Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices

The sensitivity of MIPs of 3D contrast-enhanced VIBE T1-weighted imaging for the detection of small brain metastases (≤ 5 mm) on 1.5 tesla MRI

OBJECTIVES

To evaluate whether the use of Maximum Intensity Projection (MIP) images derived from contrast-enhanced 3D-T1-weighted volumetric interpolated breath-hold examination (VIBE) would allow more sensitive detection of small (≤5 mm) brain metastases (BM) compared with source as well as 2D-T1-weighted spin-echo (SE) images.

METHODS

We performed a single center retrospective study on subjects with BM who underwent 1.5 tesla brain magnetic resonance imaging. Two readers counted the number of small BM for each of the seven sets of contrast-enhanced images created: axial 2D-T1-weighted SE, 3D-T1-weighted VIBE, 2.5 mm-thick-MIP T1-weighted VIBE, and 5 mm-thick-MIP T1-weighted VIBE; sagittal 3D-T1-weighted VIBE, 2.5 mm-thick-MIP T1-weighted VIBE, and 5 mm-thick-MIP T1-weighted VIBE. Total number of lesions detected on each image type was compared. Sensitivity, the average rates of false negatives and false positives, and the mean discrepancy were evaluated.

RESULTS

A total of 403 small BM were identified in 49 patients. Significant differences were found: in the number of true positives and false negatives between the axial 2D-T1-weighted SE sequence and all other imaging techniques; in the number of false positives between the axial 2D-T1-weighted SE and the axial 3D-T1-weighted VIBE sequences. The two image types that combined offered the highest sensitivity were 2D-T1-weighted SE and axial 2.5 mm-thick-MIP T1-weighted VIBE. The axial 2D-T1-weighted SE sequence differed significantly in sensitivity from all other sequences.

CONCLUSION

MIP images did not show a significant difference in sensitivity for the detection of small BM compared with native images.

Evaluation of respiratory bronchiolitis nodules with maximum intensity projection images

OBJECTIVE

Respiratory bronchiolitis is a disease associated with heavy smoking. Computed tomography in this disease often shows symmetrical and bilaterally ill-defined circumscribed centriacinar micronodular involvement in the upper-middle lobes. The maximum intensity projection method is a kind of image processing method and provides a better evaluation of nodules and vascular structures. Our study aimed to show whether maximum intensity projection images increase the diagnostic accuracy in the detection of micronodules in respiratory bronchiolitis.

METHODS

Two radiologists with different experiences (first reader: 10-year radiologist with cardiothoracic radiology experience and second reader: nonspecific radiologist with 2 years of experience) reviewed images of patients whose respiratory bronchiolitis diagnosis was supported by clinical findings. The evaluation was done independently of each other. Both conventional computed tomography images and maximum intensity projection images of the same patients were examined. The detection rates on conventional computed tomography and maximum intensity projection images were then compared.

RESULTS

A total of 53 patients were evaluated, of whom 48 were men and 5 were women. The first reader detected centriacinar nodules in 42 (79.2%) patients on conventional computed tomography and centriacinar nodules in all 53 (100%) patients on maximum intensity projection images. The second reader detected centriacinar nodules in 12 (22.6%) patients on conventional computed tomography images and in 48 (90.6%) patients on maximum intensity projection images. For the less experienced reader, the detection rate of micronodules in respiratory bronchiolitis in maximum intensity projection images increased statistically significantly (p<0.001).

CONCLUSION

Maximum intensity projection images in respiratory bronchiolitis increase the detectability of micronodules independently of the experience of the radiologist.

Imaging of pediatric pulmonary tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper

Pediatric pulmonary malignancy can be primary or metastatic, with the latter being by far the more common. With a few exceptions, there are no well-established evidence-based guidelines for imaging pediatric pulmonary malignancies, although computed tomography (CT) is used in almost all cases. The aim of this article is to provide general imaging guidelines for pediatric pulmonary malignancies, including minimum standards for cross-sectional imaging techniques and specific imaging recommendations for select entities.

Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

Solitary Pulmonary Nodule Evaluation: Pearls and Pitfalls

Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.

Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation.

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

Introduction Lung cancer is the most common cancer overall, and the foremost cause of cancer-related mortality. Almost all lung cancers evolve from pulmonary nodules. As multidetector CT (MDCT) scanners are now widely available, there is an increased rate of detection of pulmonary nodules. It is of utmost importance to evaluate pulmonary nodules to rule out the possibility of neoplastic diseases. With advancements in technology, there are various manual and automatic analytic software providing a wide range of post-processing techniques. Maximum intensity projection (MIP) and volume rendering (VR) techniques have been analyzed previously regarding pulmonary nodules but there is a scarcity of data in terms of low-density nodules. This study aims to delineate the comparison and supremacy of both techniques in terms of low-density nodules. Methodology The current prospective study was conducted from June 2019 to June 2020 in the Radiology Department at Dr. Ziauddin Hospital, Karachi. Chest CT scans were performed on 16 slice MDCT (Alexion 16 Multi-slice, Toshiba Medical System Corporation, Houston, TX). A consultant radiologist of six years experience and a postgraduate trainee of three years experience analyzed each patient on a workstation (Vitrea 6.2.0, Vital Images, Minnetonka, MN). SPSS 23.0 (SPSS Inc., Chicago, IL) was incorporated for data analysis. Data were expressed in the median and interquartile range (IQR). Data collected for this study were analyzed using analyzing the median difference in nodule count using Wilcoxon's signed-rank test. A p-value of <0.05 was considered significant. Results After informed consent, 236 patients were recruited for the study. MIP outperformed VR in terms of nodule detection and low-density nodules at each evaluated slab thicknesses (p<0.001). A 10-mm MIP was superior to all other techniques in terms of detection of pulmonary nodules and low-density nodules (p<0.001). MIP was also considered an easier technique as there was excellent inter-rater reliability and agreement. Conclusion This study is robust evidence regarding the supremacy of MIP. MIP outperformed VR on every slab thicknesses. The 10-mm MIP technique was superior to all others evaluated and was recorded to be an easier analyzing technique.

Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage

BACKGROUND AND OBJECTIVE

To investigate the effect of the slab thickness in maximum intensity projections (MIPs) on the candidate detection performance of a deep learning-based computer-aided detection (DL-CAD) system for pulmonary nodule detection in CT scans.

METHODS

The public LUNA16 dataset includes 888 CT scans with 1186 nodules annotated by four radiologists. From those scans, MIP images were reconstructed with slab thicknesses of 5 to 50 mm (at 5 mm intervals) and 3 to 13 mm (at 2 mm intervals). The architecture in the nodule candidate detection part of the DL-CAD system was trained separately using MIP images with various slab thicknesses. Based on ten-fold cross-validation, the sensitivity and the F₂ score were determined to evaluate the performance of using each slab thickness at the nodule candidate detection stage. The free-response receiver operating characteristic (FROC) curve was used to assess the performance of the whole DL-CAD system that took the results combined from 16 MIP slab thickness settings.

RESULTS

At the nodule candidate detection stage, the combination of results from 16 MIP slab thickness settings showed a high sensitivity of 98.0% with 46 false positives (FPs) per scan. Regarding a single MIP slab thickness of 10 mm, the highest sensitivity of 90.0% with 8 FPs/scan was reached before false positive reduction. The sensitivity increased (82.8% to 90.0%) for slab thickness of 1 to 10 mm and decreased (88.7% to 76.6%) for slab thickness of 15-50 mm. The number of FPs was decreasing with increasing slab thickness, but was stable at 5 FPs/scan at a slab thickness of 30 mm or more. After false positive reduction, the DL-CAD system, utilizing 16 MIP slab thickness settings, had the sensitivity of 94.4% with 1 FP/scan.

CONCLUSIONS

The utilization of multi-MIP images could improve the performance at the nodule candidate detection stage, even for the whole DL-CAD system. For a single slab thickness of 10 mm, the highest sensitivity for pulmonary nodule detection was reached at the nodule candidate detection stage, similar to the slab thickness usually applied by radiologists.

Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response

Early detection of brain metastases and differentiation from other neuropathologies is crucial. Although biopsy is often required for definitive diagnosis, imaging can provide useful information. After treatment commences, imaging is also performed to assess the efficacy of treatment. Contrast-enhanced magnetic resonance imaging (MRI) is the traditional imaging method for the evaluation of brain metastases, as it provides information about lesion size, morphology, and macroscopic properties. Newer MRI sequences have been developed to increase the conspicuity of detecting enhancing metastases. Other advanced MRI techniques, that have the capability to probe beyond the anatomic structure, are available to characterize micro-structures, cellularity, physiology, perfusion, and metabolism. Artificial intelligence provides powerful computational tools for detection, segmentation, classification, prediction, and prognosis. We highlight and review a few advanced MRI techniques for the assessment of brain metastases-specifically for (1) diagnosis, including differentiating between malignancy types and (2) evaluation of treatment response, including the differentiation between radiation necrosis and disease progression.

Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study

Lung cancer remains the overwhelmingly greatest cause of cancer death in the United States, accounting for more annual deaths than breast, prostate, and colon cancer combined. Accumulated evidence since the mid to late 1990s, however, indicates that low-dose CT screening of high-risk patients enables detection of lung cancer at an early stage and can reduce the risk of dying from lung cancer. CT screening is now a recommended clinical service in the United States, subject to guidelines and reimbursement requirements intended to standardize practice and optimize the balance of benefits and risks. In this review, the evidence on the effectiveness of CT screening will be summarized and the current guidelines and standards will be described in the context of knowledge gained from lung cancer screening studies. In addition, an overview of the potential advances that may improve CT screening will be presented, and the need to better understand the performance in clinical practice outside of the research trial setting will be discussed. © RSNA, 2020.

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial

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No lung nodule management algorithm exists specifically for cardiac CT.

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The DISCHARGE trial is a pragmatic prospective randomized trial currently being undertaken across 16 countries in Europe for patients with stable chest pain.

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As part of the trial a lung nodule algorithm is being evaluated based on modified lung-RADS nodule management algorithms.

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The methodology of this ‘Lung-RADS for cardiac CT’ algorithm is presented.

Purpose

In this methodology paper we describe the development of a lung nodule management algorithm specifically for patients undergoing cardiac CT.

Methods

We modified the Lung-RADS algorithm specifically to manage lung nodules incidentally detected on cardiac CT (Lung-RADS for cardiac CT). We will evaluate the modified algorithm as part of the DISCHARGE trial (www.dischargetrial.eu) in which patients with suspected coronary artery disease are randomly assigned to cardiac CT or invasive coronary angiography across Europe at 16 sites involving 3546 patients. Patients will be followed for up to four years.

Results

The major adjustments to Lung-RADS specifically for cardiac CT relate to 1) incomplete coverage of the lungs by cardiac CT compared with chest CT, and when to order a completion chest CT versus a follow up chest CT, 2) cardiac CT findings will not trigger annual lung-cancer screening, and 3) a lower threshold of at least 10 mm for classifying new ground glass nodules as probably benign (category 3).

Conclusions

The DISCHARGE trial will assess a lung nodule management algorithm designed specifically for cardiac CT in patients with stable chest pain across Europe.

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.

Thoracic computed tomographic interpretation for clinicians to aid in the diagnosis of dogs and cats with respiratory disease

In humans, high-resolution computed tomography (CT) is a key diagnostic modality for pulmonary disorders. Its success likely lies in excellent correlation of lung diseases with associated subgross anatomic changes, as assessed by histopathology, and because of a multidisciplinary approach between clinicians, radiologists and pathologists. Although thoracic CT studies have been performed in dogs and cats for nearly three decades, there is a lack of uniformity in both protocols for acquisition and in terminology used to describe lesions. Importantly, terms such as a bronchial, interstitial, and alveolar patterns are inappropriate descriptors for canine and feline thoracic CT imaging changes; instead, lung patterns should be classified as increased or decreased attenuation, nodular patterns, and linear patterns, with specific vocabulary to describe subtypes of lesions. In this manuscript, the authors provide an overview of basic CT principles, strategies to optimize and acquire high-quality diagnostic studies (inclusive of paired inspiratory and expiratory series, contrast and triphasic angiography) and provide a roadmap for systematic interpretation of thoracic CT images.

Computed Tomography Window Blending: Feasibility in Thoracic Trauma

RATIONALE AND OBJECTIVES

This study aims to demonstrate the feasibility of processing computed tomography (CT) images with a custom window blending algorithm that combines soft-tissue, bone, and lung window settings into a single image; to compare the time for interpretation of chest CT for thoracic trauma with window blending and conventional window settings; and to assess diagnostic performance of both techniques.

MATERIALS AND METHODS

Adobe Photoshop was scripted to process axial DICOM images from retrospective contrast-enhanced chest CTs performed for trauma with a window-blending algorithm. Two emergency radiologists independently interpreted the axial images from 103 chest CTs with both blended and conventional windows. Interpretation time and diagnostic performance were compared with Wilcoxon signed-rank test and McNemar test, respectively. Agreement with Nexus CT Chest injury severity was assessed with the weighted kappa statistic.

RESULTS

A total of 13,295 images were processed without error. Interpretation was faster with window blending, resulting in a 20.3% time saving (P < .001), with no difference in diagnostic performance, within the power of the study to detect a difference in sensitivity of 5% as determined by post hoc power analysis. The sensitivity of the window-blended cases was 82.7%, compared to 81.6% for conventional windows. The specificity of the window-blended cases was 93.1%, compared to 90.5% for conventional windows. All injuries of major clinical significance (per Nexus CT Chest criteria) were correctly identified in all reading sessions, and all negative cases were correctly classified. All readers demonstrated near-perfect agreement with injury severity classification with both window settings.

CONCLUSIONS

In this pilot study utilizing retrospective data, window blending allows faster preliminary interpretation of axial chest CT performed for trauma, with no significant difference in diagnostic performance compared to conventional window settings. Future studies would be required to assess the utility of window blending in clinical practice.

Evaluation of Thick-Slab Overlapping MIP Images of Contrast-Enhanced 3D T1-Weighted CUBE for Detection of Intracranial Metastases: A Pilot Study for Comparison of Lesion Detection, Interpretation Time, and Sensitivity with Nonoverlapping CUBE MIP, CUBE, and Inversion-Recovery-Prepared Fast-Spoiled Gradient Recalled Brain Volume

BACKGROUND AND PURPOSE

Early and accurate identification of cerebral metastases is important for prognostication and treatment planning although this process is often time consuming and labor intensive, especially with the hundreds of images associated with 3D volumetric imaging. This study aimed to evaluate the benefits of thick-slab overlapping MIPs constructed from contrast-enhanced T1-weighted CUBE (overlapping CUBE MIP) for the detection of brain metastases in comparison with traditional CUBE and inversion-recovery prepared fast-spoiled gradient recalled brain volume (IR-FSPGR-BRAVO) and nonoverlapping CUBE MIP.

MATERIALS AND METHODS

A retrospective review of 48 patients with cerebral metastases was performed at our institution from June 2016 to October 2017. Brain MRIs, which were acquired on multiple 3T scanners, included gadolinium-enhanced T1-weighted IR-FSPGR-BRAVO and CUBE, with subsequent generation of nonoverlapping CUBE MIP and overlapping CUBE MIP. Two blinded radiologists identified the total number and location of metastases on each image type. The Cohen κ was used to determine interrater agreement. Sensitivity, interpretation time, and lesion contrast-to-noise ratio were assessed.

RESULTS

Interrater agreement for identification of metastases was fair-to-moderate for all image types (κ = 0.222-0.598). The total number of metastases identified was not significantly different across the image types. Interpretation time for CUBE MIPs was significantly shorter than for CUBE and IR-FSPGR-BRAVO, saving at least 50 seconds per case on average (P < .001). The mean lesion contrast-to-noise ratio for both CUBE MIPs was higher than for IR-FSPGR-BRAVO. The mean contrast-to-noise ratio for small lesions (<4 mm) was lower for nonoverlapping CUBE MIP (1.55) than for overlapping CUBE MIP (2.35). For both readers, the sensitivity for lesion detection was high for all image types but highest for overlapping CUBE MIP and CUBE (0.93-0.97).

CONCLUSIONS

This study suggests that the use of overlapping CUBE MIP or nonoverlapping CUBE MIP for the detection of brain metastases can reduce interpretation time without sacrificing sensitivity, though the contrast-to-noise ratio of lesions is highest for overlapping CUBE MIP.

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.

The impact of reconstruction techniques on observer performance for the detection and characterization of small pulmonary nodules in chest CT of children under 13 years

OBJECTIVE

To compare three different reconstruction techniques of CT data for the detection of pulmonary nodules in children under 13 years. Secondly to assess the prevalence of perifissural nodular opacities.

MATERIALS AND METHODS

The study consisted of chest CTs of 31 children (median age 6.9 years, range 2.1-12.7), of whom 17 had known extra-thoracic malignancies. Four observers assessed three techniques for the presence of nodules: axial 5 mm maximum intensity projections (MIPs) used in conjunction with 1 mm slices (mode A), 1 mm slices alone (mode B) and 3 mm slices (mode C). All modes were available in 3D. Per mode sensitivities were determined above a certain threshold of reader agreement. Confidence level and reader agreement for identification of an opacity as nodule served as surrogate for quality of nodule characterization.

RESULTS

103 nodules (median size 2.0 mm) were detected. Mode A yielded the highest interreader agreement (κ 0.336) and a superior sensitivity (71%, p = 0.003) compared to mode B and C (κ 0.218, sensitivity 57% and κ 0.247, sensitivity 56%, respectively). Mode B provided the highest confidence level and interreader agreement with respect to nodule identification (mean 4.3/5, κw 0.508). Double reading improved and evened interreader agreement for all modes (κ 0.450), mode A maintained the highest sensitivity (89.1%, p = 0.05-0.08). A median of 1 intrapulmonary lymph node/patient was seen in children with and without malignancy.

CONCLUSION

MIP improves the detection of pulmonary nodules in chest CTs of children, but overall interreader agreement is only fair. Double reading represents a powerful tool to increase diagnostic reliability in chest CTs of children with a malignancy. Nodule characterization is best with 1 mm slices. Intrapulmonary lymph nodes occur in children with and without malignancy.

JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function

OBJECTIVE

The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system.

MATERIALS AND METHODS

A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis.

RESULTS

In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p < 0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%.

CONCLUSION

The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.

Evidence-based anatomical review areas derived from systematic analysis of cases from a radiological departmental discrepancy meeting

AIM

To produce short checklists of specific anatomical review sites for different regions of the body based on the frequency of radiological errors reviewed at radiology discrepancy meetings, thereby creating "evidence-based" review areas for radiology reporting.

MATERIALS AND METHODS

A single centre discrepancy database was retrospectively reviewed from a 5-year period. All errors were classified by type, modality, body system, and specific anatomical location. Errors were assigned to one of four body regions: chest, abdominopelvic, central nervous system (CNS), and musculoskeletal (MSK). Frequencies of errors in anatomical locations were then analysed.

RESULTS

There were 561 errors in 477 examinations; 290 (46%) errors occurred in the abdomen/pelvis, 99 (15.7%) in the chest, 117 (18.5%) in the CNS, and 125 (19.9%) in the MSK system. In each body system, the five most common location were chest: lung bases on computed tomography (CT), apices on radiography, pulmonary vasculature, bones, and mediastinum; abdominopelvic: vasculature, colon, kidneys, liver, and pancreas; CNS: intracranial vasculature, peripheral cerebral grey matter, bone, parafalcine, and the frontotemporal lobes surrounding the Sylvian fissure; and MSK: calvarium, sacrum, pelvis, chest, and spine.

CONCLUSION

The five listed locations accounted for >50% of all perceptual errors suggesting an avenue for focused review at the end of reporting.

The Effect of Teaching Search Strategies on Perceptual Performance

RATIONALE AND OBJECTIVES

Radiology expertise is dependent on the use of efficient search strategies. The aim of this study is to investigate the effect of teaching search strategies on trainee's accuracy in detecting lung nodules at computed tomography.

MATERIALS AND METHODS

Two search strategies, "scanning" and "drilling," were tested with a randomized crossover design. Nineteen junior radiology residents were randomized into two groups. Both groups first completed a baseline lung nodule detection test allowing a free search strategy, followed by a test after scanning instruction and drilling instruction or vice versa. True positive (TP) and false positive (FP) scores and scroll behavior were registered. A mixed-design analysis of variance was applied to compare the three search conditions.

RESULTS

Search strategy instruction had a significant effect on scroll behavior, F(1.3) = 54.2, P < 0.001; TP score, F(2) = 16.1, P < 0.001; and FP score, F(1.3) = 15.3, P < 0.001. Scanning instruction resulted in significantly lower TP scores than drilling instruction (M = 10.7, SD = 5.0 versus M = 16.3, SD = 5.3), t(18) = 4.78, P < 0.001; or free search (M = 15.3, SD = 4.6), t(18) = 4.44, P < 0.001. TP scores for drilling did not significantly differ from free search. FP scores for drilling (M = 7.3, SD = 5.6) were significantly lower than for free search (M = 12.5, SD = 7.8), t(18) = 4.86, P < 0.001.

CONCLUSIONS

Teaching a drilling strategy is preferable to teaching a scanning strategy for finding lung nodules.

Efficacy of Maximum Intensity Projection of Contrast-Enhanced 3D Turbo-Spin Echo Imaging with Improved Motion-Sensitized Driven-Equilibrium Preparation in the Detection of Brain Metastases

Objective

To evaluate the diagnostic benefits of 5-mm maximum intensity projection of improved motion-sensitized driven-equilibrium prepared contrast-enhanced 3D T1-weighted turbo-spin echo imaging (MIP iMSDE-TSE) in the detection of brain metastases. The imaging technique was compared with 1-mm images of iMSDE-TSE (non-MIP iMSDE-TSE), 1-mm contrast-enhanced 3D T1-weighted gradient-echo imaging (non-MIP 3D-GRE), and 5-mm MIP 3D-GRE.

Materials and Methods

From October 2014 to July 2015, 30 patients with 460 enhancing brain metastases (size > 3 mm, n = 150; size ≤ 3 mm, n = 310) were scanned with non-MIP iMSDE-TSE and non-MIP 3D-GRE. We then performed 5-mm MIP reconstruction of these images. Two independent neuroradiologists reviewed these four sequences. Their diagnostic performance was compared using the following parameters: sensitivity, reading time, and figure of merit (FOM) derived by jackknife alternative free-response receiver operating characteristic analysis. Interobserver agreement was also tested.

Results

The mean FOM (all lesions, 0.984; lesions ≤ 3 mm, 0.980) and sensitivity ([reader 1: all lesions, 97.3%; lesions ≤ 3 mm, 96.2%], [reader 2: all lesions, 97.0%; lesions ≤ 3 mm, 95.8%]) of MIP iMSDE-TSE was comparable to the mean FOM (0.985, 0.977) and sensitivity ([reader 1: 96.7, 99.0%], [reader 2: 97, 95.3%]) of non-MIP iMSDE-TSE, but they were superior to those of non-MIP and MIP 3D-GREs (all, p < 0.001). The reading time of MIP iMSDE-TSE (reader 1: 47.7 ± 35.9 seconds; reader 2: 44.7 ± 23.6 seconds) was significantly shorter than that of non-MIP iMSDE-TSE (reader 1: 78.8 ± 43.7 seconds, p = 0.01; reader 2: 82.9 ± 39.9 seconds, p < 0.001). Interobserver agreement was excellent (κ > 0.75) for all lesions in both sequences.

Conclusion

MIP iMSDE-TSE showed high detectability of brain metastases. Its detectability was comparable to that of non-MIP iMSDE-TSE, but it was superior to the detectability of non-MIP/MIP 3D-GREs. With a shorter reading time, the false-positive results of MIP iMSDE-TSE were greater. We suggest that MIP iMSDE-TSE can provide high diagnostic performance and low false-positive rates when combined with 1-mm sequences.

Proton Magnetic Resonance Imaging for Initial Assessment of Isolated Mycobacterium avium Complex Pneumonia

RATIONALE

Computed tomographic (CT) radiography is the reference standard for imaging Mycobacterium avium complex (MAC) lung infection. Magnetic resonance imaging (MRI) has been shown to be comparable to CT for characterizing other pulmonary inflammatory conditions, but has not been rigorously tested for imaging MAC pneumonia.

OBJECTIVES

To determine the feasibility of pulmonary MRI for imaging MAC pneumonia and to assess the degree of agreement between MRI and CT for assessing the anatomic features and lobar extent of MAC lung infections.

METHODS

Twenty-five subjects with culture-confirmed MAC pneumonia and no identified coinfecting organisms were evaluated by thoracic MRI and then by chest CT imaging performed up to 1 week later. After deidentification, first the MRI and then the CT scans were scored 2 weeks apart by two chest radiologists working independently of one another. Discrepancies were resolved by a third chest radiologist. The scans were scored for bronchiectasis, consolidation or atelectasis, abscess or sacculation, nodules, and mucus plugging using a three-point lobar scale (absent, <50% of lobe, and >50% of lobe). Agreement analyses and ordinary least products regressions were performed.

MEASUREMENTS AND MAIN RESULTS

A fixed bias was found between total CT and MRI scores, with CT scoring higher on average (median difference: 4 on a scale of 48; interquartile range: 3, 6). Fixed biases were found for bronchiectasis and consolidation or atelectasis subscale scores. Both fixed and proportional biases were found between CT and MRI mucus plugging scores. No bias was found between CT and MRI nodule scores. There was nearly perfect lobar percent agreement for more conspicuous findings such as consolidation or atelectasis and abscess or sacculation.

CONCLUSIONS

In this exploratory study of 25 adult patients with culture-proven MAC lung infection, we found moderate agreement between MRI and CT for assessing the anatomic features and lobar extent of disease. Given the feasibility of chest MRI for this condition, future work is warranted to assess the clinical impact of MRI compared with CT in assessing progression of untreated MAC infection and response to treatment over time.

Multidetector CT detection of peritoneal metastases: evaluation of sensitivity between standard 2.5 mm axial imaging and maximum-intensity-projection (MIP) reconstructions

OBJECTIVE

Our purpose was to evaluate the sensitivity of multidetector CT for the detection of peritoneal metastases between standard 2.5 mm axial imaging and maximum-intensity-projection (MIP) reconstructions.

MATERIALS AND METHODS

The Institutional Review Board approved this retrospective study and waived the need to obtain patient consent. We retrospectively identified 36 patients with pancreatic adenocarcinoma and peritoneal metastatic disease who underwent a pancreatic protocol CT examination of the abdomen and pelvis between January 2012 and January 2014. Three independent radiologists reviewed a randomized combination of standard axial (2.5 mm reconstructed thickness, 2.5 mm interval) and axial MIP reconstructions (6, 3 mm interval) over two sessions. Each reader recorded metastasis location in PACS. Subsequent consensus review by two radiologists determined the final number and size of metastases.

RESULTS

The reviewers found 328 peritoneal implants in 36 patients. After accounting for the size, location, and number of lesions as well as multiple readers, a generalized estimating equations model showed that the statistical combination of MIP with standard technique significantly increased the odds of correctly identifying a lesion (OR 2.16; 95% CI 1.86-2.51; p value < 0.0001) compared to standard technique alone. MIP reconstruction as a standalone technique was less sensitive compared to standard technique alone (OR 0.81; 95% CI 0.65-0.99; p value = 0.0468). When compared to standard axial imaging, evaluation via MIP reconstructions resulted in the identification of an additional 50 (15%), 45 (14%), and 55 (17%) lesions by Readers 1-3, respectively.

CONCLUSION

The axial 6 mm MIP series is complimentary in the CT evaluation of peritoneal metastases. MIP reconstruction evaluation identified a significant number of additional lesions, but is not adequate as a standalone technique for peritoneal cavity assessment.

Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels

OBJECTIVE

The objective of our study was to evaluate lung nodule detection rates on standard and microdose chest CT with two different computer-aided detection systems (SyngoCT-CAD, VA 20, Siemens Healthcare [CAD1]; Lung CAD, IntelliSpace Portal DX Server, Philips Healthcare [CAD2]) as well as maximum-intensity-projection (MIP) images. We also assessed the impact of different reconstruction kernels.

MATERIALS AND METHODS

Standard and microdose CT using three reconstruction kernels (i30, i50, i70) was performed with an anthropomorphic chest phantom. We placed 133 ground-glass and 133 solid nodules (diameters of 5 mm, 8 mm, 10 mm, and 12 mm) in 55 phantoms. Four blinded readers evaluated the MIP images; one recorded the results of CAD1 and CAD2. Sensitivities for CAD and MIP nodule detection on standard dose and microdose CT were calculated for each reconstruction kernel.

RESULTS

Dose for microdose CT was significantly less than that for standard-dose CT (0.1323 mSv vs 1.65 mSv; p < 0.0001). CAD1 delivered superior results compared with CAD2 for standard-dose and microdose CT (p < 0.0001). At microdose level, the best stand-alone sensitivity (97.6%) was comparable with CAD1 sensitivity (96.0%; p = 0.36; both with i30 reconstruction kernel). Pooled sensitivities for all nodules, doses, and reconstruction kernels on CAD1 ranged from 88.9% to 97.3% versus 49.6% to 73.9% for CAD2. The best sensitivity was achieved with standard-dose CT, i50 kernel, and CAD1 (97.3%) versus 96% with microdose CT, i30 or i50 kernel, and CAD1. MIP images and CAD1 had similar performance at both dose levels (p = 0.1313 and p = 0.48).

CONCLUSION

Submillisievert CT is feasible for detecting solid and ground-glass nodules that require soft-tissue kernels for MIP and CAD systems to achieve acceptable sensitivities. MIP reconstructions remain a valuable adjunct to the interpretation of chest CT for increasing sensitivity and have the advantage of significantly lower false-positive rates.

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.

Lung nodule and cancer detection in computed tomography screening

Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.

Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives

INTRODUCTION

The imaging evaluation of pulmonary nodules, often incidentally detected on imaging examinations performed for other clinical reasons, is a frequently encountered clinical circumstance. With advances in imaging modalities, both the detection and characterization of pulmonary nodules continue to evolve and improve.

AREAS COVERED

This article will review the imaging modalities used to detect and diagnose benign and malignant pulmonary nodules, with a focus on computed tomography (CT), which continues to be the mainstay for evaluation. The authors discuss recent advances in the lung nodule management, and an algorithm for the management of indeterminate pulmonary nodules.

EXPERT OPINION

There are set of criteria that define a benign nodule, the most important of which are the lack of temporal change for 2 years or more, and certain benign imaging criteria, including specific patterns of calcification or the presence of fat. Although some indeterminate pulmonary nodules are immediately actionable, generally those approaching 1 cm or larger in diameter, at which size the diagnostic accuracy of tools such as positron emission tomography (PET)/CT, single photon emission CT (SPECT) and biopsy techniques are sufficient to warrant their use. The majority of indeterminate pulmonary nodules are under 1 cm, for which serial CT examinations through at least 2 years for solid nodules and 3 years for ground-glass nodules, are used to demonstrate either benign biologic behavior or otherwise. The management of incidental pulmonary nodules involves a multidisciplinary approach in which radiology plays a pivotal role. Newer imaging and postprocessing techniques have made this a more accurate technique eliminating ambiguity and unnecessary follow-up.

Comparison of chest dual-energy subtraction digital tomosynthesis and dual-energy subtraction radiography for detection of pulmonary nodules: initial evaluations in human clinical cases

RATIONALE AND OBJECTIVES

To compare initial evaluations of chest dual-energy subtraction digital tomosynthesis (DES-DT) and dual-energy subtraction radiography (DES-R) for detection of pulmonary nodules.

MATERIALS AND METHODS

DES-DT and DES-R systems with pulsed x-rays and rapid kV switching were used to evaluate pulmonary nodules (>4-6 mm, 2 nodules; >6-8 mm, 2 nodules; >8 mm, 32 nodules). Multidetector computed tomography was used as a reference. A filtered back-projection algorithm was used to reconstruct low-voltage (60 kVp), high-voltage (120 kVp), and soft-tissue or bone-subtracted tomograms of the desired layer thicknesses from the image data acquired during a single tomographic scan. DES-R images were processed from the low- and high-voltage images. To detect the pulmonary nodules, we used both systems to examine 36 patients with and 36 patients without pulmonary nodules. Two radiologists and three doctors of pulmonary medicine (average experience, 18 years) performed receiver operating characteristic (ROC) curve analysis to evaluate the results.

RESULTS

The ROC analysis results suggested that the detection ability was significantly better for DES-DT than for DES-R (P < .0001; 95% confidence interval: DES-DT, 0.94 [0.83-0.99]; DES-R, 0.76 [0.68-0.85]; sensitivity: DES-DT, 87.7 ± 2.9%; DES-R, 53.8 ± 3.5%; specificity: DES-DT, 78.3 ± 5.6%; DES-R, 78.4 ± 3.4%; accuracy: DES-DT, 83.1 ± 3.8%, DES-R, 66.1 ± 2.0%). When the nodules were no longer superimposed over the normal structures, their characteristics and distribution could be observed much more clearly.

CONCLUSION

Compared with DES-R, DES-DT provided greater sensitivity for detection of pulmonary nodules, particularly for the larger ones.

Detection of pulmonary nodules at paediatric CT: maximum intensity projections and axial source images are complementary

BACKGROUND

Maximum intensity projection (MIP) images might be useful in helping to differentiate small pulmonary nodules from adjacent vessels on thoracic multidetector CT (MDCT).

OBJECTIVE

The aim was to evaluate the benefits of axial MIP images over axial source images for the paediatric chest in an interobserver variability study.

MATERIALS AND METHODS

We included 46 children with extra-pulmonary solid organ malignancy who had undergone thoracic MDCT. Three radiologists independently read 2-mm axial and 10-mm MIP image datasets, recording the number of nodules, size and location, overall time taken and confidence.

RESULTS

There were 83 nodules (249 total reads among three readers) in 46 children (mean age 10.4 ± 4.98 years, range 0.3-15.9 years; 24 boys). Consensus read was used as the reference standard. Overall, three readers recorded significantly more nodules on MIP images (228 vs. 174; P < 0.05), improving sensitivity from 67% to 77.5% (P < 0.05) but with lower positive predictive value (96% vs. 85%, P < 0.005). MIP images took significantly less time to read (71.6 ± 43.7 s vs. 92.9 ± 48.7 s; P < 0.005) but did not improve confidence levels.

CONCLUSION

Using 10-mm axial MIP images for nodule detection in the paediatric chest enhances diagnostic performance, improving sensitivity and reducing reading time when compared with conventional axial thin-slice images. Axial MIP and axial source images are complementary in thoracic nodule detection.

Technical parameters and interpretive issues in screening computed tomography scans for lung cancer

Lung cancer screening computed tomographies (CTs) differ from traditional chest CT scans in that they are performed at very low radiation doses, which allow the detection of small nodules but which have a much higher noise content than would be acceptable in a diagnostic chest CT. The technical parameters require a great deal of attention on the part of the user, because inappropriate settings could result in either excess radiation dose to the large population of screened individuals or in low-quality images with impaired nodule detectability. Both situations undermine the main goal of the screening program, which is to detect lung nodules using as low a radiation dose as can reasonably be achieved. Once an image has been obtained, there are unique interpretive issues that must be addressed mainly because of the very high noise content of the images and the high prevalence of incidental findings in the chest unrelated to the sought-after pulmonary nodules.

Significance of pulmonary nodules in patients with colorectal cancer

OBJECTIVES

Radiographically small pulmonary nodules (PNs) in patients with colorectal cancer are troublesome because their discovery raises concern about metastases. This study sought to establish the appropriate timing of radiological follow-up for PNs detected at initial staging evaluation of colorectal carcinoma patients.

METHODS

The medical records of 376 consecutive colorectal cancer patients who underwent curative surgery and had baseline and follow-up chest X-rays (CXR) and computed tomography (CT) were reviewed.

RESULTS

The study included 92 patients who had all CXR and chest CT available for review, at least one PN found on baseline imaging, and no synchronous neoplasms. On baseline chest CT, these 92 patients had 170 PNs altogether and 77 (45.2 %) of them were greater than 5 mm in size. Baseline CXR detected 13 PNs in 12 patients and all but 2 were larger than 5 mm. Nodule size greater than 5 mm and irregular margins were predictors of nodule growth. The mean doubling time of 24/170 (14.1 %) growing PNs was about 4 months.

CONCLUSIONS

Our findings suggest that baseline and follow-up CXR are pointless, and short-interval CT follow-up is warranted when PNs larger than 5 mm with irregular margins are detected on preoperative chest CT.

KEY POINTS

• Pulmonary nodules in colorectal cancer patients raise concern about metastasis. • Baseline and follow-up chest X-ray in colorectal cancer can be abandoned. • CT is the best technique for assessing PNs in colorectal cancer. • Short-interval CT follow-up advisable for PNs larger than 5 mm with irregular margins.

Value of multidetector computed tomography image segmentation for preoperative planning in general surgery

BACKGROUND

Using practical examples, this report aims to highlight the clinical value of patient-specific three-dimensional (3D) models, obtained segmenting multidetector computed tomography (MDCT) images, for preoperative planning in general surgery.

METHODS

In this study, segmentation and 3D model generation were performed using a semiautomatic tool developed in the authors' laboratory. Their segmentation procedure is based on the neighborhood connected region-growing algorithm that, appropriately parameterized for the anatomy of interest and combined with the optimal segmentation sequence, generates good-quality 3D images coupled with facility of use. Using a touch screen monitor, manual refining can be added to segment structures unsuitable for automatic reconstruction. Three-dimensional models of 10 candidates for major general surgery procedures were presented to the operating surgeons for evaluation. A questionnaire then was administered after surgery to assess the perceived added value of the new technology.

RESULTS

The questionnaire results were very positive. The authors recorded the diffuse opinion that planning the procedure using a segmented data set allows the surgeon to plan critical interventions with better awareness of the specific patient anatomy and consequently facilitates choosing the best surgical approach.

CONCLUSIONS

The benefit shown in this report supports a wider use of segmentation software in clinical practice, even taking into account the extra time and effort required to learn and use these systems.

Learning aspects and potential pitfalls regarding detection of pulmonary nodules in chest tomosynthesis and proposed related quality criteria

BACKGROUND

In chest tomosynthesis, low-dose projections collected over a limited angular range are used for reconstruction of an arbitrary number of section images of the chest, resulting in a moderately increased radiation dose compared to chest radiography.

PURPOSE

To investigate the effects of learning with feedback on the detection of pulmonary nodules for observers with varying experience of chest tomosynthesis, to identify pitfalls regarding detection of pulmonary nodules, and present suggestions for how to avoid them, and to adapt the European quality criteria for chest radiography and computed tomography (CT) to chest tomosynthesis.

MATERIAL AND METHODS

Six observers analyzed tomosynthesis cases for presence of nodules in a jackknife alternative free-response receiver-operating characteristics (JAFROC) study. CT was used as reference. The same tomosynthesis cases were analyzed before and after learning with feedback, which included a collective learning session. The difference in performance between the two readings was calculated using the JAFROC figure of merit as principal measure of detectability.

RESULTS

Significant improvement in performance after learning with feedback was found only for observers inexperienced in tomosynthesis. At the collective learning session, localization of pleural and subpleural nodules or structures was identified as the main difficulty in analyzing tomosynthesis images.

CONCLUSION

The results indicate that inexperienced observers can reach a high level of performance regarding nodule detection in tomosynthesis after learning with feedback and that the main problem with chest tomosynthesis is related to the limited depth resolution.

Dose reduction strategies for thoracic multidetector computed tomography: background, current issues, and recommendations

This review will summarize the current background knowledge about radiation exposure related to thoracic computed tomography (CT). It will also review the historical development in this area. This will be followed by a summary of current efforts to reduce dose with respect to predefined clinical indications. Finally, the review will indicate future strategies for further dose reduction in thoracic CT imaging and give practical recommendations for everyday use.

[Localized pure or mixed ground-glass lung opacities]

Localized ground-glass opacities (GGOs) have been recently individualized and account for between 2.9% and 19% of all pulmonary nodules detected in high-risk patients included in CT screening series for lung cancer. These opacities, nodular, lobular or flat, correspond to benign lesions (localised infectious and inflammatory diseases, focal interstitial fibrosis, and atypical alveolar hyperplasia) or malignant lesions (bronchioloalveolar carcinoma, early-stage adenocarcinoma and sometimes metastases). Localized GGOs are more likely to be malignant than solid nodules and prognosis is related to the percentage of the ground-glass component. However, doubling time of pure localized malignant GGOs is longer than mixed localized malignant GGOs and even longer than the doubling time of solid malignant nodules. Therefore, localized GGOs warrant a dedicated diagnostic workup.

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.