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Grant Details

Grant Number: 5R37CA218413-05 Interpret this number
Primary Investigator: Bansal, Aasthaa
Organization: University Of Washington
Project Title: Personalized Risk-Adaptive Surveillance Strategies in Cancer -- Praise
Fiscal Year: 2022


Cancer biomarkers are at the leading edge of Precision Medicine, and offer both tremendous opportunities and challenges. In particular, biomarker development to detect recurrence in cancer survivors is blossoming, as surveillance testing with serial biomarker measurements offers an opportunity to detect recurrence at a point when treatment may be curative. However, frequent biomarker testing may cause more harm than benefit for low-risk individuals, due to the costs and complications of unnecessary testing and increased likelihood of false positives leading to unnecessary treatment. Unfortunately, tailoring surveillance to individual patients is a complex decision-making problem that requires understanding the heterogeneity in biomarker measurements across patients and across time within patients. As a result, surveillance testing guidelines using one-size-fits- all strategies continue to be common in most cancers, despite their uncertain clinical utility. The overarching goal of the proposed research is to develop a decision-making framework to identify optimal surveillance strategies among cancer survivors. The specific aims are: Aim 1. Develop and evaluate a sequential decision- making framework by merging statistical methods for prediction modeling with economics concepts for value of information (VOI) analysis to guide individualized decisions about testing and treatment for recurrence using serial biomarker testing, with the goal of optimizing long-term patient outcomes. This aim will build on preliminary work and develop a dynamic decision-making algorithm that uses accumulated information at a given time to update predictions and guide decisions. The broad applicability of the framework will be demonstrated by considering three distinct cancer surveillance settings: colorectal cancer (CRC), prostate cancer (PrCA), and chronic myeloid leukemia (CML), which capture a range of decision-making problems in cancer surveillance. Aim 2. Apply this framework to existing electronic health record (EHR) and cohort study data to identify a risk-adaptive surveillance strategy for detecting CRC recurrence that targets high-risk patients for frequent follow-up and treatment, and recommends less frequent follow-up for low-risk patients. Aim 3. Assess the comparative effectiveness of the proposed risk-adaptive surveillance strategy versus guideline- based surveillance in CRC. Aim 4. Use existing data to evaluate the generalizability of the framework by addressing the optimal frequency of follow-up among (a) low-risk men with recurrent PrCA, for whom treatment may be safely delayed for a prolonged period, and (b) long-term survivors of CML, who achieve long-term remission but currently continue to be monitored frequently. We address a significant problem in cancer survivorship care using approaches to help resolve the uncertainty that clinicians and patients face when confronted with using new and evolving biomarker technologies to monitor for recurrence after patients have survived their primary cancer.


Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence.
Authors: Rodriguez P.J. , Heagerty P.J. , Clark S. , Khor S. , Chen Y. , Haupt E. , Hahn E.E. , Shankaran V. , Bansal A. .
Source: JCO clinical cancer informatics, 2023 Sep; 7, p. e2300066.
PMID: 37963310
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Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors.
Authors: Khor S. , Haupt E.C. , Hahn E.E. , Lyons L.J.L. , Shankaran V. , Bansal A. .
Source: JAMA network open, 2023-06-01; 6(6), p. e2318495.
EPub date: 2023-06-01.
PMID: 37318804
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Underutilization or appropriate care? Assessing adjuvant chemotherapy use and survival in 3 heterogenous subpopulations with stage II/III colorectal cancer within a large integrated health system.
Authors: Chen Y. , Shankaran V. , Hahn E.E. , Haupt E.C. , Bansal A. .
Source: Journal of managed care & specialty pharmacy, 2023 Jun; 29(6), p. 635-646.
PMID: 37276035
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Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records.
Authors: Khor S. , Heagerty P.J. , Basu A. , Haupt E.C. , Lyons L.J.L. , Hahn E.E. , Bansal A. .
Source: JCO clinical cancer informatics, 2023 Jun; 7, p. e2300004.
PMID: 37267516
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Development and Internal Validation of a Prognostic Model of the Probability of Death or Lung Transplantation Within 2 Years for Patients With Cystic Fibrosis and FEV1 ≤ 50% Predicted.
Authors: Ramos K.J. , Hee Wai T. , Stephenson A.L. , Sykes J. , Stanojevic S. , Rodriguez P.J. , Bansal A. , Mayer-Hamblett N. , Goss C.H. , Kapnadak S.G. .
Source: Chest, 2022 Oct; 162(4), p. 757-767.
EPub date: 2022-05-26.
PMID: 35643116
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Assessing surveillance utilization and value in commercially insured patients with colorectal cancer.
Authors: Suh K. , Shankaran V. , Bansal A. .
Source: The American journal of managed care, 2022-05-01; 28(5), p. e163-e169.
EPub date: 2022-05-01.
PMID: 35546589
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A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing.
Authors: Bansal A. , Heagerty P.J. , Inoue L.Y.T. , Veenstra D.L. , Wolock C.J. , Basu A. .
Source: Medical decision making : an international journal of the Society for Medical Decision Making, 2022 May; 42(4), p. 474-486.
EPub date: 2021-11-07.
PMID: 34747265
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A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.
Authors: Rodriguez P.J. , Veenstra D.L. , Heagerty P.J. , Goss C.H. , Ramos K.J. , Bansal A. .
Source: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2022 Mar; 25(3), p. 350-358.
EPub date: 2021-12-22.
PMID: 35227445
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Predictors of tyrosine kinase inhibitor adherence trajectories in patients with newly diagnosed chronic myeloid leukemia.
Authors: Clark S.E. , Marcum Z.A. , Radich J.P. , Bansal A. .
Source: Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners, 2021 Dec; 27(8), p. 1842-1852.
EPub date: 2020-11-11.
PMID: 33175653
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Bias in Mean Survival From Fitting Cure Models With Limited Follow-Up.
Authors: Othus M. , Bansal A. , Erba H. , Ramsey S. .
Source: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2020 Aug; 23(8), p. 1034-1039.
EPub date: 2020-07-31.
PMID: 32828215
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Patterns of Surveillance Advanced Imaging and Serum Tumor Biomarker Testing Following Launch of the Choosing Wisely Initiative.
Authors: Miles R.C. , Lee C.I. , Sun Q. , Bansal A. , Lyman G.H. , Specht J.M. , Fedorenko C.R. , Greenwood-Hickman M.A. , Ramsey S.D. , Lee J.M. .
Source: Journal of the National Comprehensive Cancer Network : JNCCN, 2019-07-01; 17(7), p. 813-820.
PMID: 31319393
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A Novel Tool to Evaluate the Accuracy of Predicting Survival and Guiding Lung Transplantation in Cystic Fibrosis.
Authors: Bansal A. , Mayer-Hamblett N. , Goss C.H. , Chan L.N. , Heagerty P.J. .
Source: Epidemiology (Sunnyvale, Calif.), 2019; 9(2), .
EPub date: 2019-06-17.
PMID: 31523488
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A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes.
Authors: Bansal A. , Heagerty P.J. .
Source: Diagnostic and prognostic research, 2019; 3, p. 14.
EPub date: 2019-07-25.
PMID: 31367681
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A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making.
Authors: Bansal A. , Heagerty P.J. .
Source: Medical decision making : an international journal of the Society for Medical Decision Making, 2018 Nov; 38(8), p. 904-916.
EPub date: 2018-10-14.
PMID: 30319014
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