Sensitivity For Detection Of Pulmonary Nodules Research Articles

Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings.

To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. We used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4mm in diameter. After an interval of at least 15days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time. The average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11). Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.

The utilisation of convolutional neural networks in detecting pulmonary nodules: a review.

Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, there has been a call for lung cancer screening amongst at-risk populations. The early detection of malignant pulmonary nodules in CT is one of the suggested methods proposed to diagnose early-stage lung cancer; however, the reported sensitivity of radiologists' ability to accurately detect pulmonary nodules ranges widely from 30 to 97%. 2012 saw Alex Krizhevsky present a paper titled "ImageNet Classification with Deep Convolutional Networks" in which a multilayered convolutional computational model known as a convolutional neural network (CNN) was confirmed competent in identifying and classifying 1.2 million images to a previously unseen level of accuracy. Since then, CNNs have gained attention as a potential tool in aiding radiologists' detection of pulmonary nodules in CT imaging. This review found the use of CNN is a viable strategy to increase the overall sensitivity of pulmonary nodule detection. Small, non-validated data sets, computational constraints, and incomparable studies are currently limited factors of the existing research.

Dose-Optimized Computed Tomography for Screening and Follow-Up of Solid Pulmonary Nodules in Obesity: A Phantom Study

To determine the lowest CT radiation dose-level at maintained image-quality and high sensitivity for detection of pulmonary-nodules in obesity. Single-energy CT with tin-filtration was performed in a chest-phantom with solid pulmonary-nodules simulating over-weight and obesity. CTDIvol of the standard-protocol was 0.41 mGy, subsequent scans were obtained at 1/2 and 1/4 dose-levels. Images were reconstructed using FBP and advanced-modeled iterative reconstruction (ADMIRE). Noise, image-quality, and sensitivity for nodule-detection were assessed. Noise was significantly reduced with ADMIRE (standard-dose: 73%; 1/2 dose: 73%; 1/4 dose: 71.2%; p < 0.001) compared to FBP. Image-quality was diagnostic for all images reconstructed with ADMIRE5. Sensitivity for nodule-detection was 100% at 1/2 and 1/4 dose-level for the phantom simulating over-weight and 97.37% (1/2 dose-level) and 81.58% (1/4 dose-level) for the phantom simulating obesity using ADMIRE5. In conclusion, single-energy CT with tin-filtration and ADMIRE shows potential for dose reduction in a phantom experiment down to 0.1 mGy in over-weight and 0.21 mGy obese subjects, while image quality and sensitivity for detection of solid pulmonary nodules remains high.

Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume

ObjectiveTo evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule volume.MethodsA total of 400 low-dose chest CT examinations were randomly selected from the NELSON lung cancer screening trial. CTs were evaluated by two independent readers and processed by CAD. A total of 1,667 findings marked by readers and/or CAD were evaluated by a consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume.ResultsAccording to the screening protocol, 90.9 % of the findings could be excluded from further evaluation, 49.2 % being small nodules (less than 50 mm3). Excluding small nodules reduced false-positive detections by CAD from 3.7 to 1.9 per examination. Of 151 findings that needed further evaluation, 33 (21.9 %) were detected by CAD only, one of them being diagnosed as lung cancer the following year. The sensitivity of nodule detection was 78.1 % for double reading and 96.7 % for CAD. A total of 69.7 % of nodules undetected by readers were attached nodules of which 78.3 % were vessel-attached.ConclusionsCAD is valuable in lung cancer screening to improve sensitivity of pulmonary nodule detection beyond double reading, at a low false-positive rate when excluding small nodules.Key Points • Computer-aided detection (CAD) has known advantages for computed tomography (CT). • Combined CAD/nodule size cut-off parameters assist CT lung cancer screening. • This combination improves the sensitivity of pulmonary nodule detection by CT. • It increases the positive predictive value for cancer detection.