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Tseng J, Bazan JG, Minami CA, Schonberg MA. Not Too Little, Not Too Much: Optimizing More Versus Less Locoregional Treatment for Older Patients With Breast Cancer. Am Soc Clin Oncol Educ Book 2023; 43:e390450. [PMID: 37327467 DOI: 10.1200/edbk_390450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Although undertreatment of older women with aggressive breast cancers has been a concern for years, there is increasing recognition that some older women are overtreated, receiving therapies unlikely to improve survival or reduce morbidity. De-escalation of surgery may include breast-conserving surgery over mastectomy for appropriate candidates and omitting or reducing extent of axillary surgery. Appropriate patients to de-escalate surgery are those with early-stage breast cancer, favorable tumor characteristics, are clinically node-negative, and who may have other major health issues. De-escalation of radiation includes reducing treatment course length through hypofractionation and ultrahypofractionation regimens, reducing treatment volumes through partial breast irradiation, omission of radiation for select patients, and reducing radiation dose to normal tissues. Shared decision making, which aims to facilitate patients making decisions concordant with their values, can guide health care providers and patients through complicated decisions optimizing breast cancer care.
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Affiliation(s)
| | - Jose G Bazan
- City of Hope Comprehensive Cancer Center, Duarte, CA
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2
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Rahman SA, Walker RC, Maynard N, Trudgill N, Crosby T, Cromwell DA, Underwood TJ. The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests. Ann Surg 2023; 277:267-274. [PMID: 33630434 PMCID: PMC9831040 DOI: 10.1097/sla.0000000000004794] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
OBJECTIVE The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. SUMMARY BACKGROUND DATA For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a RandomSurvival Forest (RSF) model derived from routine data from a large, well-curated, national dataset. METHODS Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time-dependent area under the curve) were validated internally using bootstrap resampling. RESULTS The study analyzed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year time-dependent area under the receiver operator curve of 83.9% [95% confidence interval (CI) 82.6%-84.9%], compared to 82.3% (95% CI 81.1%-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, circumferential resection margin involvement (tumor at < 1 mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2% to 44.7% survival at 5 years. CONCLUSIONS An RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well-calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.
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Affiliation(s)
- Saqib A Rahman
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK
| | - Robert C Walker
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | | | - Nigel Trudgill
- Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK
| | | | - David A Cromwell
- Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK
| | - Timothy J Underwood
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK
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3
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Moorthie S, Babb de Villiers C, Burton H, Kroese M, Antoniou AC, Bhattacharjee P, Garcia-Closas M, Hall P, Schmidt MK. Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention. Prev Med 2022; 159:107075. [PMID: 35526672 DOI: 10.1016/j.ypmed.2022.107075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/05/2022] [Accepted: 05/02/2022] [Indexed: 12/24/2022]
Abstract
Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.
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Affiliation(s)
- Sowmiya Moorthie
- PHG Foundation, University of Cambridge, Cambridge, UK; Cambridge Public Health, University of Cambridge School of Clinical Medicine, Forvie Site, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom.
| | | | - Hilary Burton
- PHG Foundation, University of Cambridge, Cambridge, UK
| | - Mark Kroese
- PHG Foundation, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Proteeti Bhattacharjee
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
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4
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Kerr AJ, Dodwell D, McGale P, Holt F, Duane F, Mannu G, Darby SC, Taylor CW. Adjuvant and neoadjuvant breast cancer treatments: A systematic review of their effects on mortality. Cancer Treat Rev 2022; 105:102375. [PMID: 35367784 PMCID: PMC9096622 DOI: 10.1016/j.ctrv.2022.102375] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Amanda J Kerr
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - David Dodwell
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Paul McGale
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Francesca Holt
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Fran Duane
- St Luke's Radiation Oncology Network, St. James's Hospital, Dublin, Ireland.
| | - Gurdeep Mannu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Sarah C Darby
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Carolyn W Taylor
- Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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5
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Zhao A, Larbi M, Miller K, O'Neill S, Jayasekera J. A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer. Breast 2022; 61:43-57. [PMID: 34896693 PMCID: PMC8669108 DOI: 10.1016/j.breast.2021.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 11/20/2021] [Accepted: 12/04/2021] [Indexed: 01/28/2023] Open
Abstract
The increasing attention on personalized breast cancer care has resulted in an explosion of new interactive, tailored, web-based clinical decision tools for guiding treatment decisions in clinical practice. The goal of this study was to review, compare, and discuss the clinical implications of current tools, and highlight future directions for tools aiming to improve personalized breast cancer care. We searched PubMed, Embase, PsychInfo, Cochrane Database of Systematic Reviews, Web of Science, and Scopus to identify web-based decision tools addressing breast cancer treatment decisions. There was a total of 17 articles associated with 21 unique tools supporting decisions related to surgery, radiation therapy, hormonal therapy, bisphosphonates, HER2-targeted therapy, and chemotherapy. The quality of the tools was assessed using the International Patient Decision Aid Standard instrument. Overall, the tools considered clinical (e.g., age) and tumor characteristics (e.g., grade) to provide personalized outcomes (e.g., survival) associated with various treatment options. Fewer tools provided the adverse effects of the selected treatment. Only one tool was field-tested with patients, and none were tested with healthcare providers. Future studies need to assess the feasibility, usability, acceptability, as well as the effects of personalized web-based decision tools on communication and decision making from the patient and clinician perspectives.
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Affiliation(s)
- Amy Zhao
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Maya Larbi
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA; Towson University, Maryland, USA
| | - Kristen Miller
- MedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA
| | - Suzanne O'Neill
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Jinani Jayasekera
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.
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Walsh CG, McKillop MM, Lee P, Harris JW, Simpson C, Novak LL. Risky business: a scoping review for communicating results of predictive models between providers and patients. JAMIA Open 2021; 4:ooab092. [PMID: 34805776 PMCID: PMC8598291 DOI: 10.1093/jamiaopen/ooab092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 10/01/2021] [Accepted: 10/24/2021] [Indexed: 11/25/2022] Open
Abstract
Objective Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is—or should be—communicated with patients. Materials and Methods We conducted a scoping review informed by PRISMA-ScR guidelines to identify current knowledge and gaps in this domain. Results Ten studies met inclusion criteria for full text review. The following topics were represented in the studies, some of which involved more than 1 topic: disease prevention (N = 5/10, 50%), treatment decisions (N = 5/10, 50%), medication harms reduction (N = 1/10, 10%), and presentation of cardiovascular risk information (N = 5/10, 50%). A single study included 6- and 12-month clinical outcome metrics. Discussion As predictive models are increasingly published, marketed by industry, and implemented, this paucity of relevant research poses important gaps. Published studies identified the importance of (1) identifying the most effective source of information for patient communications; (2) contextualizing risk information and associated design elements based on users’ needs and problem areas; and (3) understanding potential impacts on risk factor modification and behavior change dependent on risk presentation. Conclusion An opportunity remains for researchers and practitioners to share strategies for effective selection of predictive algorithms for clinical practice, approaches for educating clinicians and patients in effectively using predictive data, and new approaches for framing patient-provider communication in the era of artificial intelligence.
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Affiliation(s)
- Colin G Walsh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mollie M McKillop
- Center for AI Research and Evaluation, IBM Watson Health, Cambridge, Massachusetts, USA
| | - Patricia Lee
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Joyce W Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Christopher Simpson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Laurie Lovett Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Corresponding Author: Laurie Lovett Novak, PhD, MHSA, Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1475, Nashville, TN 37203, USA;
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Kim JY, Lee YS, Yu J, Park Y, Lee SK, Lee M, Lee JE, Kim SW, Nam SJ, Park YH, Ahn JS, Kang M, Im YH. Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry. Front Oncol 2021; 11:596364. [PMID: 34017679 PMCID: PMC8129587 DOI: 10.3389/fonc.2021.596364] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 02/17/2021] [Indexed: 01/06/2023] Open
Abstract
Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements: baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.
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Affiliation(s)
- Ji-Yeon Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yong Seok Lee
- Digital Health Business Team, Samsung SDS, Seoul, South Korea
| | - Jonghan Yu
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Youngmin Park
- Digital Health Business Team, Samsung SDS, Seoul, South Korea
| | - Se Kyung Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Minyoung Lee
- Digital Health Business Team, Samsung SDS, Seoul, South Korea
| | - Jeong Eon Lee
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seok Won Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seok Jin Nam
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yeon Hee Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jin Seok Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Mira Kang
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Young-Hyuck Im
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Josfeld L, Keinki C, Pammer C, Zomorodbakhsch B, Hübner J. Cancer patients' perspective on shared decision-making and decision aids in oncology. J Cancer Res Clin Oncol 2021; 147:1725-1732. [PMID: 33682014 PMCID: PMC8076112 DOI: 10.1007/s00432-021-03579-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/20/2021] [Indexed: 12/26/2022]
Abstract
Purpose Shared Decision-Making (SDM) enhances patients’ satisfaction with a decision, which in turn increases compliance with and adherence to cancer treatment. SDM requires a good patient-clinician relationship and communication, patients need information matching their individual needs, and clinicians need support on how to best involve the individual patient in the decision-making process. This survey assessed oncological patients’ information needs and satisfaction, their preferred information in patient decision aids (PDAs), and their preferred way of making decisions regarding their treatment. Methods Questionnaires were distributed among attendees of a lecture program on complementary and alternative medicine in oncology of which 220 oncological patients participated. Results Participants reported a generally high need for information—correlating with level of education—but also felt overwhelmed by the amount. The latter proved particularly important during consultation. Use of PDAs increased satisfaction with given information but occurred in less than a third of the cases. Most requested contents for PDAs were pros and cons of treatment options and lists of questions to ask. The vast majority of patients preferred SDM to deciding alone. None wanted their physician to decide for them. Conclusions There is a high demand for SDM but a lack of conclusive evidence on the specific information needs of different types of patients. Conversation between patients and clinicians needs encouragement and support. PDAs are designed for this purpose and have the potential to increase patient satisfaction. Their scarce use in consultations calls for easier access to and better information on PDAs for clinicians.
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Affiliation(s)
- Lena Josfeld
- Klinik für Innere Medizin II Hämatologie und Internistische Onkologie, Universitätsklinikum Jena, Am Klinikum 1, 07747, Jena, Germany.
| | - Christian Keinki
- Klinik für Innere Medizin II Hämatologie und Internistische Onkologie, Universitätsklinikum Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Carolina Pammer
- Klinik für Innere Medizin II Hämatologie und Internistische Onkologie, Universitätsklinikum Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Bijan Zomorodbakhsch
- üBAG/MVZ Onkologische Kooperation Harz GbR, Kösliner Str. 14, 38642, Goslar, Germany
| | - Jutta Hübner
- Klinik für Innere Medizin II Hämatologie und Internistische Onkologie, Universitätsklinikum Jena, Am Klinikum 1, 07747, Jena, Germany
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Eloranta S, Smedby KE, Dickman PW, Andersson TM. Cancer survival statistics for patients and healthcare professionals - a tutorial of real-world data analysis. J Intern Med 2021; 289:12-28. [PMID: 32656940 DOI: 10.1111/joim.13139] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 05/27/2020] [Indexed: 01/04/2023]
Abstract
Monitoring survival of cancer patients using data collected by population-based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real-world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population-based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons.
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Affiliation(s)
- S Eloranta
- From the, Department of Medicine, Division of Clinical Epidemiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - K E Smedby
- From the, Department of Medicine, Division of Clinical Epidemiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine, Division of Hematology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - P W Dickman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - T M Andersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Moser EC, Narayan G. Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits. Breast 2020; 50:25-29. [PMID: 31978814 PMCID: PMC7375673 DOI: 10.1016/j.breast.2019.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/01/2019] [Accepted: 12/12/2019] [Indexed: 01/29/2023] Open
Abstract
Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director’s role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda’s and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today.
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Affiliation(s)
- E C Moser
- UM-AI Coordinator Research, UM-AI LLC, 8 the Green. Suite #5064, Dover, DE, 19901, USA.
| | - Gayatri Narayan
- UM-AI Coordinator Research, UM-AI LLC, 8 the Green. Suite #5064, Dover, DE, 19901, USA.
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11
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Bovbjerg ML, Pillai S. Current Resources for Evidence-Based Practice, September 2019. J Obstet Gynecol Neonatal Nurs 2019; 48:568-582. [PMID: 31442383 DOI: 10.1016/j.jogn.2019.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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