Thoracic: Lung Cancer
Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers

https://doi.org/10.1016/j.jtcvs.2022.12.014Get rights and content

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.

Section snippets

Methods

Serum samples and clinical information were obtained from patients with newly detected 6- to 30-mm IPNs for this prospective specimen collection, retrospective blinded evaluation study.19 Incidentally discovered and lung cancer screening-detected IPNs from 3 cohorts (Figure E1) were used. The first cohort included patients from Vanderbilt University Medical Center and the Tennessee Valley VA Healthcare System in Nashville, Tennessee (VUMC; n = 111) who consented for research between 2003 and

Study Population

Our study population included 111 patients from a histoplasmosis-endemic region (VUMC), and 170 patients from nonendemic regions (UPMC and DECAMP; Table 1). Historical histoplasmosis prevalence was >90% in the VUMC cohort and <10% in the UPMC and DECAMP cohorts.28 Cancer prevalence was higher in the histoplasmosis-endemic cohort at 75% (83/111) compared with 44% (75/170) in the nonendemic cohort. Site level characteristics and predictions are shown in Table E1 through E6.

Prediction Model Performance in the Combined Study Population

The following results

Discussion

Noninvasive biomarkers are needed to improve the evaluation, risk stratification, and management of IPNs. We have shown an improvement in the diagnostic accuracy of IPNs with a combination biomarker approach including clinical factors (Mayo Clinic model), fungal biomarkers (histoplasmosis IgG and IgM), an imaging biomarker (radiomics), and a cancer biomarker (hsCYFRA 21-1) compared with the current standard of risk estimation (Mayo Clinic model).

Furthermore, our study has highlighted the added

Conclusions

The use of a combination biomarker model including clinical factors (Mayo Clinic model), fungal biomarkers (histoplasmosis IgG and IgM), an imaging biomarker (radiomics), and a cancer biomarker (hsCYFRA 21-1) might improve the evaluation, risk stratification, and subsequent management of IPNs (Figure 3). Although further work and validation is required, integrating a combination biomarker approach into the current diagnostic algorithm of IPNs could lead to a decrease in the number of invasive

References (30)

  • P.P. Massion et al.

    Indeterminate pulmonary nodules: risk for having or for developing lung cancer?

    Cancer Prev Res (Phila)

    (2014)
  • N. Kanaji et al.

    Serum CYFRA 21-1 but not Vimentin is associated with poor prognosis in advanced lung cancer patients

    Open Respir Med J

    (2019)
  • M. Szturmowicz et al.

    Prognostic value of serum C-reactive protein (CRP) and cytokeratin 19 fragments (Cyfra 21-1) but not carcinoembryonic antigen (CEA) in surgically treated patients with non-small cell lung cancer

    Pneumonol Alergol Pol

    (2014)
  • H. Shirasu et al.

    CYFRA 21-1 predicts the efficacy of nivolumab in patients with advanced lung adenocarcinoma

    Tumour Biol

    (2018)
  • R. Paez et al.

    Risk stratification of indeterminate pulmonary nodules

    Curr Opin Pulm Med

    (2021)
  • Cited by (0)

    This work is supported by U01CA152662 (to Drs Grogan and Deppen) and T32CA106183-18 (to Dr Marmor).

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