Natural History of Pulmonary Subsolid Nodules: A Prospective Multicenter Study

The diagnosis and treatment of lung cancer presented as ground-glass nodule

Pulmonary nodules that manifest as ground-glass opacity are particularly challenging on account of their malignant potential and heterogeneous characteristics. The "ground-glass nodule-like" lung cancer is different from the conventional lung cancer. This review focuses on the radiologic and pathologic classifications of ground-glass nodules, along with the staging and clinical management of these lesions. In addition, we discuss the lung cancer high-risk population shift and follow-up of ground-glass nodules in the light of experience from screening trials. The standard of care surgical treatment of early lung cancer is still lobectomy with systematic lymph node dissection. However, a recent research has shown that some ground-glass nodules may be treated with sublobar resections or non-surgical treatment; these findings may expand the treatment options available in the future.

New T1 classification

The IASLC staging and Prognostic Factor Committee proposed new changes to the descriptors for the 8th edition of the Tumour Node Metastasis Staging for Lung Cancer. The T1 descriptor changes include (1) T1 tumours are subclassified into T1a (< 1 cm), T1b (> 1 to < 2 cm), T1c (> 2 to < 3 cm). The corresponding changes are introduced to the overall staging: T1aN0M0 = Stage IA1; T1bN0M0 = Stage IA2; T1cN0M0 = Stage IA3. (2) The introduction of the pathological entities Adenocarcinoma-In-Situ (AIS), Minimally Invasive Adenocarcinoma, and Lepidic Predominant Adenocarcinoma. The corresponding changes on the T descriptor are as follows: Adenocarcinoma-in situ is coded as Tis (AIS); Minimally Invasive Adenocarcinoma is coded as T1a(mi). In this review, the basis for these changes will be described, and the implications on clinical practice will be discussed.

Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Thin-section computed tomography (TSCT) imaging biomarkers are uncertain to distinguish progressive adenocarcinoma from benign lesions in pGGNs. The purpose of this study was to evaluate the usefulness of TSCT characteristics for differentiating among transient (TRA) lesions, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) presenting as pure ground-glass nodules (pGGNs). Between January 2016 and January 2018, 255 pGGNs, including 64 TRA, 22 AAH, 37 AIS, 108 MIA and 24 IAC cases, were reviewed on TSCT images. Differences in TSCT characteristics were compared among these five subtypes of pGGNs. Logistic analysis was performed to identify significant factors for predicting MIA and IAC. Progressive pGGNs were more likely to be round or oval in shape, with clear margins, air bronchograms, vascular and pleural changes, creep growth, and bubble-like lucency than were non-progressive pGGNs. The optimal cut-off values of the maximum diameter for differentiating non-progressive from progressive pGGNs and IAC from non-IAC were 6.5 mm and 11.5 mm, respectively. For the prediction of IAC vs. non-IAC and non-progressive vs. progressive adenocarcinoma, the areas under the receiver operating characteristics curves were 0.865 and 0.783 for maximum diameter and 0.784 and 0.722 for maximum CT attenuation, respectively. The optimal cut-off values of maximum CT attenuation were -532 HU and -574 HU for differentiating non-progressive from progressive pGGNs and IAC from non-IAC, respectively. Maximum diameter, maximum attenuation and morphological characteristics could help distinguish TRA lesions from MIA and IAC but not from AAH. So, CT morphologic characteristics, diameter and attenuation parameters are useful for differentiating among pGGNs of different subtypes.

Synchronous multiple lung cancers presenting as multifocal pure ground glass nodules: are whole-body positron emission tomography/computed tomography and brain enhanced magnetic resonance imaging necessary?

Background

Multifocal ground glass nodules (GGNs) represent a special radiological pattern indicative of synchronous multiple lung cancers (SMLCs), especially adenocarcinoma. However, the necessity of performing whole-body positron emission tomography/computed tomography (PET-CT) scanning and brain enhanced magnetic resonance imaging (MRI) as a staging workup for multifocal pure GGN (pGGN) patients remains unclear. The purpose of this study was to determine the utility of these two imaging scans for patients with multifocal pGGNs.

Methods

This retrospective study was reviewed and approved by the ethics committee of the Cancer Hospital of the Chinese Academy of Medical Sciences. The study cohort was retrospectively selected from patients with multifocal pGGNs who underwent whole-body PET-CT examinations and/or brain enhanced MRIs between January 2010 and February 2019 at our institution. The additional value of the two exams for detecting nodal and distant metastases was evaluated.

Results

In total, 73 patients (male-to-female ratio, 20:53; median age, 57 years) with multifocal pGGNs who underwent whole-body PET-CT (55 patients) and/or brain enhanced MRI (25 patients) were enrolled. No clearly metastatic lesions were detected. Among the enrolled patients, 53 (128 pGGNs) underwent complete surgical resection. All pGGNs were adenocarcinomas and/or preneoplasias, and no lymph node metastases were found on final pathology. Whole-body PET-CT and brain enhanced MRI added no definite benefit compared with chest CT alone before surgery.

Conclusions

Whole-body PET-CT scans and brain enhanced MRIs are not necessary for patients with multifocal pGGNs.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation

OBJECTIVE

To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation.

METHODS

Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth.

RESULTS

The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth.

CONCLUSIONS

Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow.

KEY POINTS

• The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Early-Stage NSCLC: Advances in Thoracic Oncology 2018

2018 was a banner year for all thoracic oncology, but especially for early-stage NSCLC. Three seminal events occurred in the approximately 18 months from mid-2017 to the end of 2018: in June 2017 at the American Society of Clinical Oncology Annual Meeting a small, relatively unheralded study from Max Diehn's group at Stanford University reported on the use of a novel "cancer personalized profiling by deep sequencing" circulating tumor-DNA technology to identify minimal residual disease in patients after curative-intent radiation or surgery for NSCLC; in April 2018 at the American Association for Cancer Research Annual Meeting, Drew Pardoll presented a small pilot study of 21 patients who had received two doses of preoperative nivolumab; in September 2018, at the 19th World Conference on Lung Cancer, Harry J. De Koning presented the long-awaited results of the Dutch-Belgian Lung Cancer Screening Trial (NELSON). These three seminal studies, along with others which are reviewed in this paper, promise to accelerate our progress towards a world in which lung cancer is identified early, more patients undergo curative-intent treatment that achieves the promised cure, and those at risk for failure after treatment are identified early, when the cancer remains most vulnerable. The day is around the corner when lung cancer is defanged and no longer the worldwide terror it currently is. We herein present an overview of the most recent body of work that moves us inexorably towards that day.

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P >.11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P = .98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P = .0005), but significantly superior specificity (P = .02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists.

Long-Term Follow-Up of Ground-Glass Nodules After 5 Years of Stability

INTRODUCTION

Small ground-glass nodules (GGNs) or those with an indeterminate risk on low-dose computed tomography (LDCT) of the chest are recommended at 5-year follow-up, but the rationale for follow-up beyond 5 years is unclear.

METHODS

An observational study was conducted to investigate the natural course of GGNs that had been stable for 5 years by LDCT over 10 years. All eligible GGNs were detected during regular health checkups. Baseline characteristics were compared between GGNs with and without growth. Risk factors for GGN growth were evaluated.

RESULTS

A total of 208 GGNs were detected in 160 participants. GGN growth was identified in 27 (13.0%) GGNs during a follow-up of 136 months on LDCT scans. In approximately 95% of these GGNs, the initial size was less than 6 mm, with 3.2 mm of growth over 8.5 years. Biopsies were performed in 3 of 27 GGNs, revealing adenocarcinoma. In 8 of 27 cases, GGN growth preceded the development of a new solid component. In a multivariate analysis, bubble lucency (p = 0.001), a history of cancer other than lung cancer (p = 0.036), and development of a new solid component (p < 0.001) were significant risk factors for GGN growth.

CONCLUSIONS

GGNs should not be ignored, even when smaller than 6 mm and stable for 5 years, especially when a new solid component appears during follow-up.

Comparison of ultra-low dose chest CT scanning protocols for the detection of pulmonary nodules: a phantom study

PURPOSE

To test ultra-low-dose computed tomography (ULDCT) scanning protocols for the detection of pulmonary nodules (PN).

METHODS

A chest phantom containing 19 solid and 11 subsolid PNs was scanned on a third-generation dual-source computed tomography (CT) scanner. Five ULDCT scans (Sn100kVp and 120, 70, 50, 30, and 20 reference mAs, using tube current modulation), reconstructed with iterative reconstruction (IR) algorithm at strength levels 2, 3, 4, and 5, were compared with standard CT (120kVp, 150 reference mAs, using tube current modulation). PNs were subjectively assessed according to a 4-point scale: 0, nondetectable nodule; 1, detectable nodule, very unlikely to be correctly measured; 2, detectable nodule, likely to be correctly measured; 3, PN quality equal to standard of reference. PN scores were analysed according to the Lung Imaging Reporting and Data System (Lung-RADS), simulating detection of nodules at baseline and incidence screening round.

RESULTS

For the baseline round, there were 17 Lung-RADS 2, 4 Lung-RADS 3, 8 Lung-RADS 4A, and 1 Lung-RADS 4B PNs. They were detectable in any ULDCT protocol, with the exception of 1 nondetectable part-solid nodule in 1 scanning protocol (120 reference mAs; IR strength: 3). For the incidence round, there were 4 Lung-RADS 2, 14 Lung-RADS 3, 2 Lung-RADS 4A, and 10 Lung-RADS 4B PNs. Ten were nondetectable in at least one ULDCT dataset; however, they were at least detectable in ULDCT with 70 reference mAs (IR strength: 4 and 5).

CONCLUSIONS

ULDCT scanning protocols allowing the detection of PNs can be proposed for the purpose of lung cancer screening.

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

BACKGROUND

Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images.

MATERIALS AND METHODS

Open-source data sets and multicenter data sets have been used in this study. A three-dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results.

RESULTS

The sensitivity and specificity of this well-trained model were found to be 84.4% (95% confidence interval [CI], 80.5%-88.3%) and 83.0% (95% CI, 79.5%-86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10-30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three-dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment.

CONCLUSION

Under the companion diagnostics, the three-dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices.

IMPLICATIONS FOR PRACTICE

The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.

Evaluation of ground glass nodules

PURPOSE OF REVIEW

Ground glass nodules (GGNs) represent an indolent subset of lung nodules including preinvasive nonsmall-cell lung cancer associated with a favorable prognosis and low risk for progression. Increased performance of screening cat-scan (CT) for high-risk patients has identified an increasing number of GGNs. The management of these nodules is founded mostly on single institution data and currently no universally accepted recommendations help guide clinicians managing these patients.

RECENT FINDINGS

The solid component within a GGN is the key determinant of prognosis and is best defined by evaluating nodule density on mediastinal windows of a chest CT. When a GGN is small (<3 cm), associated with minimal change in size (<25% growth per year), and there is no demonstration of a significant solid component on mediastinal windows (<2 mm in diameter), patients can be safely observed with serially imaging. These imaging features also help distinguish patients that may harbor early-stage lung cancers that benefit from local treatment options.

SUMMARY

The majority of GGNs do not undergo significant progression during surveillance. Evidence of nodule progression on interval imaging may be a trigger for consideration of a local treatment option such as surgical resection. Large prospective studies are needed in the United States to validate the more robust data derived from Asian studies to help formulate formal recommendations for surveillance and treatment. Future improvements in imaging and the molecular characterization of these GGNs may further refine which patients are at risk for progression.

A population-based cohort study to evaluate the effectiveness of lung cancer screening using low-dose CT in Hitachi city, Japan

Objectives

To evaluate the effectiveness of lung cancer screening using low-dose computed tomography for the general population, we conducted a retrospective cohort study of screening for participants among Hitachi residents.

Materials and Methods

Citizens aged 50-74 who underwent low-dose computed tomography screening at least once during 1998-2006 were defined as the computed tomography group, and those who underwent X-ray screening at least once during the same period, but did not receive low-dose computed tomography screening throughout the follow-up period, were defined as the XP group. We investigated the lung cancer incidence rate, mortality rate and all-cause mortality rate for both groups from the first lung cancer screening to the end of 2012.

Results

In the computed tomography group (17 935 residents; 9790 males and 8145 females), 273 cases of lung cancer (1.5%), 72 cases of lung cancer death (0.4%), and 885 cases of all-cause death (4.9%) were observed. On the other hand, 164 cases (1.1%) of lung cancer, 80 cases (0.5%) of lung cancer death and 1188 cases (7.6%) of all-cause death were observed in the XP group (15 548 residents; 6526 males and 9022 females). The hazard ratios of the computed tomography group to the XP group adjusted for gender, age and smoking history were 1.23 for lung cancer incidence rate, 0.49 for lung cancer mortality rate and 0.57 for all-cause mortality rate. Non-smokers and light smokers (<30 pack-years) had a significantly lower lung cancer mortality (0.41 and 0.21, respectively).

Conclusion

low-dose computed tomography screening for a population including non-smokers and light smokers may be effective.

Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule

OBJECTIVE

To construct a predictive model to discriminate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs) using computed tomography (CT) histogram analysis combined with morphological characteristics and to evaluate its diagnostic performance.

MATERIALS AND METHODS

Two hundred eighty-nine patients with surgically resected solitary pGGN and pathologically diagnosed with AIS, MIA, or IAC in our institution from January 2014 to May 2018 were enrolled in our study. Two hundred twenty-six pGGNs (79 AIS, 84 MIA, and 63 IAC) were randomly selected and assigned to a model-development cohort, and the remaining 63 pGGNs (11 AIS, 29 MIA and 23 IAC) were assigned to a validation cohort. The morphological characteristics were established as model A and histogram parameters as model B. The diagnostic performances of model A, model B, and model A + B were evaluated and compared via receiver operating curve (ROC) analysis and logistic regression analysis.

RESULTS

Entropy (odd ratio [OR] = 23.25, 95%CI: 6.83-79.15, p < 0.001), microvascular sign (OR = 8.62, 95%CI: 3.72-19.98, p < 0.001) and the maximum diameter (OR = 4.37, 95%CI: 2.44-7.84, p < 0.001) were identified as independent predictors in the IAC group. The area under the ROC (Az value), accuracy, sensitivity and specificity of model A + B were 0.896, 88.1%, 79.4% and 91.4%, respectively, exhibiting a significantly higher Az value than either model A or model B alone (0.785 vs 0.896, p < 0.001; 0.849 vs 0.896, p = 0.029). Model A + B also conveyed a good diagnostic performance in the validation cohort, with an Az value of 0.851.

CONCLUSION

Histogram analysis combined with morphological characteristics exhibit a superior diagnostic performance in discriminating AIS-MIA from IAC appearing as pGGNs.

Quantitative features can predict further growth of persistent pure ground-glass nodule

Background

To evaluate whether quantitative features of persistent pure ground-glass nodules (PGGN) on the initial computed tomography (CT) scans can predict further nodule growth.

Methods

This retrospective study included 59 patients with 101 PGGNs from 2011 to 2012, who received regular CT follow-up for lung nodule surveillance. Nineteen quantitative image features consisting of 8 volumetric and 11 histogram parameters were calculated to detect lung nodule growth. For the extraction of the quantitative features, semi-automatic GrowCut segmentation was implemented on chest CT images in 3D slicer platform. Univariate and multivariate analyses were performed to identify risk factors for nodule growth.

Results

With a median follow-up of 52 months, nodule growth was detected in 10 nodules by radiological assessment and in 16 nodules by quantitative features. In univariate analysis, 3D maximum diameter (MD), volume, mass, surface area, 90% percentile, and standard deviation value (SD) of PGGN on the initial CT scan were significantly different between stable nodules and nodules with further growth. In multivariate analysis, MD [hazard ratio (HR), 3.75; 95% confidence interval (CI), 2.14-6.55] and SD (HR, 2.06; 95% CI, 1.35-3.14) were independent predictors of further nodule growth. Also, the area under the curve was 0.896 (95% CI: 0.820-0.948) and 0.813 (95% CI: 0.723-0.883) for MD with a cut-off value of 10.2mm and SD of 50.0 Hounsfield Unit (HU). Besides, the growth rate was 55.6% (n=15) of PGGNs with MD >10.2 mm and SD >50.0 HU.

Conclusions

Based on the initial CT scan, the quantitative features can predict PGGN growth more precisely. PGGN with MD >10.2 mm and SD >50.0 HU may require close follow-up or surgical intervention for the high incidence of growth.

Methods of computed tomography screening and management of lung cancer in Tianjin: design of a population-based cohort study

Objective

European lung cancer screening studies using computed tomography (CT) have shown that a management protocol based on measuring lung nodule volume and volume doubling time (VDT) is more specific for early lung cancer detection than a diameter-based protocol. However, whether this also applies to a Chinese population is unclear. The aim of this study is to compare the diagnostic performance of a volume-based protocol with a diameter-based protocol for lung cancer detection and optimize the nodule management criteria for a Chinese population.

Methods

This study has a population-based, prospective cohort design and includes 4000 participants from the Hexi district of Tianjin, China. Participants will undergo low-dose chest CT at baseline and after 1 year. Initially, detected lung nodules will be evaluated for diameter and managed according to a routine diameter-based protocol (Clinical Practice Guideline in Oncology for Lung Cancer Screening, Version 2.2018). Subsequently, lung nodules will be evaluated for volume and management will be simulated according to a volume-based protocol and VDT (a European lung nodule management protocol). Participants will be followed up for 4 years to evaluate lung cancer incidence and mortality. The primary outcome is the diagnostic performance of the European volume-based protocol compared to diameter-based management regarding lung nodules detected using low-dose CT.

Results

The diagnostic performance of volume- and diameter-based management for lung nodules in a Chinese population will be estimated and compared.

Conclusions

Through the study, we expect to improve the management of lung nodules and early detection of lung cancer in Chinese populations.

Importance of avoiding surgery delays after initial discovery of suspected non-small-cell lung cancer in clinical stage IA patients

Introduction

The natural history of consolidation on computed tomography (CT) rarely includes invasive cancers, and evidence of the ideal timing for surgical intervention via long-term follow-up studies remains unknown.

Methods

Between January 2012 and June 2017, pulmonary resection was undertaken in 293 clinical IA patients who were followed-up for > 6 months after the first detection of potential non-small-cell lung cancer (NSCLC) opacities. We evaluated the corresponding HRs and compared the recurrence risk with the CT follow-up duration.

Results

HRs calculated for the longest intervals were compared between two patient subsets: a shorter-interval surgery group (SISG: 41.3%; mean follow-up interval, 13.5±5.3 months) and a longer-interval surgery group (58.7%; mean follow-up interval, 54.9±25.6 months). On Cox multivariate regression analyses, CT consolidation (ratio >0.5), an abnormal carcinoembryonic antigen and a triple-negative mutation showed an independent association with an unfavorable prognosis, as measured by disease-free survival after the first detection of potential NSCLC opacities. The longer-interval surgery group fared significantly better than the SISG in terms of 5-year overall survival after the first detection (99.3% vs 93.1%, P<0.01); the 3-year overall survival after the first detection was significantly shorter in the high-risk SISG (presence of two factors from the three) than that in the low-risk SISG (presence of 0 or one factor; 100% vs 73.3%, P<0.01).

Conclusion

Our study indicates that the patients with potential NSCLC opacities who are able to wait for more than 2 years prior to pulmonary resection may be likely to have a favorable prognosis, whereas early judgment for surgical resection should be required for avoiding surgical delays.

A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules

Purpose To evaluate management strategies and treatment options for patients with ground-glass nodules (GGNs) by using decision-analysis models. Materials and Methods A simulation was developed for 1 000 000 hypothetical patients with GGNs undergoing follow-up per the Lung Imaging Reporting and Data System (Lung-RADS) recommendations. The initial age range was 55-75 years (mean, 64 years). Nodules could grow and develop solid components over time. Clinically significant malignancy rates were calibrated to data from the National Lung Screening Trial. Annual versus 3-year-interval follow-up of Lung-RADS category 2 nodules was compared, and different treatment strategies were tested (stereotactic body radiation therapy, surgery, and no therapy). Results Overall, 2.3% (22 584 of 1 000 000) of nodules were clinically significant malignancies; 6.3% (62 559 of 1 000 000) of nodules were treated. Only 30% (18 668 of 62 559) of Lung-RADS category 4B or 4X nodules were clinically significant malignancies. The risk of clinically significant malignancy for persistent nonsolid nodules after baseline was higher than Lung-RADS estimates for categories 2 and 3 (3% vs <1% and 1%-2%, respectively). Overall survival (OS) at 10 years was 72% (527 827 of 737 306; 95% confidence interval [CI]: 71%, 72%) with annual follow-up and 71% (526 507 of 737 306; 95% CI: 71%, 72%) with 3-year-interval follow-up (P < .01). At 10 years, OS among patients whose nodules progressed to Lung-RADS category 4B or 4X was 80% after radiation therapy (49 945 of 62 559; 95% CI: 80%, 80%), 79% after surgery (49 139 of 62 559; 95% CI: 78%, 79%), and 74% after no therapy (46 512 of 62 559; 95% CI: 74%, 75%) (P < .01). Conclusion Simulation modeling suggests that the follow-up interval for evaluating ground-glass nodules can be increased from 1 year to 3 years with minimal change in outcomes. Stereotactic body radiation therapy demonstrated the best outcomes compared with lobectomy and with no therapy for nonsolid nodules. © RSNA, 2018 Online supplemental material is available for this article.

Qualitative CT Criterion for Subsolid Nodule Subclassification: Improving Interobserver Agreement and Pathologic Correlation in the Adenocarcinoma Spectrum

RATIONALE AND OBJECTIVES

The main aim of this study was to evaluate the clinical validity and correlation with pathologic invasiveness in the pulmonary adenocarcinoma spectrum based on the novel qualitative computed tomography criterion for subsolid nodule (SSN) classification, which classified SSN into pure ground-glass nodule, heterogeneous ground-glass nodule, and part-solid nodule. In addition, we compared the performance of the conventional and novel classifications.

MATERIALS AND METHODS

The computed tomography images of 41 SSN nodules were interpreted by six observers independently, and the SSN characteristics were classified according to both the conventional and the novel classification systems. Each observer assessed 41 nodules in two different classifications separated by a minimum of 8 weeks. The kappa (κ) coefficient test was used to determine the reliability. The correlation between pulmonary adenocarcinoma spectrum and the SSN classification was analyzed with Spearman correlation coefficients.

RESULTS

Interobserver agreement (κ) was 0.702 (range 0.42-0.89) and 0.707 (range 0.58-0.88) for the conventional and the novel classifications for SSN, respectively, and intraobserver agreement (κ) was 0.92 and 0.88 for the conventional and the novel classifications for SSN, respectively. The novel SSN classification (correlation coefficient range 0.622-0.732) is more strongly correlated with the pathologic invasiveness degree of lesions in adenocarcinoma spectrum than the conventional SSN classification (correlation coefficient range 0.458-0.644).

CONCLUSIONS

The agreement between observers on the novel SSN classification system was good and had better correlation with pathologic invasiveness than the conventional SSN classification. Further studies are needed to confirm these results on interobserver agreement.

Predictive factors for invasive adenocarcinoma in patients with clinical non-invasive or minimally invasive lung cancer

Background

Pure ground glass opacity (GGO) or part-solid GGO with small solid component (≤5 mm) are likely to be non-invasive or minimally invasive lung cancer. However, those lesions sometimes are diagnosed as invasive adenocarcinoma postoperatively. The aim of this study was to determine the predictors of invasive adenocarcinoma in clinical non- or minimally invasive lung cancer.

Methods

From January 2010 to December 2017, 203 patients were diagnosed as clinical adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) identified on chest computed tomography (CT) and they underwent surgical resection. A retrospective study was performed to analyze the prediction of invasive adenocarcinoma in clinical non- or minimally invasive lung cancer.

Results

Of all clinical AIS or MIA patients, invasive adenocarcinoma was diagnosed in 55 patients (27.1%). In clinical AIS, invasive adenocarcinoma was diagnosed in 19 patients (17.9%) and 36 patients (37.1%) were diagnosed as invasive adenocarcinoma in clinical MIA (P=0.002). Tumor diameter and the presence of solid component were confirmed to be significant predictive factors for invasive adenocarcinoma in a multivariate analysis [hazard ratio (HR) 1.071, P=0.037; HR 2.573, P=0.005; respectively].

Conclusions

Large tumor size and the presence of solid component in clinical AIS or MIA are predictive factors for invasive adenocarcinoma. Therefore, early surgical intervention is recommended for those lesions.

[Factors Influencing the Progression Trend of Early Lung Cancer and CT Findings]

背景与目的

不同类型的肺部结节具有不同的体积倍增时间（volume doubling time, VDT）。目前针对不同病理类型早期肺腺癌VDT的研究较少。本研究通过回顾性分析143例早期肺腺癌的影像资料，探讨早期肺腺癌的进展趋势及相关影响因素，为临床制订其随访策略提供参考。

方法

依据2015版世界卫生组织肺肿瘤分类标准和第8版肿瘤肿瘤-淋巴结-转移（tumor-node-metastasis, TNM）分期标准，对143例早期肺腺癌进行分类及分期。参考修正版Schwartz公式计算不同病理类型肺腺癌的VDT。

结果

143例早期肺腺癌中，有50例（34.97%）出现进展，多因素分析显示影响因素包括随访时间、结节大小、病理类型、结节类型和病理分期。附壁生长为主型肺腺癌（lepidic predominant adenocarcinoma, LPA）的VDT为（594±272）d，伴少量附壁生长成分浸润性腺癌的VDT为（520±285）d，完全浸润性腺癌的VDT为（371±183）d，3类进展性早期肺腺癌的VDT有统计学差异（P=0.044）。

结论

在早期肺腺癌中，约有35%的肿瘤处于进展阶段，是否含有附壁生长成分是影响肿瘤进展速度的重要因素。

Modified Lung-RADS Improves Performance of Screening LDCT in a Population with High Prevalence of Non-smoking-related Lung Cancer

OBJECTIVES

We proposed a modification of the ACR Lung Imaging Reporting and Data System (Lung-RADS) to clarify the characteristics of subsolid nodules with categories 1-11, and to compare the diagnostic accuracy with Lung-RADS and National Lung Screening Trial criteria in an Asian population with high prevalence of adenocarcinoma.

METHODS

We analyzed a retrospective cohort of 1978 consecutive healthy subjects (72.8% nonsmoker) who underwent low-dose computed tomography from August 2013 to October 2014 (1084 men, 894 women). Lung-RADS categories 2 and 3 were modified to include subcategories of 2A/2B/2C and 3A/3B/3C, respectively. Clinical information and nodule characteristics were recorded. Receiver operating characteristic curves were used to compare diagnostic accuracy at different cutoffs.

RESULTS

Thirty-two subjects (30 nonsmokers) had pathology-proven adenocarcinoma spectrum lesions in the follow-up period (1.6 ± 0.5 years). Modified Lung-RADS, using modified Lung-RADS category 2C as cutoff, had an area under the curve (AUC) of 0.973 in predicting adenocarcinoma spectrum lesions (sensitivity of 100%, specificity of 89.3%), which was significantly higher than that of Lung-RADS (AUC = 0.815, P < .001) and National Lung Screening Trial (AUC = 0.906, P < .001). Furthermore, modified Lung-RADS showed an AUC of 0.992 in predicting invasive adenocarcinoma (sensitivity of 95%, specificity of 97.8%) when category 3B was used as cutoff.

CONCLUSIONS

Modified Lung-RADS may substantially improve sensitivity while maintaining specificity for detection of adenocarcinoma spectrum lesions in an Asian population. Compared to Lung-RADS, it has enhanced ability to differentiate invasive from indolent adenocarcinoma by more refined subclassification of subsolid nodules using two cutoff values of category 2C and 3B. The effect of using modified Lung-RADS in clinical practice must be carefully studied in prospective large cohort studies.

A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection

OBJECTIVES

To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model.

METHODS

This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity.

RESULTS

Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively).

CONCLUSIONS

Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations.

KEY POINTS

• Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.

Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter?

OBJECTIVES

The aim was to investigate the effect of clinico-radiologic variables, including total tumor (T_total) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs).

METHODS

This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, T_total size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the T_total size at each clinical T category.

RESULTS

Multivariate Cox regression analysis demonstrated that T_total size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a T_total size cutoff of 3.0 cm, PSNs with T_total > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with T_total > 3.0 cm and cT1c (p = 0.915).

CONCLUSIONS

T_total size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with T_total > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c.

KEY POINTS

• Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.

Ground-glass nodules of the lung in never-smokers and smokers: clinical and genetic insights

Pulmonary ground-glass nodules (GGNs) are hazy radiological findings on computed tomography (CT). GGNs are detected more often in never-smokers. Retrospective and prospective studies have revealed that approximately 20% of pure GGNs and 40% of part-solid GGNs gradually grow or increase their solid components, whereas others remain stable for years. Most persistent or growing GGNs are lung adenocarcinomas or their preinvasive lesions. To distinguish GGNs with growth from those without growth, GGNs should be followed for at least 5 years. Lesion size and smoking history are predictors of GGN growth. Genetic analyses of resected GGNs have suggested that EGFR mutations are also predictors for growth but a subset of KRAS- or BRAF-mutated GGNs may undergo spontaneous regression because the frequencies of KRAS or BRAF mutations decrease with the advance of pathological invasiveness. Although lobectomy is the standard surgical procedure for lung cancer, limited surgery such as wedge resection or segmentectomy for lung cancers ≤2 cm with consolidation/tumor ratio ≤0.25 can be a viable alternative based on the recent clinical trial. Further genetic analyses and clinical trials can contribute to elucidation of the biological aspects of preinvasive adenocarcinoma and the development of less invasive management strategies for patients with GGNs.

Long-Term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment

INTRODUCTION

Lung cancer presenting as subsolid nodule (SSN) can show slow growth, hence treating SSN is controversial. Our aim was to determine the long-term outcome of subjects with unresected SSNs in lung cancer screening.

METHODS

Since 2005, the Multicenter Italian Lung Detection (MILD) screening trial implemented active surveillance for persistent SSN, as opposed to early resection. Presence of SSNs was related to diagnosis of cancer at the site of SSN, elsewhere in the lung, or in the body. The risk of overall mortality and lung cancer mortality was tested by Cox proportional hazards model.

RESULTS

SSNs were found in 16.9% (389 of 2303) of screenees. During 9.3 ± 1.2 years of follow-up, the hazard ratio of lung cancer diagnosis in subjects with SSN was 6.77 (95% confidence interval: 3.39-13.54), with 73% (22 of 30) of cancers not arising from SSN (median time to diagnosis 52 months from SSN). Lung cancer-specific mortality in subjects with SSN was significantly increased (hazard ratio = 3.80; 95% confidence interval: 1.24-11.65) compared to subjects without lung nodules. Lung cancer arising from SSN did not lead to death within the follow-up period.

CONCLUSIONS

Subjects with SSN in the MILD cohort showed a high risk of developing lung cancer elsewhere in the lung, with only a minority of cases arising from SSN, and never representing the cause of death. These results show the safety of active surveillance for conservative management of SSN until signs of solid component growth and the need for prolonged follow-up because of high risk of other cancers.

Stratifin regulates stabilization of receptor tyrosine kinases via interaction with ubiquitin-specific protease 8 in lung adenocarcinoma

Previously we have reported that stratifin (SFN, 14-3-3 sigma) acts as a novel oncogene, accelerating the tumor initiation and progression of lung adenocarcinoma. Here, pull-down assay and LC-MS/MS analysis revealed that ubiquitin-specific protease 8 (USP8) specifically bound to SFN in lung adenocarcinoma cells. Both USP8 and SFN showed higher expression in human lung adenocarcinoma than in normal lung tissue, and USP8 expression was significantly correlated with SFN expression. Expression of SFN, but not of USP8, was associated with histological subtype, pathological stage, and poor prognosis. USP8 stabilizes receptor tyrosine kinases (RTKs) such as EGFR and MET by deubiquitination, contributing to the proliferative activity of many human cancers including non-small cell lung cancer. In vitro, USP8 binds to SFN and they co-localize at the early endosomes in lung adenocarcinoma cells. Moreover, USP8 or SFN knockdown leads to downregulation of tumor cellular proliferation and upregulation of apoptosis, p-EGFR or p-MET, which are related to the degradation pathway, and accumulation of ubiquitinated RTKs, leading to lysosomal degradation. Additionally, mutant USP8, which is unable to bind to SFN, reduces the expression of RTKs and p-STAT3. We also found that interaction with SFN is critical for USP8 to exert its autodeubiquitination function and avoid dephosphorylation by PP1. Our findings demonstrate that SFN enhances RTK stabilization through abnormal USP8 regulation in lung adenocarcinoma, suggesting that SFN could be a more suitable therapeutic target for lung adenocarcinoma than USP8.

Novel Asymmetrical Linear Stapler (NALS) for pathologic evaluation of true resection margin tissue

Background

The use of limited resection for lung cancer has increased with the accumulation of knowledge about early lung cancer. To decrease locoregional recurrence after a limited resection, it is important to confirm R0 resection at the true resection margin. In this study, we report a novel linear stapler that preserves the true resection margin tissue after organ resection.

Methods

We used a Novel Asymmetrical Linear Stapler (NALS) made by Meditulip. On the resected organ side of NALS, there is a single row of titanium fasteners. To verify the utility of NALS and to compare its preservation of the resection margin tissue to a conventional stapler, we performed wedge resection of the lung in a porcine animal model and examined the pathology of the true resection margin.

Results

Using NALS, we successfully divided and closed the lung tissues, as with the conventional stapler. There was no bleeding on either side or no air leakage from the remnant stapled tissue. The distance between the cutting edge and the titanium fasteners was 3.10 mm with NALS, which was sufficient to resect the true resection margin tissue for pathology evaluation. There was no squeezing artifact at the true resection margin on microscopic evaluation with NALS. With the conventional stapler, it is difficult to evaluate the pathology at the true resection margin due to the severe squeezing artifact.

Conclusions

NALS preserves the true resection margin tissue and thus should be useful for evaluating the resection margin with a frozen section biopsy in oncology surgery.

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.

[Management Strategies of Pulmonary Ground Galss Nodule]

肺部磨玻璃结节（ground glass nodule, GGN）是一种影像学表现，可能是肺部恶性肿瘤或良性病变。目前对于肺部磨玻璃结节的诊疗仍存在争议。2017年Fleischner协会和美国国立综合癌症网络（National Comprehensive Cancer Network, NCCN）都更新了GGN诊疗的指南，与之前的版本相比，手术或活检的指征更严，随访的间隔时间更长。临床工作中，GGN的大小、实性成分大小、动态随访变化和CT值都是判断手术介入时机的因素。GGN的诊疗中还存在一些误区：抗生素的使用、正电子发射型计算机断层显像（positron emission tomographycomputed tomography, PET-CT）检查、贴近胸膜的纯GGN和进入GGN的血管都是值得注意的问题。总之，GGN是一种发展缓慢的病灶，可以安全地进行随访。

Screening for Lung Cancer: CHEST Guideline and Expert Panel Report

BACKGROUND

Low-dose chest CT screening for lung cancer has become a standard of care in the United States in the past few years, in large part due to the results of the National Lung Screening Trial. The benefit and harms of low-dose chest CT screening differ in both frequency and magnitude. The translation of a favorable balance of benefit and harms into practice can be difficult. Here, we update the evidence base for the benefit, harms, and implementation of low radiation dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not.

METHODS

Approved panelists developed key questions using the PICO (population, intervention, comparator, and outcome) format to address the benefit and harms of low-dose CT screening, as well as key areas of program implementation. A systematic literature review was conducted by using MEDLINE via PubMed, Embase, and the Cochrane Library. Reference lists from relevant retrievals were searched, and additional papers were added. The quality of the evidence was assessed for each critical or important outcome of interest using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached.

RESULTS

The systematic literature review identified 59 studies that informed the response to the 12 PICO questions that were developed. Key clinical questions were addressed resulting in six graded recommendations and nine ungraded consensus based statements.

CONCLUSIONS

Evidence suggests that low-dose CT screening for lung cancer results in a favorable but tenuous balance of benefit and harms. The selection of screen-eligible patients, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can affect this balance. Additional research is needed to optimize the approach to low-dose CT screening.

Clinical Features of Ground Glass Opacity-Dominant Lung Cancer Exceeding 3.0 cm in the Whole Tumor Size

BACKGROUND

Ground glass opacity (GGO)-dominant lung adenocarcinoma sized 3.0 cm or less in the whole tumor size is widely known to have an excellent prognosis and is regarded as early lung cancer. However, the characteristics and prognosis of lung cancer showing GGO exceeding 3.0 cm remains unclear.

METHODS

From 2002 through 2012, we reviewed 3,735 lung cancers that underwent complete resection at our institution. We identified 160 lung cancers (4.3%) showing GGO exceeding 3.0 cm on thin-section computed tomography and divided them into three types by the consolidation/tumor ratio (CTR) using cutoff values of 0.25 and 0.5. We compared the characteristics and prognosis among these types.

RESULTS

Type A (CTR, 0 to ≤0.25), type B (CTR, >0.25 to ≤0.5), and type C (CTR, >0.5 to <1.0) were found in 16 (10%), 37 (23%), and 107 lesions (67%), respectively. No lymph node metastasis was found in types A and B. Recurrence was not observed in types A and B. The 5-year overall survival and disease-free survival rates were both 100% in type A, both 97.2% in type B, and 88.4% and 66.7% in type C, respectively. Patients with type C had a significantly worse prognosis than those with the other types with respect to overall survival and disease-free survival.

CONCLUSIONS

A patient with GGO-dominant lung cancer exceeding 3.0 cm can be considered to be in a group of patients with nodal-negative disease and an excellent prognosis.

Radiologic-Pathologic Correlation of Solid Portions on Thin-section CT Images in Lung Adenocarcinoma: A Multicenter Study

BACKGROUND

Measuring the size of invasiveness on computed tomography (CT) for the T descriptor size was deemed important in the 8th edition of the TNM lung cancer classification. We aimed to correlate the maximal dimensions of the solid portions using both lung and mediastinal window settings on CT imaging with the pathologic invasiveness (> 0.5 cm) in lung adenocarcinoma patients.

MATERIALS AND METHODS

The study population consisted of 378 patients with a histologic diagnosis of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IVA)-lepidic, IVA-acinar and/or IVA-papillary, and IVA-micropapillary and/or solid adenocarcinoma. A panel of 15 radiologists was divided into 2 groups (group A, 9 radiologists; and group B, 6 radiologists). The 2 groups independently measured the maximal and perpendicular dimensions of the solid components and entire tumors on the lung and mediastinal window settings. The solid proportion of nodule was calculated by dividing the solid portion size (lung and mediastinal window settings) by the nodule size (lung window setting). The maximal dimensions of the invasive focus were measured on the corresponding pathologic specimens by 2 pathologists.

RESULTS

The solid proportion was larger in the following descending order: IVA-micropapillary and/or solid, IVA-acinar and/or papillary, IVA-lepidic, MIA, and AIS. For both groups A and B, a solid portion > 0.8 cm in the lung window setting or > 0.6 cm in the mediastinal window setting on CT was a significant indicator of pathologic invasiveness > 0.5 cm (P < .001; receiver operating characteristic analysis using Youden's index).

CONCLUSION

A solid portion > 0.8 cm on the lung window setting or solid portion > 0.6 cm on the mediastinal window setting on CT predicts for histopathologic invasiveness to differentiate IVA from MIA and AIS.