Mobile cone-beam computed tomography complementing shape-sensing robotic-assisted bronchoscopy in the small pulmonary nodule sampling: A multicentre experience.

BACKGROUND AND OBJECTIVE: Shape-sensing robotic-assisted bronchoscopy (ssRAB) has expanded as an important diagnostic tool for peripheral pulmonary nodules (PPNs), with diagnostic yields ranging from 60% to 88%. However, sampling and diagnosing PPN less than 2 cm in size has historically been challenging. Mobile cone-beam computed tomography (mCBCT) has been recently integrated into ssRAB to improve diagnostic accuracy, but its added value remains uncertain. We aim to describe the role of mCBCT and determine if it provides any diagnostic advantage. METHODS: A multicentre, retrospective study on the use of ssRAB and mCBCT in two tertiary care institutions: Mayo Clinic Florida and Massachusetts General Hospital. The primary outcome was diagnostic yield and sensitivity for malignancy of ssRAB complemented with mCBCT, compared to ssRAB with the standard 2D fluoroscopy. RESULTS: A total of 192 nodules were biopsied from 173 patients. mCBCT was used in 117 (60.9%) nodules. The overall diagnostic yield was 85.4%. Diagnostic yield between subgroups with and without mCBCT was 83.8% and 88% (p = 0.417), respectively. The mCBCT group had fewer solid nodules (65.8% vs. 81.3%, p = 0.020) and a higher number of ground-glass nodules (10.3% vs. 1.3%, p = 0.016). CONCLUSION: Overall, diagnostic yield between subgroups with and without mCBCT was similar. The complementary use of mCBCT to ssRAB allows proceduralists to target more complex and subsolid PPNs with a diagnostic yield comparable to simple solid PPNs while maintaining an excellent safety profile.

Impact of an integrated classifier using biomarkers, clinical and imaging factors on clinical decisions making for lung nodules.

Background: An integrated classifier that utilizes plasma proteomic biomarker along with five clinical and imaging factors was previously shown to be potentially useful in lung nodule evaluation. This study evaluated the impact of the integrated proteomic classifier on management decisions in patients with a pretest probability of cancer (pCA) ≤50% in "real-world" clinical setting. Methods: Retrospective study examining patients with lung nodules who were evaluated using the integrated classifier as compared to standard clinical care during the same period, with at least 1-year follow-up. Results: A total of 995 patients were evaluated for lung nodules over 1 year following the implementation of the integrated classifier with 17.3% prevalence of lung cancer. 231 patients met the study eligibility criteria; 102 (44.2%) were tested with the integrated classifier, while 129 (55.8%) did not. The median number of chest imaging studies was 2 [interquartile range (IQR), 1-2] in the integrated classifier arm and 2 [IQR, 1-3] in the non-integrated classifier arm (P=0.09). The median outpatient clinic visit was 2.00 (IQR, 1.00-3.00) in the integrated classifier arm and 2.00 (IQR, 2.00-3.00) in the non-integrated classifier (P=0.004). Fewer invasive procedures were pursued in the integrated classifier arm as compared to non-integrated classifier respectively (26.5% vs. 79.1%, P<0.001). All patients in the integrated classifier arm with post-pCA (likely benign n=39) had designated benign diagnosis at 1-year follow-up. Conclusions: In patients with lung nodules with a pCA ≤50%, use of the integrated classifier was associated with fewer invasive procedures and clinic visits without misclassifying patients with likely benign lung nodules results at 1-year follow-up.