Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer

Abstract

Introduction: The incidence of pulmonary nodules is increasing with the movement toward screening for lung cancer by low-dose computed tomography. Given the large number of benign nodules detected by computed tomography, an adjunctive test capable of distinguishing malignant from benign nodules would benefit practitioners. The ability of the EarlyCDT-Lung blood test (Oncimmune Ltd., Nottingham, United Kingdom) to make this distinction by measuring autoantibodies to seven tumor-associated antigens was evaluated in a prospective registry.

Methods: Of the members of a cohort of 1987 individuals with Health Insurance Portability and Accountability Act authorization, those with pulmonary nodules detected, imaging, and pathology reports were reviewed. All patients for whom a nodule was identified within 6 months of testing by EarlyCDT-Lung were included. The additivity of the test to nodule size and nodule-based risk models was explored.

Results: A total of 451 patients (32%) had at least one nodule, leading to 296 eligible patients after exclusions, with a lung cancer prevalence of 25%. In 4- to 20-mm nodules, a positive test result represented a greater than twofold increased relative risk for development of lung cancer as compared with a negative test result. Also, when the "both-positive rule" for combining binary tests was used, adding EarlyCDT-Lung to risk models improved diagnostic performance with high specificity (>92%) and positive predictive value (>70%).

Conclusions: A positive autoantibody test result reflects a significant increased risk for malignancy in lung nodules 4 to 20 mm in largest diameter. These data confirm that EarlyCDT-Lung may add value to the armamentarium of the practitioner in assessing the risk for malignancy in indeterminate pulmonary nodules.

Keywords: Autoantibodies; CT scanning; Lung cancer; Pulmonary nodules; Risk models.

Figure 1

Patient cohorts in the clinical registry of nodule status. Of 1987 patients enrolled, 585 (29%) were excluded either on the basis of being lost to follow-up (LTFU) for all reasons, including practices closed, patients changing practices, and physicians moving practices (n = 493), or on the basis of having received a confirmed cancer diagnosis (Dx) other than lung cancer, a history (Hx) of cancer (n = 78), or an invalid autoantibody test result (n = 14). Of the remaining 1402 patients (71% of the 1987), 451 (339 without cancer and 112 with cancer) had a pulmonary nodule(s) reported, whereas the remaining 951 either did not have a nodule detected by computed tomography (CT), did not have an available CT, were reported as unknown by the physician, or had a CT but with unknown results. In the 451 patients with a nodule, the nodule was calcified in 25 individuals, 75 nodules had no clear size information, and a further 55 were outside the 6-month time window, leaving 296 patients (221 without cancer and 75 with cancer) for the inclusive cohort and 269 (217 without cancer and 52 with cancer) for the exclusive cohort. The EarlyCDT-Lung positivity rates for the excluded, no-nodule, and with-nodule groups were 8%, 11%, and 25%, respectively, with the comparison of the excluded and no-nodule groups being of borderline significance (p = 0.05) and thus giving no clear evidence of bias.

Figure 2

Partial receiver operating characteristic curves for the MAYO model and with EarlyCDT-Lung added using the both-positive rule. Curves are shown for the MAYO model only (black line); the MAYO model plus EarlyCDT-Lung (dashed line); the proportional line, which is the theoretical line if EarlyCDT-Lung was added to the MAYO model in a strictly proportional (independent) manner (dotted line); and the random line of no diagnostic discrimination (thin black line). Below approximately 8% on the x axis (92% specificity) the combined model and autoantibody test show improved sensitivity at the same specificity. The proportional line follows the observed line quite closely (inclusive cohort, 4 mm–20 mm [n = 208]).

Figure 3

Plot of positive predictive value (PPV) versus risk threshold (R) for the MAYO model. Curves are shown for the MAYO model only (black line with dots) and the MAYO model plus EarlyCDT-Lung (gray dashed line with triangles). Also shown is the plot of the percentage of subjects with an individual risk below R on the x axis (black dashed line). Adding EarlyCDT-Lung improves the PPV over the whole range. For a chosen risk threshold, a patient is risk model–positive if his or her calculated individual risk is greater than the threshold. The procedure could be as follows: Choose R (30%, for example) and then read off the model-only PPV for patients with a risk higher than R (48% in this example) and then with an EarlyCDT-Lung–positive result added (91%). Finally, read off the percentage of patients in the population with a risk higher than R (23%). The number of false-positive results is reduced at the expense of fewer cancers detected. So choose the value of R giving the most useful performance (inclusive cohort, 4 mm–20 mm [n = 208]).