LHS. The next step is for the data to feedback and change practice, that is, close the loop.
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Affiliation(s)
- Mark N. Levine
- Department of OncologyMcMaster UniversityHamiltonOntarioCanada
- Escarpment Cancer Research InstituteHamiltonOntarioCanada
| | - Joel Kemppainen
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
| | - Morgan Rosenberg
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
| | | | - Jessica Bogach
- Department of SurgeryMcMaster UniversityHamiltonOntarioCanada
| | - Tim Whelan
- Department of OncologyMcMaster UniversityHamiltonOntarioCanada
- Escarpment Cancer Research InstituteHamiltonOntarioCanada
| | - Ashirbani Saha
- Department of OncologyMcMaster UniversityHamiltonOntarioCanada
- Escarpment Cancer Research InstituteHamiltonOntarioCanada
| | - Jonathan Ranisau
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
| | - Jeremy Petch
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
- Department of MedicineMcMaster UniversityHamiltonOntarioCanada
- Population Health Research Institute, Hamilton Health SciencesHamiltonOntarioCanada
- Institute of Health Policy, Management and EvaluationUniversity of TorontoHamiltonOntarioCanada
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Bhardwaj V, Sharma A, Parambath SV, Gul I, Zhang X, Lobie PE, Qin P, Pandey V. Machine Learning for Endometrial Cancer Prediction and Prognostication. Front Oncol 2022; 12:852746. [PMID: 35965548 PMCID: PMC9365068 DOI: 10.3389/fonc.2022.852746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.
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Affiliation(s)
- Vipul Bhardwaj
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Arundhiti Sharma
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | | | - Ijaz Gul
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Xi Zhang
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peter E. Lobie
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Peiwu Qin
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Vijay Pandey
- Tsinghua Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
- *Correspondence: Vijay Pandey,
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