Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

Figure 1 Graphical representation of raters’ receiver operating characteristic curves before (A) and after (B) computer-aided diagnosis results disclosure. The Bayesian Inference Malignancy Calculator model receiver operating characteristic curve is recognizable as a staircase shaped dotted-line. A net shift towards the upper-left corner is noted from A to B.

Figure 2 Graphical representation of overall receiver operating characteristic curve comparison. Post-computer-aided diagnosis receiver operating characteristic curve (solid line) is constantly superior to the pre-computer-aided diagnosis curve (dotted line). CAD: Computer-aided diagnosis.

Figure 3 Graphical representation of benign solitary pulmonary nodule classification before (pre-computer-aided diagnosis) and after (post-computer-aided diagnosis) computer-aided diagnosis result disclosure. CAD: Computer-aided diagnosis; SPNs: Solitary pulmonary nodules.

Figure 4 Graphical representation of malignant solitary pulmonary nodule classification before (pre-computer-aided diagnosis) and after (post-computer-aided diagnosis) computer-aided diagnosis result disclosure. CAD: Computer-aided diagnosis; SPNs: Solitary pulmonary nodules.

Figure 5 Typical appearance of an indeterminate solid pulmonary nodule at high-resolution computed tomography (Philips icomputed tomography, slice thickness 1. 25 mm) in a 59-year-old female patient. The lesion was located in the left lower lobe, had a maximum diameter of 10 mm and proved to be a non-tubercular granuloma at surgery.

Figure 6 Typical appearance of a malignant solid pulmonary nodule at high-resolution computed tomography (Philips icomputed tomography, slice thickness 1. 25 mm) in a 59-year-old male patient. The lesion was located in the right upper lobe, had a maximum diameter of 24 mm and proved to be an adenocarcinoma at surgery.