Ground-glass Nodules Research Articles

Imaging findings of COPA Syndrome.

Autosomal dominant mutations in the coatomer-associated protein alpha (COPA) gene cause an immune dysregulation disorder associated with pulmonary hemorrhage, lymphoid hyperplasia, arthritis, and glomerulonephritis. To describe the thoracic, musculoskeletal, and renal imaging findings of COPA syndrome with a focus on the evolution of the pulmonary findings. With approval of the Institutional Review Board, consensus retrospective review of findings on chest radiography and computed tomography (CT), musculoskeletal radiography and magnetic resonance imaging (MRI), and renal ultrasound (US) was performed for pediatric COPA syndrome patients. COPA syndrome patients < 18 years of age presenting between 1992 and 2019 were identified from an institutional rheumatology registry. Twelve pediatric COPA syndrome patients (mean age of 6.5 years at first imaging exam; 6 females) were identified. Imaging exams available for review included 45 chest CT exams on 12 patients, 37 musculoskeletal exams on 4 patients, and 10 renal US exams on 5 patients. All 12 had abnormal chest CT exams, with findings including ground-glass opacities (12/12), cysts (8/12), septal thickening (9/12), nodules (8/12), fibrosis (7/12), crazy-paving (2/12), consolidation (1/12), hilar/mediastinal lymphadenopathy (11/12), and chest wall deformity (5/12). Nine had at least one follow-up chest CT, which showed improvement in nodules (7/9), ground-glass opacities (4/9), and lymphadenopathy (9/9), but worsening of septal thickening (3/9), cyst formation (3/9), and fibrosis (3/9). Four had musculoskeletal imaging revealing synovitis (2/4), bone erosions (1/4), tenosynovitis (1/4), enthesitis (1/4), and subcutaneous nodules (1/4). Five had at least one renal US, revealing renal size abnormalities (4/5) and cortical hyperechogenicity (3/5). The most prevalent imaging finding of COPA syndrome is diffuse lung disease related to early childhood-onset recurrent pulmonary hemorrhage and lymphoid hyperplasia that may progress to pulmonary fibrosis. Other imaging findings manifesting later in childhood or adolescence relate to arthritis and glomerulonephritis.

Relevant Risk Factor and Follow-Up of Lung Nodules in Physical Examination with Low-Dose CT Screening.

We aimed to explore the risk factors of lung nodules and lung cancer in physical examination population with low-dose multi-slice spiral CT (LDCT) screening, to provide basis for lung cancer screening and follow-up management after CT examination. The general data, serum tumor markers and CT images of 2,274 patients underwent LDCT in the Physical Examination Center of the Fourth Hospital of Hebei Medical University, China in 2019 were retrospectively analyzed and followed up for three years. The detection rate of lung nodules was 48.42%. The detection rate of lung nodules was higher in females, those over 70, those with history of smoking, passive smoking, drinking, precious history of lung diseases and family history of malignant tumors, with statistically significant differences (P<0.05). The abnormal rate of serum tumor markers (CA199, CA125 female and CYFRA211) were higher than that in the non-nodule group, with statistically significant differences (P<0.05). Multivariate logistic regression analysis showed that gender, age, history of smoking, passive smoking, family history of malignant tumors and serum tumor markers (CYFRA211 and CA199) were independent risk factors for the occurrence of lung nodules. Gender female, age>35, history of smoking, passive smoking, history of drinking, history of past lung disease, family history of malignant tumors, abnormal CYFRA211 tumor markers were detected and low dose multi-slice spiral CT image showed ground-glass nodules are risk factors for lung nodules and lung cancer, which should be paid close attention to during physical examination and follow-up.

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

ObjectiveThe purpose of the study is to investigate the performance of radiomics-based analysis in prediction of pure ground-glass nodule (pGGN) lung adenocarcinomas invasiveness using thin-section computed tomography images.MethodsA total of 382 patients surgically resected single pGGN and pathologically confirmed were enrolled in the retrospective study. The pGGN cases were divided into two groups: the noninvasive group and the invasive adenocarcinoma (IAC) group. 330 patients were randomly assigned to the training and testing cohorts with a ratio of 7:3 (245 noninvasive lesions, 85 IAC lesions), while 52 patients (30 noninvasive lesions, 22 IAC lesions) were assigned to the external validation cohort. A model, radiomics model, and combined clinical-radiographic-radiomic model were built using the LASSO and multivariate backward stepwise regression analysis on the basis of the selected and radiomics features. The area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate and compare the model performance for invasiveness discrimination among the three cohorts.ResultsThree clinical-radiographic features (including age, gender and the mean CT value) and three radiomics features were selected for model building. The combined model and radiomics model performed better than the clinical-radiographic model. The AUCs of the combined model in the training, testing, and validation cohorts were 0.856, 0.859, and 0.765, respectively. The DCA demonstrated the radiomics signatures incorporating clinical-radiographic feature was clinically useful in predicting pGGN invasiveness.ConclusionsThe proposed radiomics-based analysis incorporating the clinical-radiographic feature could accurately predict pGGN invasiveness, providing a noninvasive biomarker for the individualized and precise medical treatment of patients.

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1.

Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP. Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram. This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05). Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.

Computed Tomography Imaging Features of Lung Cancer under Artificial Intelligence Algorithm and Its Correlation with Pathology

This study aims to investigate the relationship between the detection performance of an artificial intelligence (AI) algorithm and pathology in chest computed tomography (CT) images. In this study, a new pulmonary nodule (PN) detection algorithm was designed and developed on the three-dimensional (3D) connected domain algorithm. The appropriate grayscale threshold of CT images was selected, the CT images were converted into black-and-white images, and the useless images were removed. Then, the remaining lung images were formed into a 3D black-and-white pixel matrix. Labeling statistics was carried out, and the size, property, and location of PN could be measured and determined. A self-built database of PNs undergoing chest multislice spiral CT examination was retrospectively selected, and 150 cases were randomly selected by SPSS 22.0. Image processing was performed according to the algorithm and compared with the PN detected by radiologists; finally, the detection results were counted. There were 560 benign and malignant PNs, 312 malignant, and 248 benign. The algorithm detected 498 cases, of which 478 cases were detected accurately, and the sensitivity was 95.98%. The radiologist detected 424 cases, 364 cases were accurate, and the sensitivity was 85.85%. Compared with the detection results of radiologists, the algorithm detection results of nodules in solid nodules and ground glass nodules were more accurate. The detection results of nodules in the pleural connection type, peripheral type, central type, and hilar type were more accurate and statistically significant ( P &lt; 0.05 ). The malignancy, size, property, and location of different nodules could be accurately determined through CT images under this algorithm. It provided important support for the pathological research of lung cancer and prejudged the future development of PN in patients more accurately.

PP01.12 Increasing Diagnosis of Ground Glass Nodules and Semi-Solid Lung Lesions on Chest CT Scans over the Past Decade

PP01.12 Increasing Diagnosis of Ground Glass Nodules and Semi-Solid Lung Lesions on Chest CT Scans over the Past Decade

Artificial intelligence-based radiomics for the prediction of nodal metastasis in early-stage lung cancer

We aimed to investigate the value of computed tomography (CT)-based radiomics with artificial intelligence (AI) in predicting pathological lymph node metastasis (pN) in patients with clinical stage 0–IA non-small cell lung cancer (c-stage 0–IA NSCLC). This study enrolled 720 patients who underwent complete surgical resection for c-stage 0–IA NSCLC, and were assigned to the derivation and validation cohorts. Using the AI software Beta Version (Fujifilm Corporation, Japan), 39 AI imaging factors, including 17 factors from the AI ground-glass nodule analysis and 22 radiomics features from nodule characterization analysis, were extracted to identify factors associated with pN. Multivariate analysis showed that clinical stage IA3 (p = 0.028), solid-part size (p < 0.001), and average solid CT value (p = 0.033) were independently associated with pN. The receiver operating characteristic analysis showed that the area under the curve and optimal cut-off values of the average solid CT value relevant to pN were 0.761 and -103 Hounsfield units, and the threshold provided sensitivity, specificity, and negative predictive values of 69%, 65%, and 94% in the entire cohort, respectively. Measuring the average solid-CT value of tumors for pN may have broad applications such as guiding individualized surgical approaches and postoperative treatment.

Pulmonary veno-occlusive disease in Sjogren's syndrome: a case report

BackgroundPulmonary arterial hypertension (PAH) associated with connective tissue disease (CTD) belongs to Group 1 pulmonary hypertension. Pulmonary veno-occlusive disease (PVOD), which is characterized by venous system aberrations, has been previously reported in CTD-PAH; however, it has rarely been observed in Sjogren’s syndrome (SS).Case presentationOur 28-year-old female patient was admitted to the hospital with recurrent shortness of breath even after minimal physical activity. Her chest high-resolution CT scan demonstrated pulmonary artery dilatation and bilateral ground-glass nodules. A subsequent right heart catheterization confirmed pulmonary hypertension because her mean pulmonary arterial pressure was 62 mmHg. Our inquisitive genomic assessment identified a novel EIF2AK4 mutation at c.1021 C > T (p. Gln341*), the dominant causal gene of PVOD. Histological examination demonstrated stenosis and occlusions in the pulmonary veins. Because she presented with features such as dry eyes and Raynaud's phenomenon, we performed a biopsy on the labial salivary gland, which confirmed SS. Her treatment regimen included PAH-targeted therapies (tadalafil and macitentan) in combination with hydroxychloroquine. Although she was hospitalized several times due to acute exacerbation of PAH, her disease progression was under control, and she did not demonstrate any signs of pulmonary edema even after a three-year treatment period.ConclusionHere, we report the case of an SS-PAH patient with PVOD who carried a novel biallelic EIF2AK4 mutation, and PAH-targeted therapies were well tolerated by our patient.

Maximum amplitude and mean platelet volume in the blood as biomarkers to detect lung adenocarcinoma cancer featured with ground-glass nodules

Objectives The development and progression of malignancies are closely linked to hypercoagulability. As an early type of lung adenocarcinoma, ground glass nodules (GGNs) have been detected increasingly. Blood Maximum amplitude (MA) and mean platelet volume (MPV) are related to various conditions of hypercoagulability. Therefore, the role of MA and MPV in diagnosing lung adenocarcinoma cancer featured with GGNs was investigated in this case-control study. Methods The analyzed data of this study is derived from GGNs patients and healthy individuals in West China (Airport) Hospital Sichuan University. The differences between GGNs patients and healthy individuals were determined by one-way ANOVA, logistic regression or chi-squared test. The accuracy of diagnostic was performed by receiver operating characteristic curve (ROC). The relative mRNA expressions were studied by RT-qPCR. Results 470 patients diagnosed with GGNs which benign lesions (BN group) are inflammatory and malignant lesions (LC group) are adenocarcinoma in stage IA, and 235 healthy subjects (HC group) were enrolled in this study. Levels of MA and MPV were increased in LC group compared with BN and HC group ( p &lt; 0.001, p &lt; 0.001). When we combined MA and MPV, MA and MPV presented a sensitivity (SEN) of 0.809 and a specificity (SPE) of 0.774. And the area under the curve (AUC) increased to 0.871 (0.837–0.900) when confidence interval was 95%. Conclusion This study demonstrates that there have been systemic changes in coagulation disorders in the pathogenesis of GGNs. The diagnostic ability to different lung adenocarcinoma cancer featured with GGNs from benign or healthy controls can be improved by the combination of MA and MPV. Maximum amplitude and MPV may be used as biomarkers to detect lung adenocarcinoma cancer featured with GGNs.

Multi-scale dense selective network based on border modeling for lung nodule segmentation.

Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules. In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder-decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules. We evaluate the BorDenNet rigorously on the lung public dataset LIDC-IDRI. For the segmentation of the target nodules, the average Dice score is 92.78[Formula: see text], the average sensitivity is 91.37[Formula: see text], and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People's Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules. Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency.

Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial.

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4-5 mm, 6~10 mm, 11~20 mm, and &gt;20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached &gt;95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of &gt;0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

Natural progression of persistent pure ground-glass nodules 10mm or smaller: long-term observation and risk factor assessment.

Semi-automatic segmentation was used to investigate the natural progression of pure ground-glass nodules (pGGNs) of 5-10mm in long-term follow-up and to analyze independent risk factors for subsequent growth. A total of 154 pGGNs of 5-10mm from 132 patients with 698 follow-up CT scans were retrospectively identified. Subsequently, enrolled pGGNs were semiautomatically segmented on initial and follow-up CT to obtain diameter, density and volume, thus calculating mass, volume doubling time (VDT), and mass doubling time (MDT). Kaplan‒Meier analysis and multivariate Cox proportional risk regression were performed to explore independent predictors of pGGN growth. We analyzed growth differences among different pathological results of pGGNs confirmed by surgery. The prognosis was analyzed using the total diameter or solid size of the nodules on the last preoperative CT. Among the 85 (55.2%) pGGNs with growth, 5.9%, 51.8%, and 80.0% showed growth within 1, 3, and 5years, respectively. The median VDT and MDT were 1206.4 (range 349.8-5134.4) days and 1161.3 (range 339.4-6630.4) days, respectively. The multivariate Cox risk regression analysis showed that mean CT attenuation (m-CTA) [hazard ratio (HR) = 2.098, p = 0.010] and roundness index (HR = 1.892, p = 0.021) were independent risk factors for pGGN growth. In total, 67.6% of surgically resected and growing pGGNs were invasive non-mucinous adenocarcinoma (IA), including 2 cases of endpoint events, showing a PSN with solid components of 5.6mm and a solid nodule with a diameter of 19.9mm. pGGNs of 5-10mm showed an indolent clinical course. Follow-up CT imaging of pGGNs in the latter half of the first two years should be a rational management strategy. Small pGGNs with a larger overall m-CTA and roundness index on baseline CT are more likely to grow.

Importance of Lymph Node Evaluation in ≤2-cm Pure-Solid Non-Small Cell Lung Cancer

BackgroundThe prevalence of lymph node (LN) metastasis in small-sized lung cancer varies depending on the tumor size and proportion of ground-glass opacity. We investigated occult LN metastasis and prognosis in patients with small-sized non-small cell lung cancer (NSCLC), mainly focusing on the pure-solid tumor. MethodsWe retrospectively reviewed patients with ≤2-cm clinical N0 NSCLC who underwent lung resection with curative intent from 2003 to 2017. Among them we analyzed patients who also underwent adequate complete systematic LN dissection. Pathologic results and disease-free survival of the radiologically mixed ground-glass nodule (mGGN) and pure-solid nodule (PSN) groups were analyzed. ResultsOf 1329 patients analyzed, 591 had mGGNs and PSNs. As tumor size increased, patients in the mGGN group showed no difference in LN metastasis: ≤1 cm, 2.27%; 1.0 to 1.5 cm, 2.19%; and 1.5 to 2.0 cm, 2.18% (P = .999). However the PSN group showed a significant difference in LN metastasis as the tumor size increased: ≤1 cm, 2.67%; 1.0 to 1.5 cm, 12.46%; and 1.5 to 2.0 cm, 21.31% (P < .001). In the multivariate analysis tumor size was a significant predictor of nodal metastasis in the PSN group but not in the mGGN group. In terms of 5-year disease-free survival, the mGGN group showed a better prognosis than the PSN group (94.4% vs 71.2%, P < .001). ConclusionsWe need to conduct a thorough LN dissection during surgery for small-sized NSCLC, especially for pure-solid tumors ≥ 1 cm.

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules.

Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs). 150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed.GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images.Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass. Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference.The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group (P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752. SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT.Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.

Analysis of the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types.

In this study, we aimed to analyze the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types. Between January 2017 and December 2018, a total of 657 patients (191 males, 466 females; mean age: 60.9±8.1 years; range, 34 to 80 years) with pathologically diagnosed ground-glass nodules were retrospectively analyzed. The clinicopathological characteristics and computed tomography characterizations of patients with ground-glass nodules who received surgical resection were analyzed. The clinical data including age, sex, smoking status and medical history were recorded. Computed tomography characterizations included the location and size of the tumor, the size of the consolidation components, density uniformity, shape, margin, tumor-lung interface, internal signs and surrounding signs. Based on the computed tomography imaging characteristics, a mean computed tomography value of ≥444.5 HU was more likely to indicate malignant lesions, while ≤444.5 HU indicated benign lesions. A malignant ground-glass nodules" maximum diameter of <6.78 mm, a diameter of the consolidation component of <3.88 mm, and a mean computed tomography value of <-536.5 HU were more likely to indicate atypical adenomatous hyperplasia and adenocarcinoma in situ. A maximum diameter of malignant ground-glass nodules of >11.52 mm, a diameter of the consolidation component of >6.20 mm, and a mean computed tomography value of ≥493.5 HU were more likely to indicate invasive adenocarcinomas. The focus between these parameters indicated minimally invasive adenocarcinomas. Ill-defined tumor-lung interface, irregular in shape, and smooth nodule margins suggest benign lesions while round or oval, clear tumor-lung interface, spiculation signs, lobulation signs, bubble signs, air bronchograms, pleural indentations, and vessel convergences are helpful in the diagnosis of malignant lesions. A clear tumor-lung interface, the spiculation signs, lobulation signs, and bubble signs indicate the invasion of the lesions.

Network pharmacology to explore the mechanism of traditional Chinese medicine in the treatment of ground glass nodules.

Ground glass nodules (GGNs) in the lung are considered to be a high-risk factor of lung adenocarcinoma. Immediate surgery is not recommended for GGNs patients, and low-dose computed tomography (CT) is often used for observation and follow-up, which brings high psychological and economic burden to the patient. Three traditional Chinese medicine (TCM) prescriptions for the treatment of GGNs were found through database including PubMed, Google Scholar, and China National Knowledge Infrastructure (CNKI), Scopus and so on. The possible targets of the active ingredients of the TCM preparations and the gene targets of GGNs were screened out from Traditional Chinese Medicine Systems Pharmacology (TCMSP), UniProt and GeneCards. Network visualization was realized via STRING, Cytoscape 3.7.2, Evenn, DAVID and Hiplot. Finally, molecular docking Vina and PyMOL software were performed to further explore the possibility of drug-target interactions using PubChem compounds, protein data bank (PDB) database, Autodocktools and Autodock. Three TCM preparations could target the same 13 potential therapeutic targets in GGNs. From network pharmacology, 14 signaling pathways, the functions of the significant targets, an effective ingredient in TCM prescriptions and its functions were obtained. Chinese herbal formulas containing quercetin could be a potential treatment for GGNs, targeting C-reactive protein (CRP), tumor necrosis factor (TNF), interferon gamma (IFN-γ), intercellular adhesion molecule 1 (ICAM-1), and vascular endothelial growth factor A (VEGFA) through the hypoxia-inducible factor 1 (HIF-1) pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and leukocyte transendothelial migration.

Complications of synchronous microwave ablation and biopsy versus microwave ablation alone for pulmonary sub-solid nodules: a retrospective, large sample, case-control study.

This was a retrospective, large-sample, case-control study assessing the complications associated with synchronous microwave ablation (MWA) and biopsy for pulmonary sub-solid nodules or ground-glass nodules (GGNs) versus MWA alone. We aimed to verify the safety of synchronous MWA and biopsy for treating GGNs. From May 2020 to December 2021, 326 patients with GGNs were enrolled. Among them, 164 patients underwent MWA alone (group A) and 162 patients underwent synchronous MWA and biopsy (group B). We assessed the complications, technical success, and positivity rate of the biopsy. The major complications were similar between the two groups, and included pneumothorax (group A vs. group B, 19.5% vs. 13.6%; P=0.150), hemothorax (0.6% vs. 1.2%; P=1.000), pleural effusion (1.2% vs. 0.6%; P=1.000), and pulmonary infection (4.9% vs. 6.2%; P=0.609). No massive hemoptysis, bronchopleural fistula, or air embolism developed. Minor complications including intrapulmonary hemorrhage (group A vs. group B, 28.7% vs. 62.3%, P<0.001), mild pneumothorax (20.7% vs. 29.6%, P=0.587), mild ipsilateral pleural effusion (30.5% vs. 27.8%, P=0.590), mild bilateral pleural effusion (16.5% vs. 22.2%, P=0.188), and subcutaneous emphysema (4.3% vs. 5.6%, P=0.498) were observed. The side effects, including pain, cough, post-ablation syndrome, and post-ablation chronic pain syndrome, were similar between the two groups. The positive diagnosis rate of biopsy in group B was 88.3%. Compared with MWA alone, synchronous MWA and biopsy did not increase the risk of major complications. Although some minor complications developed, synchronous MWA and biopsy is safe for treating pulmonary GGNs.

Minute pulmonary meningothelial-like nodules: associations between computed tomography and pathology features

Increased use of multislice computed tomography (CT) scans has revealed that minute pulmonary meningothelial-like nodules (MPMNs) showed as ground-glass nodules (GGNs) are frequent in patients. However, little is known about the incidence and fate of nodules. By using a cross-sectional design, this study compared the multislice CT signs and pathological results of MPMNs, and further used pathological results to explain the formation mechanism of the CT signs of MPMNs to improve the clinical understanding of the disease. The clinicopathological data of 93 cases diagnosed as MPMNs in the Jiangsu Province Hospital from January 2016 to September 2019 and the Nanjing First Hospital from January 2017 to December 2019 were examined. The related literature was reviewed, and each case's age, gender, medical history, and preoperative CT examinations were classified. Based on CT signs, this study analyzed the imaging features, including size, shape, boundary, distribution, opacity, and their relationship with pulmonary blood vessels. A total of 13 cases had immunohistochemistry results among which the lesions showed consistent positive expression of vimentin (100%), followed by epithelial cell membrane antigen (92.3%) and progesterone (8%). The MPMNs mainly occurred in individuals aged 50-59 years (32.6%). Most patients (82.6%) had neoplastic disease. All nodules (100%) manifested with a round shape and well-demarcated borders on images. The size of the nodules on CT scans ranged from 2.5 to 5.0 mm, with an average size of 3.04±1.12 mm. Most nodules were subpleural (89.1%) and showed ground-glass opacity (97.8%). The follow-up results of postoperative clinical manifestations and chest CT examination were negative in 12 patients. This study suggested that the pathological findings of MPMNs could explain the formation mechanism of the CT signs. The results can provide guidance for the diagnosis of the disease in the future.

Retracted: CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules.

[This retracts the article DOI: 10.1155/2022/8967643.].

Lung Imaging Reporting and Data System (Lung-RADS) in Radiology: Strengths, Weaknesses and Improvement

In 2019, the American College of Radiology announced Lung CT Screening Reporting & Data System (Lung-RADS) 1.1 to reduce lung cancer false positivity compared to that of Lung-RADS 1.0 for effective national lung cancer screening, and in December 2022, announced the new Lung-RADS 1.1, Lung-RADS® 2022 improvement. The Lung-RADS® 2022 measures the nodule size to the first decimal place compared to that of the Lung-RADS 1.0, to category 2 until the juxtapleural nodule size is < 10 mm, increases the size criterion of the ground glass nodule to 30 mm in category 2, and changes categories 4B and 4X to extremely suspicious. The category was divided according to the airway nodules location and shape or wall thickness of atypical pulmonary cysts. Herein, to help radiologists understand the Lung-RADS® 2022, this review will describe its advantages, disadvantages, and future improvements.