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.

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.).

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.

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.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

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.

Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management.

OBJECTIVES: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. METHODS: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen's kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. RESULTS: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58-0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. CONCLUSIONS: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. KEY POINTS: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs.

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.

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.

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.

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.

Incidental perifissural nodules on routine chest computed tomography: lung cancer or not?

OBJECTIVES: Perifissural nodules (PFNs) are a common finding on chest CT, and are thought to represent non-malignant lesions. However, data outside a lung cancer-screening setting are currently lacking. METHODS: In a nested case-control design, out of a total cohort of 16,850 patients ≥ 40 years of age who underwent routine chest CT (2004-2012), 186 eligible subjects with incident lung cancer and 511 controls without were investigated. All non-calcified nodules ≥ 4 mm were semi-automatically annotated. Lung cancer location and subject characteristics were recorded. RESULTS: Cases (56 % male) had a median age of 64 years (IQR 59-70). Controls (60 % male) were slightly younger (p<0.01), median age of 61 years (IQR 51-70). A total of 262/1,278 (21 %) unique non-calcified nodules represented a PFN. None of these were traced to a lung malignancy over a median follow-up of around 4.5 years. PFNs were most often located in the lower lung zones (72 %, p<0.001). Median diameter was 4.6 mm (range: 4.0-8.1), volume 51 mm3 (range: 32-278). Some showed growth rates < 400 days. CONCLUSIONS: Our data show that incidental PFNs do not represent lung cancer in a routine care, heterogeneous population. This confirms prior screening-based results. KEY POINTS: • One-fifth of non-calcified nodules represented a perifissural nodule in our non-screening population. • PFNs fairly often show larger size, and can show interval growth. • When morphologically resembling a PFN, nodules are nearly certainly not a malignancy. • The assumed benign aetiology of PFNs seems valid outside the screening setting.

Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval.

BACKGROUND: In the USA annual lung cancer screening is recommended. However, the optimal screening strategy (eg, screening interval, screening rounds) is unknown. This study provides results of the fourth screening round after a 2.5-year interval in the Dutch-Belgian Lung Cancer Screening trial (NELSON). METHODS: Europe's largest, sufficiently powered randomised lung cancer screening trial was designed to determine whether low-dose CT screening reduces lung cancer mortality by ≥25% compared with no screening after 10 years of follow-up. The screening arm (n=7915) received screening at baseline, after 1 year, 2 years and 2.5 years. Performance of the NELSON screening strategy in the final fourth round was evaluated. Comparisons were made between lung cancers detected in the first three rounds, in the final round and during the 2.5-year interval. RESULTS: In round 4, 46 cancers were screen-detected and there were 28 interval cancers between the third and fourth screenings. Compared with the second round screening (1-year interval), in round 4 a higher proportion of stage IIIb/IV cancers (17.3% vs 6.8%, p=0.02) and higher proportions of squamous-cell, bronchoalveolar and small-cell carcinomas (p=0.001) were detected. Compared with a 2-year interval, the 2.5-year interval showed a higher non-significant stage distribution (stage IIIb/IV 17.3% vs 5.2%, p=0.10). Additionally, more interval cancers manifested in the 2.5-year interval than in the intervals of previous rounds (28 vs 5 and 28 vs 19). CONCLUSIONS: A 2.5-year interval reduced the effect of screening: the interval cancer rate was higher compared with the 1-year and 2-year intervals, and proportion of advanced disease stage in the final round was higher compared with the previous rounds. TRIAL REGISTRATION NUMBER: ISRCTN63545820.

Risk stratification based on screening history: the NELSON lung cancer screening study.

BACKGROUND: Debate about the optimal lung cancer screening strategy is ongoing. In this study, previous screening history of the Dutch-Belgian Lung Cancer Screening trial (NELSON) is investigated on if it predicts the screening outcome (test result and lung cancer risk) of the final screening round. METHODS: 15 792 participants were randomised (1:1) of which 7900 randomised into a screening group. CT screening took place at baseline, and after 1, 2 and 2.5 years. Initially, three screening outcomes were possible: negative, indeterminate or positive scan result. Probability for screening outcome in the fourth round was calculated for subgroups of participants. RESULTS: Based on results of the first three rounds, three subgroups were identified: (1) those with exclusively negative results (n=3856; 73.0%); (2) those with ≥1 indeterminate result, but never a positive result (n=1342; 25.5%); and (3) with ≥1 positive result (n=81; 1.5%). Group 1 had the highest probability for having a negative scan result in round 4 (97.2% vs 94.8% and 90.1%, respectively, p<0.001), and the lowest risk for detecting lung cancer in round 4 (0.6% vs 1.6%, p=0.001). 'Smoked pack-years' and 'screening history' significantly predicted the fourth round test result. The third round results implied that the risk for detecting lung cancer (after an interval of 2.5 years) was 0.6% for those with negative results compared with 3.7% of those with indeterminate results. CONCLUSIONS: Previous CT lung cancer screening results provides an opportunity for further risk stratifications of those who undergo lung cancer screening. TRIAL REGISTRATION NUMBER: Results, ISRCTN63545820.

Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?

Purpose To evaluate the added value of Lung CT Screening Reporting and Data System (Lung-RADS) assessment category 4X over categories 3, 4A, and 4B for differentiating between benign and malignant subsolid nodules (SSNs). Materials and Methods SSNs on all baseline computed tomographic (CT) scans from the National Lung Cancer Trial that would have been classified as Lung-RADS category 3 or higher were identified, resulting in 374 SSNs for analysis. An experienced screening radiologist volumetrically segmented all solid cores and located all malignant SSNs visible on baseline scans. Six experienced chest radiologists independently determined which nodules to upgrade to category 4X, a recently introduced category for lesions that demonstrate additional features or imaging findings that increase the suspicion of malignancy. Malignancy rates of purely size-based categories and category 4X were compared. Furthermore, the false-positive rates of category 4X lesions were calculated and observer variability was assessed by using Fleiss κ statistics. Results The observers upgraded 15%-24% of the SSNs to category 4X. The malignancy rate for 4X nodules varied from 46% to 57% per observer and was substantially higher than the malignancy rates of categories 3, 4A, and 4B SSNs without observer intervention (9%, 19%, and 23%, respectively). On average, the false-positive rate for category 4X nodules was 7% for category 3 SSNs, 7% for category 4A SSNs, and 19% for category 4B SSNs. Of the falsely upgraded benign lesions, on average 27% were transient. The agreement among the observers was moderate, with an average κ value of 0.535 (95% confidence interval: 0.509, 0.561). Conclusion The inclusion of a 4X assessment category for lesions suspicious for malignancy in a nodule management tool is of added value and results in high malignancy rates in the hands of experienced radiologists. Proof of the transient character of category 4X lesions at short-term follow-up could avoid unnecessary invasive management. © RSNA, 2017.

Subsolid pulmonary nodule morphology and associated patient characteristics in a routine clinical population.

OBJECTIVES: To determine the presence and morphology of subsolid pulmonary nodules (SSNs) in a non-screening setting and relate them to clinical and patient characteristics. METHODS: A total of 16,890 reports of clinically obtained chest CT (06/2011 to 11/2014, single-centre) were searched describing an SSN. Subjects with a visually confirmed SSN and at least two thin-slice CTs were included. Nodule volumes were measured. Progression was defined as volume increase exceeding the software interscan variation. Nodule morphology, location, and patient characteristics were evaluated. RESULTS: Fifteen transient and 74 persistent SSNs were included (median follow-up 19.6 [8.3-36.8] months). Subjects with an SSN were slightly older than those without (62 vs. 58 years; p = 0.01), but no gender predilection was found. SSNs were mostly located in the upper lobes. Women showed significantly more often persistent lesions than men (94 % vs. 69 %; p = 0.002). Part-solid lesions were larger (1638 vs. 383 mm3; p < 0.001) and more often progressive (68 % vs. 38 %; p = 0.02), compared to pure ground-glass nodules. Progressive SSNs were rare under the age of 50 years. Logistic regression analysis did not identify additional nodule parameters of future progression, apart from part-solid nature. CONCLUSIONS: This study confirms previously reported characteristics of SSNs and associated factors in a European, routine clinical population. KEY POINTS: • SSNs in women are significantly more often persistent compared to men. • SSN persistence is not associated with age or prior malignancy. • The majority of (persistent) SSNs are located in the upper lung lobes. • A part-solid nature is associated with future nodule growth. • Progressive solitary SSNs are rare under the age of 50 years.

Malignancy estimation of Lung-RADS criteria for subsolid nodules on CT: accuracy of low and high risk spectrum when using NLST nodules.

PURPOSE: Lung-RADS proposes malignancy probabilities for categories 2 (<1%) and 4B (>15%). The purpose of this study was to quantify and compare malignancy rates for Lung-RADS 2 and 4B subsolid nodules (SSNs) on a nodule base. METHODS: We identified all baseline SSNs eligible for Lung-RADS 2 and 4B in the National Lung Screening Trial (NLST) database. Solid cores and nodule locations were annotated using in-house software. Malignant SSNs were identified by an experienced radiologist using NLST information. Malignancy rates and percentages of persistence were calculated. RESULTS: Of the Lung-RADS 2SSNs, 94.3% (1790/1897) could be located on chest CTs. Likewise, 95.1% (331/348) of part-solid nodules ≥6 mm in diameter could be located. Of these, 120 had a solid core ≥8 mm, corresponding to category 4B. Category 2 SSNs showed a malignancy rate of 2.5%, exceeding slightly the proposed rate of <1%. Category 4B SSNs showed a malignancy rate of 23.9%. In both categories one third of benign lesions were transient. CONCLUSION: Malignancy probabilities for Lung-RADS 2 and 4B generally match malignancy rates in SSNs. An option to include also category 2 SSNs for upgrade to 4X designed for suspicious nodules might be useful in the future. Integration of short-term follow-up to confirm persistence would prevent unnecessary invasive work-up in 4B SSNs. KEY POINTS: • Malignancy probabilities for Lung-RADS 2/4B generally match malignancy risks in SSNs. • Transient rate between low-risk Lung-RADS 2 and high-risk 4B lesions were similar. • Upgrade of highly suspicious Lung-RADS 2 SSNs to Lung-RADS 4X might be useful. • Up to one third of the benign high-risk Lung-RADS 4B lesions were transient. • Short-term follow-up confirming persistence would avoid unnecessary invasive work-up of 4B lesions.

Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines.

OBJECTIVES: To compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances. METHODS: From the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: Dlongest-C (PanCan), DmeanAxial (NCCN), both obtained from axial sections, and Dmean3D (Lung-RADS). Subsequently all diameter definitions were applied uniformly to all systems. Areas under the ROC curves (AUC) were used to evaluate risk discrimination. RESULTS: PanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying Dlongest-C, Dmean3D and DmeanAxial, PanCan remained superior to Lung-RADS (p < 0.001 - p = 0.001) and NCCN (p < 0.001 - p = 0.016). Diameter definition significantly influenced NCCN's performance with Dlongest-C being the worst (Dlongest-C vs. Dmean3D, p = 0.005; Dlongest-C vs. DmeanAxial, p = 0.016). CONCLUSIONS: Without follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: • PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. • Nodule size definition had no significant impact on Lung-RADS and PanCan model. • 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. • Longest diameter achieved lowest performance for all models. • Mean diameter performed equivalently when derived from axial sections and from volumetry.

Observer Variability for Classification of Pulmonary Nodules on Low-Dose CT Images and Its Effect on Nodule Management.

PURPOSE: To examine the factors that affect inter- and intraobserver agreement for pulmonary nodule type classification on low-radiation-dose computed tomographic (CT) images, and their potential effect on patient management. MATERIALS AND METHODS: Nodules (n = 160) were randomly selected from the Dutch-Belgian Lung Cancer Screening Trial cohort, with equal numbers of nodule types and similar sizes. Nodules were scored by eight radiologists by using morphologic categories proposed by the Fleischner Society guidelines for management of pulmonary nodules as solid, part solid with a solid component smaller than 5 mm, part solid with a solid component 5 mm or larger, or pure ground glass. Inter- and intraobserver agreement was analyzed by using Cohen κ statistics. Multivariate analysis of variance was performed to assess the effect of nodule characteristics and image quality on observer disagreement. Effect on nodule management was estimated by differentiating CT follow-up for ground-glass nodules, solid nodules 8 mm or smaller, and part-solid nodules smaller than 5 mm from immediate diagnostic work-up for solid nodules larger than 8 mm and part-solid nodules 5 mm or greater. RESULTS: Pair-wise inter- and intraobserver agreement was moderate (mean κ, 0.51 [95% confidence interval, 0.30, 0.68] and 0.57 [95% confidence interval, 0.47, 0.71]). Categorization as part-solid nodules and location in the upper lobe significantly reduced observer agreement (P = .012 and P < .001, respectively). By considering all possible reading pairs (28 possible combinations of observer pairs × 160 nodules = 4480 possible agreements or disagreements), a discordant nodule classification was found in 36.4% (1630 of 4480), related to presence or size of a solid component in 88.7% (1446 of 1630). Two-thirds of these discrepant readings (1061 of 1630) would have potentially resulted in different nodule management. CONCLUSION: There is moderate inter- and intraobserver agreement for nodule classification by using current recommendations for low-radiation-dose CT examinations of the chest. Discrepancies in nodule categorization were mainly caused by disagreement on the size and presence of a solid component, which may lead to different management in the majority of cases with such discrepancies. (©) RSNA, 2015.