Fleischner Society: Glossary of Terms for Thoracic Imaging.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.

Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs.

Our objective is to investigate the reliability and usefulness of anatomic point-based lung zone segmentation on chest radiographs (CXRs) as a reference standard framework and to evaluate the accuracy of automated point placement. Two hundred frontal CXRs were presented to two radiologists who identified five anatomic points: two at the lung apices, one at the top of the aortic arch, and two at the costophrenic angles. Of these 1000 anatomic points, 161 (16.1%) were obscured (mostly by pleural effusions). Observer variations were investigated. Eight anatomic zones then were automatically generated from the manually placed anatomic points, and a prototype algorithm was developed using the point-based lung zone segmentation to detect cardiomegaly and levels of diaphragm and pleural effusions. A trained U-Net neural network was used to automatically place these five points within 379 CXRs of an independent database. Intra- and inter-observer variation in mean distance between corresponding anatomic points was larger for obscured points (8.7 mm and 20 mm, respectively) than for visible points (4.3 mm and 7.6 mm, respectively). The computer algorithm using the point-based lung zone segmentation could diagnostically measure the cardiothoracic ratio and diaphragm position or pleural effusion. The mean distance between corresponding points placed by the radiologist and by the neural network was 6.2 mm. The network identified 95% of the radiologist-indicated points with only 3% of network-identified points being false-positives. In conclusion, a reliable anatomic point-based lung segmentation method for CXRs has been developed with expected utility for establishing reference standards for machine learning applications.

Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma.

OBJECTIVES: To evaluate differences in the tumor response classifications that result from clinical measurements and to compare these response classifications with overall survival for patients with malignant pleural mesothelioma (MPM). METHODS: One hundred thirty-one computed tomography (CT) scans were collected from 41 MPM patients enrolled in a clinical trial. Primary measurements had been acquired by clinical radiologists at a single center during routine clinical workflow, and the variability of these measurements was investigated. Retrospective measurements were acquired by a single radiologist in compliance with the study protocol based on the modified response evaluation criteria in solid tumors (RECIST). Differences in response classification categories by the two measurement approaches were evaluated and compared with patient survival. RESULTS: Eleven (27%) of the 41 MPM patients had primary measurements at baseline or at follow-up that deviated from the guidelines of the clinical trial protocol. Among the 41 baseline scans, no statistical difference was observed in summed tumor measurements between primary and retrospective measurements. Response classification based on primary and retrospective measurements was different in 23 (26%) of the 90 follow-up scans, and best response was the different in seven (17%) of the 41 patients. Using Harrell's C statistic as a measure of correlation, response based on retrospective measurements correlated better with survival (C = 0.62) than did response based on primary measurements (C = 0.57). CONCLUSIONS: Strict compliance with the measurement protocol yields tumor response classifications that may differ from those obtained in clinical practice. Response based on retrospective measurements correlated better with survival than did response based on primary measurements. KEY POINTS: • Response classifications could be different between clinical primary and retrospective measurements for malignant pleural mesothelioma. • Response classifications obtained by strict compliance with the trial-specific protocol correlated better with survival than the classifications based on primary measurements. • Quality assurance and radiologist training measures should be used to ensure the integrity of image-based tumor measurements in mesothelioma clinical trials.

Variable radiological lung nodule evaluation leads to divergent management recommendations.

Radiological evaluation of incidentally detected lung nodules on computed tomography (CT) influences management. We assessed international radiological variation in 1) pulmonary nodule characterisation; 2) hypothetical guideline-derived management; and 3) radiologists' management recommendations.107 radiologists from 25 countries evaluated 69 CT-detected nodules, recording: 1) first-choice composition (solid, part-solid or ground-glass, with percentage confidence); 2) morphological features; 3) dimensions; 4) recommended management; and 5) decision-influencing factors. We modelled hypothetical management decisions on the 2005 and updated 2017 Fleischner Society, and both liberal and parsimonious interpretations of the British Thoracic Society 2015 guidelines.Overall agreement for first-choice nodule composition was good (Fleiss' κ=0.65), but poorest for part-solid nodules (weighted κ 0.62, interquartile range 0.50-0.71). Morphological variables, including spiculation (κ=0.35), showed poor-to-moderate agreement (κ=0.23-0.53). Variation in diameter was greatest at key thresholds (5 mm and 6 mm). Agreement for radiologists' recommendations was poor (κ=0.30); 21% disagreed with the majority. Although agreement within the four guideline-modelled management strategies was good (κ=0.63-0.73), 5-10% of radiologists would disagree with majority decisions if they applied guidelines strictly.Agreement was lowest for part-solid nodules, while significant measurement variation exists at important size thresholds. These variations resulted in generally good agreement for guideline-modelled management, but poor agreement for radiologists' actual recommendations.

Screening for Lung Cancer: Incidental Pulmonary Parenchymal Findings.

OBJECTIVE: Incidental pulmonary findings are commonly detected at lung cancer screening chest CT. Though most of these findings are clinically insignificant, it is difficult to prospectively determine which are potentially important to clinical care. The purpose of this review is to discuss the incidental pulmonary findings commonly detected at lung cancer screening chest CT. CONCLUSION: Incidental pulmonary findings most commonly fall into one of three categories: interstitial lung disease, emphysema, and airways disease (both small and large airways).

Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society.

These recommendations for measuring pulmonary nodules at computed tomography (CT) are a statement from the Fleischner Society and, as such, incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. The recommendations address nodule size measurements at CT, which is a topic of importance, given that all available guidelines for nodule management are essentially based on nodule size or changes thereof. The recommendations are organized according to practical questions that commonly arise when nodules are measured in routine clinical practice and are, together with their answers, summarized in a table. The recommendations include technical requirements for accurate nodule measurement, directions on how to accurately measure the size of nodules at the workstation, and directions on how to report nodule size and changes in size. The recommendations are designed to provide practical advice based on the available evidence from the literature; however, areas of uncertainty are also discussed, and topics needing future research are highlighted. © RSNA, 2017 Online supplemental material is available for this article.

Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017.

The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 13, 2017.

CT Findings, Radiologic-Pathologic Correlation, and Imaging Predictors of Survival for Patients With Interstitial Pneumonia With Autoimmune Features.

OBJECTIVE: The objective of this study is to determine the CT findings and patterns of interstitial pneumonia with autoimmune features (IPAF) and to assess whether imaging can predict survival for patients with IPAF. MATERIALS AND METHODS: The study included 136 subjects who met the criteria for IPAF and had diagnostic-quality chest CT scans obtained from 2006 to 2015; a total of 74 of these subjects had pathologic samples available for review within 1 year of chest CT examination. CT findings and the presence of an usual interstitial pneumonitis (UIP) pattern of disease were assessed, as was the UIP pattern noted on pathologic analysis. Analysis of chest CT findings associated with survival was performed using standard univariate and multivariate Cox proportional hazards methods as well as the unadjusted log-rank test. Survival data were visually presented using the Kaplan-Meier survival curve estimator. RESULTS: Most subjects with IPAF (57.4%; 78/136) had a high-confidence diagnosis of a UIP pattern on CT. Substantially fewer subjects (28.7%; 39/136) had a pattern that was inconsistent with UIP noted on CT. The presence of a UIP pattern on CT was associated with smoking (p < 0.01), male sex (p < 0.01), and older age (p < 0.001). Approximately one-fourth of the subjects had a nonspecific interstitial pneumonitis pattern on CT. Of interest, nearly one-tenth of the subjects had a CT pattern that was most consistent with hypersensitivity pneumonitis rather than the customary CT patterns ascribed to lung disease resulting from connective tissue disease. Most subjects with a possible UIP pattern on CT (83.3%) had UIP diagnosed on the basis of pathologic findings. Focused multivariate analysis showed that honeycombing on CT (hazard ratio, 2.17; 95% CI, 1.05-4.47) and pulmonary artery enlargement on CT (hazard ratio, 2.08; 95% CI, 1.02-4.20) were independent predictors of survival. CONCLUSION: IPAF most often presents with a UIP pattern on CT and is associated with worse survival when concomitant honeycombing or pulmonary artery enlargement is present.

Comprehensive Genomic Profiling Identifies a Subset of Crizotinib-Responsive ALK-Rearranged Non-Small Cell Lung Cancer Not Detected by Fluorescence In Situ Hybridization.

INTRODUCTION: For patients with non-small cell lung cancer (NSCLC) to benefit from ALK inhibitors, sensitive and specific detection of ALK genomic rearrangements is needed. ALK break-apart fluorescence in situ hybridization (FISH) is the U.S. Food and Drug Administration approved and standard-of-care diagnostic assay, but identification of ALK rearrangements by other methods reported in NSCLC cases that tested negative for ALK rearrangements by FISH suggests a significant false-negative rate. We report here a large series of NSCLC cases assayed by hybrid-capture-based comprehensive genomic profiling (CGP) in the course of clinical care. MATERIALS AND METHODS: Hybrid-capture-based CGP using next-generation sequencing was performed in the course of clinical care of 1,070 patients with advanced lung cancer. Each tumor sample was evaluated for all classes of genomic alterations, including base-pair substitutions, insertions/deletions, copy number alterations and rearrangements, as well as fusions/rearrangements. RESULTS: A total of 47 patients (4.4%) were found to harbor ALK rearrangements, of whom 41 had an EML4-ALK fusion, and 6 had other fusion partners, including 3 previously unreported rearrangement events: EIF2AK-ALK, PPM1B-ALK, and PRKAR1A-ALK. Of 41 patients harboring ALK rearrangements, 31 had prior FISH testing results available. Of these, 20 were ALK FISH positive, and 11 (35%) were ALK FISH negative. Of the latter 11 patients, 9 received crizotinib based on the CGP results, and 7 achieved a response with median duration of 17 months. CONCLUSION: Comprehensive genomic profiling detected canonical ALK rearrangements and ALK rearrangements with noncanonical fusion partners in a subset of patients with NSCLC with previously negative ALK FISH results. In this series, such patients had durable responses to ALK inhibitors, comparable to historical response rates for ALK FISH-positive cases. IMPLICATIONS FOR PRACTICE: Comprehensive genomic profiling (CGP) that includes hybrid capture and specific baiting of intron 19 of ALK is a highly sensitive, alternative method for identification of drug-sensitive ALK fusions in patients with non-small cell lung cancer (NSCLC) who had previously tested negative using standard ALK fluorescence in situ hybridization (FISH) diagnostic assays. Given the proven benefit of treatment with crizotinib and second-generation ALK inhibitors in patients with ALK fusions, CGP should be considered in patients with NSCLC, including those who have tested negative for other alterations, including negative results using ALK FISH testing.

Clinical significance of noncalcified lung nodules in patients with breast cancer.

Patients with breast cancer are increasingly likely to have chest computed tomography (CT) performed. In many cases, small lung nodules will be detected, raising concern for metastases and causing considerable patient anxiety. The majority of these nodules, however, are benign, though the specific probability of malignancy is uncertain in any given case. Therefore, we analyzed the results of chest CT scans of a large number of patients with breast cancer, to determine characteristics and clinical significance of noncalcified lung nodules. 3313 patients were investigated, and 4889 CT scans from 1325 patients were retrospectively reviewed. Among the 1325 patients, 812 (59 %) had at least one noncalcified lung nodule, of which 330 (41 %) had malignant nodules, 197 (24 %) had large (≥10 mm) nodules, and 586 (72 %) had multiple nodules. Large nodules were more often malignant than benign (P < 0.001). In patients with multiple large nodules, the rate of malignancy rate was 83 %, and most of these were metastases. In the case of very small (2-4 mm) nodules, the malignancy rates for solitary and multiple nodules were 8 and 20 %, respectively. Lung metastases were more likely with breast cancer cell grade 3 (22 %) than grade 1-2 (10 %) (P < 0.001) and when patients were clinical stage 2-3 (14 %) than stage 0-1 (7.9 %) (P = 0.03). Lung metastases are highly likely in patients with multiple nodules greater than 10 mm. Higher cancer cell grades and clinical stage are also related to an increased likelihood of lung metastases. The great majority of small lung nodules in breast cancer patients are benign.

Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society.

This report is to complement the original Fleischner Society recommendations for incidentally detected solid nodules by proposing a set of recommendations specifically aimed at subsolid nodules. The development of a standardized approach to the interpretation and management of subsolid nodules remains critically important given that peripheral adenocarcinomas represent the most common type of lung cancer, with evidence of increasing frequency. Following an initial consideration of appropriate terminology to describe subsolid nodules and a brief review of the new classification system for peripheral lung adenocarcinomas sponsored by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS), six specific recommendations were made, three with regard to solitary subsolid nodules and three with regard to multiple subsolid nodules. Each recommendation is followed first by the rationales underlying the recommendation and then by specific pertinent remarks. Finally, issues for which future research is needed are discussed. The recommendations are the result of careful review of the literature now available regarding subsolid nodules. Given the complexity of these lesions, the current recommendations are more varied than the original Fleischner Society guidelines for solid nodules. It cannot be overemphasized that these guidelines must be interpreted in light of an individual's clinical history. Given the frequency with which subsolid nodules are encountered in daily clinical practice, and notwithstanding continuing controversy on many of these issues, it is anticipated that further refinements and modifications to these recommendations will be forthcoming as information continues to emerge from ongoing research.

Improved detection of focal pneumonia by chest radiography with bone suppression imaging.

OBJECTIVE: To evaluate radiologists' ability to detect focal pneumonia by use of standard chest radiographs alone compared with standard plus bone-suppressed chest radiographs. METHODS: Standard chest radiographs in 36 patients with 46 focal airspace opacities due to pneumonia (10 patients had bilateral opacities) and 20 patients without focal opacities were included in an observer study. A bone suppression image processing system was applied to the 56 radiographs to create corresponding bone suppression images. In the observer study, eight observers, including six attending radiologists and two radiology residents, indicated their confidence level regarding the presence of a focal opacity compatible with pneumonia for each lung, first by use of standard images, then with the addition of bone suppression images. Receiver operating characteristic (ROC) analysis was used to evaluate the observers' performance. RESULTS: The mean value of the area under the ROC curve (AUC) for eight observers was significantly improved from 0.844 with use of standard images alone to 0.880 with standard plus bone suppression images (P < 0.001) based on 46 positive lungs and 66 negative lungs. CONCLUSION: Use of bone suppression images improved radiologists' performance for detection of focal pneumonia on chest radiographs. KEY POINTS: Bone suppression image processing can be applied to conventional digital radiography systems. Bone suppression imaging (BSI) produces images that appear similar to dual-energy soft tissue images. BSI improves the conspicuity of focal lung disease by minimizing bone opacity. BSI can improve the accuracy of radiologists in detecting focal pneumonia.

Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography.

PURPOSE: To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. MATERIALS AND METHODS: Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. RESULTS: The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). CONCLUSION: Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose.

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification.

PURPOSE: To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate. METHODS: The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmented candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates. RESULTS: To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as "very subtle" and "extremely subtle," a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs/image, respectively. CONCLUSIONS: These results compare favorably to those described for other commercial and non-commercial CADe nodule detection systems.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Improved detection of subtle lung nodules by use of chest radiographs with bone suppression imaging: receiver operating characteristic analysis with and without localization.

OBJECTIVE: The purpose of this article is to evaluate radiologists' ability to detect subtle nodules by use of standard chest radiographs alone compared with bone suppression imaging used together with standard radiographs. MATERIALS AND METHODS: The cases used in this observer study comprised radiographs of 72 patients with a subtle nodule and 79 patients without nodules taken from the Japanese Society of Radiological Technology nodule database. A new image-processing system was applied to the 151 radiographs to create corresponding bone suppression images. Two image reading sets were used with an independent test method. The first reading included half of the patients (a randomly selected subset A) showing only the standard image and the remaining half (subset B) showing the standard image plus bone suppression images. The second reading entailed the same subsets; however, subset A was accompanied by bone suppression images, whereas subset B was shown with only the standard image. The two image sets were read by three experienced radiologists, with an interval of more than 2 weeks between the sessions. Receiver operating characteristic (ROC) curves, with and without localization, were obtained to evaluate the observers' performance. RESULTS: The mean value of the area under the ROC curve for the three observers was significantly improved, from 0.840 with standard radiographs alone to 0.863 with additional bone suppression images (p = 0.01). The area under the localization ROC curve was also improved with bone suppression imaging. CONCLUSION: The use of bone suppression images improved radiologists' performance in the detection of subtle nodules on chest radiographs.

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models. METHODS: Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets. RESULTS AND CONCLUSIONS: The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.

Managing Incidental Findings on Thoracic CT: Lung Findings. A White Paper of the ACR Incidental Findings Committee.

The ACR Incidental Findings Committee presents recommendations for managing incidentally detected lung findings on thoracic CT. The Chest Subcommittee is composed of thoracic radiologists who endorsed and developed the provided guidance. These recommendations represent a combination of current published evidence and expert opinion and were finalized by informal iterative consensus. The recommendations address commonly encountered incidental findings in the lungs and are not intended to be a comprehensive review of all pulmonary incidental findings. The goal is to improve the quality of care by providing guidance on management of incidentally detected thoracic findings.

True detection versus "accidental" detection of small lung cancer by a computer-aided detection (CAD) program on chest radiographs.

To evaluate the number of actual detections versus "accidental" detections by a computer-aided detection (CAD) system for small nodular lung cancers (