Predicting Malignancy Risk of Screen-Detected Lung Nodules-Mean Diameter or Volume.

OBJECTIVE: In lung cancer screening practice low-dose computed tomography, diameter, and volumetric measurement have been used in the management of screen-detected lung nodules. The aim of this study was to compare the performance of nodule malignancy risk prediction tools using diameter or volume and between computer-aided detection (CAD) and radiologist measurements. METHODS: Multivariable logistic regression models were prepared by using data from two multicenter lung cancer screening trials. For model development and validation, baseline low-dose computed tomography scans from the Pan-Canadian Early Detection of Lung Cancer Study and a subset of National Lung Screening Trial (NLST) scans with lung nodules 3 mm or more in mean diameter were analyzed by using the CIRRUS Lung Screening Workstation (Radboud University Medical Center, Nijmegen, the Netherlands). In the NLST sample, nodules with cancer had been matched on the basis of size to nodules without cancer. RESULTS: Both CAD-based mean diameter and volume models showed excellent discrimination and calibration, with similar areas under the receiver operating characteristic curves of 0.947. The two CAD models had predictive performance similar to that of the radiologist-based model. In the NLST validation data, the CAD mean diameter and volume models also demonstrated excellent discrimination: areas under the curve of 0.810 and 0.821, respectively. These performance statistics are similar to those of the Pan-Canadian Early Detection of Lung Cancer Study malignancy probability model with use of these data and radiologist-measured maximum diameter. CONCLUSION: Either CAD-based nodule diameter or volume can be used to assist in predicting a nodule's malignancy risk.

Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans.

OBJECTIVES: To implement a cost-effective low-dose computed tomography (LDCT) lung cancer screening program at the population level, accurate and efficient interpretation of a large volume of LDCT scans is needed. The objective of this study was to evaluate a workflow strategy to identify abnormal LDCT scans in which a technician assisted by computer vision (CV) software acts as a first reader with the aim to improve speed, consistency, and quality of scan interpretation. METHODS: Without knowledge of the diagnosis, a technician reviewed 828 randomly batched scans (136 with lung cancers, 556 with benign nodules, and 136 without nodules) from the baseline Pan-Canadian Early Detection of Lung Cancer Study that had been annotated by the CV software CIRRUS Lung Screening (Diagnostic Image Analysis Group, Nijmegen, The Netherlands). The scans were classified as either normal (no nodules ≥1 mm or benign nodules) or abnormal (nodules or other abnormality). The results were compared with the diagnostic interpretation by Pan-Canadian Early Detection of Lung Cancer Study radiologists. RESULTS: The overall sensitivity and specificity of the technician in identifying an abnormal scan were 97.8% (95% confidence interval: 96.4-98.8) and 98.0% (95% confidence interval: 89.5-99.7), respectively. Of the 112 prevalent nodules that were found to be malignant in follow-up, 92.9% were correctly identified by the technician plus CV compared with 84.8% by the study radiologists. The average time taken by the technician to review a scan after CV processing was 208 ± 120 seconds. CONCLUSIONS: Prescreening CV software and a technician as first reader is a promising strategy for improving the consistency and quality of screening interpretation of LDCT scans.