Ultra-low-dose MDCT of the chest: influence on automated lung nodule detection

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.

Respiratory surveillance for coal mine dust and artificial stone exposed workers in Australia and New Zealand: A position statement from the Thoracic Society of Australia and New Zealand

Coal mine lung dust disease (CMDLD) and artificial stone (AS) silicosis are preventable diseases which have occurred in serious outbreaks in Australia recently. This has prompted a TSANZ review of Australia's approach to respiratory periodic health surveillance. While regulating respirable dust exposure remains the foundation of primary and secondary prevention, identification of workers with early disease assists with control of further exposure, and with the aims of preserving lung function and decreasing respiratory morbidity in those affected. Prompt detection of an abnormality also allows for ongoing respiratory specialist clinical management. This review outlines a medical framework for improvements in respiratory surveillance to detect CMDLD and AS silicosis in Australia. This includes appropriate referral, improved data collection and interpretation, enhanced surveillance, the establishment of a nationwide Occupational Lung Disease Registry and an independent advisory group. These measures are designed to improve health outcomes for workers in the coal mining, AS and other dust-exposed and mining industries.

A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists Singapore

Lung cancer is the leading cause of cancer-related death around the world, being the top cause of cancer-related deaths among men and the second most common cause of cancer-related deaths among women in Singapore. Currently, no screening programme for lung cancer exists in Singapore. Since there is mounting evidence indicating a different epidemiology of lung cancer in Asian countries, including Singapore, compared to the rest of the world, a unique and adaptive approach must be taken for a screening programme to be successful at reducing mortality while maintaining cost-effectiveness and a favourable risk-benefit ratio. This review article promotes the use of low-dose computed tomography of the chest and explores the radiological challenges and future directions.

Low-dose chest CT and the impact on nodule visibility

INTRODUCTION

The need to continually optimise CT protocols is essential to ensure the lowest possible radiation dose for the clinical task and individual patient. The aim of this study was to explore the effect of reducing effective mAs on nodule detection and radiation dose across six scanners.

METHODS

An anthropomorphic chest phantom was scanned using a low-dose chest CT protocol, with the effective mAs lowered to the lowest permissible level. All other acquisition parameters remained consistent. Images were evaluated by five radiologists to determine their sensitivity in detecting six simulated nodules within the phantom. Image noise was calculated together with DLP.

RESULTS

The lowest possible mAs achievable ranged from 7 to 19 mAs. The two highest mAs setting (17 mAs + 19 mAs) had kV modulation enabled (100 kV instead of 120 kV) which consequently resulted in a higher nodule detection rate. Overall nodule detection averaged at 91% (range 80-97%). Out of a possible 180 nodules, 16 were missed, with 12 of those 16 being the same nodule. Noise was double for the Somatom Sensation scanner when compared to the others; however, this scanner did not have iterative reconstruction and it was installed over 10 years ago. There was a strong correlation between image noise and scanner age.

CONCLUSION

This study highlighted that nodules can be detected at very low effective mAs (<20 mAs) but only when other acquisition parameters are optimised i.e. iterative reconstruction and kV modulation. Nodule detection rates were affected by nodule location and image noise.

IMPLICATIONS FOR PRACTICE

This study consolidates previous findings on how to successfully optimise low-dose chest CT. It also highlights the difficulty with standardisation owing to factors such as scanner age and different vendor attributes.

Concomitant screening of coronary artery disease and lung cancer with a new ultrafast-low-dose Computed Tomography protocol: A pilot randomised trial

We performed a pilot randomised study to assess the feasibility and radiation exposure of a new computed tomography (CT) protocol that allows screening of both coronary artery disease (CAD) and lung cancer. Current or former heavy smokers at high lung cancer risk with indication to cardiac CT for suspected or known CAD were randomised to undergo concomitant CT evaluation of either cardiac or thoracic area or cardiac CT only. Out of 129 subjects deemed eligible for the study, 110 agreed to participate and were randomised to simultaneous cardiac and lung CT (Gr.A; n = 55) or cardiac CT only (Gr.B; n = 55). The feasibility (i.e. adequate visualization of coronary artery segments) was noninferior with simultaneous cardiac and lung CT compared with the standard cardiac CT (870 of 889 segments [97%] in Gr.A vs 878/890 segments [99%] in Gr.B; mean difference 2.0% [90% confidence interval: -0.3% to 4.1%]). The safety (i.e. effective radiation dose) of the concomitant cardiac and lung CT protocol was noninferior to the standard cardiac CT (1.5 [95% confidence intervals: 1.2-1.7] vs. 1.4 [95% confidence intervals: 1.1-1.6] mSv; mean difference 0.1 mSv [90% confidence interval: -0.2 to 0.3 mSv]). In the two groups, a total of 25 significant (>70%) coronary stenoses were found at cardiac CT (9/55 cases of Gr.A vs 11/55 cases of Gr.B). Pulmonary nodules >2 mm were detected in 7 of the 55 Gr.A subjects. This pilot randomised study shows that concomitant CAD and lung cancer screening by means of a new CT protocol is both feasible and safe, thus allowing a comprehensive evaluation of both cardiac and thoracic regions during one CT scanning only. (ClinicalTrials.gov Identifier: NCT03727958).

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Automatic current selection with iterative reconstruction reduces effective dose to less than 1 mSv in low-dose chest computed tomography in persons with normal BMI

Most of the recent studies have used fixed tube current while few investigators use automatic current selection (ACS) with iterative reconstruction (IR) techniques to reduce effective dose (ED) to < 1 mSv in low-dose chest computed tomography (LDCCT). We investigated whether image quality of lungs as produced by a fixed tube current (FTC) of 35 mAs can be maintained with ED < 1 mSv produced by ACS with IR techniques in LDCCT. A total of 32 participants were included. The LDCCT was performed by a FTC 35 mAs (with a kilovoltage peak of 120 kVp) in 16 participants (Group A), and by a DoseRight ACS in 16 participants (Group B). Their images were improved by IR technique. The ED was estimated by multiplying the individual dose length product (DLP) by the dose conversion factor. The image quality was assessed by the CT number, noise levels, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the regions of interest in the apex, upper lobe, and lower lobe of lung regions in the CT images. A t-test was used to evaluate the LDCCT image quality between the groups. The ED was significantly 49.2% lower in Group B than in Group A (0.71 ± 0.05 mSv vs 1.40 ± 0.02 mSv, P < .001). However, noise level, SNR, and CNR were not significantly different between Groups A and B, indicating the image quality was similar between two groups, or our setting parameters for DoseRight ACS with IR technique can achieve the image quality as good as obtained on the FTC 35 mAs with IR techniques. Our results suggest that the DoseRight ACS with IR technique reduces ED to lower than 1 mSv (averagely 0.71 mSv) yet maintains an image quality as good as produced by FTC 35 mAs with IR technique in normal BMI persons. The ACS setup thus is more preferable than the FTC to achieve the ALARA (as low as reasonably achievable) principle.

Lung cancer screening: nodule identification and characterization

The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.

A novel ultrafast-low-dose computed tomography protocol allows concomitant coronary artery evaluation and lung cancer screening

BACKGROUND

Cardiac computed tomography (CT) is often performed in patients who are at high risk for lung cancer in whom screening is currently recommended. We tested diagnostic ability and radiation exposure of a novel ultra-low-dose CT protocol that allows concomitant coronary artery evaluation and lung screening.

METHODS

We studied 30 current or former heavy smoker subjects with suspected or known coronary artery disease who underwent CT assessment of both coronary arteries and thoracic area (Revolution CT, General Electric). A new ultrafast-low-dose single protocol was used for ECG-gated helical acquisition of the heart and the whole chest. A single IV iodine bolus (70-90 ml) was used. All patients with CT evidence of coronary stenosis underwent also invasive coronary angiography.

RESULTS

All the coronary segments were assessable in 28/30 (93%) patients. Only 8 coronary segments were not assessable in 2 patients due to motion artefacts (assessability: 98%; 477/485 segments). In the assessable segments, 20/21 significant stenoses (> 70% reduction of vessel diameter) were correctly diagnosed. Pulmonary nodules were detected in 5 patients, thus requiring to schedule follow-up surveillance CT thorax. Effective dose was 1.3 ± 0.9 mSv (range: 0.8-3.2 mSv). Noteworthy, no contrast or radiation dose increment was required with the new protocol as compared to conventional coronary CT protocol.

CONCLUSIONS

The novel ultrafast-low-dose CT protocol allows lung cancer screening at time of coronary artery evaluation. The new approach might enhance the cost-effectiveness of coronary CT in heavy smokers with suspected or known coronary artery disease.

Sub-solid Nodule Detection Performance on Reduced-dose Computed Tomography with Iterative Reduction: Comparison Between 20 mA (7 mAs) and 120 mA (42 mAs) Regarding Nodular Size and Characteristics and Association with Size-specific Dose Estimate

RATIONALE AND OBJECTIVES

This study aimed to compare sub-solid nodule detection performances (SSNDP) on chest computed tomography (CT) with Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR 3D) between 7 mAs (0.21 mSv) and 42 mAs (1.28 mSv) in total and in subgroups classified by nodular size, characteristics, and location, and analyze the association of SSNDP with size-specific dose estimate (SSDE).

MATERIALS AND METHODS

As part of the Area-detector Computed Tomography for the Investigation of Thoracic Diseases Study, a Japanese multicenter research project, 68 subjects underwent chest CT with 120 kV, 0.35 seconds per rotation, and three tube currents: 240 mA (84 mAs), 120 mA (42 mAs), and 20 mA (7 mAs). The research committee of the study project outlined and approved our study protocols. The institutional review board of each institution approved this study. Axial 2-mm-thick CT images were reconstructed using AIDR 3D. Standard reference was determined by CT images at 84 mAs. Four radiologists recorded SSN presence by continuously distributed rating on CT at 7 mAs and 42 mAs. Receiver operating characteristic analysis was used to evaluate SSNDP at both doses in total and in subgroups classified by nodular longest diameter (LD) (≥5 mm), characteristics (pure and part-solid), and locations (ventral, intermediate, or dorsal; central or peripheral; and upper, middle, or lower). Detection sensitivity was compared among five groups of SSNs classified based on particular SSDE to nodule on CT with AIDR 3D at 7 mAs.

RESULTS

Twenty-two part-solid and 86 pure SSNs were identified. For larger SSNs (LD ≥ 5 mm) as well as subgroups classified by nodular locations and part-solid nodules, SSNDP was similar in both methods (area under the receiver operating characteristics curve: 0.96 ± 0.02 in CT at 7 mAs and 0.97 ± 0.01 in CT at 42 mAs), with acceptable interobserver agreements in five locations. For larger SSNs (LD ≥ 5 mm), on CT at 42 mAs, no significant differences in detection sensitivity were found among the five groups classified by SSDE, whereas on CT with 7 mAs, four groups with SSDE of 0.65 or higher were superior in detection sensitivity to the other group, with SSDE less than 0.65 mGy.

CONCLUSIONS

For SSNs with 5 mm or more in cases with normal range of body habitus, CT at 7 mAs was demonstrated to have comparable SSNDP to CT at 42 mAs regardless of nodular location and characteristics, and SSDE higher than 0.65 mGy is desirable to obtain sufficient SSNDP.

Advanced imaging tools in pulmonary nodule detection and surveillance

Lung cancer is a leading cause of death worldwide. The National Lung Screening Trial has demonstrated that lung cancer screening can reduce lung cancer specific and all cause mortality. With approval of national coverage for lung cancer screening, it is expected that an increase in exams related to pulmonary nodule detection and surveillance will ensue. Advanced imaging technologies for nodule detection and surveillance will be more important than ever. While computed tomography (CT) remains the modality of choice, other emerging modalities such as magnetic resonance imaging provides viable alternatives to CT.

Effect of radiation dose reduction and iterative reconstruction on computer-aided detection of pulmonary nodules: Intra-individual comparison

OBJECTIVE

To evaluate the effect of radiation dose reduction and iterative reconstruction (IR) on the performance of computer-aided detection (CAD) for pulmonary nodules.

METHODS

In this prospective study twenty-five patients were included who were scanned for pulmonary nodule follow-up. Image acquisition was performed at routine dose and three reduced dose levels in a single session by decreasing mAs-values with 45%, 60% and 75%. Tube voltage was fixed at 120 kVp for patients ≥ 80 kg and 100 kVp for patients < 80 kg. Data were reconstructed with filtered back projection (FBP), iDose(4) (levels 1,4,6) and IMR (levels 1-3). All noncalcified solid pulmonary nodules ≥ 4 mm identified by two radiologists in consensus served as the reference standard. Subsequently, nodule volume was measured with CAD software and compared to the reference consensus. The numbers of true-positives, false-positives and missed pulmonary nodules were evaluated as well as the sensitivity.

RESULTS

Median effective radiation dose was 2.2 mSv at routine dose and 1.2, 0.9 and 0.6 mSv at respectively 45%, 60% and 75% reduced dose. A total of 28 pulmonary nodules were included. With FBP at routine dose, 89% (25/28) of the nodules were correctly identified by CAD. This was similar at reduced dose levels with FBP, iDose(4) and IMR. CAD resulted in a median number of false-positives findings of 11 per scan with FBP at routine dose (93% of the CAD marks) increasing to 15 per scan with iDose(4) (95% of the CAD marks) and 26 per scan (96% of the CAD marks) with IMR at the lowest dose level.

CONCLUSION

CAD can identify pulmonary nodules at submillisievert dose levels with FBP, hybrid and model-based IR. However, the number of false-positive findings increased using hybrid and especially model-based IR at submillisievert dose while dose reduction did not affect the number of false-positives with FBP.

Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction

OBJECTIVE

The purpose of this study is to assess the image quality and radiation dose reduction of ultra-low-dose CT using sinogram-affirmed iterative reconstruction (SAFIRE).

SUBJECTS AND METHODS

This prospective study enrolled 25 patients who underwent three consecutive unenhanced CT scans including low-dose CT (120 kVp and 30 mAs) and two ultra-low-dose CT protocols (protocol A, 100 kVp and 20 mAs; protocol B, 80 kVp and 30 mAs) with image reconstruction using SAFIRE. The image quality and radiation dose reduction were assessed.

RESULTS

The mean (± SD) effective radiation dose was 1.06 ± 0.11, 0.44 ± 0.05, and 0.31 ± 0.03 mSv for low-dose CT, ultra-low-dose CT protocol A, and ultra-low-dose CT protocol B, respectively. Overall image quality was determined as diagnostic in 100% of low-dose CT scans, 96% of ultra-low-dose CT protocol A scans, and 88% of ultra-low-dose CT protocol B scans. All patients with nondiagnostic quality images had a body mass index (weight in kilograms divided by the square of height in meters) greater than 25. There was no statistically significant difference in detection frequencies of 14 lesion types among the three CT protocols, but pulmonary emphysema was detected in fewer patients (3/25) in ultra-low-dose CT protocol B scans compared with ultra-low-dose CT protocol A scans (5/25) or low-dose CT scans (6/25). We measured the longest dimensions of 33 small solid nodules (3.8-12.4 mm in long diameter) and found no statistically significant difference in the values afforded by the three CT protocols (p = 0.135).

CONCLUSION

Iterative reconstruction allows ultra-low-dose CT and affords acceptable image quality, allowing size measurements of solid pulmonary nodules to be made.

Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection

OBJECTIVE

The purpose of this study was to determine whether ultralow-radiation-dose chest CT can be used for quantification of lung density and for emphysema detection in participants undergoing lung cancer screening.

SUBJECTS AND METHODS

Fifty-two patients were prospectively enrolled and underwent scanning twice with low-dose CT (reference parameters, 120 kV, 50 effective mAs) and ultralow-dose CT (reference parameters, 80 kV, 4-5 effective mAs). Images were reconstructed by filtered back projection (FBP) for low-dose CT and FBP and iterative reconstruction (IR) for ultralow-dose CT. Radiation dose was recorded. Image noise, mean lung attenuation, 15th percentile of lung attenuation, and emphysema index were measured in each image series and compared. Test characteristics of ultralow-dose CT in detecting more than subtle emphysema (emphysema index≥3%) were calculated.

RESULTS

The effective dose of low-dose CT was 2.1±0.5 mSv, and that of ultralow-dose CT was 0.13±0.04 mSv. Compared with the findings for low-dose CT, absolute overestimation of emphysema index was 7% on ultralow-dose CT images reconstructed with FBP and 2% on those processed with IR. The 15th percentile of lung attenuation was underestimated by 21.3 HU on ultralow-dose FBP images and by 5.8 HU on IR images. No relevant bias was observed for mean lung attenuation. Four patients (8%) had more than subtle emphysema. The emphysema index measured at ultralow-dose CT with FBP and IR had 100% and 100% sensitivity and 92% and 96% specificity in identifying patients with more than subtle emphysema at a cutoff of greater than 12.1% for FBP and greater than 6.7% for IR.

CONCLUSION

Ultralow-dose chest CT performed for lung cancer screening can be used for quantification of lung density and for emphysema detection. IR improves the accuracy of ultralow-dose CT in this setting.

Chest CT using spectral filtration: radiation dose, image quality, and spectrum of clinical utility

OBJECTIVES

To determine the radiation dose, image quality, and clinical utility of non-enhanced chest CT with spectral filtration.

METHODS

We retrospectively analysed 25 non-contrast chest CT examinations acquired with spectral filtration (tin-filtered Sn100 kVp spectrum) compared to 25 examinations acquired without spectral filtration (120 kV). Radiation metrics were compared. Image noise was measured. Contrast-to-noise-ratio (CNR) and figure-of-merit (FOM) were calculated. Diagnostic confidence for the assessment of various thoracic pathologies was rated by two independent readers.

RESULTS

Effective chest diameters were comparable between groups (P = 0.613). In spectral filtration CT, median CTDIvol, DLP, and size-specific dose estimate (SSDE) were reduced (0.46 vs. 4.3 mGy, 16 vs. 141 mGy*cm, and 0.65 vs. 5.9 mGy, all P < 0.001). Spectral filtration CT had higher image noise (21.3 vs. 13.2 HU, P < 0.001) and lower CNR (47.2 vs. 75.3, P < 0.001), but was more dose-efficient (FOM 10,659 vs. 2,231/mSv, P < 0.001). Diagnostic confidence for parenchymal lung disease and osseous pathologies was lower with spectral filtration CT, but no significant difference was found for pleural pathologies, pulmonary nodules, or pneumonia.

CONCLUSIONS

Non-contrast chest CT using spectral filtration appears to be sufficient for the assessment of a considerable spectrum of thoracic pathologies, while providing superior dose efficiency, allowing for substantial radiation dose reduction.

KEY POINTS

• Spectral filtration enables non-contrast chest CT with very high dose efficiency. • This approach reduces CTDI vol , DLP, and SSDE (effective chest diameter 28 cm). • Lung nodules, pneumonia, and pleural pathologies can be assessed with uncompromised confidence.

Low-dose CT: technique, reading methods and image interpretation

The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality^[1]. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer^[2]. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.

Flat-panel detector computed tomography imaging: observer performance in detecting pulmonary nodules in comparison with conventional chest radiography and multidetector computed tomography

PURPOSE

The aim of this study was to compare the detectability of lung nodules on images obtained with a flat-panel detector computed tomography (FPD-CT) system and by chest radiographs (CXRs) using receiver-operating characteristic (ROC) analysis.

MATERIALS AND METHODS

FPD-CT was conducted with the patients in the sitting position. For the CXR study, the patients stood erect. Our study population consisted of 26 individuals ranging in age from 50 to 83 years. The reference standard was based on the interpretations obtained by consensus of 2 radiologists on multidetector CT images for the presence or absence of nodules. Four other radiologists independently assessed and recorded the absence or presence of lung nodules and their location on FPD-CT and CXR images. ROC analysis was used to evaluate lung nodule detectability by both imaging modalities.

RESULTS

Two radiologists identified 34 nodules whose diameter was 5 to 42 mm (mean, 19.3 mm) in 23 of the 26 study participants on the multidetector CT images. Overall, analysis of variance for ROC analysis showed that FPD-CT was significantly better in detecting nodules than CXR (P=0.02). The estimated mean Az value was 0.9818±0.0083 with FPD-CT and 0.7610±0.0908 with CXR. The sensitivity for nodule detection on FPD-CT and CXR images was 79.4% and 33.8%, respectively.

CONCLUSION

The detectability of pulmonary nodules was better on images of FPD-CT than on CXRs.

Usefulness of the CAD system for detecting pulmonary nodule in real clinical practice

Objective

We wanted to evaluate the usefulness of the computer-aided detection (CAD) system for detecting pulmonary nodules in real clinical practice by using the CT images.

Materials and Methods

Our Institutional Review Board approved our retrospective study with a waiver of informed consent. This study included 166 CT examinations that were performed for the evaluation of pulmonary metastasis in 166 patients with colorectal cancer. All the CT examinations were interpreted by radiologists and they were also evaluated by the CAD system. All the nodules detected by the CAD system were evaluated with regard to whether or not they were true nodules, and they were classified into micronodules (MN, diameter < 4 mm) and significant nodules (SN, 4 ≤ diameter ≤ 10 mm). The radiologic reports and CAD results were compared.

Results

The CAD system helped detect 426 nodules; 115 (27%) of the 426 nodules were classified as true nodules and 35 (30%) of the 115 nodules were SNs, and 83 (72%) of the 115 were not mentioned in the radiologists' reports and three (4%) of the 83 nodules were non-calcified SNs. One of three non-calcified SNs was confirmed as a metastatic nodule. According to the radiologists' reports, 60 true nodules were detected, and 28 of the 60 were not detected by the CAD system.

Conclusion

Although the CAD system missed many SNs that are detected by radiologists, it helps detect additional nodules that are missed by the radiologists in real clinical practice. Therefore, the CAD system can be useful to support a radiologist's detection performance.

A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective

As the detection and characterization of lung nodules are of paramount importance in thoracic radiology, various tools for making a computer-aided diagnosis (CAD) have been developed to improve the diagnostic performance of radiologists in clinical practice. Numerous studies over the years have shown that the CAD system can effectively help readers identify more nodules. Moreover, nodule malignancy and the response of malignant lung tumors to treatment can also be assessed using nodule volumetry. CAD also has the potential to objectively analyze the morphology of nodules and enhance the workflow during the assessment of follow-up studies. Therefore, understanding the current status and limitations of CAD for evaluating lung nodules is essential to effectively apply CAD in clinical practice.

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.