Grant Details
Grant Number: |
5R01CA264995-04 Interpret this number |
Primary Investigator: |
Wisnivesky, Juan |
Organization: |
Icahn School Of Medicine At Mount Sinai |
Project Title: |
Optimizing Lung Cancer Screening in Cancer Survivors |
Fiscal Year: |
2024 |
Abstract
The goal of the proposal is to identify optimal lung cancer (LC) screening strategies for breast (BC), prostate
(PC), colorectal (CRC) cancer survivors. We will accomplish these goals by developing and validating a novel
Multi-Racial and Ethnic Lung Cancer Model (MELCAM) that will simulate LC development, progression,
screening, treatment, and survival in a multiethnic cancer survivor population. We will then assess the
effectiveness and cost-effectiveness (CE) of different LC screening strategies for these survivors. All together,
there are >6 million BC, CRC and PC survivors in the US, and as these cancers tend to be diagnosed in early
stage, many survivors live for a long time and develop and may die from second cancers. Cancer survivors are
also at increased risk of developing LC due to relatively high rates of smoking (up to 50-60%), age, and
treatment-related side effects. As a result, >15% of LC are diagnosed in cancer survivors, and LC is the top
cause of cancer-related mortality in this population. Little is known about optimal LC screening for cancer
survivors who have been excluded from prior randomized trials (RCT) and have a different harm/benefit ratio
from screening due to competing risk of death from their first cancer and higher burden of comorbidities. Lack
of data to guide decisions about LC screening in cancer survivors has profound negative impact on
survivorship, including under and overuse of LC screening, resulting in worse outcomes and increased
healthcare costs. It is unlikely that RCT assessing the benefits of LC screening for cancer survivors will ever be
conducted. Thus, there is an urgent need to use alternative methods to determine the optimal screening
strategy for these patients. In this study, we propose using simulation modeling, an approach complementary
to RCTs, to assess the harms and benefits of LC screening in cancer survivors. The Specific Aims are to: (1)
Derive and validate a model (MELCAM), based on a well-established framework, to simulate LC screening in
BC, PC and CRC cancer survivors from diverse racial and ethnic backgrounds; (2) Determine the most
effective and CE strategies for LC screening in BC survivors; (3) Identify the optimal LC screening strategies in
PC survivors and determine their CE; and (4) Evaluate the effectiveness and CE of LC screening for CRC
survivors. To achieve these Aims, we will use data from several large, representative, population-based cancer
cohorts and robust harmonization methods to develop, calibrate, and validate MELCAM by incorporating the
development, screening, work-up, treatment and survival of LC in multiethnic survivors of BC, PC and CRC
(Aim 1). We will then use the model to simulate RCTs evaluating the effectiveness (in terms of maximizing
survival, quality of life, and other patient-centered outcomes) and CE of LC screening regimens (eligibility,
frequency and duration) in these cancer survivors (Aims 2-4). The study will be innovative in applying state-of-
the-art modeling approaches to evaluate LC screening in a diverse population of cancer survivors, and results
will have direct implications for the management of a large group of survivors.
Publications
A Cross-Sectional Analysis of the Lung Cancer Screening Eligibility Among Cancer Survivors Who Ever Smoked.
Authors: Wang Q.
, Hsu M.L.
, Lin J.J.
, Wisnivesky J.
, Cullen J.
, Dowlati A.
, Kong C.Y.
.
Source: Journal Of General Internal Medicine, 2024-02-06 00:00:00.0; , .
EPub date: 2024-02-06 00:00:00.0.
PMID: 38321314
Related Citations
Clinical and sociodemographic risk factors associated with the development of second primary cancers among postmenopausal breast cancer survivors.
Authors: Bailey S.
, Ezratty C.
, Mhango G.
, Lin J.J.
.
Source: Breast Cancer (tokyo, Japan), 2023 Mar; 30(2), p. 215-225.
EPub date: 2022-11-01 00:00:00.0.
PMID: 36316601
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