Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma

The 2023 American Association for Thoracic Surgery (AATS) Expert Consensus Document: Management of subsolid lung nodules

OBJECTIVE

Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules.

METHODS

The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel.

RESULTS

Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules.

CONCLUSIONS

Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.

Initial Clinical Images From a Second-Generation Prototype Silicon-Based Photon-Counting Computed Tomography System

RATIONALE AND OBJECTIVES

To demonstrate the feasibility and potential of using a second-generation prototype photon-counting computed tomography (CT) system to provide simultaneous high spatial resolution images and high spectral resolution material information across a range of routine imaging tasks using clinical patient exposure levels.

MATERIALS AND METHODS

The photon-counting system employs an innovative silicon-based photon-counting detector to provide a balanced approach to ultra-high-resolution spectral CT imaging. An initial cohort of volunteer subjects was imaged using the prototype photon-counting system. Acquisition technique parameters and radiation dose exposures were guided by routine clinical exposure levels used at the institution. Images were reconstructed in native slice thickness using an early version of a spectral CT reconstruction algorithm Samples of images across a range of clinical tasks were selected and presented for review.

RESULTS

Clinical cases are presented across inner ear, carotid angiography, chest, and musculoskeletal imaging tasks. Initial reconstructed images illustrate ultra-high spatial resolution imaging. The fine detail of small structures and pathologies is clearly visualized, and structural boundaries are well delineated. The prototype system additionally provides concomitant spectral information with high spatial resolution.

CONCLUSION

This initial study demonstrates that routine imaging at clinically appropriate patient exposure levels is feasible using a novel deep-silicon photon-counting detector CT system. Furthermore, a deep-silicon detector may provide a balanced approach to photon-counting CT, providing high spatial resolution imaging with simultaneous high-fidelity spectral information.

Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018-2019

Objective

This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant.

Methods

In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively.

Results

The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches.

Conclusion

The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications.

Advances in knowledge

We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines.

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing

BACKGROUND

Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes.

OBJECTIVE

We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes.

METHODS

We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time.

RESULTS

Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes.

CONCLUSIONS

Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.

Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images

Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854–0.966)) than that of all assessed radiologists (RAD 10.290 (0.201–0.379), p < 0.001, RAD 20.450 (0.352–0.548), p < 0.001, RAD 30.670 (0.578–0.762), p < 0.001, RAD 40.810 (0.733–0.887), p = 0.025, RAD 50.700 (0.610–0.790), p < 0.001). The DLAD specificity (0.775 (0.717–0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984–1.000), p < 0.001, RAD 20.970 (0.946–1.000), p < 0.001, RAD 30.980 (0.961–1.000), p < 0.001, RAD 40.975 (0.953–0.997), p < 0.001, RAD 50.995 (0.985–1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists’ false negative rate.

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

OBJECTIVES

To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images.

METHODS

The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.

RESULTS

In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs.

CONCLUSION

The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.

Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography

BACKGROUND

High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation component masks with deep learning in the prediction of high-grade components to optimize surgical strategy preoperatively.

METHODS

A total of 502 patients with pathologically confirmed high-grade adenocarcinomas were retrospectively enrolled between 2016 and 2020. The SACs attention DL model was developed to apply solid-attenuation-component-like subregion masks (tumor area ≥ - 190 HU) to guide the DL model for predicting high-grade subtypes. The SACA-DL was assessed using 5-fold cross-validation and external validation in the training and testing sets, respectively. The performance, which was evaluated using the area under the curve (AUC), was compared between SACA-DL and the DL model without SACs attention (DL_woSACs), the prior radiomics model, or the model based on the consolidation/tumor (C/T) diameter ratio.

RESULTS

We classified 313 and 189 patients into training and testing cohorts, respectively. The SACA-DL achieved an AUC of 0.91 for the cross-validation, which was significantly superior to those of the DL_woSACs (AUC = 0.88; P = 0.02), prior radiomics model (AUC = 0.85; P = 0.004), and C/T ratio (AUC = 0.84; P = 0.002). An AUC of 0.93 was achieved for external validation in the SACA-DL and was significantly better than those of the DL_woSACs (AUC = 0.89; P = 0.04), prior radiomics model (AUC = 0.85; P < 0.001), and C/T ratio (AUC = 0.85; P < 0.001).

CONCLUSIONS

The combination of solid-attenuation-component-like subregion masks with the DL model is a promising approach for the preoperative prediction of high-grade adenocarcinoma subtypes.

Tissue Fraction Correction and Visual Analysis Increase Diagnostic Sensitivity in Predicting Malignancy of Ground-Glass Nodules on [18F]FDG PET/CT: A Bicenter Retrospective Study

We investigated the role of [18F]FDG positron emission tomography/computed tomography (PET/CT) in evaluating ground-glass nodules (GGNs) by visual analysis and tissue fraction correction. A total of 40 pathologically confirmed ≥1 cm GGNs were evaluated visually and semiquantitatively. [18F]FDG uptake of GGN distinct from background lung activity was considered positive in visual analysis. In semiquantitative analysis, we performed tissue fraction correction for the maximum standardized uptake value (SUVmax) of GGN. Of the 40 GGNs, 25 (63%) were adenocarcinomas, 9 (23%) were minimally invasive adenocarcinomas (MIAs), and 6 (15%) were adenocarcinomas in situ (AIS). On visual analysis, adenocarcinoma showed the highest positivity rate among the three pathological groups (88%, 44%, and 17%, respectively). Both SUVmax and tissue-fraction−corrected SUVmax (SUVmaxTF) were in the order of adenocarcinoma > MIA > AIS (p = 0.033 and 0.018, respectively). SUVmaxTF was significantly higher than SUVmax before correction (2.4 [1.9−3.0] vs. 1.3 [0.8−1.8], p < 0.001). When using a cutoff value of 2.5, the positivity rate of GGNs was significantly higher in SUVmaxTF than in SUVmax (50% vs. 5%, p < 0.001). The diagnostic sensitivity of [18F]FDG PET/CT in predicting the malignancy of lung GGN was improved by tissue fraction correction and visual analysis.

CT-based radiomics for differentiating invasive adenocarcinomas from indolent lung adenocarcinomas appearing as ground-glass nodules: Asystematic review

PURPOSE

To provide an overview of the available studies investigating the use of computer tomography (CT) radiomics features for differentiating invasive adenocarcinomas (IAC) from indolent lung adenocarcinomas presenting as ground-glass nodules (GGNs), to identify the bias of the studies and to propose directions for future research.

METHOD

PubMed, Embase, Web of Science Core Collection were searched for relevant studies. The studies differentiating IAC from indolent lung adenocarcinomas appearing as GGNs based on CT radiomics features were included. Basic information, patient information, CT-scanner information, technique information and performance information were extracted for each included study. The quality of each study was assessed using the Radiomic Quality Score (RQS) and the Prediction model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Twenty-eight studies were included with patients ranging from 34 to 794. All of them were retrospective. Patients in three studies were from multiple centers. Most studies segmented regions of interest manually. Pyradiomics and AK software were the most frequently used for features extraction. The number of radiomics features extracted varied from 7 to 10329. Logistic regression was the most frequently chosen model. Entropy was identified as radiomics signature in seven studies. The AUC of included studies ranged from 0.77 to 0.98 in 15 validation sets. The percentage RQS ranged from 3% to 50%. According to PROBAST, the overall risk of bias (ROB) was high in 89.3% (25/28) of included studies, unclear in 7.1% (2/28) of included studies, and low in 3.6% (1/28) of included studies. All studies were low concern regarding the applicability of primary studies to the review question.

CONCLUSION

CT radiomics-based model is promising and encouraging in differentiating IAC from indolent lung adenocarcinomas, though they require methodological rigor. Well-designed studies are necessary to demonstrate their validity and standardization of methods and results can prompt their use in daily clinical practice.

Differentiating focal interstitial fibrosis from adenocarcinoma in persistent pulmonary subsolid nodules (> 5 mm and < 20 mm): the role of coronal thin-section CT images

OBJECTIVES

To investigate thin-section computed tomography (CT) features of pulmonary subsolid nodules (SSNs) with sizes between 5 and 20 mm to determine predictive factors for differentiating focal interstitial fibrosis (FIF) from adenocarcinoma.

METHODS

From January 2017 to December 2018, 169 patients who had persistent SSNs 5-20 mm in size and underwent preoperative nodule localization were enrolled. Patient characteristics and thin-section CT features of the SSNs were reviewed and compared between the FIF and adenocarcinoma groups. Univariable and multivariable analyses were used to identify predictive factors of malignancy. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of these factors.

RESULTS

Among the 169 enrolled SSNs, 103 nodules (60.9%) presented as pure ground-glass opacities (GGOs), and 40 (23.7%) were FIFs. Between the FIF and adenocarcinoma groups, there were significant differences (p< 0.05) in nodule border, shape, thickness, and coronal/axial (C/A) ratio. Multivariable analysis demonstrated that a well-defined border, a nodule thickness >4.2, and a C/A ratio >0.62 were significant independent predictors of malignancy. The performance of a model that incorporated these three predictors in discriminating FIF from adenocarcinoma achieved a high area under the ROC curve (AUC, 0.979) and specificity (97.5%).

CONCLUSIONS

For evaluating persistent SSNs 5-20 mm in size, the combination of a well-defined border, a nodule thickness > 4.2, and a C/A ratio > 0.62 is strongly correlated with malignancy. High accuracy and specificity can be achieved by using this predictive model.

KEY POINTS

• Thin-section coronal images play an important role in differentiating FIF from adenocarcinoma. • The combination of a well-defined border, nodule thickness>4.2 mm, and C/A ratio >0.62 is associated with malignancy. • This predictive model may be helpful for managing persistent SSNs between 5 and 20 mm in size.

Determining the invasiveness of ground-glass nodules using a 3D multi-task network

OBJECTIVES

The aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network.

METHODS

We propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results.

RESULTS

In the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively.

CONCLUSIONS

The proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods.

KEY POINTS

• The proposed multi-task model has achieved good classification results for the invasiveness of GGNs. • The proposed network includes a classification and segmentation branch to learn global and regional features, respectively. • The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods.

Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916-0.964) and validation set (AUC, 0.946; 95% CI, 0.907-0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule

OBJECTIVE

To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.

METHODS

We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison.

RESULTS

The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (p's value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features.

CONCLUSIONS

Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs.

ADVANCES IN KNOWLEDGE

The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics

Background

Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs.

Methods

This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks. Three deep learning networks, namely XimaNet (deep learning-based classification model), XimaSharp (classification and nodule segmentation model), and Deep-RadNet (deep learning and radiomics combined classification model, i.e., deep radiomics) were built. Three classification tasks, namely task 1: classification of AAH/AIS and MIA, task 2: classification of MIA and IAC, and task 3: classification of non-invasive adenocarcinomas and invasive adenocarcinomas (AAH/AIS&MIA and IAC) were conducted to evaluate the model performance. The Z-test was used to compare the model performance.

Results

The AUC for classification of AAH/AIS with MIA were 0.891, 0.841 and 0.779 for Deep-RadNet, XimaNet and XimaSharp respectively. The AUC for classification of MIA with IAC were 0.889, 0.785 and 0.778 for three networks and AUC for classification of AAH/AIS&MIA with IAC were 0.941, 0.892 and 0.827 respectively. The performance of deep_RadNet was better than the other two models with the Z-test (P<0.05).

Conclusions

Deep-RadNet with the visual heat map could evaluate the invasiveness of GGNs accurately and intuitively, providing a theoretical basis for individualized and accurate medical treatment of patients with GGNs.

Diagnostic value and imaging features of multi-detector CT in lung adenocarcinoma with ground glass nodule patients

This study investigated the application value and imaging features of multi-detector CT (MDCT) in the treatment of lung adenocarcinoma with ground glass nodules (GGN). The medical data of 168 patients with pulmonary GGN in Shengli Oilfield Central Hospital from January 2013 to June 2015 were analyzed. Patients with microinvasive adenocarcinoma and invasive adenocarcinoma were included in group A (invasive lung adenocarcinoma, n=98), while patients with atypical adenomatous hyperplasia and adenocarcinoma in situ were included in group B (pre-invasive lung adenocarcinoma, n=70). The imaging features of MDCT were compared. ROC curves of the size of nidus and the size of solid component were drawn for the diagnosis of invasive lung adenocarcinoma. Logistic multivariate regression analysis was used to analyze the risk factors that affected invasive lung adenocarcinoma. There were significant differences in nidus, burr, and lobes of the patients between groups A and B. The size of nidus and the size of solid component of the patients in group A were significantly higher than those of the patients in group B. The AUCs of the size of the nidus and the size of the solid component of the invasive lung adenocarcinoma were 0.891 and 0.902, respectively. The AUC of the combined diagnosis was 0.984. Size of the nidus, size of the solid component, nature of the lesion, burr, and lobes were all risk factors for invasive lung adenocarcinoma. In patients with GGN, size of the nidus and size of the solid component can be used as excellent diagnostic parameters for invasive lung adenocarcinoma, and nidus size (≥9.8 mm), size of the solid component (≥0.9 mm), the mixed GGN nature of the nidus, burr and lobes can distinguish invasive lung adenocarcinoma and pre-invasive lesions.

Value of TSCT Features for Differentiating Preinvasive and Minimally Invasive Adenocarcinoma From Invasive Adenocarcinoma Presenting as Subsolid Nodules Smaller Than 3 cm

BACKGROUND

To distinguish preinvasive (adenocarcinoma in situ/atypical adenomatous hyperplasia) and minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IA) appearing as solitary subsolid nodules (SSNs) less than 3 cm based on thin-section computed tomography (TSCT) features to guide therapeutic approaches.

METHODS

A total of 154 lesions that were histopathologically confirmed to have pre/minimally invasive adenocarcinoma (hereafter pre/MIA) and IA presenting as part-solid nodules (PSNs) or pure ground-glass nodules (pGGNs) were retrospectively reviewed. The TSCT features, including diameter, area, CT value, shape, air bronchogram, margins, and location, were compared and assessed. Receiver operating characteristic analyses were conducted to determine the cut-off values for the qualitative variables and their diagnostic performances.

RESULTS

Of 154 nodules, 89 IA, 53 MIA, eight adenocarcinoma in situ, and four atypical adenomatous hyperplasia lesions were found. Univariate and multivariate logistic regression of the pre/MIA and IA lesions were compared and analyzed among PSNs and pGGNs. Among pGGNs, a significant difference was found in the area (p = 0.004, odds ratio [OR] = 0.124, 95% confidence interval [CI] = 0.300-0.515) between the pre/MIA and IA groups. In PSNs, significant differences were found in the diameter (p = 0.001, OR = 0.171, 95% CI = 0.063-0.467) and CT value (p = 0.001, OR = 0.996, 95% CI = 0.993-0.998) between the pre/MIA and IA groups. According to the corresponding receiver operating characteristic curves, the optimal cut-off tumor area in pGGNs to differentiate pre/MIA from IA was 0.595 cm². A higher CT value of the lesion (≥ -298.500 HU) and a larger diameter (≥1.450 cm) in PSNs were significantly associated with IA.

CONCLUSION

Imaging features from TSCT contribute to distinguishing pre/MIA from IA in solitary subsolid nodules and may contribute to guide the clinical management of these lesions.

Effect of 5-Line Signs in the Prediction of Staging, Progression, and Prognosis of Peripheral Lung Carcinoma: Preliminary Observation Report

BACKGROUND

The single line of the normal interlobar fissure shown on a thin section image can be reconstructed as a 5-line sign on axial maximal intensity projection. The line between the lung nodule and the pleura is called the pleural tail sign on thin image. On the axial maximal intensity projection, it can also be reconstructed as the 5-line sign or fewer than 5 lines.

OBJECTIVE

This study aimed to observe the effect of 5-line signs in staging, progression, and prognosis of peripheral lung carcinoma.

MATERIALS AND METHODS

This study included 132 patients with peripheral lung carcinoma. Among these patients, 93 were men and 39 were women, with an age range of 27 to 82 years and a lung nodule range of 0.98 to 8.75 cm. Maximal intensity projection was reconstructed based on 1.0 or 1.25 mm of thin-slice images in multislice spiral computed tomography. Five-line signs on the margin of the nodule (mass) were observed and were classified into grades 1 to 4 according to the sharpness of the 5-line signs.

RESULTS

Multivariate logistic regression analysis showed that the sharpness of the 5-line signs was correlated with N and TNM staging of peripheral lung carcinoma (P = 0.012, P = 0.016). The lower the sharpness of the 5-line signs, the greater the number of cases of progression of the tumor (P < 0.001), and thus the higher the mortality rate and the lower the survival rate (P = 0.001). The sensitivity and specificity of predicting tumor progression were 56.3% and 93.3%, and those of tumor prognosis were 61.1% and 82.4%, respectively.

CONCLUSIONS

The sharpness of the 5-line signs has certain effects on the prediction of invasion, progression, and prognosis of lung carcinoma, particularly of small lung cancer (≤3.0 cm).

Attenuation and Morphologic Characteristics Distinguishing a Ground-Glass Nodule Measuring 5-10 mm in Diameter as Invasive Lung Adenocarcinoma on Thin-Slice CT

OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.

A comparison of ultra-high-resolution CT target scan versus conventional CT target reconstruction in the evaluation of ground-glass-nodule-like lung adenocarcinoma

Background

The aim of this study was to determine whether the clinical value of scanned computed tomography (CT) images is higher when using ultra-high-resolution CT (U-HRCT) target scanning than conventional CT target reconstruction scanning in the evaluation of ground-glass-nodule (GGN)-like lung adenocarcinoma.

Methods

A total of 91 consecutive patients with isolated GGN-like lung adenocarcinoma were included in this study from April 2017 to June 2018. U-HRCT and conventional CT scans were conducted in all enrolled patients. Two experienced thoracic radiologists independently assessed image quality and made diagnoses. Based on the pathological results, the accuracies of U-HRCT target scanning and conventional CT target reconstruction for detecting morphological features on CT, including spiculation of GGNs, bronchial vascular bundles, solid components in the nodules, burr, vacuole, air bronchial signs, and fissure distortion, were calculated. All statistical analyses were performed using SPSS 17.0 software. Enumeration data were tested using the Chi-square test. A P value of <0.05 was considered statistically significant.

Results

When both techniques were compared with the pathological findings, the detection rate for CT images obtained using U-HRCT target scanning and conventional CT target reconstruction with regard to the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules were 78% vs. 61.5%, 72.5% vs. 54.9%, 65.9% vs. 49.5%, respectively. The presence of the spiculation of GGNs, bronchial vascular bundles, and solid components in the nodules in U-HRCT target scanning was significantly higher than that in conventional CT target reconstruction (all P<0.05). However, no significant difference was observed between the two techniques with regard to the burr, vacuole, air bronchial signs, and fissure distortion (all P>0.05).

Conclusions

When viewing GGNs, the detection rate was higher for U-HRCT target scanning than for conventional CT target reconstruction, and this improvement significantly enhanced the diagnostic accuracy of early lung adenocarcinoma.

Prostate Cancer Pulmonary Metastasis Presenting as a Ground-Glass Pulmonary Nodule on 68Ga-PSMA-11 PET/CT

Ga-prostate-specific membrane antigen 11 (PSMA) PET/CT imaging accurately depicts metastatic prostate adenocarcinoma (PCa). Pulmonary metastases of PCa are often overlooked on follow-up imaging in patients after initial treatment and following androgen deprivation therapy. Here we present a rare case of biopsy-proven PCa pulmonary metastasis with a ground-glass appearance. The increased PSMA expression and the evolving CT features of the solid component of the ground-glass nodule detected by PSMA PET/CT imaging led to surgical resection and PET/CT-guided therapy.

Changes in quantitative parameters of pulmonary nonsolid nodule induced by lung inflation according to paired inspiratory and expiratory computed tomography imaging

OBJECTIVE

To evaluate quantitative parameters of nonsolid nodules on paired inspiratory and expiratory computed tomography (CT) and to examine whether these parameters are sensitive to lung inflation reflected by lung volume.

METHODS

Thirty-three patients with 41 nonsolid nodules were included in this prospective study. Paired inspiratory and low-dose respiratory plain chest CT were performed. The volume and density of nonsolid nodule(s), both lungs, the right and left lung, and five lobes, were analyzed in inspiratory and expiratory CT scans. The ratio of expiratory to inspiratory parameters was calculated and labeled as parameter_(E-I)/I. To standardize the changes in nonsolid nodule quantitative parameters, the ratio of nonsolid nodule parameter to lung parameter was also calculated. Quantitative parameters were compared between inspiratory and expiratory CT.

RESULTS

Nonsolid nodule volumes on expiratory CT were reduced by 19.8% ± 12.9%, while the density was increased by 11.4% ± 8.8%. The volume of nonsolid nodules was significantly greater on inspiratory compared with expiratory CT (p < 0.001). The density of nonsolid nodules was significantly greater on expiratory than inspiratory CT (p < 0.001). The volume_(E-I)/I was significantly greater than density_(E-I)/I both in nonsolid nodules and lung. The volume_(E-I)/I and density_(E-I)/I of nonsolid nodules were independent of size. The density_(E-I)/I of nonsolid nodule was greater in the lower lobe than that in the upper lobe (p = 0.002).

CONCLUSION

Volume changes in nonsolid nodules were more sensitive than density changes in expiratory phase. The density of lower lobe nodules was more susceptible to respiration. Expiratory scanning is not recommended for quantification of nonsolid nodules and/or follow-up.

KEY POINTS

• The nonsolid nodule volume on expiratory CT was reduced by 19.8% ± 12.9%. • The nonsolid nodule density on expiratory CT was increased by 11.4% ± 8.8%. • The volume _(E-I)/I and density _(E-I)/I of nonsolid nodules were independent of size.

Expression of p53 in ground-glass nodule of lung cancer and non-lung cancer patients

The present study investigated the expression of p53 in ground-glass nodule (GGN) of lung cancer and non-lung cancer patients, and explored the correlation with prognosis. A total of 120 GGN patients admitted to the Department of Respiratory Medicine in the Second Affiliated Hospital of Zhejiang University School of Medicine during the period from March 2010 to March 2014 were selected. These patients included 60 lung cancer patients and 60 non-tumor patients. Biopsy or surgical specimens were collected. Fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) were used to detect p53 gene and protein expression in the two groups of GGN tissues. All patients were followed up for 3 years and the relationship between p53 protein expression and the overall survival (OS) of the two groups of patients was analyzed. In GGN cells of non-cancer patients, p53 absence was observed in 6 cases and the absence rate was 10.0%. In GGN cells of cancer patients, the absence rate was significantly higher than that of non-cancer GGN group (p<0.05). The positive rate of p53-positive cases in non-tumor patients GGN group was lower than that of in GGN tissues of lung cancer patients (p<0.05). There were no deaths in the GGN non-cancer group (n=60) within 3 years, while 43 deaths occurred in GGN lung cancer group. The median survival time and the 3-year survival rate of patients with p53 positive was lower than that of p53-negative patients (p<0.05). p53 was overexpressed in GGN of lung cancer patients, and p53 overexpression is significantly correlated with poor prognosis of lung cancer patients. p53 plays an important role in transformation from GGN to lung cancer. Detection of p53 expression in GGN tissue may provide guidance for the diagnosis and prognosis of lung cancer.

Identification of epidermal growth factor receptor mutations in pulmonary adenocarcinoma using dual-energy spectral computed tomography

OBJECTIVES

To explore the role of dual-energy spectral computed tomography (DESCT) quantitative characteristics for the identification of epidermal growth factor receptor (EGFR) mutation status in a cohort of East Asian patients with pulmonary adenocarcinoma.

MATERIALS AND METHODS

Patients with lung adenocarcinoma who underwent both DESCT chest examination and EGFR test were retrospectively selected from our institution's database. The DESCT visual morphological features and quantitative parameters, including the CT number at 70 keV, normalized iodine concentration (NIC), normalized water concentration, and slopes of the spectral attenuation curves (slope λ HU [Hounsfield unit]), were evaluated or calculated. The patients were divided into two groups: the EGFR mutation group and EGFR wild-type group. Statistical analyses were performed to identify the DESCT quantitative parameters for diagnosis of EGFR mutation status.

RESULTS

EGFR mutations were detected in 66 (55.0%) of the 120 enrolled patients. The univariate analysis revealed that sex, smoking history, CT texture, NIC, and slope λ HU were significantly associated with EGFR mutation status (p = 0.037, 0.001, 0.047, 0.010, and 0.018, respectively). The multivariate logistic analysis revealed that smoking history (odds ratio [OR] = 3.23, p = 0.005) and NIC (OR = 58.026, p = 0.049) were the two significant predictive factors associated with EGFR mutations. Based on this analysis, the smoking history and NIC were combined to determine the predictive value for EGFR mutations with the area under the curve of 0.702.

CONCLUSIONS

NIC may be a potential quantitative DESCT parameter for predicting EGFR mutations in patients with pulmonary adenocarcinoma.

KEY POINTS

• DESCT can provide multiple quantitative image parameters compared to conventional CT. • Identification of the radio-genomic relation between DESCT and EGFR status can help to define molecular subcategories of lung adenocarcinoma, which is valuable for personalized clinical targeted therapy. • NIC may be a potential DESCT quantitative parameter for predicting EGFR mutations in pulmonary adenocarcinoma.

High versus low attenuation thresholds to determine the solid component of ground-glass opacity nodules

Objectives

To evaluate and compare the diagnostic accuracy of high versus low attenuation thresholds for determining the solid component of ground-glass opacity nodules (GGNs) for the differential diagnosis of adenocarcinoma in situ (AIS) from minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA).

Methods

Eighty-six pathologically confirmed GGNs < 3 cm observed in 86 patients (27 male, 59 female; mean age, 59.3 ± 11.0 years) between January 2013 and December 2015 were retrospectively included. The solid component of each GGN was defined using two different attenuation thresholds: high (-160 Hounsfield units [HU]) and low (-400 HU). According to the presence or absence of solid portions, each GGN was categorized as a pure GGN or part-solid GGN. Solid components were regarded as indicators of invasive foci, suggesting MIA or IA.

Results

Among the 86 GGNs, there were 57 cases of IA, 19 of MIA, and 10 of AIS. Using the high attenuation threshold, 44 were categorized as pure GGNs and 42 as part-solid GGNs. Using the low attenuation threshold, 13 were categorized as pure GGNs and 73 as part-solid GGNs. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for the invasive focus were 55.2%, 100%, 100%, 22.7%, and 60.4%, respectively, for the high attenuation threshold, and 93.4%, 80%, 97.2%, 61.5%, and 91.8%, respectively, for the low attenuation threshold.

Conclusion

The low attenuation threshold was better than the conventional high attenuation threshold for determining the solid components of GGNs, which indicate invasive foci.

The "solid" component within subsolid nodules: imaging definition, display, and correlation with invasiveness of lung adenocarcinoma, a comparison of CT histograms and subjective evaluation

OBJECTIVE

To validate three proposed definitions of the "solid" component of subsolid nodules, as compared to CT histograms and the use of different window settings, for discriminating the invasiveness of adenocarcinomas in a manner that facilitates routine clinical assessment.

METHODS

We retrospectively analyzed 328 pathologically confirmed lung adenocarcinomas, manifesting as subsolid nodules. Three-dimensional CT histograms were generated by setting 11 CT attenuation intervals from - 400 to 50 HU, at 50 HU intervals, and the voxel percentage within each CT attenuation interval was generated automatically. Three definitions of the "solid" component were proposed, and 10 medium window settings were set to evaluate the "solid" component. The diagnostic performance of the three definitions for identifying invasive adenocarcinoma was compared with that of CT histogram analysis and subjective evaluation with medium window settings.

RESULTS

A parallel diagnosis using five intervals with the largest AUC (AUC ≥ 0.797) demonstrated good differential diagnostic performance, with 78% sensitivity and 73.7% specificity. Definition 2 (visibility in the mediastinum window) yielded higher accuracy (75.6%) than the other two definitions (p < 0.01). A medium window setting of - 50 WL/2 WW gave a larger AUC than the other nine medium window settings as well as definition 2, with 82.5% specificity and 88.5% PPV, which was higher than those of parallel diagnosis with CT histogram and definition 2.

CONCLUSION

Using - 50 WL/2 WW is the optimum approach for evaluating the "solid" component and discriminating invasiveness, superior to using 3D CT histograms and definition 2, and convenient in routine clinical assessment.

KEY POINTS

• - 50 WL/2 WW gave a larger AUC than definition 2. • The specificity of - 50 WL/2 WW was higher than CT histograms. • - 50 WL/2 WW offers the best evaluation of the solid component.

Contrast analysis of the relationship between the HRCT sign and new pathologic classification in small ground glass nodule-like lung adenocarcinoma

PURPOSE

To perform contrast analysis of the relationship between high-resolution computed tomography (HRCT) signs and new pathologic classification of small GGNs-like lung adenocarcinoma.

MATERIALS AND METHODS

The HRCT data from 145 pathologically confirmed cases of small GGNs of lung adenocarcinoma were analysed retrospectively. The 145 cases of GGNs were divided into pre-invasive (PI) group (n = 46), micro-invasive adenocarcinoma (MIA) group (n = 48), and invasive adenocarcinoma (IAC) group (n = 51). HRCT imaging sign of GGNs in each group was assessed and compared.

RESULTS

Significant differences in GGN size were found among the three groups (P < 0.05). The presence of a tumour-lung interface in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05), but no significant difference was found between the MIA and IAC groups. The presence of a pleural indentation sign in the IAC group was significantly higher than that in the other two groups (P < 0.05), but no significant difference was noted between the latter two groups. Significant differences were found in the lobulated and spicule signs among the three groups (P < 0.05). The presence of a microvascular sign in the MIA and IAC groups was significantly higher than that in the PI group (P < 0.05). No significant difference was found in the GGN density, vacuole sign, air bronchus sign and notch sign among the three groups.

CONCLUSIONS

The HRCT signs of GGNs could be used to differentiate among pre-invasive lesions, micro-invasive lesions and invasive lung adenocarcinoma.

Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule

OBJECTIVES

To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.

METHODS

This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.

RESULTS

355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.

CONCLUSIONS

The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma.

KEY POINTS

• The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.

Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature

BACKGROUND AND OBJECTIVES

To improve lung nodule classification efficiency, we propose a lung nodule CT image characterization method. We propose a multi-directional feature extraction method to effectively represent nodules of different risk levels. The proposed feature combined with pattern recognition model to classify lung adenocarcinomas risk to four categories: Atypical Adenomatous Hyperplasia (AAH), Adenocarcinoma In Situ (AIS), Minimally Invasive Adenocarcinoma (MIA), and Invasive Adenocarcinoma (IA).

METHODS

First, we constructed the reference map using an integral image and labelled this map using a K-means approach. The density distribution map of the lung nodule image was generated after scanning all pixels in the nodule image. An exponential function was designed to weight the angular histogram for each component of the distribution map, and the features of the image were described. Then, quantitative measurement was performed using a Random Forest classifier. The evaluation data were obtained from the LIDC-IDRI database and the CT database which provided by Shanghai Zhongshan hospital (ZSDB). In the LIDC-IDRI, the nodules are categorized into three configurations with five ranks of malignancy ("1" to "5"). In the ZSDB, the nodule categories are AAH, AIS, MIA, and IA.

RESULTS

The average of Student's t-test p-values were less than 0.02. The AUCs for the LIDC-IDRI database were 0.9568, 0.9320, and 0.8288 for Configurations 1, 2, and 3, respectively. The AUCs for the ZSDB were 0.9771, 0.9917, 0.9590, and 0.9971 for AAH, AIS, MIA and IA, respectively.

CONCLUSION

The experimental results demonstrate that the proposed method outperforms the state-of-the-art and is robust for different lung CT image datasets.

Diagnostic accuracy and complications of CT-guided core needle lung biopsy of solid and part-solid lesions

OBJECTIVE

To evaluate whether diagnostic accuracy and complications of CT-guided core needle biopsy (CNB) differ for solid and part-solid lung lesions Methods: This retrospective study included 354 consecutive patients from April 2012 to July 2016 who underwent CT-guided CNB of lung lesions by a radiologist. Patient demographics, lung lesions' characteristics; solid or part-solid, underlying pulmonary disease, distance of path, procedure time, complications (hemorrhage or pneumothorax), histopathological results of biopsy specimens and final diagnosis were reviewed. The diagnostic yields, biopsy-related factors and complications were compared for patients with solid lesions and patients with part-solid lesions. Factors related to true diagnoses and complications were analyzed statistically.

RESULTS

The biopsies of part-solid lesions take more time (p = 0.021). Non-diagnostic biopsies were not statistically different between solid and part-solid lesions (p = 0.804). There was no significant difference in the diagnostic yields including sensitivity, specificity, accuracy, positive predictive value and negative predictive value for solid and part-solid lesions statistically. The occurrence of hemorrhage on postbiopy follow-up CT was significantly higher (p = 0.016) for part-solid lesions. The occurrence of symptomatic major hemorrhage (p = 0.859) and pneumothorax (p = 0.106) was not significantly different between solid and part-solid lesions.

CONCLUSION

The diagnostic accuracy of CT-guided CNB for diagnosing malignancy was comparable for solid and part-solid lesions. The frequency of hemorrhage on the follow up CT was higher in patients with part-solid lesions, but there were no significant differences in major hemorrhage and pneumothorax for solid and part-solid lesions. Advances in knowledge: The diagnostic yield of CT-guided CNB for diagnosing malignancy is comparable for solid and part-solid lesions. Although the post procedural hemorrhage occurs more frequently in part-solid lesions, the occurrence of symptomatic major hemorrhage is not significantly different. Therefore, CT-guided CNB should be considered for histopathological confirmation of intrapulmonary lesions regardless of the presence of ground-glass opacity portion.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma

Objective

To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma.

Materials and Methods

From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model.

Results

Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130).

Conclusion

Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.

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

Purpose

To evaluate whether, and to which extent, experienced radiologists are able to visually correctly differentiate transient from persistent subsolid nodules from a single CT examination alone and to determine CT morphological features to make this differentiation.

Materials and methods

We selected 86 transient and 135 persistent subsolid nodules from the National Lung Screening Trial (NLST) database. Four experienced radiologists visually assessed a predefined list of morphological features and gave a final judgment on a continuous scale (0–100). To assess observer performance, area under the receiver operating characteristic (ROC) curve was calculated. Statistical differences of morphological features between transient and persistent lesions were calculated using Chi-square. Inter-observer agreement of morphological features was evaluated by percentage agreement.

Results

Forty-nine lesions were excluded by at least 2 observers, leaving 172 lesions for analysis. On average observers were able to differentiate transient from persistent subsolid nodules ≥ 10 mm with an area under the curve of 0.75 (95% CI 0.67–0.82). Nodule type, lesion margin, presence of a well-defined border, and pleural retraction showed significant differences between transient and persistent lesions in two observers. Average pair-wise percentage agreement for these features was 81%, 64%, 47% and 89% respectively. Agreement for other morphological features varied from 53% to 95%.

Conclusion

The visual capacity of experienced radiologists to differentiate persistent and transient subsolid nodules is moderate in subsolid nodules larger than 10 mm. Performance of the visual assessment of CT morphology alone is not sufficient to generally abandon a short-term follow-up for subsolid nodules.

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of ≤3 cm

AIM

To differentiate retrospectively the morphological characteristics at high-resolution computed tomography (CT) between minimally invasive pulmonary adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IAC) appearing as subsolid nodules (SNs) with a diameter of ≤3 cm and to provide information to help operative decision-making.

MATERIALS AND METHODS

The patient notes of 260 patients with SNs of ≤3 cm in diameter (98 with MIA and 162 with IAC) confirmed at surgery and histopathology from September 2008 to June 2012 were reviewed retrospectively at the Department of Radiology, Weifang Respiratory Disease Hospital. Sixty-seven patients had pure ground-glass nodules (PGGNs) and 193 had mixed ground-glass nodules (MGGNs). Patients were grouped according to the final pathology: minimally invasive MIA and IAC. The HRCT characteristics were compared between the two groups.

RESULTS

There were statistically significant differences in the pattern, shape, diameter of solid components, proportion of solid components, CT radiodensity values of the ground-glass and solid components, borders, margins, air bronchograms, microvascular signs, and pleural indentations of the nodules between the two groups (all p<0.05). Multivariate and receiver operating characteristic (ROC) analyses indicated significant predictors of MIAs were as follows: small lesion diameter (≤14.7 mm), solid components ≤7 mm, <50% of solid components, low CT radiodensity values of the solid components (≤-107 HU), air bronchograms in the ground-glass opacity components, and microvascular signs.

CONCLUSION

The morphological characteristics at high-resolution CT can be used to differentiate between MIAs and IACs appearing as SNs with a diameter of ≤3 cm and provide information to help operative decision-making.

Virtual fluoroscopy during transbronchial biopsy for locating ground-glass nodules not visible on X-ray fluoroscopy

Background

Virtual fluoroscopy (VF) is a novel guided technique that provides ray summation images of target lesions similar to X-ray fluoroscopy. Endobronchial ultrasound with a guide sheath (EBUS-GS) is a useful modality for imaging ground-glass nodules (GGNs) but is not ideal for GGNs that cannot be detected on X-ray fluoroscopy. We evaluated whether the addition of VF to EBUS-GS improved the diagnostic yield.

Methods

Consecutive patients who had undergone diagnostic bronchoscopy for GGNs that were not detected on X-ray fluoroscopy between September 2012 and January 2016 were retrospectively enrolled. The patients were divided into two groups: a non-VF group [performed using conventional thin-section computed tomography (CT), X-ray fluoroscopy, EBUS-GS, and virtual bronchoscopy for reference], and a VF group (performed using additional VF to non-VF group). We then compared the diagnostic yields between the two groups and performed a multivariate analysis to identify factors associated with an increased diagnostic yield.

Results

A total of 74 patients (VF, 35 patients; non-VF, 39 patients) were enrolled and were included in the analysis. The diagnostic yield was significantly higher in the VF group (77.1%) than in the non-VF group (51.2%, P=0.030). There were no clinically significant complications in either group. In the multivariate analysis, a positive bronchus sign [odds ratio (ORs), 5.41; 95% confidence interval (CI), 1.36-21.40] and the use of VF (odds ratio, 3.68; 95% confidence interval, 1.16-11.60) were significantly associated with successful bronchoscopic diagnosis.

Conclusions

The addition of VF to EBUS-GS helped to identify GGNs that were not visible on X-ray fluoroscopy.

A retrospective clinicopathological study of lung adenocarcinoma: Total tumor size can predict subtypes and lymph node involvement

PURPOSE

To analyze the predictive ability of total tumor size in lung adenocarcinoma subtype and lymph node involvement.

MATERIALS AND METHODS

1018 patients, ≤3cm tumor, were enrolled. The maximum diameter and other variables of each tumor were measured.

RESULTS

The optimal cut-off value for total tumor size in differentiating AIS and MIA from IAC was <1.15cm, in distinguishing lymph node involvement, it was 1.65cm.

CONCLUSIONS

Total tumor size could be a reliable predictor of lung adenocarcinoma subtype and lymph node involvement irrespective of ground glass, part solid and solid characteristics.

[Lung Nodules Assessment--Analysis of Four Guidelines]

近20年来，随着计算机断层扫描（computed tomography, CT）技术的提高和肺癌高危人群筛查的普及，越来越多的肺部小结节被发现，然而肺结节的定性诊断仍有很多困难。肺结节是临床上一种常见的现象，恶性结节早期发病比较隐匿，如果不进行早期干预，其病程迅速、恶性程度强、预后差。如果能在早期阶段对病灶进行手术切除，将会明显改善肺癌患者的预后。目前针对肺结节的处理指南层出不穷，但各大指南均未达成统一的共识。本文拟对在国内影响最大的四个指南：美国国家综合癌症网络非小细胞肺癌（non-small cell lung cancer, NSCLC）临床实践指南、美国胸科医师协会肺癌诊疗指南、Fleischner-Society肺结节处理策略指南、肺结节的评估亚洲共识指南所推荐的肺结节诊断和处理策略进行介绍和分析。

Comparison of Positron Emission Tomography Using 2-[18F]-fluoro-2-deoxy-D-glucose and 3-deoxy-3-[18F]-fluorothymidine in Lung Cancer Imaging

Background:

The detection of solitary pulmonary nodules (SPNs) that may potentially develop into a malignant lesion is essential for early clinical interventions. However, grading classification based on computed tomography (CT) imaging results remains a significant challenge. The 2-[¹⁸F]-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography (PET)/CT imaging produces both false-positive and false-negative findings for the diagnosis of SPNs. In this study, we compared ¹⁸F-FDG and 3-deoxy-3-[¹⁸F]-fluorothymidine (¹⁸F-FLT) in lung cancer PET/CT imaging.

Methods:

The binding ratios of the two tracers to A549 lung cancer cells were calculated. The mouse lung cancer model was established (n = 12), and micro-PET/CT analysis using the two tracers was performed. Images using the two tracers were collected from 55 lung cancer patients with SPNs. The correlation among the cell-tracer binding ratios, standardized uptake values (SUVs), and Ki-67 proliferation marker expression were investigated.

Results:

The cell-tracer binding ratio for the A549 cells using the ¹⁸F-FDG was greater than the ratio using ¹⁸F-FLT (P < 0.05). The Ki-67 expression showed a significant positive correlation with the ¹⁸F-FLT binding ratio (r = 0.824, P < 0.01). The tumor-to-nontumor uptake ratio of ¹⁸F-FDG imaging in xenografts was higher than that of ¹⁸F-FLT imaging. The diagnostic sensitivity, specificity, and the accuracy of ¹⁸F-FDG for lung cancer were 89%, 67%, and 73%, respectively. Moreover, the diagnostic sensitivity, specificity, and the accuracy of ¹⁸F-FLT for lung cancer were 71%, 79%, and 76%, respectively. There was an obvious positive correlation between the lung cancer Ki-67 expression and the mean maximum SUV of ¹⁸F-FDG and ¹⁸F-FLT (r = 0.658, P < 0.05 and r = 0.724, P < 0.01, respectively).

Conclusions:

The ¹⁸F-FDG uptake ratio is higher than that of ¹⁸F-FLT in A549 cells at the cellular level. ¹⁸F-FLT imaging might be superior for the quantitative diagnosis of lung tumor tissue and could distinguish lung cancer nodules from other SPNs.

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.

[Advances in Diagnosis and Treatment of Multiple Primary Lung Cancer]

多原发性肺癌的发病率和检出率逐年升高。目前临床上诊断多原发性肺癌(multiple primary lung cancer, MPLC)主要参照Martini-Melamed标准和美国胸科医师协会(American College of Chest Physicians, ACCP)标准, 综合考虑临床表现、影像学特征、组织学类型和分子遗传学特征。组织学类型不同的MPLC诊断相对容易, 而组织学类型相同的MPLC诊断仍相当困难。DNA倍体分析、基因突变检测、微卫星多态性分析等分子生物学技术为MPLC的正确诊断提供了新手段, 可评估各病灶的克隆性关系, 帮助鉴别MPLC与转移。MPLC的首选治疗方案为根治性手术, 术式应考虑患者肺功能储备等因素, 选择肺叶切除、肺段切除或楔形切除; 对于不能根治性切除的病灶, 可综合化疗、放疗、立体定向放疗(stereotactic ablative radiotherapy, SABR)、射频消融(radiofrequency ablation, RFA)、分子靶向治疗等。

Predictive value of mutant p53 expression index obtained from nonenhanced computed tomography measurements for assessing invasiveness of ground-glass opacity nodules

Purpose

To predict p53 expression index (p53-EI) based on measurements from computed tomography (CT) for preoperatively assessing pathologies of nodular ground-glass opacities (nGGOs).

Methods

Information of 176 cases with nGGOs on high-resolution CT that were pathologically confirmed adenocarcinoma was collected. Diameters, total volumes (TVs), maximum (MAX), average (AVG), and standard deviation (STD) of CT attenuations within nGGOs were measured. p53-EI was evaluated through immunohistochemistry with Image-Pro Plus 6.0. A multiple linear stepwise regression model was established to calculate p53-EI prediction from CT measurements. Receiver-operating characteristic curve analysis was performed to compare the diagnostic performance of variables in differentiating preinvasive adenocarcinoma (PIA), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC).

Results

Diameters, TVs, MAX, AVG, and STD showed significant differences among PIAs, MIAs, and IACs (all P-values <0.001), with only MAX being incapable to differentiate MIAs from IACs (P=0.106). The mean p53-EIs of PIAs, MIAs, and IACs were 3.4±2.0, 7.2±1.9, and 9.8±2.7, with significant intergroup differences (all P-values <0.001). An equation was established by multiple linear regression as: p53-EI prediction =0.001* TVs +0.012* AVG +0.022* STD +9.345, through which p53-EI predictions were calculated to be 4.4%±1.0%, 6.8%±1.3%, and 8.5%±1.4% for PIAs, MIAs, and IACs (Kruskal–Wallis test P<0.001; Tamhane’s T2 test: PIA vs MIA P<0.001, MIA vs IAC P<0.001), respectively. Although not significant, p53-EI prediction has a little higher area under the curve (AUC) than the actual one both in differentiating MIAs from PIAs (AUC 0.938 vs 0.914, P=0.263) and in distinguishing IACs from MIAs (AUC 0.812 vs 0.786, P=0.718).

Conclusion

p53-EI prediction of nGGOs obtained from CT measurements allows accurately estimating lesions’ pathology and invasiveness preoperatively not only from radiology but also from pathology.