CT Screening for Lung Cancer: Suspiciousness of Nodules according to Size on Baseline Scans

Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial

Screening with low-dose CT (LDCT) in a high-risk population, as defined by age and smoking behavior, reduces lung cancer-related mortality. However, LDCT screening presents a major challenge. Numerous, mostly benign, nodules are seen in the lungs during screening. The question is how to distinguish the malignant from the benign nodules. Various studies use different protocols for nodule management. The Dutch-Belgian NELSON (Nederlands-Leuvens Longkanker Screenings Onderzoek) trial, the largest European lung cancer screening trial, used distinctions based on nodule volumetric assessment and growth rate. This review discusses key findings from the NELSON study regarding the characteristics of screening-detected nodules, including nodule size and its volumetric assessment, growth rate, subtype, and their associated malignancy risk. These results are compared with findings from other screening studies and current recommendations for lung nodule management. By examining differences in nodule management strategies and providing a comprehensive overview of outcomes specific to lung cancer screening, this review aims to contribute to the broader discussion on optimizing lung nodule management in screening programs.

Factors Associated With Timeliness and Equity of Access to Outpatient MRI Examinations

OBJECTIVE

The role of MRI in guiding patients' diagnosis and treatment is increasing. Therefore, timely MRI performance prevents delays that can impact patient care. We assessed the timeliness of performing outpatient MRIs using the socio-ecological model approach and evaluated multilevel factors associated with delays.

METHODS

This institutional review board-approved study included outpatient MRI examinations ordered between October 1, 2021, and December 31, 2022, for performance at a large quaternary care health system. Mean order-to-performed (OtoP) interval (in days) and prolonged OtoP interval (defined as >10 days) for MRI orders with an expected date of 1 day to examination performance were measured. Logistic regression was used to assess patient-level (demographic and social determinants of health), radiology practice-level, and community-level factors associated with prolonged OtoP interval.

RESULTS

There were 126,079 MRI examination orders with expected performance within 1 day placed during the study period (56% of all MRI orders placed). After excluding duplicates, there were 97,160 orders for unique patients. Of the MRI orders, 48% had a prolonged OtoP interval, and mean OtoP interval was 18.5 days. Factors significantly associated with delay in MRI performance included public insurance (odds ratio [OR] = 1.11, P < .001), female gender (OR = 1.11, P < .001), radiology subspecialty (ie, cardiac, OR = 1.71, P < .001), and patients from areas that are most deprived (ie, highest Area Deprivation Index quintile, OR = 1.70, P < .001).

DISCUSSION

Nearly half of outpatient MRI orders were delayed, performed >10 days from the expected date selected by the ordering provider. Addressing multilevel factors associated with such delays may help enhance timeliness and equity of access to MRI examinations, potentially reducing diagnostic errors and treatment delays.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Radiologic Features of Nodules Attached to the Mediastinal or Diaphragmatic Pleura at Low-Dose CT for Lung Cancer Screening

Background Pulmonary noncalcified nodules (NCNs) attached to the fissural or costal pleura with smooth margins and triangular or lentiform, oval, or semicircular (LOS) shapes at low-dose CT are recommended for annual follow-up instead of immediate workup. Purpose To determine whether management of mediastinal or diaphragmatic pleura-attached NCNs (M/DP-NCNs) with the same features as fissural or costal pleura-attached NCNs at low-dose CT can follow the same recommendations. Materials and Methods This retrospective study reviewed chest CT examinations in participants from two databases. Group A included 1451 participants who had lung cancer that was first present as a solid nodule with an average diameter of 3.0-30.0 mm. Group B included 345 consecutive participants from a lung cancer screening program who had at least one solid nodule with a diameter of 3.0-30.0 mm at baseline CT and underwent at least three follow-up CT examinations. Radiologists reviewed CT images to identify solid M/DP-NCNs, defined as nodules 0 mm in distance from the mediastinal or diaphragmatic pleura, and recorded average diameter, margin, and shape. General descriptive statistics were used. Results Among the 1451 participants with lung cancer in group A, 163 participants (median age, 68 years [IQR, 61.5-75.0 years]; 92 male participants) had 164 malignant M/DP-NCNs 3.0-30.0 mm in average diameter. None of the 164 malignant M/DP-NCNs had smooth margins and triangular or LOS shapes (upper limit of 95% CI of proportion, 0.02). Among the 345 consecutive screening participants in group B, 146 participants (median age, 65 years [IQR, 59-71 years]; 81 female participants) had 240 M/DP-NCNs with average diameter 3.0-30.0 mm. None of the M/DP-NCNs with smooth margins and triangular or LOS shapes were malignant after a median follow-up of 57.8 months (IQR, 46.3-68.1 months). Conclusion For solid M/DP-NCNs with smooth margins and triangular or LOS shapes at low-dose CT, the risk of lung cancer is extremely low, which supports the recommendation of Lung Imaging Reporting and Data System version 2022 for annual follow-up instead of immediate workup. © RSNA, 2024 See also the editorial by Goodman and Baruah in this issue.

Lung Cancer Screening in Asia: An Expert Consensus Report

INTRODUCTION

The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

METHOD

A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

RESULTS

Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

CONCLUSIONS

Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Radio-Guided Lung Surgery: A Feasible Approach for a Cancer Precision Medicine

BACKGROUND

Radio-guided surgery is a reliable approach used for localizing ground-glass opacities, lung nodules, and metastatic lymph nodes. Lung nodules, lymph node metastatic involvement, and ground-glass opacities often represent a challenge for surgical management and clinical work-up.

METHODS

PubMed research was conducted from January 1997 to June 2023 using the keywords "radioguided surgery and lung cancer".

RESULTS

Different studies were conducted with different tracers: technetium-99m-albumin macroaggregates, cyanoacrylate combined to technetium-99m-sulfur colloid, indium-111-pentetreotide, and fluorine-18-deoxyglucose. A study proposed naphthalocyanine radio-labeled with copper-64. Radio-guided surgery has been demonstrated to be a reliable approach in localizing a lesion, and has a low radiological burden for personnel exposure and low morbidity. The lack of necessity to conduct radio-guided surgery under fluoroscopy or echography makes this radio-guided surgery an easy way of performing precise surgical procedures.

CONCLUSIONS

Radio-guided surgery is a feasible approach useful for the intraoperative localization of ground-glass opacities, lung nodules, and metastatic lymph nodes. It is a valid alternative to the existing approaches due to its low cost, associated low morbidity, the possibility to perform the procedure after several hours, the low radiation dose applied, and the small amount of time that is required to perform it.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study

Objective

The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.

Methods

A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R²(determination coefficient).

Results

The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation.

Conclusion

The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

The value of preoperative positron emission tomography/computed tomography in differentiating the invasive degree of hypometabolic lung adenocarcinoma

OBJECTIVES

To investigate the value of preoperative positron emission tomography/computed tomography (PET/CT) in differentiating the invasive degree of hypometabolic lung adenocarcinoma.

METHODS

We retrospectively analyzed the data of patients who underwent PET/CT examination, high-resolution computed tomography, and surgical resection for low-metabolism lung adenocarcinoma in our hospital between June 2016 and December 2021. We also investigated the relationship between the preoperative PET/CT findings and the pathological subtype of hypometabolic lung adenocarcinoma.

RESULTS

A total of 128 lesions were found in 113 patients who underwent resection for lung adenocarcinoma, including 20 minimally invasive adenocarcinomas (MIA) and 108 invasive adenocarcinomas (IAC), whose preoperative PET/CT showed low metabolism. There were significant differences in the largest diameter (Dmax), lesion type, maximum standard uptake value (SUVmax), SUVindex (the ratio of SUVmax of lesion to SUVmax of contralateral normal lung paranchyma), fasting blood glucose, lobulation, spiculation, and pleura indentation between the MIA and IAC groups (p < 0.05). Multivariate logistic regression analysis showed that the Dmax (odds ratio (OR) = 1.413, 95% confidence interval (CI: 1.155-1.729, p = 0.001)) and SUVmax (OR = 12.137, 95% CI: 1.068-137.900, p = 0.044) were independent risk factors for predicting the hypometabolic IAC (p < 0.05). Receiver operating characteristic (ROC) curve analysis showed that the Dmax ≥ 10.5 mm and SUVmax ≥ 0.85 were the cut-off values for differentiating MIA from IAC, with high sensitivity (84.3% and 75.9%, respectively) and specificity (84.5% and 85.0%, respectively), the Combined Diagnosis showed higher sensitivity (91.7%) and specificity (85.0%).

CONCLUSIONS

The PET/CT findings correlated with the subtype of hypometabolic lung adenocarcinoma. The parameters Dmax and SUVmax were independent risk factors for predicting IAC, and the sensitivity of Combined Diagnosis prediction is better.

Expression and clinical significance of CD31, CD34, and CD105 in pulmonary ground glass nodules with different vascular manifestations on CT

Blood vessel passage on CT exerts a vital part in early diagnosis as well as treatment of carcinoma of the lungs. Intratumoral microvascular density (iMVD) has gradually become the focus of research on biological behavior, appearance, and evolution of malignant tumors nowadays. The aim of this paper was to verify whether there is a correlation between the iMVD and the vascular morphology of ground glass nodules (GGNs). A total of 109 patients with pulmonary GGN were classified into three groups (I,II, and III) according to the vascular morphology on CT, and their expression of CD31-, CD34-, and CD105-labeled iMVD was detected by the streptoavidin–biotin method, statistically analyzing the iMVD values of each group. The expression of CD31, CD34, and CD105 in different lung tissues was significantly different, with remarkably higher iMVD in lung cancer tissues than in adjacent normal lung tissues. In the imaging sort of types I, II, and III according to the means of vascular passage, the iMVD expression of CD31, CD34, and CD105 was significantly different between groups. These data suggest that the presence and the abnormal morphology of vessels seen within GGNs indicate the occurrence and progression of lung cancer in pathology. It offers a strong theoretical foundation for early diagnosis of carcinoma of the lungs, thus providing a more precise clinical diagnosis and prognosis of early-stage lung cancer.

Nodules, Navigation, Robotic Bronchoscopy, and Real-Time Imaging

The process of detection, diagnosis, and management of lung nodules is complex due to the heterogeneity of lung pathology and a relatively low malignancy rate. Technological advances in bronchoscopy have led to less-invasive diagnostic procedures and advances in imaging technology have helped to improve nodule localization and biopsy confirmation. Future research is required to determine which modality or combination of complimentary modalities is best suited for safe, accurate, and cost-effective management of lung nodules.

Incidental Pulmonary Nodules Found on Shoulder Arthroplasty Preoperative CT Scans

With current emphasis on preoperative templating of anatomical and reverse shoulder arthroplasty (aTSA and rTSA, respectively), patients often receive thin slice (<1.0 mm) computerized tomography (CT) scans of the operative shoulder, which includes about two-thirds of the ipsilateral lung. The purpose of this study is to evaluate the prevalence and management of incidentally detected pulmonary nodules on preoperative CT scans for shoulder arthroplasty. In this single-center retrospective study, we queried records of aTSA and rTSA patients from 2015 to 2020 who received preoperative CT imaging of the shoulder. Compared to patients with negative CT findings, there were significantly more females (63.8% vs. 46.4%; P = .011), COPD (13.0% vs. 4.7%; P = .015), and asthma (18.8% vs. 6.9%; P = .003) among the patients with incidental nodules on CT. Binary logistic regression confirmed that female sex (odds ratio = 2.00; 95% CI = 1.04 to 3.88; P = .037), COPD history (OR = 3.02; 95% CI = 1.05 to 8.65; P = .040), and asthma history (OR = 3.17; 95% CI = 1.30 to 7.77; P = .011) were significantly associated with an incidental nodule finding. Incidental pulmonary nodules found on shoulder arthroplasty preoperative CT scans are often low risk in size with low risk of malignancy, and do not require further workup. This study may provide guidance to orthopedic surgeons on how to manage patients with incidental pulmonary nodules to increase chances of early cancer detection, avoid unnecessary referrals, reduce potentially harmful radiation exposure of serial CT scans, and improve cost efficiency.

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images

Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.

Correlation between CT imaging characteristics and pathological diagnosis for subcentimeter pulmonary nodules

Background

Advances in chest computed tomography (CT) have resulted in more frequent detection of subcentimeter pulmonary nodules (SCPNs), some of which are non‐benign and may represent invasive lung cancer. The present study aimed to explore the correlation between pathological diagnosis and the CT imaging manifestations of SCPNs.

Methods

This retrospective study included patients who underwent pulmonary resection for SCPNs at Shandong Provincial Hospital in China. Lesions were divided into five categories according to their morphological characteristics on CT: cotton ball, solid‐filled with spiculation, solid‐filled with smooth edges, mixed‐density ground‐glass, and vacuolar. We further analyzed lesion size, enhancement patterns, vascular aggregation, and SCPN traversing. Chi‐square tests, Fisher's exact tests, and Welch's one‐way analysis of variance were used to examine the correlation between CT imaging characteristics and pathological type.

Results

There were statistically significant differences in the morphological distributions of SCPNs with different pathological types, including benign lesions and malignant lesions at different stages (p < 0.01). The morphological distributions of the four subtypes of invasive lung adenocarcinoma also exhibited significant differences (p < 0.01). In addition, size and enhancement patterns differed significantly among different pathological types of SCPNs.

Conclusion

Different pathological types of SCPNs exhibit significant differences based on their morphological category, size, and enhancement pattern on CT imaging. These CT characteristics may assist in the qualitative diagnosis of SCPNs.

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules]

With the widespread use of high-resolution multislice spiral computed tomography and the popularization of regular physical examinations, the prevalence of clinically diagnosed subcentimeter pulmonary nodules is increasing. Subcentimeter pulmonary nodules have low malignant probability, however, the diagnosis and management are of high difficulty and it is likely to misdiagnose and miss malignant nodules. Therefore, the evaluation and management of subcentimeter pulmonary nodules have always been the key points of clinical work. This article reviews and summarizes the progress in the evaluation and management of subcentimeter pulmonary nodules.

Pulmonary nodule radiological diagnostic algorithm in lung cancer screening

Publications of the final results of the two largest randomized lung cancer screening (LCS) trials in the United States and Europe confirmed the reduction in the mortality rate associated with the use of screening with low-dose computed tomography (LDCT). Results of these trials led to widespread acceptance of LCS in properly defined high-risk populations, and its implementation in the clinical practice. Many countries started preparation for national LCS and refreshed still open debate about lung nodule management. Detection of lung cancer in the early stage with a reduction of lung cancer mortality requires dedicated programs with optimized protocols, including a specified pulmonary nodule diagnostic algorithm. The screening protocol should be clear with a precise nodule diameter or volume threshold, based on which a positive screen result is defined. The application of risk-prediction models and the introduction of the semiautomated assessment of detected nodules improves screening accuracy and should be applied in LCS protocols as verified tools to aid radiological diagnosis. In this review, we have summarized recent data about the radiological protocols from the most important LCS programs and pulmonary diagnostic algorithms. These protocols should be taken into consideration in the ongoing and planned LCS programs.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Management of Nodules Attached to the Costal Pleura at Low-Dose CT Screening for Lung Cancer

Background Pulmonary nodule features have been used to differentiate benign from malignant nodules. Purpose To determine the frequency of solid noncalcified nodules attached to the costal pleura (CP-NCNs) at baseline low-dose CT and to identify key features of benignity. Materials and Methods A retrospective review was performed of baseline low-dose CT scans obtained in 8730 participants in the Mount Sinai Early Lung and Cardiac Action Program screening cohort between 1992 and 2019. Participants with one or more solid CP-NCNs between 3.0 mm and 30.0 mm in average diameter were included. For each CP-NCN, the size, location, shape (lentiform, oval, or semicircular [LOS]; triangular; polygonal; round; or irregular), margin (smooth or nonsmooth), and attachment to the costal pleura (broad or narrow) were documented. The manifestation of emphysema and fibrosis within a 10-mm radius of the CP-NCN was determined. Multivariable logistic regression analysis, with synthetic minority oversampling techniques, was used. Results The 569 eligible participants (average age, 62 years ± 9 [standard deviation]; 343 women) had 943 solid CP-NCNs, of which 934 (99.0%) were benign and nine (1.0%) were malignant. Multivariable analysis showed that five shapes could be consolidated into three (LOS and/or triangular, round and/or polygonal, and irregular shape); pleural attachment was not a significant independent predictor (odds ratio, 1.24; P = .70); and interaction terms of size with shape (odds ratio, 0.73; P = .005) and margin were significant (odds ratio, 0.80; P = .001). All 603 CP-NCNs less than 10.0 mm with LOS or triangular shapes and smooth margins were benign. Conclusion All baseline noncalcified solid nodules attached to the costal pleura less than 10.0 mm in average diameter with lentiform, oval, semicircular, or triangular shapes and smooth margins were benign; thus, for these nodules, an annual repeat scan in 1 year, rather than a more immediate work-up, is recommended. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Godoy in this issue.

Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network

PURPOSE

The purpose of this study is to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT).

PATIENTS AND METHODS

We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group.

RESULTS

The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer.

CONCLUSION

The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management.

Follow-up of incidental pulmonary nodules and association with mortality in a safety-net cohort

Background Though incidental pulmonary nodules are common, rates of guideline-recommended surveillance and associations between surveillance and mortality are unclear. In this study, we describe adherence (categorized as complete, partial, late and none) to guideline-recommended surveillance among patients with incidental 5-8 mm pulmonary nodules and assess associations between adherence and mortality. Methods This was a retrospective cohort study of 551 patients (≥35 years) with incidental pulmonary nodules conducted from September 1, 2008 to December 31, 2016, in an integrated safety-net health network. Results Of the 551 patients, 156 (28%) had complete, 87 (16%) had partial, 93 (17%) had late and 215 (39%) had no documented surveillance. Patients were followed for a median of 5.2 years [interquartile range (IQR), 3.6-6.7 years] and 82 (15%) died during follow-up. Adjusted all-cause mortality rates ranged from 2.24 [95% confidence interval (CI), 1.24-3.25] deaths per 100 person-years for complete follow-up to 3.30 (95% CI, 2.36-4.23) for no follow-up. In multivariable models, there were no statistically significant associations between the levels of surveillance and mortality (p > 0.16 for each comparison with complete surveillance). Compared with complete surveillance, adjusted mortality rates were non-significantly increased by 0.45 deaths per 100 person-years (95% CI, -1.10 to 2.01) for partial, 0.55 (95% CI, -1.08 to 2.17) for late and 1.05 (95% CI, -0.35 to 2.45) for no surveillance. Conclusions Although guideline-recommended surveillance of small incidental pulmonary nodules was incomplete or absent in most patients, gaps in surveillance were not associated with statistically significant increases in mortality in a safety-net population.

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

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

Diagnostic success of transthoracic needle biopsy and PET-CT for 1 to 2 cm solid indeterminate pulmonary nodules

OBJECTIVES

Among the nodule types, the most controversial group are indeterminate solid nodules from 1 to 2 cm in size with the first choice being transthoracic needle biopsy (TTNB) or positron emission tomography (PET-CT) or both methods together. However, no single diagnostic algorithm could be applied to all cases. This research discusses the diagnostic success of PET-CT and TTNB.

MATERIALS AND METHODS

407 Patients who referred to our hospital for any reason, with solid nodules with the size from 1 to 2 cmincidentally identified on the thoracic CT tests were investigated. Among the patients who underwent biopsy, 312 cases had PET-CT, and maximum SUV (SUVmax) values of the nodules were examined. Values of ≥2.5 were accepted as hypermetabolic.

RESULTS

The mean age of the patients was 61 ± 10.8 years. 84 patients were female (20.6%) and 323 were male (79.4%). For TTNB; sensitivity, specificity and accuracy rates of all cases, who were correctly diagnosed, were 76.9%, 83.3% and 78.9%, respectively (P < .001). The 2.5 SUVmax cutoff value had sensitivity of 91%, specificity of 35.6%, accuracy of 75% (P = .034). The cutoff value of 49 years of age, nodule size of 16.4 mm, gender and 2.5 SUVmax value had high accuracy for benign-malignant differentiation. No statistically significant difference was found in the upper lobe localization of nodule.

CONCLUSION

A positive result from TTNB is a reliable finding; however, a negative result is not definitive. The high negative predictive value of PET-CT is effective in preventing the unnecessary biopsies and surgical procedures.

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Diagnosis and management of peripheral lung nodule

A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung. Frequently, it is an incidental finding on chest radiographs and chest CT scans. Determining the probability of malignancy is the first step in the evaluation of SPN. This can be done by looking at specific risk factors and the rate of radiographic progression. Subsequent management is guided by the type of the nodule. Patients with solid nodules and low pretest probability can be followed radiographically; those with high probability, who are good surgical candidates, can be referred for surgical resection. When the pretest probability is in the intermediate range additional testing such as biopsy should be done. Various modalities are now available to obtain tissue diagnosis. These modalities differ in their yield and complication rate. Patients with SPN should be well informed of each approach's risks and benefits and should be able to make an informed decision regarding the different diagnostic and therapeutic modalities.

Identification of Nonaggressive Pulmonary Nodules Using an Optimized Scoring System

Supplemental Digital Content is available in the text.

Purpose:

The purpose of this study was to define the optimal scoring method for identifying benign intrapulmonary lymph nodes.

Materials and Methods:

Subjects for this study were selected from the COPDGene study, a large multicenter longitudinal observational cohort study. A retrospective case-control analysis was performed using identified nodules on a subset of 377 patients who demonstrated 765 pulmonary nodules on their baseline computed tomography (CT) study. Nodule characteristics of 636 benign nodules (which resolved or showed <20% growth rate at 5 y follow-up) were compared with 51 nodules that occurred in the same lobe as a reported malignancy. Two radiologists scored each pulmonary nodule on the basis of intrapulmonary lymph node characteristics. A simple scoring strategy weighing all characteristics equally was compared with an optimized scoring strategy that weighed characteristics on the basis of their relative importance in identifying benign pulmonary nodules.

Results:

A total of 479 of 636 benign pulmonary nodules had the majority of lymph node characteristics, whereas only 1 subpleural nodule with the majority of lymph node characteristics appeared to be malignant. Only 279 of 479 (58%) of benign pulmonary nodules with the majority of lymph node characteristics were intrafissural or subpleural. The optimized scoring strategy showed improved performance compared with the simple scoring strategy with average area under the curve of 0.80 versus 0.55. Optimized cutoff scores showed negative likelihood values for both readers of <0.2. A simulation showed a potential reduction in CT utilization of up to 36% for Fleischner criteria and up to 5% for LUNG-RADS.

Conclusions:

Nodules with the majority of lymph node characteristics, regardless of location, are likely benign, and weighing certain lymph node characteristics greater than others can improve overall performance. Given the potential to reduce CT utilization, lymph node characteristics should be considered when recommending appropriate follow-up.

The clinical significance of indeterminate pulmonary nodules in melanoma patients at baseline and during follow-up chest CT

Objective

This study aims to determine an appropriate timeline to monitor indeterminate pulmonary nodules (IPN) in melanoma patients to confirm metastatic origin.

Materials and Methods

588 clinically non-metastatic melanoma patients underwent curative intent surgery during 3 years. Patients with baseline chest CT and at least one follow-up (FU) CT were retrospectively analyzed to assess for IPN. Patients with definitely benign nodules, metastases and non-melanoma malignancies were excluded. Change in volume from first to FU CT, initial diameter (D1) and volume (V1), distance from pleura, peripheral and perifissural location, density and clinical stage were evaluated. Nodules were volumetrically measured on CTs and were considered metastases if they increased in size between two CTs or if increase was accompanied by multiple new nodules or extrapulmonary metastases.

Results

148 patients were included. Two out of 243 baseline IPN detected in 70 patients, increased significantly in volume in 3 and 5 months and were proven metastases. During FU, 86% of 40 interval IPN detected in 28 patients, were proven metastases. Interval nodule (p < 0.0001, HR:243,CI:[57.32,1033.74]), 3-month volume change (OR:1.023,CI:[1.014,1.033]), V1 (OR:1.006,CI:[1.003,1.009]), D1 (OR:1.424,CI:[1.23,1.648]), distance from pleura (OR:1.03,CI:[1.003,1.059]), and combined stage IIC + III (OR:11.29,CI:[1.514,84.174]), were associated with increased risk for metastasis. 43%, 72% and 94% of patients with IPN were confirmed with metastases in the first FU CT at 3, 6 and 12 months respectively.

Conclusion

Baseline IPN are most likely benign, while interval IPN are high risk for metastasis. Absence of volume increase of IPN within 6 months excluded metastasis in most patients.

Prognostic indicators of pulmonary metastasis in patients with renal cell carcinoma who have undergone radical nephrectomy

The aim of the present study was to validate prognostic indicators of pulmonary metastasis in patients with renal cell carcinoma (RCC) that have undergone nephrectomy treatment. The data from 356 patients who underwent nephrectomy were investigated and subsequently divided into 2 groups, according to the pulmonary metastasis status. The risk factors for pulmonary metastasis were examined in all patients. In the subgroup analysis, the risk factors were additionally verified in patients with pulmonary nodules using univariate and multivariate logistic regression analyses. The status of pulmonary nodules and pulmonary metastasis were confirmed through preoperative chest radiography by two radiologists. Pulmonary metastasis was observed in 33 (9.3%) patients with a median follow-up time of 54.4 months (interquartile range, 38.8-71.8). Patients with pulmonary nodules indicated significantly increased rates of pulmonary metastasis, compared with patients without pulmonary nodules (24.2 vs. 6.1%; P<0.001). In multivariate analysis, the presence of pulmonary nodules [hazard ratio (HR)=3.15; P=0.0262], albumin (HR=0.42; P=0.0490) and pTstage (HR=3.63; P=0.0475) were indicated to be independent prognostic markers for pulmonary metastasis. In subgroup analysis, pTstage was the only independent prognostic indicator for pulmonary metastasis in these patients (HR=9.81; P=0.0033). In patients with RCC, the presence of pulmonary nodules was associated with pulmonary metastasis. Furthermore, pTstage is a negative prognostic indicator in patients with pulmonary nodules. Therefore, a chest radiologic short-term follow-up is required for these patients.

Solid Indeterminate Pulmonary Nodules Less Than or Equal to 250 mm3: Application of the Updated Fleischner Society Guidelines in Clinical Practice

Background

The latest version of the Fleischner Society guidelines for management of incidental pulmonary nodules was published in 2017. The main purpose of these guidelines is to reduce the number of unnecessary computed tomography (CT) examinations during the follow-up of small indeterminate nodules.

Objective

The present study aimed to evaluate the performance of these guidelines for management of solid indeterminate pulmonary nodules (SIPNs) ≤ 250 mm³.

Materials and Methods

During a 7-year period, we retrospectively reviewed the chest CT scans of 672 consecutive patients with SIPNs. The study sample was selected according to the following inclusion criteria: solitary SIPN; diameter ≥ 3 mm; volume ≤ 250 mm³; two or more CT scans performed with the same scanner and same acquisition/reconstruction protocol; thin-section 1-mm images in DICOM format; histologic diagnosis or follow-up ≥ 2 years; and no oncological history. Applying these criteria, a total of 27 patients with single SIPNs ≤ 250 mm³ were enrolled. For each SIPN, the volume and doubling time were calculated using semiautomatic software throughout the follow-up period. For each SIPN, we applied the Fleischner Society guidelines, and the recommended management was compared to what was actually done.

Results

A significant volumetric increase was detected in 5/27 (18.5%) SIPNs; all growing nodules were observed in high-risk patients. In these SIPNs, a histologic diagnosis of malignancy was obtained. Applying the Fleischner Society recommendations, all five malignant nodules would have been identified. None of the SIPNs < 100 mm³ in low-risk patients showed significant growth during the follow-up period. The application of the new guidelines would have led to a significant reduction in follow-up CT examinations (Hodges-Lehmann median difference, -2 CT scans; p = 0.0001).

Conclusion

The application of the updated Fleischner Society guidelines has been shown to be effective in the management of SIPNs ≤ 250 mm³ with a significant reduction in radiation dose.

Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists

Background

The study was conducted to evaluate the performance of a state‐of‐the‐art commercial deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing pulmonary nodules.

Methods

Pulmonary nodules in 346 healthy subjects (male: female = 221:125, mean age 51 years) from a lung cancer screening program conducted from March to November 2017 were screened using a DL‐CAD system and double reading independently, and their performance in nodule detection and characterization were evaluated. An expert panel combined the results of the DL‐CAD system and double reading as the reference standard.

Results

The DL‐CAD system showed a higher detection rate than double reading, regardless of nodule size (86.2% vs. 79.2%; P < 0.001): nodules ≥ 5 mm (96.5% vs. 88.0%; P = 0.008); nodules < 5 mm (84.3% vs. 77.5%; P < 0.001). However, the false positive rate (per computed tomography scan) of the DL‐CAD system (1.53, 529/346) was considerably higher than that of double reading (0.13, 44/346; P < 0.001). Regarding nodule characterization, the sensitivity and specificity of the DL‐CAD system for distinguishing solid nodules > 5 mm (90.3% and 100.0%, respectively) and ground‐glass nodules (100.0% and 96.1%, respectively) were close to that of double reading, but dropped to 55.5% and 93%, respectively, when discriminating part solid nodules.

Conclusion

Our DL‐CAD system detected significantly more nodules than double reading. In the future, false positive findings should be further reduced and characterization accuracy improved.

CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions

OBJECTIVE

To explore the CT characteristics of small lung nodules and improve the diagnosis of pulmonary ground-glass nodules less than 10  mm in size.

METHODS

We retrospectively analyzed CT images of 161 pulmonary nodules (less than 10  mm in size) with spiculation, lobulation, vacuoles, and pleural indentation and compared these images with pathological results or follow-up CT images. The relationships between the ground-glass nodules (GGNs) and blood vessels were observed. The GGN-vessel relationship was divided into four types, Type I (pass-by), Type II (pass-through), Type III (distorted/dilated), Type IV (complicated). The vessels traveling through a GGN were divided into three categories, category A (arteries), category B (veins), category C (arteries and veins).

RESULTS

161 GGNs were divided into three groups (benign group, pre-invasive group, and adenocarcinoma group) according to their pathological diagnosis. Significant differences in density of nodules were observed among the three different groups (p < 0.05). Significant differences in the shape (round/round-like or not) of the nodules were observed between the benign group and the pre-invasive group and between the pre-invasive group and the adenocarcinoma group (p < 0.05). No significant differences in the presence of vacuoles were observed between the benign group and the pre-invasive group or between the pre-invasive group and the adenocarcinoma group (p ＞0.05), but a significant difference was observed between the benign group and the adenocarcinoma group (p < 0.05). The differences in the vascularization of the lesions among the three groups were statistically significant (p < 0.05). No significant differences or correlations were observed between vascular categories and GGN groups (p > 0.05).

CONCLUSION

For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, combined with Type III or Type IV GGN-vessel relationship may strongly suggest malignant.

ADVANCES IN KNOWLEDGE

Previous studies mainly focused on CT diagnosis of pulmonary nodules (≤ 3  cm in diameter), but this study focused on ground-glass nodules less than 10  mm in diameter, which had not been fully studied. For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, especially the GGN-vessel relationship manifest as Type III (distorted/dilated) or Type IV (complicated) may strongly suggest malignant.

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection

BACKGROUND AND OBJECTIVE

In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks. To efficiently address these problems, we propose a novel framework using the ensemble of 2D CNNs using single views, which outperforms existing 3D CNN-based methods.

METHODS

Our ensemble of 2D CNNs utilizes single-view 2D patches to improve both computational and memory efficiency compared to previous techniques exploiting 3D CNNs. We first categorize non-nodules on the basis of features encoded by an autoencoder. Then, all 2D CNNs are trained by using the same nodule samples, but with different types of non-nodules. By extending the learning capability, this training scheme resolves difficulties of extracting representative features from non-nodules with large appearance variations. Note that, instead of manual categorization requiring the heavy workload of radiologists, we propose to automatically categorize non-nodules based on the autoencoder and k-means clustering.

RESULTS

We performed extensive experiments to validate the effectiveness of our framework based on the database of the lung nodule analysis 2016 challenge. The superiority of our framework is demonstrated through comparing the performance of five frameworks trained with differently constructed training sets. Our proposed framework achieved state-of-the-art performance (0.922 of the competition performance metric score) with low computational demands (789K of parameters and 1024M of floating point operations per second).

CONCLUSION

We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.

Serum microRNAs improving the diagnostic accuracy in lung cancer presenting with pulmonary nodules

Background

MicroRNA (miRNA) is an approach for early diagnosing of cancer. We validated a panel of miRNAs (hsa-miR-199a-3p, hsa-miR-148a-3p, hsa-miR-210-3p, hsa-miR-378d and hsa-miR-138-5p) to aid early diagnosis of lung adenocarcinoma by blood test in lung cancer presenting with pulmonary nodules.

Methods

A total of 369 individuals who were detected pulmonary nodules by computed tomography (CT) scan were enrolled into this study. These patients included 274 pulmonary malignant or borderline lung diseases and 122 lung benign pulmonary nodules. When the lung nodules were detected by combining with CT scan, we got patient blood samples in 2 days. Patients' serum was collected within 2 days prior to miRNAs analyses. We performed miRNAs panel by reverse transcription-polymerase chain reaction (RT-PCR).

Results

The sensitivity of miRNAs panel was 34.0% and the specificity of miRNAs panel was 90.2%. In invasive adenocarcinoma, the sensitivity of miRNAs panel was 44.7%. The overall false positive rate of CT imaging for nodules and glass ground nodules (GGNs) was 33.1%. When miRNAs panel test positive patients combined with the nodule size, the false positive rate was decreased to 3.2%.

Conclusions

The greatest impact of using the miRNAs panel CT scan was decreasing the false positive. miRNAs panel can improve the diagnosis of lung cancer presenting with nodules combined with CT.

Assessment of the cancer risk factors of solitary pulmonary nodules

There are no large samples or exact prediction models for assessing the cancer risk factors of solitary pulmonary nodules (SPNs) in the Chinese population. We retrospectively analyzed the clinical and imaging data of patients with SPNs who underwent computer tomography guided needle biopsy in our hospital from Jan 1st of 2011 to March 30th of 2016. These patients were divided into a development data set and a validation data set. These groups included 1078 and 344 patients, respectively. A prediction model was developed from the development data set and was validated with the validation data set using logistic regression. The predictors of cancer in our model included female gender, age, pack-years of smoking, a previous history of malignancy, nodule size, lobulated and spiculated edges, lobulation alone and spiculation alone. The Area Under the Curves, sensitivity and specificity of our model in the development and validation data sets were significantly higher than those of the Mayo model and VA model (p < 0.001). We established the largest sampling risk prediction model of SPNs in a Chinese cohort. This model is particularly applicable to SPNs > 8 mm in size. SPNs in female patients, as well as SPNs featuring a combination of lobulated and spiculated edges or lobulated edges alone, should be evaluated carefully due to the probability that they are malignant.

Pulmonary Nodule Management in Lung Cancer Screening: A Pictorial Review of Lung-RADS Version 1.0

The number of screening-detected lung nodules is expected to increase as low-dose computed tomography screening is implemented nationally. Standardized guidelines for image acquisition, interpretation, and screen-detected nodule workup are essential to ensure a high standard of medical care and that lung cancer screening is implemented safely and cost effectively. In this article, we review the current guidelines for pulmonary nodule management in the lung cancer screening setting.

Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: the NELSON study

PURPOSE

New nodules after baseline are regularly found in low-dose CT lung cancer screening and have a high lung cancer probability. It is unknown whether morphological and location characteristics can improve new nodule risk stratification by size.

METHODS

Solid non-calcified nodules detected during incidence screening rounds of the randomised controlled Dutch-Belgian lung cancer screening (NELSON) trial and registered as new or previously below detection limit (15 mm³) were included. A multivariate logistic regression analysis with lung cancer as outcome was performed, including previously established volume cut-offs (<30 mm³, 30-<200 mm³ and ≥200 mm³) and nodule characteristics (location, distribution, shape, margin and visibility <15 mm³ in retrospect).

RESULTS

Overall, 1280 new nodules were included with 73 (6%) being lung cancer. Of nodules ≥30 mm³ at detection and visible <15 mm³ in retrospect, 22% (6/27) were lung cancer. Discrimination based on volume cut-offs (area under the receiver operating characteristic curve (AUC): 0.80, 95% CI 0.75 to 0.84) and continuous volume (AUC: 0.82, 95% CI 0.77 to 0.87) was similar. After adjustment for volume cut-offs, only location in the right upper lobe (OR 2.0, P=0.012), central distribution (OR 2.4, P=0.001) and visibility <15 mm³ in retrospect (OR 4.7, P=0.003) remained significant predictors for lung cancer. The Hosmer-Lemeshow test (P=0.75) and assessment of bootstrap calibration curves indicated adequate model fit. Discrimination based on the continuous model probability (AUC: 0.85, 95% CI 0.81 to 0.89) was superior to volume cut-offs alone, but when stratified into three risk groups (AUC: 0.82, 95% CI 0.78 to 0.86), discrimination was similar.

CONCLUSION

Contrary to morphological nodule characteristics, growth-independent characteristics may further improve volume-based new nodule lung cancer prediction, but in a three-category stratification approach, this is limited.

TRIAL REGISTRATION NUMBER

ISRCTN63545820; pre-results.

Management of Incidental Lung Nodules

The incidental pulmonary nodule is commonly encountered when interpreting chest CTs. The management of pulmonary nodules requires a multidisciplinary approach entailing integration of nodule size and features, clinical risk factors, and patient preference and comorbidities. Guidelines have been issued for the management of both solid and subsolid nodules, with the Fleischner Society issuing revised guidelines in 2017. This article focuses on the CT imaging characteristics and clinical behavior of pulmonary nodules, with review of the current management guidelines that reflect this knowledge.

Controversies on lung cancers manifesting as part-solid nodules

PURPOSE

Summarise survival of patients with resected lung cancers manifesting as part-solid nodules (PSNs).

METHODS

PubMed/MEDLINE and EMBASE databases were searched for all studies/clinical trials on CT-detected lung cancer in English before 21 December 2015 to identify surgically resected lung cancers manifesting as PSNs. Outcome measures were lung cancer-specific survival (LCS), overall survival (OS), or disease-free survival (DFS). All PSNs were classified by the percentage of solid component to the entire nodule diameter into category PSNs <80% or category PSNs ≥80%.

RESULTS

Twenty studies reported on PSNs <80%: 7 reported DFS and 2 OS of 100%, 6 DFS 96.3-98.7%, and 11 OS 94.7-98.9% (median DFS 100% and OS 97.5%). Twenty-seven studies reported on PSNs ≥80%: 1 DFS and 2 OS of 100%, 19 DFS 48.0%-98.0% (median 82.6%), and 16 reported OS 43.0%-98.0% (median DFS 82.6%, OS 85.5%). Both DFS and OS were always higher for PSNs <80%.

CONCLUSION

A clear definition of the upper limit of solid component of a PSN is needed to avoid misclassification because cell-types and outcomes are different for PSN and solid nodules. The workup should be based on the size of the solid component.

KEY POINTS

• Lung cancers manifesting as PSNs are slow growing with high cure rates. • Upper limits of the solid component are important for correct interpretation. • Consensus definition is important for the management of PSNs. • Median disease-free-survival (DFS) increased with decreasing size of the nodule.

Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers

Purpose

To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans.

Methods

A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004.

Results

Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002).

Conclusions

Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Experience improves the performance of endobronchial ultrasound-guided transbronchial biopsy for peripheral pulmonary lesions: A learning curve at a medical centre

Background

Endobronchial ultrasound(EBUS)-guided transbronchial biopsy(TBB) is the preferred diagnostic tool for peripheral pulmonary lesions(PPLs) and mastering this procedure is an important task in the training of chest physicians. Little has been published about the learning experience of physicians with this technique, particularly at an institutional level. We aimed to establish a learning curve for EBUS-guided TBB for PPLs at a medical center.

Methods

Between 2008 and 2015, consecutive patients with PPLs referred for EBUS-guided TBB at National Taiwan University Hospital were enrolled. To build the learning curve, the diagnostic yield of TBB (plus brushings and washings) was calculated and compared. Meanwhile, lesion characteristics, and procedure-related features and complications were obtained to analyze associations with TBB yield and safety profile.

Results

A total of 2144 patients were included and EBUS-guided TBB was diagnostic for 1547(72%). The TBB yield was 64% in 2008 and reached a plateau of 72% after 2010. It took approximately 400 EBUS-guided procedures to achieve stable proficiency. Further analysis showed that improvement in diagnostic yield over time was mainly observed in PPLs, in cases in which the diameter was ≤2 cm or the EBUS probe could not be positioned within. Complication rates were low, with 1.8% and 0.5% for pneumothorax and hemorrhage, respectively.

Conclusions

Even though EBUS-guided TBB is an easy-to-learn technique, it takes 3 years or around 400 procedures for a medical center to achieve a better and stable performance. In particular, the diagnostic yield for lesions without the probe within or those sized ≤2 cm could improve with time.

A novel hybrid technique for localization of subcentimeter lung nodules

BACKGROUND

It is technically challenging to locate non-visible, non-palpable subcentimeter ground-glass nodules (GGNs) of lung during video-assisted thoracic surgery (VATS). Computed tomography (CT)-guided marking of small pulmonary nodules using microcoils has been reported to be a practical method of preoperative localization, whereas dislodgement of microcoils remains to be a bothersome complication. The objective of this study was to assess the viability and effectiveness of a newly developed hybrid technique, which combines induced controllable pneumothorax and CT-guided microcoil marking procedure to reduce the risk of microcoil dislodgement.

METHODS

After induced minor pneumothorax, 35 patients with subcentimeter GGNs underwent CT-guided marking with microcoils prior to VATS sublobar resection or lobectomy. Histopathological analysis was performed after surgeries.

RESULTS

All of 37 nodules were successfully marked before VATS. Segmentectomy was performed in 8 cases, wedge resection in 19 cases and lobectomy in 8 cases. All nodules were completely removed with marking microcoils. Dislodgement of microcoils was not observed in all cases and mild pulmonary hemorrhage occurred in one case. No other complications occurred.

CONCLUSIONS

The newly developed hybrid technique which combines induced controllable pneumothorax and CT-guided marking using microcoils was feasible and reliable for VATS resection of subcentimeter GGNs, meanwhile significantly lowered the risk of microcoil dislocation.

Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial

OBJECTIVE

The objective of our study was to determine how often death occurred from lung cancers that manifested as part-solid nodules in the National Lung Screening Trial (NLST).

MATERIALS AND METHODS

NLST radiologists classified nodules as solid, ground-glass, or mixed. All lung cancers classified as mixed nodules by NLST radiologists were reviewed by four experienced radiologists and reclassified as solid, nonsolid, or part-solid nodules. When possible, volume doubling times (VDTs) were calculated separately for the entire nodule and for the solid component of the nodule.

RESULTS

Of 88 screening-diagnosed lung cancer cases identified by the NLST radiologists as mixed nodules, study radiologists confirmed that 19 were part-solid nodules. All the part-solid nodules were present at baseline (time 0), and none of the patients with a part-solid nodule had lymph node enlargement at CT before diagnosis or metastases at resection. Multilobar stage IV (T4N0M1) bronchioloalveolar carcinoma was diagnosed in one patient 25.0 months after study randomization, and the patient died 67.9 months after randomization. All 18 patients with a solitary or dominant part-solid nodule underwent surgery, and none died of lung cancer. From randomization, the average time to diagnosis was 18.6 months and the average time of follow-up was 79.2 months. On the last CT examination performed before diagnosis, the average size of the solid component of the part-solid nodules was 9.2 mm (SD, 4.9); the solid component was larger than 10 mm in five patients. The median VDT based on the entire nodule was 476 days, and the median VDT based on the solid component alone was 240 days.

CONCLUSION

None of the patients with lung cancer manifesting as a solitary or dominant part-solid nodule had lymph node enlargement or metastases at pathology, and none died of lung cancer within the follow-up time of the NLST.

Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening

Currently, lung cancer screening by low-dose chest CT is implemented in the United States for high-risk persons. A disadvantage of lung cancer screening is the large number of small-to-intermediate sized lung nodules, detected in around 50% of all participants, the large majority being benign. Accurate estimation of nodule size and growth is essential in the classification of lung nodules. Currently, manual diameter measurements are the standard for lung cancer screening programs and routine clinical care. However, European screening studies using semi-automated volume measurements have shown higher accuracy and reproducibility compared to diameter measurements. In addition to this, with the optimization of CT scan techniques and reconstruction parameters, as well as advances in segmentation software, the accuracy of nodule volume measurement can be improved even further. The positive results of previous studies on volume and diameter measurements of lung nodules suggest that manual measurements of nodule diameter may be replaced by semi-automated volume measurements in the (near) future.

Analysis of small nucleolar RNAs in sputum for lung cancer diagnosis

Molecular analysis of sputum presents a noninvasive approach for diagnosis of lung cancer. We have shown that dysregulation of small nucleolar RNAs (snoRNAs) plays a vital role in lung tumorigenesis. We have also identified six snoRNAs whose changes are associated with lung cancer. Here we investigated if analysis of the snoRNAs in sputum could provide a potential tool for diagnosis of lung cancer. Using qRT-PCR, we determined expressions of the six snoRNAs in sputum of a training set of 59 lung cancer patients and 61 cancer-free smokers to develop a biomarker panel, which was validated in a testing set of 67 lung cancer patients and 69 cancer-free smokers for the diagnostic performance. The snoRNAs were robustly measurable in sputum. In the training set, a panel of two snoRNA biomarkers (snoRD66 and snoRD78) was developed, producing 74.58% sensitivity and 83.61% specificity for identifying lung cancer. The snoRNA biomarkers had a significantly higher sensitivity (74.58%) compared with sputum cytology (45.76%) (P < 0.05). The changes of the snoRNAs were not associated with stage and histology of lung cancer (All P >0.05). The performance of the biomarker panel was confirmed in the testing cohort. We report for the first time that sputum snoRNA biomarkers might be useful to improve diagnosis of lung cancer.

[First time revealed small formations of lungs (under 2 cm in diameter). Dynamic follow-up or surgery?]

AIM

To develop the treatment algorithm in patients with first time revealed lung lesions smaller than 2 cm.

MATERIAL AND METHODS

The study included 110 patients with pathological lung lesions with small dimensions who have been treated in the Burdenko Clinic of Faculty Surgery for the period 1997-2013. All patients underwent surgical removal of lung tissue using different surgical approaches: 44 cases of videothoracoscopic resections, 43 video-assisted minithoracotomies, 23 minithoracotomies.

RESULTS

There were 25 patients with lung cancer, 38 cases of benign tumours (hamartoma and tuberculoma) and 10 patients with disseminated tuberculosis thar required special treatment.

CONCLUSION

Small pulmonary formations (from 0.5 to 2 cm) can be removed without morphological verification prior to surgery. Optimal surgical approach should be selected depending on the amount and size of formations. Management of solitary lung formation smaller than 0.5 cm that was newly diagnosed by computed tomography should include dynamic follow-up and performance of computed tomography in 3-6-12 months.

CT Screening for Lung Cancer: Part-Solid Nodules in Baseline and Annual Repeat Rounds

OBJECTIVE

The purpose of this study was to assess the frequencies of identifying participants with part-solid nodules, of diagnostic pursuit, of diagnoses of lung cancer, and long-term lung cancer survival in baseline and annual repeat rounds of CT screening in the International Early Lung Cancer Action Project.

MATERIALS AND METHODS

Screenings were performed under a common protocol. Participants with solid, nonsolid, and part-solid nodules and the diagnoses of lung cancer were documented.

RESULTS

Part-solid nodules were identified in 2892 of 57,496 (5.0%) baseline screening studies; 567 (19.6%) of these nodules resolved or decreased in size. Diagnostic pursuit led to the diagnosis of adenocarcinoma in 79 cases, all clinical stage I. At resection, one nodule (12-mm solid component) had a single N2 metastasis. A new part-solid nodule was identified in 541 of 64,677 (0.8%) annual repeat screenings; 377 (69.7%) of these nodules resolved or decreased in size. In eight cases among the 541, the diagnosis of adenocarcinoma manifesting as a part solid nodule was made; on retrospective review the nodule originally had been a nonsolid nodule. In another 20 cases, the cancer originally had manifested as a nonsolid nodule but had progressed to become part-solid at annual repeat screening before any diagnosis was pursued. These 28 annual repeat cases of lung cancer were all pathologic stage IA. Of the 107 cases of lung cancer (79 baseline cases and 28 annual repeat cases), 106 were surgically resected, and one baseline case was followed up with imaging for 4 years. The lung cancer survival rate was 100% with a median follow-up period from diagnosis of 89 months (interquartile range, 52-134 months).

CONCLUSION

Lung cancers manifesting as part-solid nodules at repeat screening studies all started as nonsolid nodules. Among 107 cases of adenocarcinoma manifesting as a part-solid nodule, a single lymph node metastasis was found in a single case (solid component, 12 mm).

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules

Purpose: We propose a systematic methodology to quantify incidentally identified pulmonary nodules based on observed radiological traits (semantics) quantified on a point scale and a machine-learning method using these data to predict cancer status.Experimental Design: We investigated 172 patients who had low-dose CT images, with 102 and 70 patients grouped into training and validation cohorts, respectively. On the images, 24 radiological traits were systematically scored and a linear classifier was built to relate the traits to malignant status. The model was formed both with and without size descriptors to remove bias due to nodule size. The multivariate pairs formed on the training set were tested on an independent validation data set to evaluate their performance.Results: The best 4-feature set that included a size measurement (set 1), was short axis, contour, concavity, and texture, which had an area under the receiver operator characteristic curve (AUROC) of 0.88 (accuracy = 81%, sensitivity = 76.2%, specificity = 91.7%). If size measures were excluded, the four best features (set 2) were location, fissure attachment, lobulation, and spiculation, which had an AUROC of 0.83 (accuracy = 73.2%, sensitivity = 73.8%, specificity = 81.7%) in predicting malignancy in primary nodules. The validation test AUROC was 0.8 (accuracy = 74.3%, sensitivity = 66.7%, specificity = 75.6%) and 0.74 (accuracy = 71.4%, sensitivity = 61.9%, specificity = 75.5%) for sets 1 and 2, respectively.Conclusions: Radiological image traits are useful in predicting malignancy in lung nodules. These semantic traits can be used in combination with size-based measures to enhance prediction accuracy and reduce false-positives. Clin Cancer Res; 23(6); 1442-9. ©2016 AACR.