Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

OBJECTIVES

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

MATERIALS AND METHODS

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

RESULTS

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

CONCLUSION

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules.

CLINICAL RELEVANCE STATEMENT

The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis.

KEY POINTS

• According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic

OBJECTIVE

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia.

METHODS

This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors.

RESULTS

A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020.

CONCLUSIONS

Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans

INTRODUCTION

It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time.

MATERIALS AND METHODS

We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters.

RESULTS

The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals.

CONCLUSION

This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.

Risk factors for loss of pulmonary function after wedge resection for peripheral ground-glass opacity dominant lung cancer

OBJECTIVES

This study aimed to identify the risk factors for pulmonary functional deterioration after wedge resection for early-stage lung cancer with ground-glass opacity, which remain unclear, particularly in low-risk patients.

METHODS

We analysed 237 patients who underwent wedge resection for peripheral early-stage lung cancer in JCOG0804/WJOG4507L, a phase III, single-arm confirmatory trial. The changes in forced expiratory volume in 1 s were calculated pre- and postoperatively, and a cutoff value of -10%, the previously reported reduction rate after lobectomy, was used to divide the patients into 2 groups: the severely reduced group (≤-10%) and normal group (>-10%). These groups were compared to identify predictors for severe reduction.

RESULTS

Thirty-seven (16%) patients experienced severe reduction. Lesions with a total tumour size ≥1 cm were significantly more frequent in the severely reduced group than in the normal group (89.2% vs 71.5%; P = 0.024). A total tumour size of ≥1 cm [odds ratio (OR), 3.287; 95% confidence interval (CI), 1.114-9.699: P = 0.031] and pleural indentation (OR, 2.474; 95% CI, 1.039-5.890: P = 0.041) were significant predictive factors in the univariable analysis. In the multivariable analysis, pleural indentation (OR, 2.667; 95% CI, 1.082-6.574; P = 0.033) was an independent predictive factor, whereas smoking status and total tumour size were marginally significant.

CONCLUSIONS

Of the low-risk patients who underwent pulmonary wedge resection for early-stage lung cancer, 16% experienced severe reduction in pulmonary function. Pleural indentation may be a risk factor for severely reduced pulmonary function in pulmonary wedge resection.

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

PURPOSE

To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules.

METHODS

A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC).

RESULTS

The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896.

CONCLUSIONS

A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

Risk and Time to Diagnosis of Lung Cancer in Incidental Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer in incidental pulmonary nodules, as well as the time until cancer growth is detected.

PATIENTS AND METHODS

This retrospective study examined patients with incidental nodules detected on chest computed tomography (CT) in 2017. Characteristics of the dominant nodule were automatically extracted from CT reports, and cancer diagnoses were manually verified by a thoracic radiologist. Nodules were categorized per Fleischner Society guideline categories: solid <6 mm, solid 6 to 8 mm, solid >8 mm, subsolid <6 mm, ground glass nodules ≥6 mm, and part-solid nodules ≥6 mm. The time to nodule growth was determined by CT reports.

RESULTS

A total of 3180 patients (nodules) were included, of which 155 (5%) were diagnosed with lung cancer. By category, 7/1601 (0.4%) solid nodules <6 mm, 11/713 (1.5%) solid nodules 6 to 8 mm, 71/446 (15.9%) solid nodules >8 mm, 1/124 (0.8%) subsolid nodules <6 mm, 29/202 (14.4%) ground glass nodules ≥6 mm, and 36/94 (37.9%) part-solid nodules ≥6 mm were malignant. Of solid lung cancers <6 mm, growth was observed in 1/4 imaged by 1 year and 2/5 by 2 years; of solid lung cancers 6 to 8 mm, growth was observed in 3/10 imaged by 1 year and 6/10 by 2 years.

CONCLUSION

Solid nodules <6 mm have a very low risk of malignancy and may not require routine follow-up. However, when malignant, growth is often not observed until 2 or more years later; therefore, stability at 1 to 2 years does not imply benignity.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography

PURPOSE

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images.

MATERIALS AND METHODS

Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently.

RESULTS

The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set.

CONCLUSION

The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods

PURPOSE

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period.

METHODS

Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth.

RESULTS

For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8 days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9 days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions.

CONCLUSION

Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Retrospective Analysis of Subsolid Nodules' Frequency Using Chest Computed Tomography Detection in an Outpatient Population

INTRODUCTION

The study was designed to evaluate the frequency of detection and the characteristics of subsolid nodules (SSNs) in outpatients' chest computed tomography (CT) scans from a private hospital in Southern Brazil.

METHODS

A retrospective analysis of all chest CT scans was performed in adult patients from ambulatory care (non-lung cancer screening population) over a thirty-day period. Inclusion criteria were age > 18 years and lung-scanning protocols, including standard-dose high-resolution chest CT (HRCT), enhanced CT, CT angiography, and low-dose chest CT (LDCT). SSNs main features collected were mean diameter, number, density (pure or heterogenous ground glass nodules and part-solid), and localization. TheLungRADS system and the updated Fleischner Society's pulmonary nodules recommendations were used for categorization only for study purposes, although not specifically fitting the population. The presence of emphysema, as well as calcified and solid nodules were also addressed. Statistical analysis was performed using R software, categorial variables are shown as absolute or relative frequencies, and continuous variables as mean and interquartile ranges.

RESULTS

Chest computed tomography were performed in 756 patients during the study period (September 2019), and 650 met the inclusion criteria. The IQR for age was 53/73 years; most participants were female (58.3%) and 10.6% had subsolid nodules detected.

CONCLUSIONS

The frequency of SSNs detection in patients in daily clinical practice, not related to screening populations, is not negligible. Regardless of the final etiology, follow-up is often indicated, given the likelihood of malignancy for persistent lesions.

Initial low-dose computed tomography screening results and summary of participant characteristics: based on the latest Chinese guideline

Background

Low-dose computed tomography (LDCT) has been promoted as a promising screening strategy for early detection of lung cancer. China released the latest lung cancer screening guideline in 2021. The compliance of the individuals who received LDCT for lung cancer screening with the guideline is unknown yet. It is necessary to summarize the distribution of guideline-defined lung cancer-related risk factors in the Chinese population so as to inform the selection of target population for the future lung cancer screening.

Methods

A single-center, cross-sectional study design was adopted. All participants were individuals who underwent LDCT at a tertiary teaching hospital in Hunan, China, between 1 January and 31 December 2021. LDCT results were derived along with guideline-based characteristics for descriptive analysis.

Results

A total of 5,486 participants were included. Over one-quarter (1,426, 26.0%) of the participants who received screening did not meet the guideline-defined high-risk population, even among non-smokers (36.4%). Most of the participants (4,622, 84.3%) were found to have lung nodules, while no clinical intervention was required basically. The detection rate of positive nodules varied from 46.8% to 71.2% when using different cut-off values for positive nodules. Among non-smoking women, ground glass opacity appeared to be more significantly common compared with non-smoking men (26.7% vs. 21.8%).

Conclusion

Over one-quarter of individuals who received LDCT screening did not meet the guideline-defined high-risk populations. Appropriate cut-off values for positive nodules need to be continuously explored. More precise and localized criteria for high-risk individuals are needed, especially for non-smoking women.

Long-term follow-up of persistent pulmonary subsolid nodules: Natural course of pure, heterogeneous, and real part-solid ground-glass nodules

BACKGROUND

Previous studies have suggested the applicability of three classifications of subsolid nodules (SSNs). However, few studies have unraveled the natural history of the three types of SSNs.

METHODS

A retrospective study from two medical centers between November 2007 and November 2017 was conducted to explore the long-term follow-up results of three different types of SSNs, which were divided into pure ground-glass nodules (pGGNs), heterogeneous ground-glass nodules (hGGNs), and real part-solid nodules (rPSNs).

RESULTS

A total of 306 consecutive patients, including 361 SSNs with long-term follow-up, were reviewed. The median growth times of pGGNs, hGGNs, and rPSNs were 7.7, 6.0, and 2.0 years, respectively. For pGGNs, the median period of development into rPSNs was 4.6 years, while that of hGGNs was 1.8 years, and the time from pGGNs to hGGNs was 3.1 years (p < 0.05). In SSNs with an initial lung window consolidation tumor ratio (LW-CTR) >0.5 and mediastinum window (MW)-CTR >0.2, all cases with growth were identified within 5 years. Meanwhile, in SSNs whose LW-CTR and MW-CTR were 0, it took over 5 years to detect nodular growth. Pathologically, 90.6% of initial SSNs with LW-CTR >0 were invasive carcinomas (invasive adenocarcinoma and micro-invasive adenocarcinoma). Among patients with rPSNs in the initial state, 100.0% of the final pathological results were invasive carcinoma. Cox regression showed that age (p = 0.038), initial maximal diameter (p < 0.001), and LW-CTR (p = 0.002) were independent risk factors for SSN growth.

CONCLUSIONS

pGGNs, hGGNs, and rPSNs have significantly different natural histories. Age, initial nodule diameter, and LW-CTR are important risk factors for SSN growth.

Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas

OBJECTIVE

The purpose of the study is to investigate the performance of radiomics-based analysis in prediction of pure ground-glass nodule (pGGN) lung adenocarcinomas invasiveness using thin-section computed tomography images.

METHODS

A total of 382 patients surgically resected single pGGN and pathologically confirmed were enrolled in the retrospective study. The pGGN cases were divided into two groups: the noninvasive group and the invasive adenocarcinoma (IAC) group. 330 patients were randomly assigned to the training and testing cohorts with a ratio of 7:3 (245 noninvasive lesions, 85 IAC lesions), while 52 patients (30 noninvasive lesions, 22 IAC lesions) were assigned to the external validation cohort. A model, radiomics model, and combined clinical-radiographic-radiomic model were built using the LASSO and multivariate backward stepwise regression analysis on the basis of the selected and radiomics features. The area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate and compare the model performance for invasiveness discrimination among the three cohorts.

RESULTS

Three clinical-radiographic features (including age, gender and the mean CT value) and three radiomics features were selected for model building. The combined model and radiomics model performed better than the clinical-radiographic model. The AUCs of the combined model in the training, testing, and validation cohorts were 0.856, 0.859, and 0.765, respectively. The DCA demonstrated the radiomics signatures incorporating clinical-radiographic feature was clinically useful in predicting pGGN invasiveness.

CONCLUSIONS

The proposed radiomics-based analysis incorporating the clinical-radiographic feature could accurately predict pGGN invasiveness, providing a noninvasive biomarker for the individualized and precise medical treatment of patients.

The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis

OBJECTIVES

To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT.

METHODS

A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger's test.

RESULTS

Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth.

CONCLUSIONS

The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth.

KEY POINTS

• Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

Evaluation and Management of Indeterminate Pulmonary Nodules on Chest Computed Tomography in Asymptomatic Subjects: The Principles of Nodule Guidelines

With the rapidly increasing number of chest computed tomography (CT) examinations, the question of how to manage lung nodules found in asymptomatic patients has become increasingly important. Several nodule management guidelines have been developed that can be applied to incidentally found lung nodules (the Fleischner Society guideline), nodules found during lung cancer screening (International Early Lung Cancer Action Program protocol [I-ELCAP] and Lung CT Screening Reporting and Data System [Lung-RADS]), or both (American College of Chest Physicians guideline [ACCP], British Thoracic Society guideline [BTS], and National Comprehensive Cancer Network guideline [NCCN]). As the radiologic nodule type (solid, part-solid, and pure ground glass) and size are significant predictors of a nodule's nature, most guidelines categorize nodules in terms of these characteristics. Various methods exist for measuring the size of nodules, and the method recommended in each guideline should be followed. The diameter can be manually measured as a single maximal diameter or as an average of two-dimensional diameters, and software can be used to obtain volumetric measurements. It is important to properly evaluate and measure nodules and familiarize ourselves with the relevant guidelines to appropriately utilize medical resources and minimize unnecessary radiation exposure to patients.

Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases

Objectives

If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer.

Methods

Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models.

Results

A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05).

Conclusion

Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.

Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests

Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening

BACKGROUND

Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS).

METHODS

A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.

RESULTS

The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively).

CONCLUSIONS

The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.

Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature

Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT).

Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index.

Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905).

Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Evaluating the Patient With a Pulmonary Nodule: A Review

IMPORTANCE

Pulmonary nodules are identified in approximately 1.6 million patients per year in the US and are detected on approximately 30% of computed tomographic (CT) images of the chest. Optimal treatment of an individual with a pulmonary nodule can lead to early detection of cancer while minimizing testing for a benign nodule.

OBSERVATIONS

At least 95% of all pulmonary nodules identified are benign, most often granulomas or intrapulmonary lymph nodes. Smaller nodules are more likely to be benign. Pulmonary nodules are categorized as small solid (<8 mm), larger solid (≥8 mm), and subsolid. Subsolid nodules are divided into ground-glass nodules (no solid component) and part-solid (both ground-glass and solid components). The probability of malignancy is less than 1% for all nodules smaller than 6 mm and 1% to 2% for nodules 6 mm to 8 mm. Nodules that are 6 mm to 8 mm can be followed with a repeat chest CT in 6 to 12 months, depending on the presence of patient risk factors and imaging characteristics associated with lung malignancy, clinical judgment about the probability of malignancy, and patient preferences. The treatment of an individual with a solid pulmonary nodule 8 mm or larger is based on the estimated probability of malignancy; the presence of patient comorbidities, such as chronic obstructive pulmonary disease and coronary artery disease; and patient preferences. Management options include surveillance imaging, defined as monitoring for nodule growth with chest CT imaging, positron emission tomography-CT imaging, nonsurgical biopsy with bronchoscopy or transthoracic needle biopsy, and surgical resection. Part-solid pulmonary nodules are managed according to the size of the solid component. Larger solid components are associated with a higher risk of malignancy. Ground-glass pulmonary nodules have a probability of malignancy of 10% to 50% when they persist beyond 3 months and are larger than 10 mm in diameter. A malignant nodule that is entirely ground glass in appearance is typically slow growing. Current bronchoscopy and transthoracic needle biopsy methods yield a sensitivity of 70% to 90% for a diagnosis of lung cancer.

CONCLUSIONS AND RELEVANCE

Pulmonary nodules are identified in approximately 1.6 million people per year in the US and approximately 30% of chest CT images. The treatment of an individual with a pulmonary nodule should be guided by the probability that the nodule is malignant, safety of testing, the likelihood that additional testing will be informative, and patient preferences.

Single-Stage Pulmonary Resection via a Combination of Single Hookwire Localization and Video-Assisted Thoracoscopic Surgery for Synchronous Multiple Pulmonary Nodules

Purpose: To retrospectively analyze the incidence and predictors of complications related to hookwire localization in patients with single and multiple nodules, and to evaluate the usefulness of a single-stage surgical method of single hookwire localization combined with video-assisted thoracoscopic surgery (VATS) in synchronous multiple pulmonary nodules (SMPNs). Methods: A total of 200 patients who underwent computed tomography (CT)-guided hookwire localization and subsequent VATS resection were enrolled in this study. For each patient, only 1 indeterminate nodule was implanted with a hookwire. There were 145 patients in the single-nodule group (Group S) and 55 in the multiple-nodule group (Group M). Univariate and binary logistic regression analyses were used to assess incidence and predictors of complications associated with hookwire localization. Results: The technical success rate of hookwire implantation was 97.5%. The incidence of pneumothorax and hookwire dislodgement was 17.0% and 2.5%, respectively. Binary logistic regression analysis showed that 1 transpleural puncture through the pleura (odds ratio [OR] = 0.433, P = .033) was the only independent protective factor for pneumothorax, and pneumothorax (OR = 26.114, P < .01) was the only independent risk factor for dislodgement. The volume of blood loss during VATS was significantly higher in group M than in group S, and the time of postoperative hospitalization was significantly longer in group M than in group S. About 44 patients in group M with additional 58 nodules without localization had undergone direct surgical resection simultaneously, and bilateral surgery was performed in 13 patients (29.5%). The intrathoracic recurrence rate was 4.8% during follow-up CT. Conclusion: Single-stage surgery via an approach of single hookwire localization combined with VATS is feasible and safe for SMPNs.

The cognitive and perceptual processes that affect observer performance in lung cancer detection: a scoping review

INTRODUCTION

Early detection of malignant pulmonary nodules through screening has been shown to reduce lung cancer-related mortality by 20%. However, perceptual and cognitive factors that affect nodule detection are poorly understood. This review examines the cognitive and visual processes of various observers, with a particular focus on radiologists, during lung nodule detection.

METHODS

Four databases (Medline, Embase, Scopus and PubMed) were searched to extract studies on eye-tracking in pulmonary nodule detection. Studies were included if they used eye-tracking to assess the search and detection of lung nodules in computed tomography or 2D radiographic imaging. Data were charted according to identified themes and synthesised using a thematic narrative approach.

RESULTS

The literature search yielded 25 articles and five themes were discovered: 1 - functional visual field and satisfaction of search, 2 - expert search patterns, 3 - error classification through dwell time, 4 - the impact of the viewing environment and 5 - the effect of prevalence expectation on search. Functional visual field reduced to 2.7° in 3D imaging compared to 5° in 2D radiographs. Although greater visual coverage improved nodule detection, incomplete search was not responsible for missed nodules. Most radiological errors during lung nodule detection were decision-making errors (30%-45%). Dwell times associated with false-positive (FP) decisions informed feedback systems to improve diagnosis. Interruptions did not influence diagnostic performance; however, it increased viewing time by 8% and produced a 23.1% search continuation accuracy. Comparative scanning was found to increase the detection of low contrast nodules. Prevalence expectation did not directly affect diagnostic accuracy; however, decision-making time increased by 2.32 seconds with high prevalence expectations.

CONCLUSION

Visual and cognitive factors influence pulmonary nodule detection. Insights gained from eye-tracking can inform advancements in lung screening. Further exploration of eye-tracking in lung screening, particularly with low-dose computed tomography (LDCT), will benefit the future of lung cancer screening.

Prospective evaluation of watchful waiting for early-stage lung cancer with ground-glass opacity: a single-arm confirmatory multicenter study: Japan Clinical Oncology Group study JCOG1906 (EVERGREEN study)

Among early-stage lung cancers that show ground-glass nodules on chest computed tomography, the lung cancer surgical study group of the Japan Clinical Oncology Group previously reported that nodules of ≤2 cm with a consolidation/tumor ratio of ≤0.25 were considered radiologically non-invasive cancers with a low risk of metastasis. However, there is no consensus based on high-quality evidence about the optimal timing of surgical intervention for radiologically non-invasive cancers. In June 2020, we launched a multi-institutional, single-arm confirmatory trial to evaluate the efficacy and safety of watchful waiting for patients with radiologically non-invasive lung cancer. It is planned that a total of 720 patients will be enrolled from 49 institutes over a 5-year period. The primary endpoint is 10-year overall survival (OS). The secondary endpoints are 5-year OS, operation-free survival, proportion of extensive lung resection, proportion of unresectable progression, lung cancer-specific survival and adverse events.

Lung-RADS Version 1.0 versus Lung-RADS Version 1.1: Comparison of Categories Using Nodules from the National Lung Screening Trial

Background The American College of Radiology updated Lung Imaging Reporting and Data System (Lung-RADS) version 1.0 to version 1.1 in May 2019, with the two key changes involving perifissural nodules (PFNs) and ground-glass nodules (GGNs) now designated as a negative screening result. This study examines the effects of these changes using National Lung Screening Trial (NLST) data. Purpose To determine the frequency of PFNs and GGNs reclassified from category 3 or 4A to the more benign category 2 in the updated Lung-RADS version 1.1, as compared with Lung-RADS version 1.0, using CT scans from the NLST. Materials and Methods In this secondary analysis of the NLST, the authors studied all noncalcified nodules (NCNs) found on the incident scan. Nodules were evaluated using criteria from both Lung-RADS version 1.0 and version 1.1, which were compared to determine changes in the number of nodules deemed benign. A McNemar test was used to assess statistical significance. Results A total of 2813 patients (mean age ± standard deviation, 62 years ± 5; 1717 men) with 4408 NCNs were studied. Of the largest 1092 solid NCNs measuring at least 6 mm but less than 10 mm, 216 (19.8%) were deemed PFNs (category 2) using Lung-RADS version 1.1. Eleven of the 1092 solid NCNs (1.0%) were malignant, but none were PFNs. Of 161 GGNs, three (1.9%) were category 3 according to Lung-RADS version 1.0, of which two (66.7%) were down-classified to category 2 with version 1.1. One of the three down-categorized GGNs (version 1.1) proved to be malignant (false-negative finding). Statistically significant improvement for Lung-RADS version 1.1 was found for total nodules (P < .01) and PFNs (P < .01), but not GGNs (P = .48). Conclusion This secondary analysis of National Lung Screening Trial data shows that Lung Imaging Reporting and Data System version 1.1 decreased the number of false-positive results. This was related to the down-classification of perifissural nodules in the range of 6 up to 10 mm. The increase in allowable nodule size for ground-glass nodules in category 2 from 20 mm (version 1.0) to 30 mm (version 1.1) showed no benefit. © RSNA, 2021 See also the editorial by Mayo and Lam in this issue.

Management of pulmonary ground-glass opacities: a position paper from a panel of experts of the Italian Society of Thoracic Surgery (SICT)

OBJECTIVES

A significant gap in our knowledge of how to manage pulmonary ground-glass opacities (GGOs) still exists. Accordingly, there is a lack of consensus among clinicians on this topic. The Italian Society of Thoracic Surgery (Società Italiana di Chirurgia Toracica, SICT) promoted a national expert meeting to provide insightful guidance for clinical practice. Our goal was to publish herein the final consensus document from this conference.

METHODS

The working panel of the PNR group (Pulmonary Nodules Recommendation Group, a branch of the SICT) together with 5 scientific supervisors (nominated by the SICT) identified a jury of expert thoracic surgeons who organized a multidisciplinary meeting to propose specific statements (n = 29); 73 participants discussed and voted on statements using a modified Delphi process (repeated iterations of anonymous voting over 2 rounds with electronic support) requiring 70% agreement to reach consensus on a statement.

RESULTS

Consensus was reached on several critical points in GGO management, in particular on the definition of GGO, radiological and radiometabolic evaluation, indications for a non-surgical biopsy, GGO management based on radiological characteristics, surgical strategies (extension of pulmonary resection and lymphadenectomy) and radiological surveillance. A list of 29 statements was finally approved.

CONCLUSIONS

The participants at this national expert meeting analysed this challenging topic and provided a list of suggestions for health institutions and physicians with practical indications for GGO management.

Arc concave sign on thin-section computed tomography：A novel predictor for invasive pulmonary adenocarcinoma in pure ground-glass nodules

OBJECTIVE

We aimed to investigate the risk factors of invasive pulmonary adenocarcinoma, especially to report and validate the use of our newly identified arc concave sign in predicting invasiveness of pure ground-glass nodules (pGGNs).

METHODS

From January 2015 to August 2018, we retrospectively enrolled 302 patients with 306 pGGNs ≤ 20 mm pathologically confirmed (141 preinvasive lesions and 165 invasive lesions). Arc concave sign was defined as smooth and sunken part of the edge of the lesion on thin-section computed tomography (TSCT). The degree of arc concave sign was expressed by the arc chord distance to chord length ratio (AC-R); deep arc concave sign was defined as AC-R larger than the optimal cut-off value. Logistic regression analysis was used to identify the independent risk factors of invasiveness.

RESULTS

Arc concave sign was observed in 65 of 306 pGGNs (21.2 %), and deep arc concave sign (AC-R > 0.25) were more common in invasive lesions (P = 0.008). Under microscope, interlobular septal displacements were found at tumour surface. Multivariate analysis indicated that irregular shape (OR, 3.558; CI: 1.374-9.214), presence of deep arc concave sign (OR, 3.336; CI: 1.013-10.986), the largest diameter > 10.1 mm (OR, 4.607; CI: 2.584-8.212) and maximum density > -502 HU (OR, 6.301; CI: 3.562-11.148) were significant independent risk factors of invasive lesions.

CONCLUSIONS

Arc concave sign on TSCT is caused by interlobular septal displacement. The degree of arc concave sign can reflect the invasiveness of pGGNs. Invasive lesions can be effectively distinguished from preinvasive lesions by the presence of deep arc concave sign, irregular shape, the largest diameter > 10.1 mm and maximum density > -502 HU in pGGNs ≤ 20 mm.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume

Purpose

Materials and Methods

Results

Conclusion

Digital tomosynthesis and ground glass nodules: Optimization of acquisition protocol. A phantom study

INTRODUCTION

Ground-glass nodules may be the expression of benign conditions, pre-invasive lesions or malignancies. The aim of our study was to evaluate the capability of chest digital tomosynthesis (DTS) in detecting pulmonary ground-glass opacities (GGOs).

METHODS

An anthropomorphic chest phantom and synthetic nodules were used to simulate pulmonary ground-glass nodules. The nodules were positioned in 3 different regions (apex, hilum and basal); then the phantom was scanned by multi-detector CT (MDCT) and DTS. For each set (nodule-free phantom, nodule in apical zone, nodule in hilar zone, nodule in basal zone) seven different scans (n = 28) were performed varying the following technical parameters: Cu-filter (0.1-0.3 mm), dose rateo (10-25) and X-ray tube voltage (105-125 kVp). Two radiologists in consensus evaluated the DTS images and provided in agreement a visual score: 1 for unidentifiable nodules, 2 for poorly identifiable nodules, 3 for nodules identifiable with fair certainty, 4 for nodules identifiable with absolute certainty.

RESULTS

Increasing the dose rateo from 10 to 15, GGOs located in the apex and in the basal zone were better identified (from a score = 2 to a score = 3). GGOs located in the hilar zone were not visible even with a higher dose rate. Intermediate density GGOs had a good visibility score (score = 3) and it did not improve by varying technical parameters. A progressive increase of voltage (from 105 kVp to 125 kVp) did not provide a better nodule visibility.

CONCLUSION

DTS with optimized technical parameters can identify GGOs, in particular those with a diameter greater than 10 mm.

IMPLICATIONS FOR PRACTICE

DTS could have a role in the follow-up of patients with known GGOs identified in lung apex or base region.

Limited usefulness of serum carcinoembryonic antigen and carbohydrate antigen 19-9 levels for gastrointestinal and whole-body cancer screening

The diagnostic performance of serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA) 19-9 levels for multiple-organ cancer screening has not been fully elucidated. However, they are widely used for real-world opportunistic screening of multiple-organ cancers. This study aimed to examine the diagnostic performance of these serum markers in multiple-organ cancer screening. Data from asymptomatic individuals subjected to opportunistic cancer screening were analyzed. The diagnostic performance of CEA and CA 19-9 was assessed for (A) upper/lower gastrointestinal cancers and (B) whole-body cancers (including both gastrointestinal and other organ cancers) using the results of upper/lower gastrointestinal endoscopy and whole-body imaging as reference. Data from 12,349 and 7616 screened individuals were used to assess the diagnostic performance of CEA and CA 19-9 for (A) and (B), respectively. For (A), the sensitivity and positive predictive value (PPV) of CEA (cut-off: 5 ng/mL) were 7.8% and 3.7%, respectively; those of CA19-9 (cut-off: 37 U/mL) were 7.4% and 2.7%, respectively. For (B), the sensitivity and PPV of CEA were 6.6% and 4.1%, respectively, and those of CA19-9 were 10.8% and 5.8%, respectively. Considering even multiple cancers, the sensitivity and PPV of CEA and CA 19-9 were low, thus confirming their limited usefulness in multiple-organ cancer screening.

Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives

In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA.

Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model.

Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive model's performance.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm²; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm²), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

Growth and Clinical Impact of 6-mm or Larger Subsolid Nodules after 5 Years of Stability at Chest CT

Background It remains unclear whether 5 years of stability is sufficient to establish the benign behavior of subsolid nodules (SSNs) of the lung. There are no guidelines for the length of follow-up needed for these SSNs. Purpose To investigate the incidence of interval growth of pulmonary SSNs 6 mm or greater in diameter after 5 years of stability and their clinical outcome. Materials and Methods This retrospective study assessed SSNs 6 mm or greater that were stable for 5 years after detection (January 2002 to December 2018). The incidence of interval growth after 5 years of stability and the clinical and radiologic features of these SSNs were investigated. Clinical stage shifts of growing SSNs, presence of metastasis, and overall survival were assessed during the follow-up period. Subgroup analysis was performed in patients with nonenhanced thin-section (section thickness ≤1.5 mm) CT for interval growth after 5 years of stability. Results A total of 235 SSNs in 235 patients (mean age, 64 years ± 10 [standard deviation]; 132 women) were evaluated. There were 212 pure ground-glass nodules and 24 part-solid nodules. During follow-up (median, 112 months; range, 84-208 months), five of the 235 SSNs (2%; three primary ground-glass nodules and two part-solid nodules) showed interval growth. Three of these five growing SSNs were 10 mm or greater. Three of the five SSNs with interval growth had clinical stage shifts after growth (from Tis [in situ] to T1mi [minimally invasive] in one lesion; from T1mi to T1a in two lesions). There were no deaths or metastases from lung cancer during follow-up. Of 160 SSNs imaged with section thickness of 1.5 mm or less, two (1%) grew; both lesions were 10 mm or greater. Conclusion Only 2% of subsolid pulmonary nodules greater than or equal to 6 mm that had been stable for 5 years showed subsequent growth. At median follow-up of 9 years (after the initial 5-year period of stability), growth of those lung nodules had no clinical effect. © RSNA, 2020 See also the editorial by Naidich and Azour in this issue.

Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction

Objectives

To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods

This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic–radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed.

Results

Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic–radiomics model (AUC 0.77; 95% CI, 0.69–0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62–0.81) and Rad-score (AUC 0.72; 95% CI, 0.63–0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model.

Conclusions

The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs.

Key Points

• CT-based radiomics analysis helps invasive prediction manifested as pGGNs.

• The combined radiographic–radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs.

• Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06776-y) contains supplementary material, which is available to authorized users.

Pre-operative CT localization for patients with subsolid opacities expecting video-assisted thoracoscopic surgery-single center experience of fluorescent iodized emulsion and hook-wire localization technique

OBJECTIVE

To describe our clinical experience of using combination of fluorescent iodized emulsion, indocyanine green & lipiodol, and hook-wire in pre-operative CT localization of patients with subsolid lung lesions prior to video-assisted thoracoscopic surgery (VATS).

METHODS

A retrospective review between June 2018 and July 2019 of consecutive Chinese patients whom underwent VATS for subsolid lung lesions with pre-operative CT localization done with combination of fluorescent iodized emulsion and hook-wire technique in a tertiary hospital (Tuen Mun Hospital, Hong Kong SAR). The duration and complications related to the localization procedure were recorded The clinical records, operative findings and pathology reports were retrieved from the hospital electronic clinical management system.

RESULTS

Combination fluorescent iodized emulsion with hook-wire enabled accurate localization and resection of all subsolid lung lesions in VATS. No major complications were reported.

CONCLUSION

Combination of fluorescent iodized emulsion and hook-wire placement under CT guidance is a simple, safe and cost- effective procedure that enabled accurate localization and resection of subsolid nodule in VATS.

ADVANCES IN KNOWLEDGE

VATS has been the mainstay for indeterminate pulmonary nodules for diagnostic and/or curative purpose. The main problem that surgeons may encounter during operation is the difficulty in locating the target lesion particularly for subsolid lesions. Many pre-operative localization methods have been developed in this regard. With the novel technique that we described, we were able to overcome disadvantages of most described methods.

Low-dose CT lung cancer screening in never-smokers and smokers: results of an eight-year observational study

Background

This was an observational study of Japanese participants who underwent low-dose computed tomographic (LDCT) lung cancer screening between February 2004 and March 2012, to evaluate the lung cancers in never-smokers and smokers.

Methods

The study population consisted of a total of 12,114 subjects [never-smokers, 6,021 (49.70%); smokers with <30 pack-years of smoking, 3,785 (31.24%); smokers with ≥30 pack-years of smoking, 2,305 (19.03%); unknown smoking status, 3 (0.02%)]. The odds ratio (OR) of lung cancer detection according to the smoking status adjusted for age and gender was evaluated.

Results

A total of 152 lung cancers were diagnosed in 133 patients [never-smokers, 66 (49.6%); smokers with <30 pack-years of smoking, 31 (23.3%); smokers with ≥30 pack-years of smoking, 36 (27.1%)]; therefore, 72.9% of lung cancer patients did not meet the National Lung Screening Trial (NLST) criterion of smokers with ≥30 pack-years of smoking. The OR of lung cancer detection in smokers with ≥30 pack-years of smoking was higher than that in the never-smokers (OR =1.71, 95% CI: 1.04-2.82, P=0.03) and that in smokers with <30 pack-years of smoking (OR =1.71, 95% CI: 1.04-2.80, P=0.03), while the OR of lung cancer detection in smokers with <30 pack-years of smoking was the same as that in the never-smokers (OR =1.00, 95% CI: 0.62-1.61, P=0.99).

Conclusions

Although the OR of lung cancer detection in smokers with ≥30 pack-years of smoking was higher than that in the never-smokers and smokers with <30 pack-years of smoking, approximately 70% of lung cancer patients might be missed if we only adopted the NLST criterion of smokers with ≥30 pack-years of smoking. Therefore, never-smokers and smokers with <30 pack-years of smoking should be included in the target population for LDCT lung cancer screening in Japan.

Incidence and natural course of CT-detected pulmonary ground-glass nodules in Chinese women with breast cancer: a retrospective, single-center, long-term follow-up study in 4682 consecutive patients

Background

Increased use of thin-section computed tomography (CT) scans has revealed that small lung nodules, termed ground-glass nodules, are frequent in primary breast cancer patients and are associated with pre-invasive or invasive pulmonary adenocarcinomas. However, little is known of the incidence and fate of ground-glass nodules.

Purpose

To elucidate the incidence and natural course of CT-detected pulmonary ground-glass nodules in Chinese women with breast cancer.

Material and Methods

We retrospectively reviewed data from female breast cancer patients who underwent lung CT scans and who were followed for ≥3 months after the initial scan to identify the incidence of ground-glass nodules and any changes in them during the follow-up period.

Results

Between January 2008 and April 2018, 693 out of 4682 breast cancer patients (14.8%) had persistent lung ground-glass nodules as detected by CT scan. The median age was 52 years (interquartile range [IQR] = 45–62 years). Median nodule size was 4.9 mm in diameter on initial CT scan. Frequency of growth was 7.5% (52/693 patients). Median volume doubling time was 1092 days (IQR = 719–1808 days) for 39 growing in size nodules. Initial nodule size, nodule type, and follow-up period were independent predictors of nodule growth.

Conclusion

Most pulmonary ground-glass nodules in breast cancer patients were stable during long-term follow-up; most growing nodules had an indolent clinical course, suggesting that nodules should be monitored until growth is detected. This information is clinically relevant for accurate diagnosis of cancer stage and for appropriate treatment plans for patients with lung ground-glass nodules.

Developing a lung nodule management protocol specifically for cardiac CT: Methodology in the DISCHARGE trial

•

No lung nodule management algorithm exists specifically for cardiac CT.

•

The DISCHARGE trial is a pragmatic prospective randomized trial currently being undertaken across 16 countries in Europe for patients with stable chest pain.

•

As part of the trial a lung nodule algorithm is being evaluated based on modified lung-RADS nodule management algorithms.

•

The methodology of this ‘Lung-RADS for cardiac CT’ algorithm is presented.

Purpose

In this methodology paper we describe the development of a lung nodule management algorithm specifically for patients undergoing cardiac CT.

Methods

We modified the Lung-RADS algorithm specifically to manage lung nodules incidentally detected on cardiac CT (Lung-RADS for cardiac CT). We will evaluate the modified algorithm as part of the DISCHARGE trial (www.dischargetrial.eu) in which patients with suspected coronary artery disease are randomly assigned to cardiac CT or invasive coronary angiography across Europe at 16 sites involving 3546 patients. Patients will be followed for up to four years.

Results

The major adjustments to Lung-RADS specifically for cardiac CT relate to 1) incomplete coverage of the lungs by cardiac CT compared with chest CT, and when to order a completion chest CT versus a follow up chest CT, 2) cardiac CT findings will not trigger annual lung-cancer screening, and 3) a lower threshold of at least 10 mm for classifying new ground glass nodules as probably benign (category 3).

Conclusions

The DISCHARGE trial will assess a lung nodule management algorithm designed specifically for cardiac CT in patients with stable chest pain across Europe.

Glycomic Signatures of Plasma IgG Improve Preoperative Prediction of the Invasiveness of Small Lung Nodules

Preoperative assessment of tumor invasiveness is essential to avoid overtreatment for patients with small-sized ground-glass nodules (GGNs) of 10 mm or less in diameter. However, it is difficult to determine the pathological state by computed tomography (CT) examination alone. Aberrant glycans has emerged as a tool to identify novel potential disease biomarkers. In this study, we used a lectin microarray-based strategy to investigate whether glycosylation changes in plasma immunoglobulin G (IgG) provide additional information about the invasiveness of small GGNs before surgery. Two independent cohorts (discovery set, n = 92; test set, n = 210) of GGN patients were used. Five of 45 lectins (Sambucus nigra agglutinin, SNA; Datura stramonium agglutinin, DSA; Galanthus nivalis agglutinin, GNA; Euonymus europaeus lectin, EEL; and Vicia villosa agglutinin, VVA) were identified as independent factors associated with pathological invasiveness of small GGNs (p < 0.01). Receiver-operating characteristic (ROC) curve analysis indicated the combination of these five lectins could significantly improve the accuracy of CT in diagnosing invasive GGNs, with an area under the curve (AUC) of 0.792 (p < 0.001), a sensitivity of 74.6%, and specificity of 74.4%, which was superior to current clinical biomarkers. These results suggest that the multilectin assay based on plasma IgG glycosylation may be a useful in vitro complementary test to enhance preoperative determination of the invasiveness of GGNs and guide surgeons to select proper clinical management to avoid overtreatment.

A simple prediction model using fluorodeoxyglucose-PET and high-resolution computed tomography for discrimination of invasive adenocarcinomas among solitary pulmonary ground-glass opacity nodules

OBJECTIVE

To analyze the FDG-PET and high-resolution computed tomography (HRCT) features of early lung adenocarcinoma manifesting as solitary ground-glass opacity nodules (GGNs), and to establish a new risk model for predicting the invasiveness of early lung adenocarcinoma.

METHODS

We retrospectively analyzed the data of clinical stage IA lung adenocarcinoma patients who received preoperative PET/CT and HRCT examination. Patients were divided into invasive adenocarcinoma (IVA) group and preinvasive minimally invasive adenocarcinoma (MIA) group. The correlations between FDG-PET parameters, HRCT parameters and histopathological invasiveness, and their predictive efficacy were analyzed. A mathematical model for predicting histopathological invasiveness of early lung adenocarcinoma was established and assessed.

RESULTS

This study enrolled 56 patients, 48 were in IVA group and 8 were in preinvasive MIA group. Compared with those in preinvasive MIA group, GGNs in IVA group showed larger diameter, higher ground-glass opacity (GGO) density and more pleural indentation signs (70.8%) on HRCT; they also showed higher maximum standardized uptake value (SUV) and SUV index on FDG-PET (P = 0.001-0.037). Logistic regression analysis found a risk model for predicting IVA of solitary GGNs that were established by CTGGO and SUV index. Receiver operating characteristic curves showed that this model had the highest area under the curve (AUC), sensitivity, specificity and accuracy (AUC, 0.948; sensitivity, 95.8%; specificity, 87.5%; accuracy, 94.6%).

CONCLUSION

Using HRCT combined with FDG-PET to establish the corresponding mathematical prediction model has the potential to identify IVA in early lung adenocarcinoma preoperatively.

Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial

BACKGROUND

In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules.

METHODS

First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category.

RESULTS

Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4-8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05-0.67).

CONCLUSIONS

If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.

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.

[Diagnosis and Treatment of Pulmonary Ground-glass Nodules]

Recent widespread use of high resolution computed tomography (HRCT) for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass nodule (GGN). However, it had been proved that GGN was well associated with lung cancer in previous studies. Therefore, the classification, imaging characteristics, pathological type, follow-up, suggested managements and other clinical concerns of GGN were reviewed in this paper.
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