Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.
Abstact
<b>Rationale:</b> Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. <b>Objectives:</b> To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. <b>Methods:</b> 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 (<i>n</i> = 170) and validated in cohorts 2-4 (total <i>n</i> = 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. <b>Measurements and Main Results:</b> 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; <i>P</i> < 2 × 10<sup>-16</sup>). 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. <b>Conclusions:</b> 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.
Authors
- Antic SL
- Atwater T
- Balagurunathan Y
- Balar AB
- Barad U
- Barón AE
- Bauza J
- Billatos E
- Bornhop DJ
- Chen H
- Chen SC
- Deppen SA
- Diergaarde B
- Feser WJ
- Gillies RJ
- Grogan EL
- Helmey S
- Hirsch E
- Kaizer A
- Kammer MN
- Kussrow AK
- Lakhani DA
- Landman B
- Mahapatra S
- Maldonado F
- Massion PP
- Miller YE
- New M
- Qian J
- Rioth M
- Rowe DJ
- Sandler K
- Schabath MB
- Shah C
- Spira A
- Strong J
- Walker RC
- Webster RL
- Wilson DO