What do we know about ground-glass opacity nodules in the lung?

An Investigation of the Immune Microenvironment and Genome during Lung Adenocarcinoma Development

Background: Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are two consecutive pathological processes that occur before invasive lung adenocarcinoma (LUAD). However, our understanding of the immune editing patterns during the progression of LUAD remains limited. Furthermore, we know very little about whether alterations in driver genes are involved in forming the tumor microenvironment (TME). Therefore, it is necessary to elucidate the regulatory role of TME in LUAD development from multiple dimensions, including immune cell infiltration, molecular mutation events, and oncogenic signaling pathways. Methods: We collected 145 surgically resected pulmonary nodule specimens, including 28 cases of AIS, 52 cases of MIA, and 65 cases of LUAD. Immunohistochemistry (IHC) was used to detect the expression of immune markers CD3, CD4, CD8, CD68 and programmed death ligand 1 (PD-L1). Genomic data and TMB generated by targeted next-generation sequencing (NGS). Results: LUAD exhibited higher levels of immune cell infiltration, tumor mutation burden (TMB), and activation of oncogenic pathways compared to AIS and MIA. In LUAD, compared to epidermal growth factor receptor (EGFR) single mutation and wild-type (WT) samples, cases with EGFR co-mutations showed a more pronounced rise in the CD4/CD8 ratio and CD68 infiltration. Patients with low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) mutation have higher TMB and PD-L1 expression. The transition from AIS to LUAD tends to shift the TME towards the PD-L1+CD8+ subtype (adaptive resistance). Progression-associated mutations (PAMs) were enriched in the lymphocyte differentiation pathway and related to exhausted cells' phenotype. Conclusion: Tumor-infiltrating immune cells tend to accumulate as the depth of LUAD invasion increases, but subsequently develop into an immune exhaustion and immune escape state. Mutations in EGFR and LRP1B could potentially establish an immune niche that fosters tumor growth. PAMs in LUAD may accelerate disease progression by promoting T cell differentiation into an exhausted state.

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography

BACKGROUND

There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features.

METHODS

A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve.

RESULTS

The CTRV at attenuation threshold of -250 HU (CTRV_- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV_- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05).

CONCLUSIONS

The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV_- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.

The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management

The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.

Prevalence of ground glass nodules in preschool children: a cross-sectional study

BACKGROUND

Following increased screening efforts and the use of thin-slice computed tomography (CT), there has been a considerable increase in the incidence of ground-glass nodules (GGNs) in adults. As a result, we have more and more treatments for ground-glass nodules in adults, but few in children. Most think development pattern of pulmonary GGNs is lung inflammation, tumor, or tuberculosis that are more related to acquired or environmental factors. By studying the incidence of pulmonary GGNs in preschool children, we sought to determine whether we had ground glass nodules in the lung before we were teenagers, but we didn't pay attention to them until later. If the hypothesis holds, we may change the cognition and treatment strategies of ground glass nodules. Even not, there are few epidemiological studies with big data that can fill this gap.

METHODS

We retrospectively collected the data of all preschool children who had undergone CT at the Children's Hospital of Zhejiang University School of Medicine from 2013 to 2020. These data were filtered according to the following exclusion criteria: severe artifacts, data with identical names to the original data; and patients without follow-up records (≥3 months). Inclusion criteria: must have undergone thin-slice CT (≤1.25 mm) at the first and last follow-up. Two thoracic radiologists with 5 years of experience and another senior one assessed the images.

RESULTS

There were a total of 13,361 cases after relevant exclusions, 311 patients were finally enrolled. Clinical features: age at diagnosis (year): 3.56±1.84, female: 147, male: 164, follow-up interval (month): 6.90±4.74, leukemia: 99, pneumonia: 21, lung cyst: 8, space-occupying lesions outside the lungs: 69, foreign body in respiratory tract: 6. After manual screening and reading, only 1 patient meets all requirements. The results showed that between 2013 and 2020, the incidence of GGNs that could be basically determined in the Children's Hospital of Zhejiang University School of Medicine was 0.32%.

CONCLUSIONS

There have been few previous studies of GGNs in children, and based on our study, we found that there is still some associated morbidity for preschool children, it is rarely found when they are young.

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.

Ground glass opacity: can we correlate radiological and histological features to plan clinical decision making?

BACKGROUND

The spectrum of ground glass opacity (GGO) is a diagnostic and clinical management quandary. The role of computed tomographic scans in detecting malignant GGO has inter-observer variability. Pure GGO have been traditionally thought to be predominantly benign in nature and has long volume doubling times. This study was undertaken to correlate the findings of radiology and histology of ground glass opacities at our institute.

METHODS

This study is a retrospective observational study of patients who underwent lung resection surgery for radiology proven ground glass opacities between January 2010 and December 2018. A total of 115 patients were included in the study based on inclusion and exclusion criteria and were analysed.

RESULTS

The patients were divided into two groups; pure GGO (n = 50), mixed GGO (n = 65). The pathological tumour size was ≤ 2 cm in 51% of the patients and 27 patients had the size between 2.1 and 3.0 cm. The predominant histopathologic feature was lepidic predominance in 54 patients followed by 24 patients with acinar predominance. Among patients with radiological tumour size of ≤ 2 cm, pure GGO was present in 48% of the patients. Among patients with pure GGO, 96% of the patients had no solid component. 44 patients had only single CT scan before proceeding to surgery. All these patients had mixed GGO.

CONCLUSION

Our study concludes pure GGOs, though lacking solid component have a high propensity to be malignant. The role of repeated CT surveillance in this context without offering curative surgery may be questionable.

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.

Design of lung nodules segmentation and recognition algorithm based on deep learning

BACKGROUND

Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules.

RESULTS

3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907.

CONCLUSION

The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

Data Analysis Strategies in Medical Imaging

Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence has allowed for detailed quantification of radiographic characteristics of tissues using predefined engineered algorithms or deep learning methods. Precedents in radiology as well as a wealth of research studies hint at the clinical relevance of these characteristics. However, critical challenges are associated with the analysis of medical imaging data. Although some of these challenges are specific to the imaging field, many others like reproducibility and batch effects are generic and have already been addressed in other quantitative fields such as genomics. Here, we identify these pitfalls and provide recommendations for analysis strategies of medical imaging data, including data normalization, development of robust models, and rigorous statistical analyses. Adhering to these recommendations will not only improve analysis quality but also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources. Clin Cancer Res; 24(15); 3492-9. ©2018 AACR.

Nodule Classification on Low-Dose Unenhanced CT and Standard-Dose Enhanced CT: Inter-Protocol Agreement and Analysis of Interchangeability

Objective

To measure inter-protocol agreement and analyze interchangeability on nodule classification between low-dose unenhanced CT and standard-dose enhanced CT.

Materials and Methods

From nodule libraries containing both low-dose unenhanced and standard-dose enhanced CT, 80 solid and 80 subsolid (40 part-solid, 40 non-solid) nodules of 135 patients were selected. Five thoracic radiologists categorized each nodule into solid, part-solid or non-solid. Inter-protocol agreement between low-dose unenhanced and standard-dose enhanced images was measured by pooling κ values for classification into two (solid, subsolid) and three (solid, part-solid, non-solid) categories. Interchangeability between low-dose unenhanced and standard-dose enhanced CT for the classification into two categories was assessed using a pre-defined equivalence limit of 8 percent.

Results

Inter-protocol agreement for the classification into two categories {κ, 0.96 (95% confidence interval [CI], 0.94-0.98)} and that into three categories (κ, 0.88 [95% CI, 0.85-0.92]) was considerably high. The probability of agreement between readers with standard-dose enhanced CT was 95.6% (95% CI, 94.5-96.6%), and that between low-dose unenhanced and standard-dose enhanced CT was 95.4% (95% CI, 94.7-96.0%). The difference between the two proportions was 0.25% (95% CI, -0.85-1.5%), wherein the upper bound CI was markedly below 8 percent.

Conclusion

Inter-protocol agreement for nodule classification was considerably high. Low-dose unenhanced CT can be used interchangeably with standard-dose enhanced CT for nodule classification.

Is a 5-mm diameter an appropriate cut-off value for the diagnosis of atypical adenomatous hyperplasia and adenocarcinoma in situ on chest computed tomography and pathological examination?

Background

Preinvasive lesions, such as atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), usually appear as pure ground-glass nodules (pGGNs) on thin-section computed tomography (TSCT). AAH is usually less than 5 mm wide on imaging and pathological examinations. We aimed to determine whether a 5-mm cut-off value was appropriate for the diagnosis of AAH and AIS.

Methods

We retrospectively analyzed the performance of TSCT in evaluating 80 pathologically confirmed preinvasive lesions (33 AAH lesions in 31 patients and 47 AIS lesions in 45 patients). We compared the following characteristics between the AAH and AIS groups: lesion diameter, density, rim, lobulation, spiculation, vacuole sign, aerated bronchus sign, pleural indentation sign, and pathological findings.

Results

All 80 lesions appeared as pGGNs. On TSCT, the average diameter of AAH lesions (6.0±1.64 mm) was significantly smaller than that of AIS lesions (8.7±3.16 mm; P<0.001). The area under the curve (AUC) for diameter was 0.792, and the best diagnostic cut-off value was 6.99 mm. On gross pathological examination, the average diameter of AAH lesions (4.6±1.99 mm) was significantly smaller that of AIS lesions (6.8±2.06 mm; P<0.001). The AUC was 0.794, and the best diagnostic cut-off value was 4.5 mm. The vacuole sign was common in AIS (P=0.021). AAH did not significantly differ from AIS (P>0.05) in terms of average CT value, uniformity of density, morphology, rim, lobulation, spiculation, pleural indentation sign, and aerated bronchus sign.

Conclusions

Lesion size and the vacuole sign were beneficial in the diagnosis of AAH and AIS. The vacuole sign was common in AIS. The best diagnostic cut-off value of nodular diameter for differentiating between AAH and AIS was 6.99 mm on TSCT and 4.5 mm on gross pathology.

Ground glass opacities management in the lung cancer screening era

Pulmonary ground glass opacity (GGO) is becoming an important clinical dilemma in oncology as its diagnosis in clinical practice is increasing due to the introduction of low dose computed tomography (CT) scan and screening. The incidence of cancer in GGO has been reported as high as 63%. The purpose of this manuscript is to review best available evidence papers on management of GGO in lung cancer to address the following questions: (I) how to correlate CT findings with malignancy; (II) when and who operate? (III) how to perform intraoperative detection of intrapulmonary GGO? (IV) wedge, segmentectomy or lobectomy? Taking a cue from a clinical scenario, a review on PubMed was conducted. The words search included: "Lung ground glass opacity". The research was limited to human and adults. We considered all published articles from 1990 to April 2017, which reported on at least sufficient data, to be eligible. The literature search was limited to articles in English. A total of 1,211 articles have been found. Interestingly, while in 1991, only one paper was published on low-dose high-resolution CT, in 2016, 126 papers have been published. Most cited and recent papers have been chosen for discussion. Many recent papers have been published from Asian groups. It is clearly not possible to conclude from these data what is the best strategy for GGO in the lung cancer screening era. Certainly, when there is uncertainty, personal opinion and experience should not influence decision making, on the contrary decision should be taken by a multidisciplinary team.

The impact of chemotherapy on persistent ground-glass nodules in patients with lung adenocarcinoma

Backgrounds

To evaluate the response of persistent ground glass nodules (GGNs) in patients with lung adenocarcinoma treated with platinum-based chemotherapy on computed tomography (CT).

Methods

We retrospectively studied patients with GGNs that met the following criteria: (I) GGNs found in patients with lung adenocarcinoma, which persist for more than 3 months; (II) patients treated with platinum-based (cisplatin or carboplatin) chemotherapy for at least 2 cycles; (III) ground glass proportion ¡Ý50%. For each patient, if more than two CTs satisfied the inclusion criteria, then the baseline and last CTs were used for analysis, defined as CT1 and CT2. A total of 91 persistent pulmonary GGNs in 51 patients fulfilled the inclusion criteria. We defined growth as a nodule ¡Ý2 mm increase in diameter or showing up a solid portion. GGN response to therapy was assessed and compared with the baseline CT. Differences in CT findings were analyzed using a paired t-test and Pearson ¦Ö² test.

Results

Between 2010 and 2015, 25 of the 51 (49%) were male and 26 of the 51 (51%) were female. The average age at time of detection of a GGN was 63.8 (range, 36-84) years. Mean follow-up duration was 24.1¡À17.9 months. During the follow-up periods, on a per-nodule basis, 94.5% of GGNs (n=86) remained unchanged in size. Only 5.5% GGNs (n=5) in 5 patients increased in size. The nodules CT feature in each lung adenocarcinoma clinical stage show no difference. No significant difference was found in the size, attenuation, volume, and mass of GGN between baseline and post-treatment measurements, regardless of the type of chemotherapy (P>0.05).

Conclusions

The clinical course of GGNs in patients with lung adenocarcinoma is predominantly indolent, and platinum-based chemotherapy may have no effect on the growth of persistent GGNs.

[Progressions on Diagnosis and Treatment of Ground-glass Opacity]

近年来，肺部磨玻璃影（ground-glass opacity, GGO）逐渐得到了肿瘤科和胸外科医生的普遍关注。GGO是指肺部CT表现为密度轻微增加，增加程度小于实性改变，呈模糊的云雾状，并可见其内血管和支气管纹理。GGO多数情况下呈惰性，但也可进一步发展为肺腺癌，这使其治疗方案的选择颇为棘手；近年来GGO发现率的日益增加也使其关注度得到大大提升。许多报道都从组织学、放射诊断学、治疗学等多个方面对GGO的诊治进行了探讨。本文综述了近10年来学界对GGO的诊断和处理的进展，希望临床医生能更好地认识这个问题，在临床工作中收集并总结更多循证学证据，以指导未来的临床诊治方案的选择。