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Withdrawn by IRB on 10-16-2023
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The study team hypothesizes that incidentally discovered pulmonary nodules are often under captured and/or not surveilled in accordance with published guidelines in the Montefiore Health System, which cares for a large proportion of Black and Hispanic patients. Incidental Pulmonary Nodules (IPNs) require a pragmatic approach to follow-up and management, especially in racially disparate populations who have greater potential for lung cancer morbidity and mortality.
An estimated 235,760 people will be newly diagnosed with lung cancer in 2021 and over 130,000 people will die as a result. Computed tomography (CT) screening has demonstrated reduced lung cancer mortality, and is now recommended for adults aged 50 to 80 years with a 20-pack-year smoking history who are current smoker or have quit within 15 years. Along with the early identification of at-risk pulmonary nodules, screening programs also have the ability to direct patients into pathways for protocolized follow-up. Despite defined eligibility requirements, lung cancer screening (LCS) is underutilized, with less than 10% of eligible patients undergoing screening. Screening is particularly challenging in minority and socioeconomically disadvantaged populations, who face an increased risk of death due to lung cancer. Of over 50,000 patients enrolled in the National Lung Screening Trial, for example, only 4.5% of participants identified as Black and 1.7% identified as Hispanic.
Outside of screening programs, lung nodules are commonly detected incidentally on imaging done for other reasons. Each year, more than 1.5 million patients are diagnosed with an incidental pulmonary nodule. Of these, 5-9% are estimated to represent cancers, higher than the malignancy rate noted in lung cancer screening programs. Guidelines exist for the follow-up of IPNs, however compliance is often poor. IPNs may be overlooked in the context of the other illnesses for which imaging is obtained. Tests may also be ordered by providers without continuity of care, as occurs in the emergency department (ED). Patients may thus be unaware of incidental findings or receive inadequate direction for follow-up when there is no clear chain of responsibility. Racially disparate populations are specifically at risk and often face barriers to accessing primary care providers (PCPs), leading to increased use of the ED. In one study, a higher proportion of Black and Hispanic patients (38.3% and 28.1%, respectively) had initial imaging identifying an IPN performed in the ED compared to White patients (10.7%), who were more likely to have outpatient scans.
Previous studies indicate that only 38% of patients receive guideline concordant care once diagnosed with an IPN. Lung cancer was diagnosed in 8% of these patients undergoing such care compared to only 1% in those who received less intensive evaluation. Similarly, the median time to diagnosis of a lung cancer was 1.3 months in the guideline concordant care group versus 12 months in the less intensive evaluation patients, again underscoring the importance of appropriate mechanisms of follow-up. The consequences of a missed nodule are clear.
Data suggest that racial and ethnic disparities exist in the follow-up of IPNs. In a study of 1,562 patients with an IPN requiring follow-up at a tertiary care center, only 49.1% of Hispanic patients and 55.1% of Black patients were notified of IPNs compared to 79.5% of White patients. Similarly, non-White patients had significantly lower rates of ordering and adherence to follow-up imaging and had an increased odds of delaying follow-up. While this discrepancy in care has been identified, few solutions exist to bridge the gap and underrepresented patients remain at higher risk of delayed diagnosis until advanced stages of disease.
Further compounding the difficulty in managing underserved patients with IPNs is the lack of programs for formalized follow-up, specifically in urban areas. In an advisory board meeting of major medical centers within New York City, only one formal nodule evaluation program associated with a center's ED was identified. New York Presbyterian Hospital Weill Cornell identified 539 patients with IPNs over a two-year period. After radiologic review, chest radiologists referred 289 patients for further consultation and of these 142 (26.3% of original population) were referred for evaluation by a pulmonologist or oncologist. While the results of this investigation and rates of cancer diagnoses are currently being tabulated, the large proportion of patients referred for concerning findings is quite notable.
Within New York City, the Montefiore Health System is uniquely positioned to conduct clinical research and bridge health care disparities by engaging underserved and underrepresented communities. The health system is comprised of 11 hospitals in the Bronx, Westchester, and the Hudson Valley in New York. The main campuses include two high volume EDs, including one of the five busiest in the country, and serve a diverse population of nearly 1.5 million residents in the Bronx. Previous data from Montefiore have demonstrated that of 855 primary lung cancers diagnosed between 2013 and 2016, only 417 (55%) were found in patients with an in-network PCP, illustrating the need for a better support system and for a systematic approach to identify and guide these patients. Furthermore, of the 175 of these patients who were eligible for LCS, only 33 had completed screening. Among screened patients, 64% were diagnosed with stage I/II non-small cell lung cancer, compared to only 29% of the lung cancers found outside of screening. In this latter group, 46% were diagnosed with metastatic disease. This demonstrates not only the value of screening in this at-risk population, but also emphasizes the need for prompt follow-up of incidentally detected lung abnormalities. A large proportion of this population in whom lung cancers were identified outside of screening was comprised of Black (46%) and Hispanic (34%) patients with a median per capita income of only approximately $20,000.12. The evaluation and implementation of a nodule detection program may thus extend care and improve the potential for survival in patients with reduced access to health care, where the ED may function as the primary care hub.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group A | Aim 1 (Part A). To utilize natural language processing (NLP) to identify all ED patients with incidentally detected lung nodules found on chest radiographs or chest or abdominal CT scans, and to develop a standardized referral and notification process Aim 1.1) Utilization of NLP to screen radiologic reports and identify nodules meeting criteria for follow-up per Fleischner Society Guidelines Aim 1.2) Creation of an electronic medical record-based notification system to alert patients and providers of the identification of an IPN that requires follow-up, tracked by a dedicated patient navigator Aim 1.3) Establishment of a multidisciplinary lung nodule management team, hereafter referred to as the lung nodule clinic, to ensure guideline-directed management of nodules with emphasis on high risk nodules as identified in subsequent aims | ||
| Group B | Aim 2 (Part B). To clinically risk stratify patients with IPNs utilizing artificial intelligence (AI) processing of known clinical risks factors for pulmonary malignancy, such as age, smoking history, and history of malignancy, along with radiographic risk classifiers including nodule location, size, and imaging features. Aim 2.1) Development of an integrated classifier based on automated scanning and data retrieval from the electronic medical record (EMR) to stratify patients with IPNs as low, intermediate or high risk for malignancy, with factor analysis to assess contributions of individual factors to the model Aim 2.2) Prospective evaluation of the integrated classifier and comparison of automated integrated classifier to established manual risk calculators | ||
| Group C | Aim 3 (Part C). To investigate biologic risk classifiers that may aid in the risk stratification of pulmonary nodules Aim 3.1) Evaluation of a blood-based gene expression assay for risk stratification of pulmonary nodules using biobanked specimens Aim 3.2) Prospective collection of plasma from patients enrolled in lung nodule clinic and evaluation of gene expression to assess malignancy risk |
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| Measure | Description | Time Frame |
|---|---|---|
| Capture of incidentally identified lung nodules utilizing the electronic medical record | To capture all incidentally identified lung nodules and create a multidisciplinary team for lung nodule management. Radiology and pulmonary providers will identify and report IPNs >=6mm denoted as positive imaging result at Montefiore. NLP will be used to identify IPNs using the Lung Orchestrator and IPNs will be captured by automated assessment of radiology. Cardiothoracic Imaging will assist in nodule identification using NLP. Following confirmation of a nodule requiring follow-up, the Lung Orchestrator will place the patient in a work queue. Patients diagnosed with lung cancer the year prior to the program will be reviewed retrospectively with NLP for determination on whether the approach increases adherence to IPN follow-up guidelines. | Through completion of chart review, up to 1 year |
| Performance comparison of EMR-based algorithm to establish risk | Patients will be followed to develop a multi-component integrated risk classifier for stratification into low or intermediate malignancy risk. Risk calculators (Brock University; Mayo Clinic) will be used for assessment and evaluations designed to automatically populate in the EMR. Stratification will be based upon demographic factors, such as age, gender, race, smoking, and cancer history. Imaging characteristics such as emphysema, nodule location, size, and concerning features will be used. The aim is to develop an automated risk calculating algorithm based upon AI-mediated processing of clinical data and radiomics. These assessments will allow for real-time risk identification and the investigation of a novel model will incorporate data from a more diverse population. | Through development of algorithm, up to 1 year |
| Evaluation of LiquidLung plasma-based gene expression assay for biomarker subset selection using biobanked specimens | This assay is applicable for all concerning pulmonary nodules, represented by those patients referred to the lung nodule clinic. The lung nodule clinic manages both incidental and screen-detected lesions. Montefiore Health System has an established lung cancer screening program with over 1,000 screenings completed in the last year. Patients with suspicious nodules are recommended follow-up and also have potential to benefit from testing via non-invasive means. |
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| Measure | Description | Time Frame |
|---|---|---|
| Performance of LiquidLung plasma-based gene expression assay on patients with incidental or screen-detected lung nodules | As an exploratory aim, the performance (utility/feasibility) of the LiquidLung assay will be assessed in patients enrolled into Montefiore's lung nodule clinic for the ability of the assay to classify incidental or screen-detected lung nodules as benign or malignant. | Through evaluation of assay performance, up to 1.5 years |
Inclusion Criteria:
Part A of the study seeks to capture IPNs and standardize mechanism for management will employ the following inclusion criteria:
Part B of the study will seek to develop an algorithm for incidental lung nodule risk stratification utilizing data from the electronic medical record. Inclusion criteria are:
The third part (Part C) of the study is a substudy examining plasma-based expression of markers associated with lung cancer in patients with lung nodules, and will include the following patients:
Exclusion Criteria:
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The target population for the study consists of patients with an identified incidental pulmonary nodule seen at the Montefiore Medical Center after Feburary 1, 2023.
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| Name | Affiliation | Role |
|---|---|---|
| Neel Chudgar, MD | Montefiore Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Montefiore Medical Center-Albert Einstein College of Medicine | The Bronx | New York | 10461 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33433946 | Background | Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021 Jan;71(1):7-33. doi: 10.3322/caac.21654. Epub 2021 Jan 12. | |
| 21714641 | Background | National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29. |
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Once the trial data collection is completed the potential for sharing data to expand the trial may occur.
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| Through evaluation of assay, up to 1.5 years |
| 33687470 | Background | US Preventive Services Task Force; Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB, Davis EM, Donahue KE, Doubeni CA, Kubik M, Landefeld CS, Li L, Ogedegbe G, Owens DK, Pbert L, Silverstein M, Stevermer J, Tseng CW, Wong JB. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2021 Mar 9;325(10):962-970. doi: 10.1001/jama.2021.1117. |
| 28152136 | Background | Jemal A, Fedewa SA. Lung Cancer Screening With Low-Dose Computed Tomography in the United States-2010 to 2015. JAMA Oncol. 2017 Sep 1;3(9):1278-1281. doi: 10.1001/jamaoncol.2016.6416. |
| 32546140 | Background | Lake M, Shusted CS, Juon HS, McIntire RK, Zeigler-Johnson C, Evans NR, Kane GC, Barta JA. Black patients referred to a lung cancer screening program experience lower rates of screening and longer time to follow-up. BMC Cancer. 2020 Jun 16;20(1):561. doi: 10.1186/s12885-020-06923-0. |
| 24710850 | Background | Wiener RS, Gould MK, Slatore CG, Fincke BG, Schwartz LM, Woloshin S. Resource use and guideline concordance in evaluation of pulmonary nodules for cancer: too much and too little care. JAMA Intern Med. 2014 Jun;174(6):871-80. doi: 10.1001/jamainternmed.2014.561. |
| 30798793 | Background | Hanchate AD, Dyer KS, Paasche-Orlow MK, Banerjee S, Baker WE, Lin M, Xue WD, Feldman J. Disparities in Emergency Department Visits Among Collocated Racial/Ethnic Medicare Enrollees. Ann Emerg Med. 2019 Mar;73(3):225-235. doi: 10.1016/j.annemergmed.2018.09.007. Epub 2018 Oct 26. |
| 32771492 | Background | Schut RA, Mortani Barbosa EJ Jr. Racial/Ethnic Disparities in Follow-Up Adherence for Incidental Pulmonary Nodules: An Application of a Cascade-of-Care Framework. J Am Coll Radiol. 2020 Nov;17(11):1410-1419. doi: 10.1016/j.jacr.2020.07.018. Epub 2020 Aug 7. |
| 29937386 | Background | Su CT, Bhargava A, Shah CD, Halmos B, Gucalp RA, Packer SH, Ohri N, Haramati LB, Perez-Soler R, Cheng H. Screening Patterns and Mortality Differences in Patients With Lung Cancer at an Urban Underserved Community. Clin Lung Cancer. 2018 Sep;19(5):e767-e773. doi: 10.1016/j.cllc.2018.05.019. Epub 2018 Jun 5. |
| 30373653 | Background | Dziadzko MA, Novotny PJ, Sloan J, Gajic O, Herasevich V, Mirhaji P, Wu Y, Gong MN. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care. 2018 Oct 30;22(1):286. doi: 10.1186/s13054-018-2194-7. |
| 32642255 | Background | Kammer MN, Massion PP. Noninvasive biomarkers for lung cancer diagnosis, where do we stand? J Thorac Dis. 2020 Jun;12(6):3317-3330. doi: 10.21037/jtd-2019-ndt-10. |
| 26214244 | Result | Gould MK, Tang T, Liu IL, Lee J, Zheng C, Danforth KN, Kosco AE, Di Fiore JL, Suh DE. Recent Trends in the Identification of Incidental Pulmonary Nodules. Am J Respir Crit Care Med. 2015 Nov 15;192(10):1208-14. doi: 10.1164/rccm.201505-0990OC. |
| 28240562 | Result | MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, Mehta AC, Ohno Y, Powell CA, Prokop M, Rubin GD, Schaefer-Prokop CM, Travis WD, Van Schil PE, Bankier AA. Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017. Radiology. 2017 Jul;284(1):228-243. doi: 10.1148/radiol.2017161659. Epub 2017 Feb 23. |
| 31132331 | Result | Kang SK, Garry K, Chung R, Moore WH, Iturrate E, Swartz JL, Kim DC, Horwitz LI, Blecker S. Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports. J Am Coll Radiol. 2019 Nov;16(11):1587-1594. doi: 10.1016/j.jacr.2019.04.026. Epub 2019 May 24. |
| 33434126 | Result | Mobiny A, Yuan P, Cicalese PA, Moulik SK, Garg N, Wu CC, Wong K, Wong ST, He TC, Nguyen HV. Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification. IEEE Trans Med Imaging. 2021 Oct;40(10):2869-2879. doi: 10.1109/TMI.2021.3051089. Epub 2021 Sep 30. |
| 29496499 | Result | Silvestri GA, Tanner NT, Kearney P, Vachani A, Massion PP, Porter A, Springmeyer SC, Fang KC, Midthun D, Mazzone PJ; PANOPTIC Trial Team. Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules: Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial. Chest. 2018 Sep;154(3):491-500. doi: 10.1016/j.chest.2018.02.012. Epub 2018 Mar 1. |