Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions.

PURPOSE: To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS: From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS: Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION: The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules.

Purpose: To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs). Patients and Methods: From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated. The CT features of target GGNs and intrapulmonary concomitant lesions in these two groups were analyzed and compared, and the role of intrapulmonary concomitant lesions in improving differentiation was evaluated. Results: The intrapulmonary concomitant lesions were more common in patients with non-neoplastic GGNs than in those with neoplastic ones (87.88% vs 82.18%, P = 0.015). Specifically, patients with non-neoplastic GGNs had a higher incidence of multiple solid nodules (SNs), patchy ground-glass opacity/consolidation, and fibrosis/calcification in any lung fields (each P < 0.05). Logistic regression analysis indicated that patients < 44 years old, diameter < 7.35 mm, irregular shape, and coarse margin or ill-defined boundary for target GGN, pleural thickening, and concomitant SNs in the same lobe and fibrosis or calcification in any lung field were independent indicators for predicting non-neoplastic GGNs. The AUC of the model for predicting non-neoplastic GGNs increased from 0.894 to 0.926 (sensitivity, 83.10%; specificity, 87.10%) after including the concomitant lesions in the patients' clinical characteristics and CT features of target GGNs (P < 0.0001). Conclusion: Besides the patients' clinical characteristics and CT features of target GGNs, the concomitant multiple SNs in the same lobe and fibrosis/calcification in any lung field should be considered in further differentiating non-neoplastic and neoplastic GGNs.

Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT.

BACKGROUND: Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs. RESULTS: From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001). CONCLUSION: In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant. CRITICAL RELEVANCE STATEMENT: The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection. KEY POINTS: • The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.

Significance of marginal vessels in differentiating peripheral small-cell lung cancer and benign lung tumor.

BACKGROUND: Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis. PURPOSE: To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs. MATERIAL AND METHODS: A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared. RESULTS: Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%). CONCLUSION: For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.

Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study.

BACKGROUND: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. METHODS: The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN-vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. RESULTS: Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p < 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). CONCLUSIONS: In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.

The influence of different previous cancer histories on the diagnostic efficacy of Lung Imaging Reporting and Data System.

Background: For lung cancer screening in patients with previous malignant tumors, Lung Imaging Reporting and Data System (Lung-RADS) and other lung cancer screening tools are controversial in terms of requirements for the previous cancer history. This study investigated the effect of the length and type of malignancy history on the diagnostic efficacy of Lung Imaging Reporting and Data System (Lung-RADS) 2022 in pulmonary nodules (PNs). Methods: Chest computed tomography and clinical data of PNs in patients with a history of cancer who underwent surgical resection in The First Affiliated Hospital of Chongqing Medical University from January 1, 2018, to November 30, 2021, were retrospectively collected and evaluated based on Lung-RADS. All PNs were divided into 2 groups: the prior lung cancer (PLC) and the prior extrapulmonary cancer (PEPC) groups. Each group was divided into the ≥5 years and <5 years groups based on the duration of cancer history. The diagnostic agreement of Lung-RADS was evaluated based on the pathological diagnosis of nodules after operation. The diagnostic agreement rate (AR) of Lung-RADS and the composition ratios of different types between different groups were calculated and compared. Results: A total of 451 patients with 565 PNs were included in this study. These patients were divided into the PLC group (<5 years: 135 cases, 175 PNs; ≥5 years: 9 cases, 12 PNs) and the PEPC group (<5 years: 219 cases, 278 PNs; ≥5 years: 88 cases, 100 PNs). The diagnostic AR of partial solid nodules (93.0%; 95% CI: 88.7-97.2%) and solid nodules (88.1%; 95% CI: 84.1-92.1%) was close (P=0.13), while both were higher than that of the pure ground-glass nodules (24.0%; 95% CI: 17.5-30.4%; all P values <0.001). Within 5 years, the composition ratio of PNs and the diagnostic AR (PLC: 58.9%, 95% CI: 51.5-66.2%; PEPC: 76.6%, 95% CI: 71.6-81.6%) between the PLC and PEPC groups were all different (all P values <0.001), and the others [composition ratio of PNs & the diagnostic AR: PLC (≥5 years) vs. PEPC (≥5 years); PLC (<5 years) vs. PLC (≥5 years); PEPC (<5 years) vs. PEPC (≥5 years)] were similar (all P values >0.05; range: 0.10-0.93). Conclusions: The length of prior cancer history may affect the diagnostic agreement of Lung-RADS, especially for patients with prior lung cancer within 5 years.

Reticulation Sign on Thin-Section CT: Utility for Predicting Invasiveness of Pure Ground-Glass Nodules.

BACKGROUND. Pure ground-glass nodules (pGGNs) may represent a diverse range of histologic entities of varying aggressiveness. OBJECTIVE. The purpose of this study was to evaluate the use of the reticulation sign on thin-section CT images for predicting the invasiveness of pGGNs. METHODS. This retrospective study included 795 patients (mean age, 53.4 ± 11.1 [SD] years; 254 men, 541 women) with a total of 876 pGGNs on thin-section CT that underwent resection between January 2015 and April 2022. Two fellowship-trained thoracic radiologists independently reviewed unenhanced CT images to assess the pGGNs for a range of features, including diameter, attenuation, location, shape, air bronchogram, bubble lucency, vascular change, lobulation, spiculation, margins, pleural indentation, and the reticulation sign (defined as multiple small linear opacities resembling a mesh or a net); differences were resolved by consensus. The relationship between the reticulation sign and lesion invasiveness on pathologic assessment was evaluated. RESULTS. On pathologic assessment, the 876 pGGNs included 163 nonneoplastic and 713 neoplastic pGGNs (323 atypical adenomatous hyperplasias [AAHs] or adenocarcinomas in situ [AISs], 250 minimally invasive adenocarcinomas [MIAs], and 140 invasive adenocarcinomas [IACs]). Interobserver agreement for the reticulation sign, expressed as kappa, was 0.870. The reticulation sign was detected in 0.0% of nonneoplastic lesions, 0.0% of AAHs/AISs, 6.8% of MIAs, and 54.3% of IACs. The reticulation sign had sensitivity of 24.0% and specificity of 100.0% for a diagnosis of MIA or IAC and sensitivity of 54.3% and specificity of 97.7% for a diagnosis of IAC. In multivariable regression analyses including all of the assessed CT features, the reticulation sign was a significant independent predictor of IAC (OR, 3.64; p = .001) but was not a significant independent predictor of MIA or IAC. CONCLUSION. The reticulation sign, when observed in a pGGN on thin-section CT, has high specificity (albeit low sensitivity) for invasiveness and is an independent predictor of IAC. CLINICAL IMPACT. Those pGGNs that show the reticulation sign should be strongly suspected to represent IAC; this suspicion may guide risk assessments and follow-up recommendations.

Correlations Between Inflammatory Cell Infiltration and Relative Density and the Boundary Manifestation of Pulmonary Non-Neoplastic Ground Glass Nodules.

Purpose: To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT). Materials and Methods: From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively. Results: Nongranulomatous nodules (283, 89.0%) with fibrous tissue proliferation and/or inflammatory cells as the predominant pathological findings were the most common non-neoplastic GGNs, followed by granulomatous nodules (28, 8.8%). Among nongranulomatous GGNs, cases with more and less/no inflammatory cells were 15 (10.9%) and 122 (89.1%) in 137 well-defined ones with smooth margin, 16 (24.6%) and 49 (75.4%) in 65 well-defined ones with coarse margin, 43 (91.5%) and 4 (8.5%) in 47 ill-defined ones with higher ΔCT (>151HU), and 4 (11.8%) and 30 (88.2%) in 34 ill-defined ones with lower ΔCT (< 151HU). The proportion of cases with more inflammatory cells in well-defined nodules was similar to that in ill-defined ones with lower ΔCT (P = 0.587) but significantly lower than that in ill-defined ones with higher ΔCT (P < 0.001). Among the granulomatous nodules, ill-defined cases with higher ΔCT (16, 57.1%) were the most common, and they (7/8, 87.5%) frequently had changes during short-term follow-up. Conclusion: Nongranulomatous nodules are the most common non-neoplastic GGNs, their diverse boundary manifestations closely correlate with degree of inflammatory cell infiltration and density difference.

A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.

BACKGROUND: Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE: This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS: The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS: Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.

Quantitative evaluation of density variability in the lesion-lung boundary zone to differentiate pulmonary subsolid nodules.

Background: Transition of the CT values from nodule to peripheral normal lung is related to pathological changes and may be a potential indicator for differential diagnosis. This study investigated the significance of the standard deviation (SD) values in the lesion-lung boundary zone when differentiating between benign and neoplastic subsolid nodules (SSNs). Methods: From January 2012 to July 2021, a total of 229 neoplastic and 84 benign SSNs confirmed by pathological examination were retrospectively and nonconsecutively enrolled in this study. The diagnostic study was not registered with a clinical trial platform, and the study protocol was not published. Computed tomography (CT) values of the ground-glass component (CT1), adjacent normal lung tissue (CT2), and lesion-lung boundary zone (CT3) were measured consecutively. The SD of CT3 was recorded to assess density variability. The CT1, CT2, CT3, and SD values were compared between benign and neoplastic SSNs. Results: No significant differences in CT1 and CT2 were observed between benign and neoplastic SSNs (each P value >0.05). CT3 (-736.1±51.0 vs. -792.6±73.9; P<0.001) and its SD (135.6±29.6 vs. 83.6±20.6; P<0.001) in neoplastic SSNs were significantly higher than those in benign SSNs. Moreover, the SD increased with the invasiveness degree of neoplastic SSNs (r=0.657; P<0.001). The receiver operating characteristic (ROC) curve revealed that the area under the curve was 0.927 (95% CI: 0.896-0.959) when using the SD (cutoff value =106.98) as a factor to distinguish SSNs, which increased to 0.966 (95% CI: 0.934-0.985) when including nodules with a CT1 of ≥-715 Hounsfield units (HU) only (cutoff of SD 109.9, sensitivity 0.930, and specificity 0.914). Conclusions: The SD as an objective index is valuable for differentiating SSNs, especially for those with a CT1 of ≥-715 HU, which have a higher possibility of neoplasm if the SD is >109.9.

Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography.

BACKGROUND: Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis. METHODS: From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy. RESULTS: Compared to patients with benign patchy GGOs, malignant cases were older (P <  0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P <  0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135-6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027-14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062-40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124-8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079-12.482; P < 0.001). CONCLUSIONS: In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.

Clinical and Computed Tomography Characteristics of Solitary Pulmonary Nodules Caused by Fungi: A Comparative Study.

Purpose: To clarify the clinical and computed tomography (CT) indicators in distinguishing pulmonary nodules caused by fungal infection from lung cancers. Methods: From January 2013 to April 2022, 68 patients with solitary fungal nodules (64 were solid and 4 were mixed ground-glass nodules) and 140 cases with solid cancerous nodules with similar size were enrolled. Their clinical characteristics and CT manifestations of the solid nodules were summarized and compared, respectively. Results: Compared with patients with lung cancers, cases were younger (51.2 ± 11.5 vs 61.3 ± 10.2 years) and non-smokers (72.1% vs 57.9%) and immunocompromised (44.1% vs 17.9%) individuals were more common in patients with fungal nodules (each P < 0.05). The air crescent sign (ACS) (34.4% vs 0%), halo sign (HS) (23.4% vs 4.3%), and satellite lesions (45.3% vs 2.9%) were more frequently detected in fungal nodules than in cancerous ones (each P < 0.05). Air bronchogram similarly occurred in fungal and cancerous nodules, whereas the natural ones were more common in the former (100% vs 16.7%, P = 0.000). However, the fungal nodules had a lower enhancement degree (29.0 ± 19.2 HU vs 40.3 ± 28.3 HU, P = 0.038) and frequency of hilar and/or mediastinal lymph node enlargement (2.9% vs 14.3%, P = 0.013) compared with the cancerous nodules. Conclusion: In the younger, non-smoking and immunocompromised patients, a solitary pulmonary solid nodule with ACS, HS, satellite lesions and/or natural air bronchogram but without significant enhancement, fungal infection is a probable diagnosis.

Sensitivity and specificity of computed tomography hypodense sign when differentiating pulmonary inflammatory and malignant mass-like lesions.

Background: Hypodense sign (HyS) reportedly is associated with pulmonary fungal infection, while it also common in many non-fungal lesions. This study aims to determine the significance of a HyS presented on contrast-enhanced computed tomography (CECT) when distinguishing pulmonary inflammatory from malignant mass-like lesions. Methods: From January 2013 to January 2021, we retrospectively evaluated the clinical and computed tomography (CT) data of patients with pathologically confirmed pulmonary inflammatory lesions (ILs) and malignant lesions (MLs). We analyzed and compared the CT features of the HyS in MLs and ILs, and then evaluated whether the HyS helped to differentiate MLs and ILs. Results: There were significant differences in age and tumor markers between patients with ILs and MLs (both P<0.05). Compared with that in MLs, the occurrence of the HyS in ILs was higher (62.81% vs. 28.81%; P<0.0001). In ILs, more HyS were single, round or oval, well-defined, and had lower enhancement (ΔCT). Logistic regression analysis revealed that an ill-defined boundary, peripheral fibrosis, presence of a well-defined HyS, and a ΔCT value of the HyS <9.5 Hounsfield units (HU) were independent indicators for predicting ILs. After including the HyS CT features, the area under the curve (AUC) of the model predicting ILs increased from 0.953 to 0.986 with a sensitivity of 96.03% and a specificity of 94.03% (P=0.0027). Conclusions: The HyS is more common in ILs than in MLs. A single, regular, and well-defined HyS with a ΔCT value of <9.5 HU on CECT is highly suggestive of ILs. Combining the HyS with other morphological features could improve the diagnosis accuracy of pulmonary mass-like lesions.

Clinical and Computed Tomography Characteristics for Early Diagnosis of Peripheral Small-cell Lung Cancer.

PURPOSE: To investigate the clinical and computed tomography (CT) characteristics of peripheral small-cell lung cancer (pSCLC) to improve its early diagnosis. PATIENTS AND METHODS: In total, 70, 132, 69, and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC, peripheral non-small cell lung cancer (pNSCLC), benign lung tumor (pBLT), and inflammatory lesion (pIL) were enrolled in this study retrospectively. The clinical and CT data of studied patients with different lesions were analyzed and compared by univariate analysis. Multivariate analysis was used to reveal the key features to distinguish pSCLC from pNSCLC, pBLT, and pIL, respectively. RESULTS: Univariate and multivariate analysis of the clinical and CT characteristics of studied patients indicated that 1) compared with pNSCLC and pIL, vessel convergence, spiculation, and peripheral ground-glass opacity were less common in pSCLC; 2) density homogeneity (OR = 38.84-120.21, P < 0.05), bronchial cutoff sign (OR = 10.00-60.13, P = 0.001), hilar lymph node enlargement (OR = 22.81-95.08, P < 0.0001) (pSCLC vs pNSCLC, pBLT, and pIL), male sex (OR = 5.53-10.92, P < 0.05) (pSCLC vs pNSCLC and pBLT), and emphysema (OR = 36.57-56.63, P < 0.05) (pSCLC vs pBLT and pIL) were significantly and independently associated with pSCLC. Type I and II bronchial cutoff sign, especially type I, were closely related to pSCLC. CONCLUSION: Peripheral solid nodules with homogeneous density, bronchial cutoff sign, hilar lymph node enlargement, but without spiculation or vessel convergence in male patients with emphysema should be highly suspected of pSCLC.

Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography.

BACKGROUND: Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features. METHODS: Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs. RESULTS: A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions. CONCLUSIONS: Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.

Clinical and Computed Tomography (CT) Characteristics of Pulmonary Nodules Caused by Cryptococcal Infection.

PURPOSE: To investigate the clinical and computed tomography (CT) characteristics of pulmonary nodules caused by cryptococcal infection and gain a greater insight and understanding that will reduce misdiagnosis. MATERIALS AND METHODS: A total of 47 patients with confirmed pulmonary cryptococcosis (PC) manifested as nodules were retrospectively enrolled from January 2013 to December 2020. The clinical and CT data of patients with single and multiple nodules were analyzed and compared with emphasis on exploring the characteristics of the solitary ones. RESULTS: Among the 47 patients, single and multiple nodules were detected in 25 (53.2%) and 22 (46.8%) patients, respectively, with similar clinical characteristics. The diameter of solitary nodules was smaller than that of the largest ones of the multiple nodules (P = 0.000). Compared with multiple nodules, less solitary nodules were located in the subpleural zone (P = 0.031) and had a cavity or vacuole (P = 0.049). Regarding the solitary nodules, 22 (88.0%) and 3 (12.0%) were solid and subsolid, respectively. Eighteen (81.8%) solid solitary nodules exhibited either one of air bronchogram or vacuole, satellite lesions, and halo sign. Twenty-three (92.0%) of 25 patients with solitary nodules had follow-up CT data, and most of them had no changes (12, 52.1%) or increased in size and/or density (8, 34.8%), but only a few (3, 13.0%) decreased with or without anti-inflammatory therapy. CONCLUSION: The clinical characteristics of patients with solitary and multiple nodules caused by PC are similar. The solitary nodules are smaller, more scattered, but less necrotic than multiple ones. For solitary nodules with air bronchogram or vacuole, satellite lesions, or halo sign, PC should be considered as one of the differential diagnosis.

Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings.

PURPOSE: Solitary pulmonary inflammatory nodules (SPINs) are frequently misdiagnosed as malignancy. We aimed to investigate CT features and pathological findings of SPINs for improving diagnosis strategies. PATIENTS AND METHODS: In this retrospective study, 225 and 310 consecutive patients with confirmed SPINs and lung cancerous nodules were enrolled from January 2013 to December 2020. Nodules were classified into different types based on the key CT features: I, homogeneous and well-defined nodules with smooth (Ia), coarse (Ib), or spiculated margins (Ic); II, nodules with blurred boundaries, peripheral patches, or both; III, nodules exhibiting heterogeneous density; and IV, polygonal nodules. The pathological findings of SPINs were simultaneously studied and summarized. RESULTS: Among the 225 SPINs, type I (Ia, Ib, and Ic), II, III, and IV were 137 (60.9%) (47 [20.9%], 33 [14.7%], and 57 [25.3%]), 62 (27.6%), 12 (5.3%) and 14 (6.2%), respectively. Correspondingly, those in 310 cancerous nodules were 275 (88.7%) (119 [38.4%], 70 [22.6%], and 86 [27.7%]), 20 (6.5%), 15 (4.8%), and 0, respectively. Compared with lung cancers, type I nodules were less common but type II and IV nodules were more common in SPINs (each P < 0.0001). Though the frequencies of subtype I (P = 0.095) and type III (P = 0.796) nodules were similar between two groups, their specific CT features were significantly different. The main pathological findings of each type of SPINs were most extensively identical (82.2 - 100%). CONCLUSION: Between cancerous nodules and SPINs, differences in overall or specific CT features exist. The type II and IV nodules are highly indicative of SPINs, and each type of SPINs have almost similar pathological findings.

Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography.

PURPOSE: To investigate the clinical and computed tomography (CT) characteristics of absorbable pulmonary solid nodules (PSNs) and to clarify CT features for distinguishing absorbable PSNs from malignant ones. MATERIALS AND METHODS: From January 2015 to February 2021, a total of 316 patients with 348 PSNs (171 absorbable and 177 size-matched malignant) were retrospectively enrolled. Their clinical and CT data were analyzed and compared to determine CT features for predicting absorbable PSNs. RESULTS: Between absorbable and malignant PSNs, there were significant differences in patients' age, lesions' locations, shapes, homogeneity, borders, distance from the pleura, vacuoles, air bronchograms, lobulation, spiculation, halo sign, multiple concomitant nodules and pleural indentation (each P < 0.05). Multivariate analysis revealed that the independent predictors of absorbable PSNs were the following: patient age ≤55 years (OR, 2.660; 95% CI, 1.432-4.942; P = 0.002), homogeneous density (OR, 2.487; 95% CI, 1.107-5.590; P = 0.027), ill-defined border (OR, 5.445; 95% CI, 1.661-17.846; P = 0.005), halo sign (OR, 3.135; 95% CI, 1.154-8.513; P = 0.025), multiple concomitant nodules (OR, 8.700; 95% CI, 4.401-17.197; P<0.001), and abutting pleura (OR, 3.759; 95% CI, 1.407-10.044; P = 0.008). The indicators for malignant PSNs were the following: lobulation (OR, 3.904; 95% CI, 1.956-7.791; P<0.001), spiculation (OR, 4.980; 95% CI, 2.202-11.266, P<0.001), and pleural indentation (OR, 4.514; 95% CI, 1.223-16.666; P = 0.024). CONCLUSION: In patients younger than 55 years, PSNs with homogeneous density, ill-defined border, halo sign, multiple concomitant nodules, and abutting pleura should be highly suspected as absorbable ones.

Significance of intra-nodular vessel sign in differentiating benign and malignant pulmonary ground-glass nodules.

BACKGROUND: The presence of pulmonary vessels inside ground-glass nodules (GGNs) of different nature is a very common occurrence. This study aimed to reveal the significance of pulmonary vessels displayed in GGNs in their diagnosis and differential diagnosis. RESULTS: A total of 149 malignant and 130 benign GGNs confirmed by postoperative pathological examination were retrospectively enrolled in this study. There were significant differences in size, shape, nodule-lung interface, pleural traction, lobulation, and spiculation (each p < 0.05) between benign and malignant GGNs. Compared with benign GGNs, intra-nodular vessels were more common in malignant GGNs (67.79% vs. 54.62%, p = 0.024), while the vascular categories were similar (p = 0.663). After adjusting the nodule size and the distance between the nodule center and adjacent pleura [radius-distance ratio, RDR], the occurrences of internal vessels between them were similar. The number of intra-nodular vessels was positively correlated with nodular diameter and RDR. Vascular changes were more common in malignant than benign GGNs (52.48% vs. 18.31%, p < 0.0001), which mainly manifested as distortion and/or dilation of pulmonary veins (61.19%). The occurrence rate, number, and changes of internal vessels had no significant differences among all the pre-invasive and invasive lesions (each p > 0.05). CONCLUSIONS: The incidence of internal vessels in GGNs is mainly related to their size and the distance between nodule and pleura rather than the pathological nature. However, GGNs with dilated or distorted internal vessels, especially pulmonary veins, have a higher possibility of malignancy.