Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Abstract

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.

Keywords: biomarkers; diagnostic imaging; lung neoplasms; tumor.

Figure 1.

Distribution of risk scores and receiver operating characteristics (ROC) curves for the Mayo model (Mayo) risk scores and the combined biomarker model (CBM) risk scores in the training cohort and validation cohorts. (A) Distribution of risk of malignancy for the Mayo (teal) and CBM (orange) for the training cohort (VUMC n = 170) and three validation cohorts (DECAMP n = 99, UPMC n = 99, and UC Denver n = 88). (B) ROC curves, and (C) precision/recall curves with 95% confidence interval (100 bootstrap samples) for two models. Numbers indicate area under the curve with the range of the 95% confidence interval in parentheses. DECAMP = Detection of Early Lung Cancer among Military Personnel; UC Denver = University of Colorado Denver; UPMC = Univeristy of Pittsburgh Medical Center; VUMC = Vanderbilt University Medical Center.

Figure 2.

Comparison of the Mayo model (Mayo) risk versus the combined biomarker model (CBM) risk in the pooled cohort (N = 456), with emphasis placed on patients ruled in and ruled out. (A) The risk distribution, (B) receiver operating characteristics curves, (C) precision-recall curves, (D) raw reclassification, and (E) reclassification frequency counts for the CBM. (D) Reclassification by the CBM. Left graph shows benign patient score distribution; right graph shows cancer patient score distribution (Mayo risk on the x-axis, CBM risk on the y-axis). Gray lines demarcate British Thoracic Society guideline thresholds of 0.1 and 0.7 risk. Intermediate-risk (int) patients who were correctly ruled in (for cancer) or ruled out (for benign) are in orange, and incorrectly ruled in or out are in teal. (E) Frequency counts show number of patients in each risk group. Note that this visual provides raw results of reclassification, and the bias-corrected clinical reclassification index (cNRI, listed in Table 2) provides an unbiased and more generalizable estimate of the reclassification rate.