Volumetric analysis of pulmonary nodules: reducing the discrepancy between the diameter-based volume calculation and voxel-counting method.

Background: When assessing the volume of pulmonary nodules on computed tomography (CT) images, there is an inevitable discrepancy between values based on the diameter-based volume calculation and the voxel-counting method, which is derived from the Euclidean distance measurement method on pixel/voxel-based digital image. We aimed to evaluate the ability of a modified diameter measurement method to reduce the discrepancy, and we determined a conversion equation to equate volumes derived from different methods. Methods: Two different anthropomorphic phantoms with subsolid and solid nodules were repeatedly scanned under various settings. Nodules in CT images were detected and segmented using a fully automated algorithm and the volume was calculated using three methods: the voxel-counting method (Vvc ), diameter-based volume calculation (Vd ), and a modified diameter-based volume calculation (Vd+ 1), in which one pixel spacing was added to the diameters in the three axes (x-, y-, and z-axis). For each nodule, Vd and Vd +1 were compared to Vvc by computing the absolute percentage error (APE) as follows: APE =100 × (V - Vvc )/Vvc . Comparisons between APEd and APEd+1 according to CT parameter setting were performed using the Wilcoxon signed-rank test. The Jonckheere-Terpstra test was used to evaluate trends across the four different nodule sizes. Results: The deep learning-based computer-aided diagnosis (DL-CAD) successfully detected and segmented all nodules in a fully automatic manner. The APE was significantly less with Vd+1 than with Vd (Wilcoxon signed-rank test, P<0.05) regardless of CT parameters and nodule size. The APE median increased as the size of the nodule decreased. This trend was statistically significant (Jonckheere-Terpstra test, P<0.001) regardless of volume measurement method (diameter-based and modified diameter-based volume calculations). Conclusions: Our modified diameter-based volume calculation significantly reduces the discrepancy between the diameter-based volume calculation and voxel-counting method.

Incidence Lung Cancer after a Negative CT Screening in the National Lung Screening Trial: Deep Learning-Based Detection of Missed Lung Cancers.

We aimed to analyse the CT examinations of the previous screening round (CTprev) in NLST participants with incidence lung cancer and evaluate the value of DL-CAD in detection of missed lung cancers. Thoracic radiologists reviewed CTprev in participants with incidence lung cancer, and a DL-CAD analysed CTprev according to NLST criteria and the lung CT screening reporting & data system (Lung-RADS) classification. We calculated patient-wise and lesion-wise sensitivities of the DL-CAD in detection of missed lung cancers. As per the NLST criteria, 88% (100/113) of CTprev were positive and 74 of them had missed lung cancers. The DL-CAD reported 98% (98/100) of the positive screens as positive and detected 95% (70/74) of the missed lung cancers. As per the Lung-RADS classification, 82% (93/113) of CTprev were positive and 60 of them had missed lung cancers. The DL-CAD reported 97% (90/93) of the positive screens as positive and detected 98% (59/60) of the missed lung cancers. The DL-CAD made false positive calls in 10.3% (27/263) of controls, with 0.16 false positive nodules per scan (41/263). In conclusion, the majority of CTprev in participants with incidence lung cancers had missed lung cancers, and the DL-CAD detected them with high sensitivity and a limited false positive rate.