Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT

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.

Time-Resolved 3D cardiopulmonary MRI reconstruction using spatial transformer network

The accurate visualization and assessment of the complex cardiac and pulmonary structures in 3D is critical for the diagnosis and treatment of cardiovascular and respiratory disorders. Conventional 3D cardiac magnetic resonance imaging (MRI) techniques suffer from long acquisition times, motion artifacts, and limited spatiotemporal resolution. This study proposes a novel time-resolved 3D cardiopulmonary MRI reconstruction method based on spatial transformer networks (STNs) to reconstruct the 3D cardiopulmonary MRI acquired using 3D center-out radial ultra-short echo time (UTE) sequences. The proposed reconstruction method employed an STN-based deep learning framework, which used a combination of data-processing, grid generator, and sampler. The reconstructed 3D images were compared against the start-of-the-art time-resolved reconstruction method. The results showed that the proposed time-resolved 3D cardiopulmonary MRI reconstruction using STNs offers a robust and efficient approach to obtain high-quality images. This method effectively overcomes the limitations of conventional 3D cardiac MRI techniques and has the potential to improve the diagnosis and treatment planning of cardiopulmonary disorders.

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.

Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)

Objective. We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary magnetic resonance imaging.Approach. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space. Specifically, we model the deformation map at each time instant as the output of a CNN-based generator that has the same weight for all time-frames, driven by a low-dimensional latent vector. The time series of latent vectors account for the dynamics in the dataset, including respiratory motion and bulk motion. The template image volume, the parameters of the generator, and the latent vectors are learned directly from the k-t space data in an unsupervised fashion.Main results. Our experimental results show improved reconstructions compared to state-of-the-art methods, especially in the context of bulk motion during the scans.Significance. The proposed unsupervised motion-compensated scheme jointly estimates the latent vectors that capture the motion dynamics, the corresponding deformation maps, and the reconstructed motion-compensated images from the raw k-t space data of each subject. Unlike current motion-resolved strategies, the proposed scheme is more robust to bulk motion events during the scan.

Breast Computed Tomography: Diagnostic Performance of the Maximum Intensity Projection Reformations as a Stand-Alone Method for the Detection and Characterization of Breast Findings

OBJECTIVES

This study aimed to evaluate the diagnostic performance of the maximum intensity projection (MIP) reformations of breast computed tomography (B-CT) images as a stand-alone method for the detection and characterization of breast imaging findings.

MATERIALS AND METHODS

A total of 160 women undergoing B-CT between August 2018 and December 2020 were retrospectively included; 80 patients with known breast imaging findings were matched with 80 patients without imaging findings according to age and amount of fibroglandular tissue (FGT). A total of 71 benign and 9 malignant lesions were included. Images were evaluated using 15-mm MIP in 3 planes by 2 radiologists with experience in B-CT. The presence of lesions and FGT were evaluated, using the BI-RADS classification. Interreader agreement and descriptive statistics were calculated.

RESULTS

The interreader agreement of the 2 readers for finding a lesion (benign or malignant) was 0.86 and for rating according to BI-RADS classification was 0.82. One of 9 cancers (11.1%) was missed by both readers due to dense breast tissue. BI-RADS 1 was correctly applied to 73 of 80 patients (91.3%) by reader 1 and to 74 of 80 patients (92.5%) by reader 2 without recognizable lesions. BI-RADS 2 or higher with a lesion in at least one of the breasts was correctly applied in 69 of 80 patients (86.3%) by both readers. For finding a malignant lesion, sensitivity was 88.9% (95% confidence interval [CI], 51.75%-99.72%) for both readers, and specificity was 99.3% (95% CI, 96.4%-100%) for reader 1 and 100% (95% CI, 97.20%-100.00%) for reader 2.

CONCLUSIONS

Evaluation of B-CT images using the MIP reformations may help to reduce the reading time with high diagnostic performance and confidence.

Características clinicopatológicas de nódulos pulmonares: Experiencia en Clínica Reina Sofía, Bogotá, Colombia

Introducción. El cáncer de pulmón es la primera causa de mortalidad por cáncer a nivel mundial, lo que hace que sea considerado un problema de salud pública. Existen diferentes hallazgos imagenológicos que hacen sospechar la presencia de cáncer de pulmón, uno de los cuales son los nódulos pulmonares; sin embargo, estos también pueden verse en entidades benignas. Métodos. Se incluyeron 66 pacientes con biopsia de nódulo pulmonar en la Clínica Reina Sofía, en la ciudad de Bogotá, D.C., Colombia, entre el 1° de marzo del 2017 y el 28 de febrero del 2020. Se analizaron las características demográficas de los pacientes, las características morfológicas e histopatológicas de los nódulos pulmonares y la correlación entre sus características imagenológicas e histopatológicas. Resultados. El 69,2 % de los nódulos estudiados tenían etiología maligna, de estos el 55,5 % era de origen metástasico y el 44,5 % eran neoplasias primarias de pulmón, con patrón sólido en el 70,6 % de los casos. El patrón histológico más frecuente fue adenocarcinoma. Respecto a las características radiológicas, en su mayoría los nódulos malignos medían de 1 a 2 cm, de morfología lisa y distribución múltiple, localizados en lóbulos superiores. Conclusiones. La caracterización de los nódulos pulmonares brinda información relevante que orienta sobre los diagnósticos más frecuentes en nuestro medio, cuando se estudian nódulos sospechosos encontrados incidentalmente o en el seguimiento de otro tumor. Como el nódulo es la manifestación del cáncer temprano del pulmón, establecer programas de tamización que permitan el diagnóstico oportuno, es hoy día una imperiosa necesidad, para reducir la mortalidad.

Prospective Study of Spatial Distribution of Missed Lung Nodules by Readers in CT Lung Screening Using Computer-assisted Detection

PURPOSE

To evaluate the spatial patterns of missed lung nodules in a real-life routine screening environment.

MATERIALS AND METHODS

In a screening institute, 4,822 consecutive adults underwent chest CT, and each image set was independently interpreted by two radiologists in three steps: (1) independently interpreted without computer-assisted detection (CAD) software, (2) independently referred to the CAD results, (3) determined by the consensus of the two radiologists. The locations of nodules and the detection performance data were semi-automatically collected using a CAD server integrated into the reporting system. Fisher's exact test was employed for evaluating findings in different lung divisions. Probability maps were drawn to illustrate the spatial distribution of radiologists' missed nodules.

RESULTS

Radiologists significantly tended to miss lung nodules in the bilateral hilar divisions (p < 0.01). Some radiologists had their own spatial pattern of missed lung nodules.

CONCLUSION

Radiologists tend to miss lung nodules present in the hilar regions significantly more often than in the rest of the lung.

Late presentation of lung adenocarcinoma in a stable solitary pulmonary nodule: A case presentation and review of the literature

A 67-year-old patient has been followed by our pulmonary clinic for Chronic obstructive pulmonary disease (COPD) and a stable pulmonary nodule. Solitary pulmonary nodule (SPN) was detected on the lung cancer screening by low dose computed tomography (CT) scan of the chest. It remained stable on repeat CT scan at 6, 12 and 24-months interval. Yearly lung cancer low dose CT scans of the chest showed stability of the SPN for 12 years. A mechanical fall necessitating trauma workup unveiled increase in size of the nodule from 4 mm to 11 mm within one year of the previous screening CT chest. Biopsy and Histopathology confirmed the diagnosis of lung adenocarcinoma. The patient then underwent right upper lobectomy followed by chemoradiation therapy. Current guidelines do not recommend follow up for a solitary pulmonary nodules less than 6 mm nodule if it remains stable for 12-24 months. Our case report of the late presentation of lung adenocarcinoma in a stable solitary pulmonary nodule suggests the need to exercise increased caution in the management of incidental pulmonary nodules.

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.

Breast mass assessment on chest CT: Axial, sagittal, coronal or maximal intensity projection?

OBJECTIVES

The goal of this work is to determine the optimal projection to detect breast masses on Chest CT.

METHODS

Institutional Review Board (HIPPA compliant) approval was obtained with a waiver of consent. 10 image pairs of Chest CT images containing breast masses were selected for review by 10 chest radiologists: the pairs consisted of axial, sagittal, coronal and axial MIP images (MIP images) with each projection compared to a MIP and with one another. For each pair, the image where the mass was most conspicuous was recorded.

RESULTS

MIPs were preferred to any cross sectional projection 82% of the time; sagittal (63%) or coronal (63%) images were preferred to the axial projection. When sagittal and coronal images were compared there was no preference.

CONCLUSIONS

MIP images should be obtained and reviewed for breast pathology; sagittal or coronal projections may provide additional information.

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.

Diagnostic Accuracy of Maximum Intensity Projection in Diagnosis of Malignant Pulmonary Nodules

Introduction Pulmonary nodules are frequently encountered during chest imaging, and its evaluation is usually done by chest radiograph and computed tomography (CT) scan of chest. High resolution of multidetector CT (MDCT) has improved the nodule detection. Post processing techniques such as maximum intensity projection (MIP) can further improve the sensitivity of MDCT for nodule detection. Failure to diagnose malignancy in pulmonary nodules can delay the treatment. Therefore, the aim of this study was to determine the diagnostic accuracy of MIP in the diagnosis of malignant pulmonary nodules taking histopathology findings as gold standard. Materials and methods A retrospective cross-sectional study was conducted at Dow Institute of Radiology, Dow University of Health Sciences, from 1 December 2018 till 30 June 2019. Both male and female patients aged 18 years and above who underwent CT scan of chest with suspicion of pulmonary nodules were included. Patients already diagnosed with malignant pulmonary nodules and presenting for follow-up were excluded. Contrast-enhanced CT chest was performed on a multi-slice scanner. MIP reconstruction and evaluation was performed on the workstation. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP were calculated taking histopathology findings as gold standard. Results A total of 202 patients were included in this study. The mean age of the patients was 55.87 ± 13.08 years. A total of 103 patients (51.0%) were males and 99 patients (49.0%) were females. There were 131 (64.9%) nodules with smooth margins and 71 (35.1%) nodules with irregular margins. The mean size of nodule was 3.1 ± 0.7 cm. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of MIP in diagnosing malignant pulmonary nodules were found to be 85.82%, 82.35%, 90.55%, 74.67%, and 84.65%, respectively, taking histopathology findings as gold standard. The nodules >3 cm in size had a higher sensitivity for diagnosing malignant pulmonary nodules. Smooth margin nodule had high sensitivity, specificity, and diagnostic accuracy for diagnosing malignant pulmonary nodules. Conclusion MIP images have high sensitivity, specificity, and diagnostic accuracy in the diagnosis of malignant pulmonary nodules. The utilization of MIP images can aid in the detection of malignant pulmonary nodules and help in formulating early treatment strategies for the patients. Other post processing techniques such as volume rendering and computer-aided detection can help in further improving patient care.

Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance

Purpose

To compare sensitivity in the detection of lung nodules between the deep learning (DL) model and radiologists using various patient population and scanning parameters and to assess whether the radiologists' detection performance could be enhanced when using the DL model for assistance.

Materials and Methods

A total of 12 754 thin-section chest CT scans from January 2012 to June 2017 were retrospectively collected for DL model training, validation, and testing. Pulmonary nodules from these scans were categorized into four types: solid, subsolid, calcified, and pleural. The testing dataset was divided into three cohorts based on radiation dose, patient age, and CT manufacturer. Detection performance of the DL model was analyzed by using a free-response receiver operating characteristic curve. Sensitivities of the DL model and radiologists were compared by using exploratory data analysis. False-positive detection rates of the DL model were compared within each cohort. Detection performance of the same radiologist with and without the DL model were compared by using nodule-level sensitivity and patient-level localization receiver operating characteristic curves.

Results

The DL model showed elevated overall sensitivity compared with manual review of pulmonary nodules. No significant dependence regarding radiation dose, patient age range, or CT manufacturer was observed. The sensitivity of the junior radiologist was significantly dependent on patient age. When radiologists used the DL model for assistance, their performance improved and reading time was reduced.

Conclusion

DL shows promise to enhance the identification of pulmonary nodules and benefit nodule management.© RSNA, 2019Supplemental material is available for this article.

Management of pulmonary nodules

Pulmonary nodules are frequently detected during clinical practice and require a structured approach in their management in order to identify early lung cancers and avoid harm from over investigation. The article reviews the 2015 British Thoracic Society guidelines for the management of pulmonary nodules and the evidence behind them.

Operative Planning in Thoracic Surgery: A Pilot Study Comparing Imaging Techniques and Three-Dimensional Printing

BACKGROUND

Careful preoperative planning in thoracic surgery is essential for positive outcomes, especially in video-assisted thoracic surgery (VATS), where palpation and 3-dimensional (3D) imaging is restricted. This study evaluated the ability of different imaging techniques, such as computed tomography (CT) scanning, maximal intensity projection imaging, 3D reconstruction, and 3D printing, to define the anatomy of the hilar structures before anatomical lung resection.

METHODS

All patients undergoing elective lung resections by VATS for cancer under a single surgeon were identified over a 3-month period. The surgeon was asked to record the number of pulmonary artery branches supplying the lobe to be resected by using the preoperative CT scans, maximal intensity projection images, and 3D-reconstructed CT images. The lung hilum in 3 patients was printed. These were then compared with the intraoperative findings.

RESULTS

The preoperative imaging of 16 patients was analyzed. The lung hilum was printed in a further 3 patients. Although not statistically significant, the 3D prints of the hilum were the most accurate measurement, with a correlation of 0.92. CT, 3D-reconstructed CT, and maximal intensity projection images tended to underrecognize the number of arterial branches and therefore scored between 0.26 and 0.39 in absolute agreement with the number of arteries found at operation.

CONCLUSIONS

3D printing in the planning of thoracic surgery may suggest a benefit over contemporary available imaging modalities, and the use of 3D printing in practicing operations is being established.

Management of incidental lung nodules <8 mm in diameter

Due to the increase of incidentally detected pulmonary nodules and the information obtained from several screening programs, updated guidelines with new recommendations for the management of small pulmonary nodules have been proposed. These international guidelines coincide in proposing periodic follow-up for small nodules, less than 8 mm of diameter. Fleischner and British Thoracic Society guidelines are the most recent and popular guidelines for incidental pulmonary nodules management. They have specific recommendations according to nodule characteristics (density and size) and cancer risk of the patient. Both guidelines separate recommendations for solid and subsolid nodules. Predictive risk models have been developed to improve the nodule management. In certain cases follow up may not be the best option. We discuss the scenarios and options to achieve a histologic diagnosis of these tiny pulmonary nodules.

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.

Missed Lung Cancer

The chest radiograph is one of the most commonly used imaging studies and is the modality of choice for initial evaluation of many common clinical scenarios. Over the last two decades, chest computed tomography has been increasingly used for a wide variety of indications, including respiratory illnesses, trauma, oncologic staging, and more recently lung cancer screening. Diagnostic radiologists should be familiar with the common causes of missed lung cancers on imaging studies in order to avoid detection and interpretation errors. Failure to detect these lesions can potentially have serious implications for both patients as well as the interpreting radiologist.

Preliminary study of time maximum intensity projection computed tomography imaging for the detection of early ischemic change in patient with acute ischemic stroke

Noncontrast computed tomography (NCCT) has been used for the detection of early ischemic change (EIC); however, correct interpretation of NCCT findings requires much clinical experience. This study aimed to assess the accuracy of time maximum intensity projection computed tomography technique (tMIP), which reflects the maximum value for the time phase direction from the dynamic volume data for each projected plane, for detection of EIC, against that of NCCT.Retrospective review of NCCT, cerebral blood volume in CT perfusion (CTP-CBV), and tMIP of 186 lesions from 280 regions evaluated by Alberta Stroke Program Early CT Score (ASPECTS) in 14 patients with acute middle cerebral artery stroke who had undergone whole-brain CTP using 320-row area detector CT was performed. Four radiologists reviewed EIC on NCCT, CTP-CBV, and tMIP in each ASPECTS region at onset using the continuous certainty factor method. Receiver operating characteristic analysis was performed to compare the relative performance for detection of EIC. The correlations were evaluated.tMIP-color showed the best discriminative value for detection of EIC. There were significant differences in the area under the curve for NCCT and tMIP-color, CTP-CBV (P < .05). Scatter plots of ASPECTS showed a positive significant correlation between NCCT, tMIP-gray, tMIP-color, and the follow-up study (NCCT, r = 0.32, P = .0166; tMIP-gray, r = 0.44, P = .0007; tMIP-color, r = 0.34, P = .0104).Because tMIP provides a high contrast parenchymal image with anatomical and vascular information in 1 sequential scan, it showed greater accuracy for detection of EIC and predicted the final infarct extent more accurately than NCCT based on ASPECTS.

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.

Gross tumor volume dependency on phase sorting methods of four-dimensional computed tomography images for lung cancer

Purpose

To see the gross tumor volume (GTV) dependency according to the phase selection and reconstruction methods, we measured and analyzed the changes of tumor volume and motion at each phase in 20 cases with lung cancer patients who underwent image-guided radiotherapy.

Materials and Methods

We retrospectively analyzed four-dimensional computed tomography (4D-CT) images in 20 cases of 19 patients who underwent image-guided radiotherapy. The 4D-CT images were reconstructed by the maximum intensity projection (MIP) and the minimum intensity projection (Min-IP) method after sorting phase as 40%–60%, 30%–70%, and 0%–90%. We analyzed the relationship between the range of motion and the change of GTV according to the reconstruction method.

Results

The motion ranges of GTVs are statistically significant only for the tumor motion in craniocaudal direction. The discrepancies of GTV volume and motion between MIP and Min-IP increased rapidly as the wider ranges of duty cycles are selected.

Conclusion

As narrow as possible duty cycle such as 40%–60% and MIP reconstruction was suitable for lung cancer if the respiration was stable. Selecting the reconstruction methods and duty cycle is important for small size and for large motion range tumors.

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.

MIP Improves Detection of Brain Metastases

PURPOSE

This study aimed to compare the sensitivity for detection of brain metastases using postcontrast 3-dimensional, T1W-gradient echo sequence (3DT1W) and maximum intensity projections (MIPs) obtained from the same data set.

MATERIALS AND METHODS

A prospective analysis of patients with known brain metastases was performed. We compared 1-mm postcontrast 3DT1W with 6-mm MIP reconstructions obtained from the same images (MIP-3DT1) in 95 patients using 1.5 (42 patients) and 3 T (53 patient). Two independent readers analyzed all studies and the examinations were presented in anonymized and random fashion for a total of 190 interpretations per observer. One reader had more than 20 years of experience and the second reader had 1 year of experience.

RESULTS

The least experienced observer found 542 brain metastases on postcontrast non-MIP 3DT1W and 605 with the MIP-3DT1 technique. For this observer, use of MIP resulted in increased number of detected metastases in 36% of patients regardless of field strength. The more experienced observer found 589 brain metastases on non-MIP 3DT1W and 621 with the MIP-3DT1 technique and the use of the latter also resulted in increased detection of metastases in 33% of patients regardless of field strength.

CONCLUSIONS

In our study, we found that using MIP-3DT1 reconstructions of previously obtained postcontrast 3DT1W improved detection of brain metastases. This improvement was experienced by both the junior and experienced neuroradiologists and was also better at 3.0 T than at 1.5 T.

Interval lung cancer after a negative CT screening examination: CT findings and outcomes in National Lung Screening Trial participants

OBJECTIVES

This study retrospectively analyses the screening CT examinations and outcomes of the National Lung Screening Trial (NLST) participants who had interval lung cancer diagnosed within 1 year after a negative CT screen and before the next annual screen.

METHODS

The screening CTs of all 44 participants diagnosed with interval lung cancer (cases) were matched with negative CT screens of participants who did not develop lung cancer (controls). A majority consensus process was used to classify each CT screen as positive or negative according to the NLST criteria and to estimate the likelihood that any abnormalities detected retrospectively were due to lung cancer.

RESULTS

By retrospective review, 40/44 cases (91%) and 17/44 controls (39%) met the NLST criteria for a positive screen (P < 0.001). Cases had higher estimated likelihood of lung cancer (P < 0.001). Abnormalities included pulmonary nodules ≥4 mm (n = 16), mediastinal (n = 8) and hilar (n = 6) masses, and bronchial lesions (n = 6). Cancers were stage III or IV at diagnosis in 32/44 cases (73%); 37/44 patients (84%) died of lung cancer, compared to 225/649 (35%) for all screen-detected cancers (P < 0.0001).

CONCLUSION

Most cases met the NLST criteria for a positive screen. Awareness of missed abnormalities and interpretation errors may aid lung cancer identification in CT screening.

KEY POINTS

• Lung cancer within a year of a negative CT screen was rare. • Abnormalities likely due to lung cancer were identified retrospectively in most patients. • Awareness of error types may help identify lung cancer sooner.

Imaging of Small Airways Diseases

Small airways disease, or bronchiolitis, encompasses many conditions that result in bronchiolar inflammation and/or fibrosis. Bronchioles are distal airways within secondary pulmonary lobules that are only visible on imaging when abnormal. High-resolution computed tomography plays an important role in diagnosing small airways disease. The predominant direct high-resolution computed tomography sign of bronchiolitis includes centrilobular nodules, whereas air trapping is the main indirect finding. This article reviews bronchiolar anatomy, discusses the differential diagnosis for cellular and constrictive bronchiolitis with a focus on key imaging features, and discusses how to distinguish important mimics.

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.

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.

Improved detection of bone metastases from lung cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images: comparison with standard ¹⁸F-FDG-PET/CT

RATIONALE AND OBJECTIVES

To evaluate the performance of a dedicated computed tomography (CT) software called "bone reading" generating rib unfolded images for improved detection of rib metastases in patients with lung cancer in comparison to readings of 5- and 1-mm axial CT images and (18)F-Fluordeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT).

MATERIALS AND METHODS

Ninety consecutive patients who underwent (18)F-FDG-PET/CT and chest CT scanning between 2012 and 2014 at our institution were analyzed retrospectively. Chest CT scans with 5- and 1-mm slice thickness were interpreted blindly and separately focused on the detection of rib metastases (location, number, cortical vs. medullary, and osteoblastic vs. sclerotic). Subsequent image analysis of unfolded 1 mm-based CT rib images was performed. For all three data sets the reading time was registered. Finally, results were compared to those of FDG-PET. Validation was based on FDG-PET positivity for osteolytic and mixed osteolytic/osteoblastic focal rib lesions and follow-up for sclerotic PET-negative lesions.

RESULTS

A total of 47 metastatic rib lesions were found on FDG-PET/CT plus another 30 detected by CT bone reading and confirmed by follow-up CT. Twenty-nine lesions were osteolytic, 14 were mixed osteolytic/osteoblastic, and 34 were sclerotic. On a patient-based analysis, CT (5 mm), CT (1 mm), and CT (1-mm bone reading) yielded a sensitivity, specificity, and accuracy of 76.5/97.3/93, 81.3/97.3/94, and 88.2/95.9/92, respectively. On segment-based (unfolded rib) analysis, the sensitivity, specificity, and accuracy of the three evaluations were 47.7/95.7/67, 59.5/95.8/77, and 94.8/88.2/92, respectively. Reading time for 5 mm/1 mm axial images and unfolded images was 40.5/50.7/21.56 seconds, respectively.

CONCLUSIONS

The use of unfolded rib images in patients with lung cancer improves sensitivity and specificity of rib metastasis detection in comparison to 5- and 1-mm CT slice reading. Moreover, it may reduce the reading time.

British Society for Medical Mycology best practice recommendations for the diagnosis of serious fungal diseases

Invasive fungal diseases are an important cause of morbidity and mortality in a wide range of patients, and early diagnosis and management are a challenge. We therefore did a review of the scientific literature to generate a series of key recommendations for the appropriate use of microbiological, histological, and radiological diagnostic methods for diagnosis of invasive fungal diseases. The recommendations emphasise the role of microscopy in rapid diagnosis and identification of clinically significant isolates to species level, and the need for susceptibility testing of all Aspergillus spp, if treatment is to be given. In this Review, we provide information to improve understanding of the importance of antigen detection for cryptococcal disease and invasive aspergillosis, the use of molecular (PCR) diagnostics for aspergillosis, and the crucial role of antibody detection for chronic and allergic aspergillosis. Furthermore, we consider the importance of histopathology reporting with a panel of special stains, and emphasise the need for urgent (<48 hours) and optimised imaging for patients with suspected invasive fungal infection. All 43 recommendations are auditable and should be used to ensure best diagnostic practice and improved outcomes for patients.

Picture the future: emerging imaging modalities

The past century has witnessed accelerated development in imaging modalities. Better anatomical visualisation and improved data analysis have improved survival rates. Through emerging functional, molecular and structural imaging modalities, better anatomical visualisation has been extended to cellular and molecular detail, improving diagnosis and management of diseases. This article reviews the advances made in emerging imaging modalities as well as their potential applications in targeted therapy.

Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking

PURPOSE

To determine the effectiveness of radiologists' search, recognition, and acceptance of lung nodules on computed tomographic (CT) images by using eye tracking.

MATERIALS AND METHODS

This study was performed with a protocol approved by the institutional review board. All study subjects provided informed consent, and all private health information was protected in accordance with HIPAA. A remote eye tracker was used to record time-varying gaze paths while 13 radiologists interpreted 40 lung CT images with an average of 3.9 synthetic nodules (5-mm diameter) embedded randomly in the lung parenchyma. The radiologists' gaze volumes ( GV gaze volume s) were defined as the portion of the lung parenchyma within 50 pixels (approximately 3 cm) of all gaze points. The fraction of the total lung volume encompassed within the GV gaze volume s, the fraction of lung nodules encompassed within each GV gaze volume (search effectiveness), the fraction of lung nodules within the GV gaze volume detected by the reader (recognition-acceptance effectiveness), and overall sensitivity of lung nodule detection were measured.

RESULTS

Detected nodules were within 50 pixels of the nearest gaze point for 990 of 992 correct detections. On average, radiologists searched 26.7% of the lung parenchyma in 3 minutes and 16 seconds and encompassed between 86 and 143 of 157 nodules within their GV gaze volume s. Once encompassed within their GV gaze volume , the average sensitivity of nodule recognition and acceptance ranged from 47 of 100 nodules to 103 of 124 nodules (sensitivity, 0.47-0.82). Overall sensitivity ranged from 47 to 114 of 157 nodules (sensitivity, 0.30-0.73) and showed moderate correlation (r = 0.62, P = .02) with the fraction of lung volume searched.

CONCLUSION

Relationships between reader search, recognition and acceptance, and overall lung nodule detection rate can be studied with eye tracking. Radiologists appear to actively search less than half of the lung parenchyma, with substantial interreader variation in volume searched, fraction of nodules included within the search volume, sensitivity for nodules within the search volume, and overall detection rate.

Evidence based imaging strategies for solitary pulmonary nodule

Solitary pulmonary nodule (SPN) is defined as a rounded opacity ≤3 cm in diameter surrounded by lung parenchyma. The majority of smokers who undergo thin-section CT have SPNs, most of which are smaller than 7 mm. In the past, multiple follow-up examinations over a two-year period, including CT follow-up at 3, 6, 12, 18, and 24 months, were recommended when such nodules are detected incidentally. This policy increases radiation burden for the affected population. Nodule features such as shape, edge characteristics, cavitation, and location have not yet been found to be accurate for distinguishing benign from malignant nodules. When SPN is considered to be indeterminate in the initial exam, the risk factor of the patients should be evaluated, which includes patients' age and smoking history. The 2005 Fleischner Society guideline stated that at least 99% of all nodules 4 mm or smaller are benign; when nodule is 5-9 mm in diameter, the best strategy is surveillance. The timing of these control examinations varies according to the nodule size (4-6, or 6-8 mm) and the type of patients, specifically at low or high risk of malignancy concerned. Noncalcified nodules larger than 8 mm diameter bear a substantial risk of malignancy, additional options such as contrast material-enhanced CT, positron emission tomography (PET), percutaneous needle biopsy, and thoracoscopic resection or videoassisted thoracoscopic resection should be considered.

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.

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.

Non-solid lung nodules on low-dose computed tomography: comparison of detection rate between 3 visualization techniques

Objective: To compare various visualization techniques for the detection of non-solid nodules in low-dose lung cancer screening computed tomography (CT) scans. Methods: An enriched sample of 216 male lung cancer screening subjects aged 60.4 ± 6.0 years was used. Two blinded independent readers searched for non-solid nodules on 5-mm multiplanar reconstructions, 1-mm slices and 7-mm maximum intensity projections (trial protocol). The reference standard was a consensus diagnosis of all non-solid nodules reported at least once. Results: Twenty-three individuals (10.6%) had in total 34 non-solid nodules. Interobserver agreement was good (Cohen kappa 0.89–0.95). For both observers, we found no differences between the 3 viewing techniques (P > 0.13). Conclusion: In low-dose lung cancer screening CT scans, we were unable to find a viewing technique superior to that used in the trial by experienced observers who focused on non-solid 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.

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography

OBJECTIVE

The aim of this study was to evaluate whether a computer-aided diagnosis (CAD) system improves interobserver agreement in the interpretation of lung nodules at low-dose computed tomography (CT) screening for lung cancer.

MATERIALS AND METHODS

Baseline low-dose screening CT examinations from 134 participants enrolled in the National Lung Screening Trial were reviewed by 7 chest radiologists. All participants consented to the use of their deidentified images for research purposes. Screening results were classified as positive when noncalcified nodules larger than 4 mm in diameter were present. Follow-up evaluation was recommended according to the nodule diameter: 4 mm or smaller, more than 4 to 8 mm, and larger than 8 mm. When multiple nodules were present, recommendations were based on the largest nodule. Readers initially assessed the nodule presence visually and measured the average nodule diameter manually. Revision of their decisions after reviewing the CAD marks and size measurement was allowed. Interobserver agreement evaluated using multirater κ statistics was compared between initial assessment and that with CAD.

RESULTS

Multirater κ values for the positivity of the screening results and follow-up recommendations were improved from moderate (κ = 0.53-0.54) at initial assessment to good (κ = 0.66-0.67) after reviewing CAD results. The average percentage of agreement between reader pairs on the positivity of screening results and follow-up recommendations per case was also increased from 77% and 72% at initial assessment to 84% and 80% with CAD, respectively.

CONCLUSION

Computer-aided diagnosis may improve the reader agreement on the positivity of screening results and follow-up recommendations in the assessment of low-dose screening CT.