Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

OBJECTIVE: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. METHODS AND MATERIALS: In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty. RESULTS: The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group. CONCLUSION: The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance. CLINICAL RELEVANCE STATEMENT: Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction. KEY POINTS: • Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.

Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.

BACKGROUND: Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting. METHOD: We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists. RESULTS: On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6). CONCLUSIONS: The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.

Early-stage lung cancer can be diagnosed after identifying an abnormal spot on a chest CT scan ordered for other medical reasons. These spots or lung nodules can be overlooked by radiologists, as they are not necessarily the focus of an examination and can be as small as a few millimeters. Software using Artificial Intelligence (AI) technology has proven to be successful for aiding radiologists in this task, but its performance is understudied outside a lung cancer screening setting. We therefore developed and validated AI software for the detection of cancerous nodules or non-cancerous nodules that would need attention. We show that the software can reliably detect these nodules in a non-screening setting and could potentially aid radiologists in daily clinical practice.

Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.

Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; P = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; P = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; P < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; P < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT02837809 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Horst and Nishino in this issue.

Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals.

OBJECTIVE: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. METHODS: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. RESULTS: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. CONCLUSION: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. CLINICAL RELEVANCE STATEMENT: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. KEY POINTS: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.

Explainable emphysema detection on chest radiographs with deep learning.

We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these with 4 radiological signs of pulmonary emphysema identified from the literature. A patient with ≥2 of these signs present is considered emphysema positive. Using separate deep learning systems for frontal and lateral images we predict the presence of each of the four visual signs and use these to determine emphysema positivity. The ROC and AUC results on a set of 422 held-out cases, labeled by both radiologists, are reported. Comparison with a black-box model which predicts emphysema without the use of explainable visual features is made on the annotations from both radiologists, as well as the subset that they agreed on. DeLong's test is used to compare with the black-box model ROC and McNemar's test to compare with radiologist performance. In 422 test cases, emphysema positivity was predicted with AUCs of 0.924 and 0.946 using the reference standard from each radiologist separately. Setting model sensitivity equivalent to that of the second radiologist, our model has a comparable specificity (p = 0.880 and p = 0.143 for each radiologist respectively). Our method is comparable with the black-box model with AUCs of 0.915 (p = 0.407) and 0.935 (p = 0.291), respectively. On the 370 cases where both radiologists agreed (53 positives), our model achieves an AUC of 0.981, again comparable to the black-box model AUC of 0.972 (p = 0.289). Our proposed method can predict emphysema positivity on chest radiographs as well as a radiologist or a comparable black-box method. It additionally produces labels for four visual signs to ensure the explainability of the result. The dataset is publicly available at https://doi.org/10.5281/zenodo.6373392.

Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists.

PURPOSE: To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists. MATERIALS AND METHODS: In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans. The reference standard was set by histopathologic examination for cancer-positive scans and imaging follow-up for at least 2 years for cancer-negative scans. The test datasets were applied to the three top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julian de Wit and Daniel Hammack (JWDH), and Aidence. Model outputs were compared with an observer study of 11 radiologists that assessed the same test datasets. Each scan was scored on a continuous scale by both the deep learning algorithms and the radiologists. Performance was measured using multireader, multicase receiver operating characteristic analysis. RESULTS: The area under the receiver operating characteristic curve (AUC) was 0.877 (95% CI: 0.842, 0.910) for grt123, 0.902 (95% CI: 0.871, 0.932) for JWDH, and 0.900 (95% CI: 0.870, 0.928) for Aidence. The average AUC of the radiologists was 0.917 (95% CI: 0.889, 0.945), which was significantly higher than grt123 (P = .02); however, no significant difference was found between the radiologists and JWDH (P = .29) or Aidence (P = .26). CONCLUSION: Deep learning algorithms developed in a public competition for lung cancer detection in low-dose CT scans reached performance close to that of radiologists.Keywords: Lung, CT, Thorax, Screening, Oncology Supplemental material is available for this article. © RSNA, 2021.

CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?

The purpose of this case-cohort study was to investigate whether the frequency and computed tomography (CT) features of pulmonary nodules posed a risk for the future development of lung cancer (LC) at a different location. Patients scanned between 2004 and 2012 at two Dutch academic hospitals were cross-linked with the Dutch Cancer Registry. All patients who were diagnosed with LC by 2014 and a random selection of LC-free patients were considered. LC patients who were determined to be LC-free at the time of the scan and all LC-free patients with an adequate scan were included. The nodule count and types (solid, part-solid, ground-glass, and perifissural) were recorded per scan. Age, sex, and other CT measures were included to control for confounding factors. The cohort included 163 LC patients and 1178 LC-free patients. Cox regression revealed that the number of ground-glass nodules and part-solid nodules present were positively correlated to future LC risk. The area under the receiver operating curve of parsimonious models with and without nodule type information were 0.827 and 0.802, respectively. The presence of subsolid nodules in a clinical setting may be a risk factor for future LC development in another pulmonary location in a dose-dependent manner. Replication of the results in screening cohorts is required for maximum utility of these findings.

Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three -cohorts -collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 -malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 -nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts. Results The DL algorithm significantly outperformed the PanCan model in the full cohort (AUC, 0.93 [95% CI: 0.89, 0.96] vs 0.90 [95% CI: 0.86, 0.93]; P = .046). The algorithm performed comparably to thoracic radiologists in cancer-enriched cohorts with both random benign nodules (AUC, 0.96 [95% CI: 0.93, 0.99] vs 0.90 [95% CI: 0.81, 0.98]; P = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; P = .26). Conclusion The deep learning algorithm showed excellent performance, comparable to thoracic radiologists, for malignancy risk estimation of pulmonary nodules detected at screening CT. This algorithm has the potential to provide reliable and reproducible malignancy risk scores for clinicians, which may help optimize management in lung cancer screening. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tammemägi in this issue.

Association between the number and size of intrapulmonary lymph nodes and chronic obstructive pulmonary disease severity.

PURPOSE: One of the main pathophysiological mechanisms of chronic obstructive pulmonary disease is inflammation, which has been associated with lymphadenopathy. Intrapulmonary lymph nodes can be identified on CT as perifissural nodules (PFN). We investigated the association between the number and size of PFNs and measures of COPD severity. MATERIALS AND METHODS: CT images were obtained from COPDGene. 50 subjects were randomly selected per GOLD stage (0 to 4), GOLD-unclassified, and never-smoker groups and allocated to either "Healthy," "Mild," or "Moderate/severe" groups. 26/350 (7.4%) subjects had missing images and were excluded. Supported by computer-aided detection, a trained researcher prelocated non-calcified opacities larger than 3 mm in diameter. Included lung opacities were classified independently by two radiologists as either "PFN," "not a PFN," "calcified," or "not a nodule"; disagreements were arbitrated by a third radiologist. Ordinal logistic regression was performed as the main statistical test. RESULTS: A total of 592 opacities were included in the observer study. A total of 163/592 classifications (27.5%) required arbitration. A total of 17/592 opacities (2.9%) were excluded from the analysis because they were not considered nodular, were calcified, or all three radiologists disagreed. A total of 366/575 accepted nodules (63.7%) were considered PFNs. A maximum of 10 PFNs were found in one image; 154/324 (47.5%) contained no PFNs. The number of PFNs per subject did not differ between COPD severity groups (p = 0.50). PFN short-axis diameter could significantly distinguish between the Mild and Moderate/severe groups, but not between the Healthy and Mild groups (p = 0.021). CONCLUSIONS: There is no relationship between PFN count and COPD severity. There may be a weak trend of larger intrapulmonary lymph nodes among patients with more advanced stages of COPD.

COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System.

Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020.

Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system.

There is a growing interest in the automated analysis of chest X-Ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis. In this work we evaluate the latest version of CAD4TB, a commercial software platform designed for this purpose. Version 6 of CAD4TB was released in 2018 and is here tested on a fully independent dataset of 5565 CXR images with GeneXpert (Xpert) sputum test results available (854 Xpert positive subjects). A subset of 500 subjects (50% Xpert positive) was reviewed and annotated by 5 expert observers independently to obtain a radiological reference standard. The latest version of CAD4TB is found to outperform all previous versions in terms of area under receiver operating curve (ROC) with respect to both Xpert and radiological reference standards. Improvements with respect to Xpert are most apparent at high sensitivity levels with a specificity of 76% obtained at a fixed 90% sensitivity. When compared with the radiological reference standard, CAD4TB v6 also outperformed previous versions by a considerable margin and achieved 98% specificity at the 90% sensitivity setting. No substantial difference was found between the performance of CAD4TB v6 and any of the various expert observers against the Xpert reference standard. A cost and efficiency analysis on this dataset demonstrates that in a standard clinical situation, operating at 90% sensitivity, users of CAD4TB v6 can process 132 subjects per day at an average cost per screen of $5.95 per subject, while users of version 3 process only 85 subjects per day at a cost of $8.38 per subject. At all tested operating points version 6 is shown to be more efficient and cost effective than any other version.

Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial.

BACKGROUND: There are limited data from randomized trials regarding whether volume-based, low-dose computed tomographic (CT) screening can reduce lung-cancer mortality among male former and current smokers. METHODS: A total of 13,195 men (primary analysis) and 2594 women (subgroup analyses) between the ages of 50 and 74 were randomly assigned to undergo CT screening at T0 (baseline), year 1, year 3, and year 5.5 or no screening. We obtained data on cancer diagnosis and the date and cause of death through linkages with national registries in the Netherlands and Belgium, and a review committee confirmed lung cancer as the cause of death when possible. A minimum follow-up of 10 years until December 31, 2015, was completed for all participants. RESULTS: Among men, the average adherence to CT screening was 90.0%. On average, 9.2% of the screened participants underwent at least one additional CT scan (initially indeterminate). The overall referral rate for suspicious nodules was 2.1%. At 10 years of follow-up, the incidence of lung cancer was 5.58 cases per 1000 person-years in the screening group and 4.91 cases per 1000 person-years in the control group; lung-cancer mortality was 2.50 deaths per 1000 person-years and 3.30 deaths per 1000 person-years, respectively. The cumulative rate ratio for death from lung cancer at 10 years was 0.76 (95% confidence interval [CI], 0.61 to 0.94; P = 0.01) in the screening group as compared with the control group, similar to the values at years 8 and 9. Among women, the rate ratio was 0.67 (95% CI, 0.38 to 1.14) at 10 years of follow-up, with values of 0.41 to 0.52 in years 7 through 9. CONCLUSIONS: In this trial involving high-risk persons, lung-cancer mortality was significantly lower among those who underwent volume CT screening than among those who underwent no screening. There were low rates of follow-up procedures for results suggestive of lung cancer. (Funded by the Netherlands Organization of Health Research and Development and others; NELSON Netherlands Trial Register number, NL580.).

Typical CT Features of Intrapulmonary Lymph Nodes: A Review.

Several studies investigated the appearance of intrapulmonary lymph nodes (IPLNs) at CT with pathologic correlation. IPLNs are benign lesions and do not require follow-up after initial detection. There are indications that IPLNs represent a considerable portion of incidentally found pulmonary nodules seen at high-resolution CT. The reliable and accurate identification of IPLNs as benign nodules may substantially reduce the number of unnecessary follow-up CT examinations. Typical CT features of IPLNs are a noncalcified solid nodule with sharp margins; a round, oval, or polygonal shape; distanced 15 mm or less from the pleura; and most being located below the level of the carina. The term perifissural nodule (PFN) was coined based on some of these characteristics. Standardization of those CT criteria are a prerequisite for accurate nodule classification. However, four different definitions of PFNs can currently be found in the literature. Furthermore, there is considerable variation in the reported interobserver agreement, malignancy rate, and prevalence of PFNs. The purpose of this review was to provide an overview of what is known about PFNs. In addition, knowledge gaps in defining PFNs will be discussed. A decision tree to guide clinicians in classifying nodules as PFNs is provided. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by White and Rubin in this issue.

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning.

PURPOSE: To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset. MATERIALS AND METHODS: In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. A total of 1100 scans from 1100 patients were used in this study, and 400 were selected for development of the algorithm. For testing, a dataset of 50 scans was annotated to assess the segmentation accuracy and was compared against the splenic index equation. In a qualitative observer experiment, an enriched set of 100 scan-pairs was used to evaluate whether the algorithm could aid a radiologist in assessing splenic volume change. The reference standard was set by the consensus of two other independent radiologists. A Mann-Whitney U test was conducted to test whether there was a performance difference between the algorithm and the independent observer. RESULTS: The algorithm and the independent observer obtained comparable Dice scores (P = .834) on the test set of 50 scans of 0.962 and 0.964, respectively. The radiologist had an agreement with the reference standard in 81% (81 of 100) of the cases after a visual classification of volume change, which increased to 92% (92 of 100) when aided by the algorithm. CONCLUSION: A segmentation method based on deep learning can accurately segment the spleen on CT scans and may help radiologists to detect abnormal splenic volumes and splenic volume changes.Supplemental material is available for this article.© RSNA, 2020.

Classification of CT Pulmonary Opacities as Perifissural Nodules: Reader Variability.

Purpose To study interreader variability for classifying pulmonary opacities at CT as perifissural nodules (PFNs) and determine how reliably radiologists differentiate PFNs from malignancies. Materials and Methods CT studies were obtained retrospectively from the National Lung Screening Trial (2002-2009). Nodules were eligible for the study if they were noncalcified, solid, within the size range of 5 to 10 mm, and scanned with a section thickness of 2 mm or less. Six radiologists classified 359 nodules in a cancer-enriched data set as PFN, non-PFN, or not applicable. Nodules classified as not applicable by at least three radiologists were excluded, leaving 316 nodules for post-hoc statistical analysis. Results The study group contained 22.2% cancers (70 of 316). The median proportion of nodules classified as PFNs was 45.6% (144 of 316). All six radiologists uniformly classified 17.7% (56 of 316) of the nodules as PFNs. The Fleiss κ was 0.50. Compared with non-PFNs, nodules classified as PFNs were smaller and more often located in the lower lobes and attached to a fissure (P < .001). Thirteen (18.6%) of 70 cancers were misclassified 21 times as PFNs. Individual readers' misclassification rates ranged from 0% (0 of 125) to 4.9% (eight of 163). Of 13 misclassified malignancies, 11 were in the upper lobes and two were attached to a fissure. Conclusion There was moderate interreader agreement when classifying nodules as perifissural nodules. Less than 2.5% of perifissural nodule classifications were misclassified lung cancers (21 of 865) in this cancer-enriched study. Allowing nodules classified as perifissural nodules to be omitted from additional follow-up in a screening setting could substantially reduce the number of unnecessary scans; excluding perifissural nodules in the upper lobes would greatly decrease the misclassification rate.

Brock malignancy risk calculator for pulmonary nodules: validation outside a lung cancer screening population.

OBJECTIVE: To assess the performance of the Brock malignancy risk model for pulmonary nodules detected in routine clinical setting. METHODS: In two academic centres in the Netherlands, we established a list of patients aged ≥40 years who received a chest CT scan between 2004 and 2012, resulting in 16 850 and 23 454 eligible subjects. Subsequent diagnosis of lung cancer until the end of 2014 was established through linking with the National Cancer Registry. A nested case-control study was performed (ratio 1:3). Two observers used semiautomated software to annotate the nodules. The Brock model was separately validated on each data set using ROC analysis and compared with a solely size-based model. RESULTS: After the annotation process the final analysis included 177 malignant and 695 benign nodules for centre A, and 264 malignant and 710 benign nodules for centre B. The full Brock model resulted in areas under the curve (AUCs) of 0.90 and 0.91, while the size-only model yielded significantly lower AUCs of 0.88 and 0.87, respectively (p<0.001). At 10% malignancy risk, the threshold suggested by the British Thoracic Society, sensitivity of the full model was 75% and 81%, specificity was 85% and 84%, positive predictive values were 14% and 10% at negative predictive value (NPV) of 99%. The optimal threshold was 6% for centre A and 8% for centre B, with NPVs >99%. DISCUSSION: The Brock model shows high predictive discrimination of potentially malignant and benign nodules when validated in an unselected, heterogeneous clinical population. The high NPV may be used to decrease the number of nodule follow-up examinations.

Lung cancer risk to personalise annual and biennial follow-up computed tomography screening.

BACKGROUND: All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models. METHODS: We included participants in the CT arm of the National Lung Screening Trial (2002-2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping. RESULTS: Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively. CONCLUSIONS: The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.

In vivo growth of 60 non-screening detected lung cancers: a computed tomography study.

Current pulmonary nodule management guidelines are based on nodule volume doubling time, which assumes exponential growth behaviour. However, this is a theory that has never been validated in vivo in the routine-care target population. This study evaluates growth patterns of untreated solid and subsolid lung cancers of various histologies in a non-screening setting.Growth behaviour of pathology-proven lung cancers from two academic centres that were imaged at least three times before diagnosis (n=60) was analysed using dedicated software. Random-intercept random-slope mixed-models analysis was applied to test which growth pattern most accurately described lung cancer growth. Individual growth curves were plotted per pathology subgroup and nodule type.We confirmed that growth in both subsolid and solid lung cancers is best explained by an exponential model. However, subsolid lesions generally progress slower than solid ones. Baseline lesion volume was not related to growth, indicating that smaller lesions do not grow slower compared to larger ones.By showing that lung cancer conforms to exponential growth we provide the first experimental basis in the routine-care setting for the assumption made in volume doubling time analysis.

Visual discrimination of screen-detected persistent from transient subsolid nodules: An observer study.

PURPOSE: To evaluate whether, and to which extent, experienced radiologists are able to visually correctly differentiate transient from persistent subsolid nodules from a single CT examination alone and to determine CT morphological features to make this differentiation. MATERIALS AND METHODS: We selected 86 transient and 135 persistent subsolid nodules from the National Lung Screening Trial (NLST) database. Four experienced radiologists visually assessed a predefined list of morphological features and gave a final judgment on a continuous scale (0-100). To assess observer performance, area under the receiver operating characteristic (ROC) curve was calculated. Statistical differences of morphological features between transient and persistent lesions were calculated using Chi-square. Inter-observer agreement of morphological features was evaluated by percentage agreement. RESULTS: Forty-nine lesions were excluded by at least 2 observers, leaving 172 lesions for analysis. On average observers were able to differentiate transient from persistent subsolid nodules ≥ 10 mm with an area under the curve of 0.75 (95% CI 0.67-0.82). Nodule type, lesion margin, presence of a well-defined border, and pleural retraction showed significant differences between transient and persistent lesions in two observers. Average pair-wise percentage agreement for these features was 81%, 64%, 47% and 89% respectively. Agreement for other morphological features varied from 53% to 95%. CONCLUSION: The visual capacity of experienced radiologists to differentiate persistent and transient subsolid nodules is moderate in subsolid nodules larger than 10 mm. Performance of the visual assessment of CT morphology alone is not sufficient to generally abandon a short-term follow-up for subsolid nodules.