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Combining Commercial Cancer Protein Biomarkers and Benign Fungal Antibodies Improves Diagnostic Accuracy in Pulmonary
Hudson M Holmes1,2, Kevin C McGann1, Sheau-Chiann Chen2
1Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN.
Objective:
To evaluate whether adding cancer and histoplasma biomarkers to existing clinical prediction models improves diagnostic accuracy for malignancy in indeterminate pulmonary nodules (IPNs).
Summary Background Data:
IPNs remain a diagnostic challenge, particularly in regions where granulomatous disease exists. Clinical prediction models such as the Mayo model perform moderately well but only use clinical and radiographic information. Blood-based biomarkers for cancer proteins and fungal exposure may enhance diagnostic discrimination.
Methods:
Two geographically distinct cohorts with 6-30 mm IPNs were studied from the Ohio River Valley (ORV) and the Mountain West (MW). Mayo scores, serum levels of 4 cancer proteins (CYFRA 21‑1, CEA, CA‑125, and HE‑4), and histoplasma IgG and IgM antibodies were measured. Area under the receiver operating curve (AUC) and 95% confidence intervals were calculated for individual components and combinations. Multivariable logistic regression models were built for pairwise combinations and for all variables together (full model). Risk reclassification was assessed in each cohort.
Results:
We studied 366 patients: 282 from the ORV cohort and 84 from the MW cohort. Mayo model AUCs were 0.74 (0.67-0.80) and 0.68 (0.59-0.78), respectively. Combining cancer biomarkers with fungal antibodies yielded AUCs of 0.74 (0.68-0.80) and 0.76 (0.64-0.86). The full model achieved AUCs of 0.80 (0.75-0.87) and 0.79 (0.70-0.88). In intermediate-risk nodules, the full model correctly upgraded malignancy risk in 46.3% and 42.9% of patients in each cohort.
Conclusions:
Integrating cancer biomarkers, fungal antibodies, and clinical prediction tools improves diagnostic performance for IPNs across distinct geographic regions. A combined biomarker-clinical model offers a feasible strategy to better differentiate benign from malignant nodules.
Insights
Adding cancer biomarkers and fungal antibodies to clinical models significantly improves diagnostic accuracy for indeterminate pulmonary nodules (IPNs). This combined approach better distinguishes malignant from benign nodules.
Area of Science:
- Pulmonology
- Oncology
- Infectious Disease
Background:
- Indeterminate pulmonary nodules (IPNs) present a diagnostic challenge, especially in areas with granulomatous diseases.
- Current clinical prediction models, like the Mayo model, offer moderate accuracy using only clinical and radiological data.
- Blood-based biomarkers for cancer and fungal exposure may improve diagnostic discrimination.
Purpose of the Study:
- To assess if incorporating cancer and histoplasma biomarkers enhances diagnostic accuracy for malignancy in IPNs.
- To evaluate the performance of combined biomarker and clinical prediction models.
Main Methods:
- Two distinct cohorts (Ohio River Valley and Mountain West) with IPNs (6-30 mm) were analyzed.
- Serum levels of cancer biomarkers (CYFRA 21‑1, CEA, CA‑125, HE‑4) and histoplasma antibodies (IgG, IgM) were measured alongside Mayo scores.
- Multivariable logistic regression and receiver operating curve analysis (AUC) were used to evaluate diagnostic performance.
Main Results:
- The full model, integrating all variables, achieved AUCs of 0.80 and 0.79 in the two cohorts.
- Combining cancer biomarkers with fungal antibodies improved AUCs to 0.74 and 0.76.
- The full model correctly reclassified malignancy risk in 46.3% and 42.9% of intermediate-risk patients.
Conclusions:
- Integrating cancer biomarkers, fungal antibodies, and clinical prediction tools significantly improves diagnostic performance for IPNs.
- A combined biomarker-clinical model provides a practical method for differentiating benign from malignant pulmonary nodules.
