Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Compensated Interferometry Measures of CYFRA 21-1 Improve Diagnosis of Lung Cancer.

Diagnosis of lung cancer patients with indeterminate pulmonary nodules (IPNs) presents a significant clinical challenge, with morbidity and management costs of $28 billion/year. We show that a quantitative free-solution assay (FSA), coupled with a compensated interferometric reader (CIR), improves the diagnostic performance of CYFRA 21-1 as a lung cancer biomarker. FSA-CIR is a rapid, mix-and-read, isothermal, label- and enzyme-free, matrix-insensitive, and target and probe-agnostic assay. Operating FSA-CIR at ∼40, 0.75 µL samples/day delivered a serum CYFRA 21-1 limit of quantification (LOQ) of 81 pg/mL with intra-assay and interassay CVs of 4.9% and 9.6% for four-day replicate determinations. Blinded analysis of a 225 patient cohort, consisting of 75 nonmalignant nodules, 45 adenocarcinomas, 44 squamous cell carcinomas, and 61 small cell lung cancers, gave a clear separation of cases and controls, not observed in the Cobas ECL analysis. The area under the curve (AUC) for the Mayo model increased from 0.595 to 0.923 when combined with the FSA-CIR CYFRA 21-1 measurements. In a population with nodules between 6 and 30 mm, the AUC increased from 0.567 to 0.943. In this subgroup, the positive predictive value (PPV) for all tumors by the CYFRA 21-1 assay was 98.7%. Our results demonstrate increased performance of the CYFRA 21-1 assay using FSA-CIR and represents a proof of concept for redefining the performance characteristics of this important candidate biomarker.