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. 2023 Sep;166(3):669-678.e4.
doi: 10.1016/j.jtcvs.2022.12.014. Epub 2022 Dec 23.

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers

Hannah N Marmor  1 , Michael N Kammer  2 , Stephen A Deppen  3 , Maren Shipe  1 , Valerie F Welty  4 , Khushbu Patel  2 , Caroline Godfrey  1 , Ehab Billatos  5 , James G Herman  6 , David O Wilson  7 , Amanda K Kussrow  8 , Darryl J Bornhop  8 , Fabien Maldonado  2 , Heidi Chen  4 , Eric L Grogan  9
Affiliations

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers

Hannah N Marmor et al. J Thorac Cardiovasc Surg. 2023 Sep.

Abstract

Objective: Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer.

Methods: A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach.

Results: A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16).

Conclusions: The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.

Keywords: biomarker; cancer; nodule; pulmonary.

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Figures

Figure 1.
Figure 1.. Receiver Operating Characteristics Curve Comparing Lung Cancer Prediction Models Among Combined (1a), Histoplasmosis-Endemic (1b), and Non-Endemic Cohorts (1c).
Figure 1a displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the combined cohort (n=281). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1b displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the Histoplasmosis-endemic cohort (VUMC, n=111). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1c displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the non-endemic cohort (UPMC and DECAMP, n=170). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1a. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the combined dataset (VUMC/UPMC/DECAMP, n=281) Figure 1b. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the endemic cohort (VUMC, n=111) Figure 1c. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the non-endemic cohorts (UPMC/DECAMP, n=170)
Figure 1.
Figure 1.. Receiver Operating Characteristics Curve Comparing Lung Cancer Prediction Models Among Combined (1a), Histoplasmosis-Endemic (1b), and Non-Endemic Cohorts (1c).
Figure 1a displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the combined cohort (n=281). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1b displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the Histoplasmosis-endemic cohort (VUMC, n=111). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1c displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the non-endemic cohort (UPMC and DECAMP, n=170). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1a. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the combined dataset (VUMC/UPMC/DECAMP, n=281) Figure 1b. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the endemic cohort (VUMC, n=111) Figure 1c. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the non-endemic cohorts (UPMC/DECAMP, n=170)
Figure 1.
Figure 1.. Receiver Operating Characteristics Curve Comparing Lung Cancer Prediction Models Among Combined (1a), Histoplasmosis-Endemic (1b), and Non-Endemic Cohorts (1c).
Figure 1a displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the combined cohort (n=281). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1b displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the Histoplasmosis-endemic cohort (VUMC, n=111). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1c displays AUCs and optimism-corrected AUCs (opt) for lung cancer prediction models among the non-endemic cohort (UPMC and DECAMP, n=170). The combination biomarker model including Mayo, Histoplasmosis EIA, radiomics, and hsCYFRA 21–1 exhibits the greatest AUC. Figure 1a. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the combined dataset (VUMC/UPMC/DECAMP, n=281) Figure 1b. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the endemic cohort (VUMC, n=111) Figure 1c. Area under the receiver operating characteristics curves (AUCs) and optimism-corrected AUCs (opt) for lung cancer prediction models in the non-endemic cohorts (UPMC/DECAMP, n=170)
Figure 2.
Figure 2.. Risk Reclassification for Malignant and Benign Nodules Using the Combination Biomarker Model (CBM).
Reclassification of malignant and benign IPNs by the CBM (x-axis: Mayo score, y-axis: CBM score). Vertical and horizontal lines represent 10% and 70% risk thresholds (<10% low, >70% high). Green boxes represent IPNs correctly reclassified as high risk for cancer (true positives) or low risk for benign (true negatives) using the CBM. Red boxes represent IPNs incorrectly reclassified as low risk for cancer (false negatives) or high risk for benign (false positives) using the CBM. CBM, combination biomarker model (Mayo, Histoplasmosis IgM and IgG, radiomics, hsCYFRA 21–1)
Figure 3.
Figure 3.. Graphical abstract demonstrating improved diagnostic accuracy of IPNs with a combination clinical risk factor plus imaging, cancer, and fungal biomarker approach
See this image and copyright information in PMC

References

    1. Gould MK, Tang T, Liu ILA, et al. Recent Trends in the Identification of Incidental Pulmonary Nodules. Am J Respir Crit Care Med. 2015;192(10):1208–1214. doi: 10.1164/rccm.201505-0990OC - DOI - PubMed
    1. Verdial FC, Madtes DK, Cheng GS, et al. Multidisciplinary Team-Based Management of Incidentally Detected Lung Nodules. Chest. 2020;157(4):985–993. doi: 10.1016/j.chest.2019.11.032 - DOI - PMC - PubMed
    1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–249. doi: 10.3322/caac.21660 - DOI - PubMed
    1. Choi HK, Ghobrial M, Mazzone PJ. Models to Estimate the Probability of Malignancy in Patients with Pulmonary Nodules. Ann Am Thorac Soc. 2018;15(10):1117–1126. doi: 10.1513/AnnalsATS.201803-173CME - DOI - PubMed
    1. Tanner NT, Porter A, Gould MK, Li XJ, Vachani A, Silvestri GA. Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation. Chest. 2017;152(2):263–270. doi: 10.1016/j.chest.2017.01.018 - DOI - PMC - PubMed

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