Investigating Incidental Pulmonary Nodules in Underserved Communities

NCT05738031 | Withdrawn DMC

• 1 other identifier: 2021-13475
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Lung; Node

Outcome Measures

Primary Outcomes (3)

• 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.

  Through evaluation of assay, up to 1.5 years

Other Outcomes (1)

• Performance of LiquidLung plasma-based gene expression assay on patients with incidental or screen-detected lung nodules

  Through evaluation of assay performance, up to 1.5 years

Study Arms (3)

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• Above the age of 18
• Underwent an imaging study for any purpose other than lung cancer screening at a Montefiore Medical Center facility that identifies a pulmonary nodule
• Imaging results captured into the Epic electronic medical record
• Referred to the lung nodule clinic
• Underwent an in-person or telephone/telehealth encounter at a Montefiore Medical Center facility during the study period
• Lung cancers diagnosed at a Montefiore Medical Center facility between April 1, 2021 and March 31 2022 will also be included for retrospective review and comparison to the cohort of lung cancers diagnosed following the instillation of the IPN program herein described
• Above the age of 18
• Underwent an imaging study for any purpose other than lung cancer screening at a Montefiore Medical Center facility that identifies a pulmonary nodule
• Imaging results captured into the Epic electronic medical record
• Referred to the lung nodule clinic
• Underwent an in-person or telephone/telehealth encounter at a Montefiore Medical Center facility during the study period
• 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:
• Above the age of 18
• Underwent an imaging study, including imaging performed for lung cancer screening, at a Montefiore Medical Center facility that identifies a pulmonary nodule
• Imaging results captured into the Epic electronic medical record

You may not qualify if:

• \<18 years of age
• No evidence of a lung nodule through imaging

Sponsors & Collaborators

• Montefiore Medical Center: lead

Study Sites (1)

Montefiore Medical Center-Albert Einstein College of Medicine

The Bronx, New York, 10461, United States

Location

Related Publications (16)

• 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.

  PMID: 33433946 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.

  PMID: 21714641 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.

  PMID: 33687470 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.

  PMID: 28152136 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.

  PMID: 32546140 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.

  PMID: 24710850 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.

  PMID: 30798793 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.

  PMID: 32771492 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.

  PMID: 29937386 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.

  PMID: 30373653 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.

  PMID: 32642255 BACKGROUND

• 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.

  PMID: 26214244 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.

  PMID: 28240562 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.

  PMID: 31132331 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.

  PMID: 33434126 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.

  PMID: 29496499 RESULT

Study Officials

• Neel Chudgar, MD

  Montefiore Medical Center

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 3, 2023
• First Posted: February 21, 2023
• Study Start: October 1, 2023
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: November 18, 2023

Record last verified: 2023-11

Locations

US (1)