Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography

Abstract

Aims and Objectives:To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location.Materials and Methods:A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated.Observations and Results:Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4–10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance.Conclusion:CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.

Lung Cancer in India–Part I

“…Rare manifestations are also described. 216 In the country, now various diagnostic modalities are widely available that includes chest radiograph, CT scan 217,218 , PET scan 219 , and various other invasive and semi-invasive methods. Molecular methods are now widely available in the country [251][252][253] and are discussed in greater detail in the 2nd part of the review.…”

Comparison of Maximum Intensity Projection and Volume Rendering in Detecting Pulmonary Nodules on Multidetector Computed Tomography

“…Post-processing software provides various reading techniques such as average, MIP, MinIP, and VR. Recent technical advancements based on artificial intelligence, i.e., computer-aided detection have also shown promising results [ 11 ]. However, there is an increased ratio of false-positive results [ 11 , 12 ], recorded to be 33% in one study [ 11 ].…”

Unsupervised contrastive learning based transformer for lung nodule detection

“…Therefore, major efforts have been made on early and accurate detection of lung nodules in different aspects, such as imaging technologies (NLST 2017, Niu et al 2022), diagnosis workflows (MacMahon et al 2005), and computer aided detection and computer aided diagnostic systems (Messay et al 2010). Particularly, recent results indicate that computer aided detection/diagnosis (CAD) systems empowered by artificial intelligence (AI) algorithms as the second or concurrent reader can improve the performance of lung nodule detection on chest radiographs (Yoo et al 2021) and computed tomography (CT) images (Roos et al 2010, Prakashini et al 2016.…”