Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection

CT characteristics of resolving ground-glass opacities in a lung cancer screening programme

PURPOSE

This study aimed at evaluating the computed tomography (CT) characteristics of resolving localized ground-glass opacities (GGOs) in a screening programme for lung cancer.

MATERIAL AND METHODS

280 patients at high-risk for lung cancer (221 men, 59 women; mean age, 58.6 years), divided into four groups (lung cancer history (n = 83), head and neck cancer history (n = 63), symptomatic (n = 88) and asymptomatic (n = 46) cigarette smokers), were included in a prospective trial with annual low-dose CT for lung cancer screening. We retrospectively reviewed all localized GGOs, analyzed the CT characteristics on initial CT scans and changes during follow-up (median 29.1 months). Variables associated with resolution of GGOs were tested using chi-square or Mann-Whitney tests.

RESULTS

A total of 75 GGOs were detected in 37 patients; 54.7% were present at baseline and 45.3% appeared on annual CT. During follow-up, 56.2% persisted and 43.8% disappeared. The resolving localized GGOs were significantly more often lobular GGOs (p = 0.006), polygonal in shape (p = 0.02), mixed (p = 0.003) and larger (p < 0.0001) than non-resolving localized GGOs.

CONCLUSION

Localized GGOs are frequent and many disappeared on follow-up. CT characteristics of resolving GGOs show significant differences compared to persistent ones. This study emphasizes the importance of short-term CT follow-up in subjects with localized GGOs.

Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance

OBJECTIVE

The diagnostic performance of radiologists using incremental CAD assistance for lung nodule detection on CT and their temporal variation in performance during CAD evaluation was assessed.

METHODS

CAD was applied to 20 chest multidetector-row computed tomography (MDCT) scans containing 190 non-calcified > or =3-mm nodules. After free search, three radiologists independently evaluated a maximum of up to 50 CAD detections/patient. Multiple free-response ROC curves were generated for free search and successive CAD evaluation, by incrementally adding CAD detections one at a time to the radiologists' performance.

RESULTS

The sensitivity for free search was 53% (range, 44%-59%) at 1.15 false positives (FP)/patient and increased with CAD to 69% (range, 59-82%) at 1.45 FP/patient. CAD evaluation initially resulted in a sharp rise in sensitivity of 14% with a minimal increase in FP over a time period of 100 s, followed by flattening of the sensitivity increase to only 2%. This transition resulted from a greater prevalence of true positive (TP) versus FP detections at early CAD evaluation and not by a temporal change in readers' performance. The time spent for TP (9.5 s +/- 4.5 s) and false negative (FN) (8.4 s +/- 6.7 s) detections was similar; FP decisions took two- to three-times longer (14.4 s +/- 8.7 s) than true negative (TN) decisions (4.7 s +/- 1.3 s).

CONCLUSIONS

When CAD output is ordered by CAD score, an initial period of rapid performance improvement slows significantly over time because of non-uniformity in the distribution of TP CAD output and not to a changing reader performance over time.

Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases

OBJECTIVES

To evaluate the efficacy of the computer-aided detection (CAD) system and thin-slab maximum intensity projection (MIP) technique in the detection of pulmonary nodules at multidetector computed tomography (CT) in patients who underwent metastatectomy.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board and patients' informed consent was waived. Forty-nine consecutive patients who underwent pulmonary metastatectomy were enrolled. Four chest radiologists analyzed preoperative 1-mm section CT images and recorded the locus of each nodule candidate. Afterward, they reevaluated the images once using CAD software and once with thin-slab MIP given the results of 1-mm section CT alone. The reference standard for nodule presence was established by a consensus panel and pathologic records for malignant nodules.

RESULTS

A total of 514 nodules were identified by a consensus panel. Of 212 nodules surgically removed, 121 nodules were malignant. The sensitivity of each observer in detecting malignant nodules with thin-section CT scans alone was 91%, 88%, 87%, and 86% for observers A- to D, respectively. With CAD, sensitivity increased significantly to 95%, 95%, 94%, and 95% (P< 0.05 for observer B-D), and using MIP increased to 94%, 96%, 91%, and 92% (P < 0.05 for observer B-D), respectively. There were no significant differences in sensitivity between CAD and MIP for the detection of malignant nodules. The average number of false-positive findings per patient was 0.8 with thin-section CT alone, 1.1 with CAD, and 1.4 with MIP.

CONCLUSIONS

In candidates for metastatectomy, reading with the aid of either CAD or MIP significantly improved the detection of malignant nodules compared with using thin-section CT alone.

Effect of slab thickness on the CT detection of pulmonary nodules: use of sliding thin-slab maximum intensity projection and volume rendering

OBJECTIVE

The objective of this study was to evaluate the detection rates of pulmonary nodules on CT as a function of slab thickness using sliding thin-slab maximum intensity projection (MIP) and volume rendering (VR).

SUBJECTS AND METHODS

Eighty-eight oncology patients (33 women, 55 men; mean age, 59 years; age range, 18-81 years) who routinely underwent chest CT examinations were prospectively included. Two radiologists independently evaluated each CT examination for the presence of pulmonary nodules using MIP and VR, with each image reconstructed using three different slab thicknesses (5, 8, 11 mm). The standard of reference was the maximum number of detected nodules, which were classified by localization and size, judged to be true-positives by a consensus panel. Interreader agreement was assessed by kappa value on a nodule-by-nodule basis. Sensitivities for both reconstruction techniques and for the three slab thicknesses were calculated using the proportion procedure for survey data with the patient as the primary sample unit and were compared using the Wilcoxon's signed rank test with Bonferroni correction for both readers separately.

RESULTS

One thousand fifty-eight true-positive nodules were detected. Interreader agreement was fair to moderate. Sensitivity for pulmonary nodules was superior for 8-mm MIP (reader 1, 84%; reader 2, 81%) and was significantly better than the sensitivities of all other tested techniques for both readers (p < 0.001 each) independent of nodule localization and size (except for one reader's analysis of 8-mm MIP versus 11-mm MIP for nodules > 8 mm). A higher sensitivity was achieved using MIP than VR.

CONCLUSION

MIP with a slab thickness of 8 mm is superior in the detection of pulmonary nodules to all other tested techniques.

Multi slice computed tomography in the study of pulmonary metastases

PURPOSE

This study was undertaken to assess the performance of 16-slice computed tomography (MSCT) using Multi-Planar Reformatting (MPR), Maximum Intensity Projection (MIP) and Volume Rendering (VR) reconstructions to study pulmonary metastases.

MATERIALS AND METHODS

CT studies of 32 patients with pulmonary metastases were retrospectively reviewed. Images were assessed for the following parameters: number, size, location, distribution of the nodules and the presence of the "mass-vessel sign". These parameters were evaluated by two observers on axial-source images and on MPR, MIP and VR reconstructions. Sensitivity of each reconstruction and interobserver agreement were calculated.

RESULTS

Two-dimensional (2D) axial images and MIP and VR reconstructions exhibited 100% sensitivity for lesions >10 mm. For nodules 6-10 mm, sensitivity was 49%-55% for the 2D images, 90% for MIP and 80%-85% for VR reconstructions. For metastasis <or= 5 mm, sensitivity was 22% for 2D images, 87%-89% for MIP and 55%-58% for VR reconstructions. Coronal and sagittal MPR, MIP and VR did not improve the detection rate compared with the corresponding axial images. MIP and VR provided overlapping results in detecting the "mass-vessel sign".

CONCLUSIONS

MIP are the most sensitive reconstructions for detecting small pulmonary nodules.