Benign and Malignant Part-Solid Nodules: Differentiation Via Thin-Section Computed Tomography

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This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section 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. The binary logistic regression analysis was performed to reveal predictors of malignant PSNs. Results: A total of 119 PSNs in 117 patients (mean age, 56 years ± 11 [standard deviation]; 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). Between benign and malignant PSNs, there were significant differences in patients’ age and smoking history. Regarding CT characteristics, malignant lesions and benign lesions significantly differed in CT features of 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 nodule, and 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 internal solid components were significant independent predictors distinguishing malignant PSNs. Conclusions: Well-defined and lobulated PSNs with irregular and scattered solid components have a high probability of being malignant. Cancer Biology Part-solid nodules CT Malignancy Benignity Solid component Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background With the widespread application of CT screening for lung cancer, the increasing detection of ground-glass nodules (GGNs) attracts attention because of their high possibility of being early lung cancer [ 1 – 4 ]. GGNs, based on the absence or presence of a solid component, can be further classified into pure GGNs and part-solid nodules (PSNs) [ 5 ]. PSN has been reported to be associated with lung adenocarcinoma, and its malignant rate is higher than that of pure GGNs and solid nodules [ 6 , 7 ]. Pathologically, PSNs were mainly confirmed as minimally invasive adenocarcinoma and invasive adenocarcinoma, while pure GGNs were mostly adenocarcinoma in situ and sometimes minimally adenocarcinoma [ 8 ]. Therefore, an aggressive approach to treatment is usually recommended for PSNs, especially for lesions with solid components larger than 5 mm [ 5 ]. However, PSN is a nonspecific radiological manifestation, which could also be found in various benign conditions, such as inflammatory disease, focal interstitial fibrosis, or alveolar hemorrhage [ 9 , 10 ]. And some of them could be spontaneously resolved during follow-up. Oh et al. [ 11 ] found that a significant portion (48.7%) of PSNs disappeared over follow-up, and most of them were resolved within 3 months after initial detection. Therefore, quite a few PSNs could be benign lesions (BLs), and it is important to differentiate BLs from malignant lesions (MLs) based on initial CT data for the further management of PSNs. Previous studies have investigated the differences in CT features of PSNs with different natures [ 11 – 13 ]. Yang et al. [ 12 ] reported that large size, well-defined border, and spiculation favored the diagnosis of MLs, and Lee et al. [ 13 ] revealed that lesion multiplicity, large solid component, and ill-defined lesion border indicated transient lesions. Although the differences in size and CT value of internal solid component were also studied [ 12 , 13 ], these studies mainly compared the overall CT features of nodules rather than comprehensively analyzed the differences between their various components. What’s more, some results were acquired based on the enrolled cases confirmed only by follow-up, which limited the clinical value of their findings. The current study enrolled patients with benign PSNs confirmed by follow-up or pathology and malignant lesions by pathology, and the CT features of whole nodule, internal solid component and peripheral GGO component were analyzed and compared, respectively. The results may provide more information for differentiating benign and malignant PSNs. Methods Study Population From March 2016 to January 2020, patients with pathologically confirmed benign GGNs or resolved GGNs were collected, and cases with pathologically confirmed malignant GGNs during the same period were also collected. Then cases that met the following criteria were enrolled in this study. Inclusion criteria: (1) patients with pulmonary GGNs manifesting as PSNs; (2) patients’ clinical data were complete. Exclusion criteria: (1) CT images with the thickness more than 1 mm; (2) presence of artifacts on CT images affecting evaluation. Finally, 117 patients with 119 PSNs were included in this study. The patients’ selection procedure was shown in Fig. 1. CT examinations All patients underwent non-contrast chest CT with a 128-slice multi-detector CT scanner (SOMATOM Definition Flash system, Siemens Medical Systems) while holding their breath after inspiration, and were scanned from thoracic inlet to lung base. CT examinations were performed with the following parameters: tube voltage, 110 – 120 kV; tube current, 50 – 150 mAs; beam pitch, 1.0; detector collimation, 0.6 mm; rotation time, 0.5 s; reconstruction thickness, 1.0 mm or 0.625 mm; reconstruction interval, 1.0 mm or 0.625 mm, and reconstruction kernel, medium-sharp algorithm. Analysis of CT Features CT images of all patients were reviewed by two radiologists with more than ten-year experience of chest CT interpretation, who were blinded to clinical data and pathological results. Any divergences of the two radiologists during evaluation were resolved by consensus. CT images were analyzed with lung window setting (window level, -600 HU; width, 1600 HU) by using Picture Archiving and Communication System and multiplanar reconstruction. The overall CT features of each PSN were evaluated: (a) lesion size (mean of the longest diameter and the perpendicular diameter on axial images), (b) lesion area (the largest area of entire PSN on axial images), (c) location, (d) lesion shape (round, oval, or irregular), (e) lesion border (well-defined or ill-defined), (f) lesion margin (smooth or coarse), (g) lobulation, (h) spiculation, (i) air bronchogram, (j) bubble lucency, (k) pleural indentation, (l) pulmonary vessel changes (distorted, dilated, or both). For GGO component, its density and uniformity (homogeneous or heterogeneous) were also evaluated. CT features that were analyzed for solid component included (a) area (the largest area of internal solid component on axial images), (b) solid ratio (largest area of solid component divided by largest lesion area), (c) density, (d) number (solitary or multiple), (e) shape (round, oval, or irregular), (f) border (well-defined or ill-defined), (g) margin of well-defined solid component (smooth or coarse), (h) distribution (concentrated or scattered), (i) location (central, eccentric). We did not record the size for multiple and irregular solid components because it would preclude reliable and accurate evaluation. Clinical and pathological data Patients’ clinical and laboratory data were recorded through Electronic Medical Record System. Clinical data, including patient age, sex, smoking history (never-smoker, ex-smoker, or current smoker), smoking amount and history of cancer were recorded. Laboratory findings, such as white blood cell (WBC) count, blood eosinophil count, and presence of blood eosinophilia, were also recorded. These laboratory examinations were performed before operation and within a week after CT examination. All existing histopathologic slides were reviewed by two pathologists and histopathologic analysis was performed according to the 2015 World Health Organization classification of tumors of the lung, pleura, thymus, and heart [14]. Statistical Analysis Continuous data are expressed as mean ± standard deviation, whereas categorical variables are presented as numbers and percentages. Continuous data were analyzed by using the analysis of Variance or Wilcoxon rank sum test, and categorical data were analyzed by Pearson χ 2 test or Fisher’s exact test. A P value less than .05 was considered to indicate a statistically significant difference. All statistical analyses were performed by using SPSS 20.0 (SPSS, Chicago, Ill). Binary logistic regression analysis was performed to identify variables that could be used in differentiating benign from malignant PSNs. Because of multi-collinearity in some clinical data and CT features, the least absolute shrinkage and selection operator (LASSO) was used to further select features. Characteristics with a P value of less than .05 at univariate analysis were used as the independent variables for LASSO analysis, and then the selected variables were put into binary logistic regression analysis. Receiver operating characteristic analyses were conducted for the variables with statistically significant differences on logistic regression analysis. Ethics The study protocol was reviewed and approved by the Ethics Committee of The First affiliated Hospital of Chongqing Medical University (IRB No: 2019-062). The recommendations of the Declaration of Helsinki for biomedical research involving human subjects were also followed. Results Clinical features One hundred and seventeen patients (mean age, 56 years ± 11; age range, 24 –79 years) were enrolled in this study. Of the 117 patients, 70 (59.8%) were women (mean age, 56 years ± 11; age range, 29 – 84 years), and 47 (40.2%) were men (mean age, 55 years ± 11; age range, 25 – 76 years). Thirty-one (31/117, 26.5%) patients had a history of smoking with the mean number of 26 pack-years (range, 0.8 – 80 pack-years) and 10 (8.5%) patients had a history of cancer. Only two patients had blood eosinophilia and were diagnosed with benign nodules. The clinical data of 117 patients with BLs and MLs were summarized in Table 1. Among 117 patients with 119 PSNs, there were 73 (62.4%) patients with 75 (63.0%) MLs, including 12 (16.0%) adenocarcinomas in situ (AISs), 22 (29.3%) minimally invasive adenocarcinomas (MIAs), and 41 (54.7%) invasive adenocarcinomas (IAs). Of 44 (37.6%) patients with 44 (37.0%) BLs, 17 (38.6%) were completely resolved during follow-up and 27 (61.4%) were confirmed by postoperative pathologic examination. The patients with MLs were older than patients with BLs (P=0.005), and most of them were nonsmokers (P=0.005). CT features of benign and malignant PSNs The overall CT findings of benign and malignant PSNs were showed in Table 2. The MLs were significantly larger than BLs (P<0.001), and were more likely to have well-defined border (P<0.001) and heterogeneous GGO (P=0.004). Regarding the morphological features, MLs showed lobulation (P<0.001), bubble lucency (P=0.001) and pleural indentation (P<0.001) more frequently than did BLs (Fig. 2). And all PSNs with spiculation, air bronchogram and pulmonary vessel changes were MLs. The CT findings of solid components in BLs and MLs were summarized in Table 3. The solid components in MLs were significantly larger than those in BLs (P < .001), and were more likely to be multiple (P<0.001), irregular (P<0.001), scattered (P<0.001) and eccentric (P=0.008) (Fig. 2, Fig. 3). While solid components in BLs were usually single, round, and central (Fig. 4) and were more likely to be ill-defined (P<0.001). 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 lesion, 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 solid components were significant predictors distinguishing malignant PSNs. Receiver operating characteristic analysis revealed that the areas under the curve for the border and lobulation of nodule, and shape and distribution of solid components were 0.710 (95% CI, 0.613-0.808; P<0.001), 0.775 (95% CI, 0.691- 0.858; P<0.001), 0.737 (95% CI, 0.638- 0.837; P<0.001), and 0.731 (95% CI, 0.642- 0.820; P<0..001), respectively (Fig. 5). Discussion Most PSNs are confirmed as MLs with invasiveness, but there are still some benign PSNs and some of them could disappear spontaneously [15]. As for treatment, BLs just require follow up, while MLs need early surgical resection [16]. Therefore, it is important to differentiate benign from malignant PSNs. This study investigated the clinical data of patients and CT features of benign and malignant PSNs, especially the differences of solid components. It was found that patients with MLs were more likely to be older and nonsmokers, and BLs and MLs significantly differed in CT features of whole nodule, internal solid component and GGO. In summarize, well-defined border and lobulation of nodule, and irregular shape and scattered distribution of internal solid components were significant indicators of malignant PSNs. Most previous studies [17-20] mainly investigated the overall CT features of benign and malignant GGNs, and found significant differences in lesion size, border, lobulation spiculation, bubble lucency, air bronchogram, vascular convergence sign, and pleural indentation between BLs and MLs. In addition, some researches [11, 12] investigated the overall CT features of BLs and MLs appearing as PSNs. Oh et al. [11] analyzed 86 histologically confirmed or transient PSNs (29 malignant and 57 transient lesions), and found that MLs were more likely to be larger and have spiculation than BLs. According to Yang et al. [12] a larger size, well-defined border, and spiculation had higher predictive value for malignant PSNs. Although there are some differences among these studies because of different inclusion criteria, most of present findings are consistent with the previous results. Compared with pure GGNs, the solid components in PSNs make them more characteristic. Therefore, it deserves to be meticulously analyzed and may provide additional findings for differential diagnosis of BLs and MLs. Previous researches [12,13,18,21] only investigated the size or/and CT value of solid component. Yang et al. [12] found that MLs were more likely to have a larger solid component with higher CT value than BLs, but its morphological manifestations were not well studied. The present study conducted a comprehensive analysis of solid component, and revealed that PSNs with larger, irregular, multiple, scattered, and eccentric solid components had a higher probability of malignancy, while BLs usually had single, round, and ill-defined solid components. However, there was no significant difference in density between MLs and BLs. In addition to the size and CT value of solid component, the current study found more characteristics with predictive value, which could provide new information for more accurate diagnosis. Therefore, solid component is an important part of PSN and has great significance in diagnosis. Pathologically, solid components in MLs were confirmed as tumor invasion with or without alveolar collapse or/and fibroblastic proliferation [22]. Different cell differentiation degrees and growth rates result in irregular shape of solid components, and the multiple scattered solid components may be related to multiple origins of invasions. Regarding benign PSNs, most of them were inflammatory lesions. The internal solid components usually represent a large number of inflammatory cells, and GGO represents exudates and fibrotic changes [1]. This indicates that inflammation in solid component is more severe than that in peripheral GGO, and the peripheral GGO might be secondary to central inflammation. Thus, in benign PSNs, the border of solid component and its peripheral GGO were usually ill-defined. Our study had two limitations. First, the sample size of benign PSNs is small. Therefore, more cases are needed in future studies to confirm our results' accuracy. Second, variations in nodule measurement and characterization of lesions might exist due to different radiologists. Thus, we measured relevant parameters for three times and evaluated CT features of PSNs using multiplanar reconstruction. The consistent results of radiologists were used to ensure the reliability of results. Conclusions Benign and malignant PSNs had significant differences in CT features. Well-defined and lobulated PSNs with irregular, multiple, and scattered solid components have a high probability of being malignant. In contrast, PSNs with sole, round, and blurred solid component indicate benign lesions, and follow-up should be firstly considered for them. Abbreviations PSN: Part-solid nodule; GGN: Ground-glass nodule; BL: Benign lesion; ML: Malignant lesion; GGO: Ground-glass opacity; OR: Odds ratio; CI: Confidence interval; ROC: Receiver operating characteristic; WBC: White blood cell. Declarations Ethics approval and consent to participate: This retrospective study was approved by the medical ethical committee of the First Affiliated Hospital of Chongqing Medical University and the requirement for informed consent was waived. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by the National Natural Science Foundation of China (81601545) and Chongqing Health and Family Planning Commission Foundation (2016MSXM018) of China. Author contributions: CZG designed the study, reviewed and edited the manuscript. WJL and FJL participated in data interpretation and writing-original draft preparation. YWT and BJF contributed to the statistical analysis. All authors have approved the final version of the work. Acknowledgments: Not applicable. Author information 1 Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People’s Republic of China. Wang-jia Li, Fa-jin Lv, Bin-jie Fu, Zhi-gang Chu. 2 Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People’s Republic of China. References Park CM, Goo JM, Lee HJ, Lee CH, Chun EJ, Im JG. Nodular ground-glass opacity at thin-section CT: histologic correlation and evaluation of change at follow-up. Radiographics. 2007,27(2):391-408. Mao R, She Y, Zhu E, Chen D, Dai C, Wu C, et al. Proposal for Restaging of Invasive Lung Adenocarcinoma Manifesting as Pure Ground Glass Opacity. Ann Thorac Surg. 2019,107(5):1523-1531. She Y, Zhao L, Dai C, Chen Y, Zha J, Xie H, et al. Preoperative nomogram for identifying invasive pulmonary adenocarcinoma in patients with pure ground-glass nodule: A multi-institutional study. Oncotarget. 2017,8(10):17229-17238. Kim HY, Shim YM, Lee KS, Han J, Yi CA, Kim YK. 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IASLC/ATS/ERS international multidisciplinary classification of lung adenocarcinoma: novel concepts and radiologic implications. J Thorac Imaging. 2012,27(6):340-53. Tables Table1. Clinical features of 117 patients Clinical features Patients with Benign PSNs (n=44) Patients with Malignant PSNs (n=73) P Value Age (y) 52 ± 11 58 ± 11 0.005 * Sex Male 21 (47.7) 26 (35.6) 0.196 ‡ Female 23 (52.3) 47 (64.4) Smoking history Never-smoker 25 (56.8) 61 (83.6) Ex-smoker 5 (11.4) 3 (4.1) 0.005 § Current smoker 14 (31.8) 9 (12.3) Smoking amount (pack-years) 23 ± 20 27 ± 16 0.404 & History of cancer Present 2 (4.5) 8 (11.0) 0.316 § Absent 42 (95.5) 65 (89.0) WBC count (uL -1 ) 6872 ± 2931 5631 ± 2214 0.051 § Blood eosinophil count (uL -1 ) 154 ± 223 100 ± 92 0.930 § Blood eosinophilia ≥500 per microlite 2 (4.5) 0 (0.0) 0.139 § <500 per microliter 42 (95.5) 73 (100.0) Note. Data are presented as n (%) or Means ± SD. * Analysis of Variance, ANOVA. ‡ Calculated with the Pearson χ 2 test. § Calculated with the Fisher exact test. & Wilcoxon rank sum test. Table2. The overall CT findings of benign and malignant PSNs CT features Benign PSNs (n=44) Malignant PSNs (n=75) P Value Lesion size (mm) 11.1 ± 4.3 15.2 ± 4.8 ＜0.001 * Lesion area (mm 2 ) 118.4 ± 89.8 199.4 ± 116.8 ＜0.001 & Density of GGO (HU) -635 ± 84 -616 ± 81 0.241 * Location Right upper lobe 16 (36.4) 33 (44.0) Right middle lobe 1 (2.3) 2 (2.7) Right lower lobe 7 (15.9) 6 (8.0) 0.528 § Left upper lobe 12 (27.3) 25 (33.3) Left lower lobe 8 (18.2) 9 (12.0) Lesion shape Round/Oval 13 (29.5) 17 (22.7) 0.404 ‡ Irregular 31 (70.5) 58 (77.3) Lesion border Well-defined 12 (27.3) 52 (69.3) ＜0.001 ‡ Ill-defined 32 (72.7) 23 (30.7) Margin of well-defined border Smooth 3 (25.0) 5 (9.6) 0.164 § Coarse 9 (75.0) 47 (90.4) Uniformity of GGO Homogeneous 28 (63.6) 27 (36.0) 0.004 ‡ Heterogeneous 16 (36.4) 48 (64.0) Lobulation 2 (4.5) 45 (60.0) ＜0.001 ‡ Spiculation 0 (0.0) 22 (29.3) ＜0.001 ‡ Air bronchogram 0 (0.0) 18 (24.0) ＜0.001 ‡ Bubble lucency 1 (2.3) 19 (25.3) 0.001 ‡ Pleural indentation 3 (6.8) 35 (46.7) ＜0.001 ‡ Pulmonary vessel changes 0 (0.0) 33 (44.0) ＜0.001 ‡ Note. Data are presented as n (%) or Means ± SD. * Analysis of Variance, ANOVA. ‡ Calculated with the Pearson χ 2 test. § Calculated with the Fisher exact test. & Wilcoxon rank sum test. Table3. The CT findings of solid component within Benign and Malignant PSNs CT features Patients with Benign PSNs (n=44) Patients with Malignant PSNs (n=75) P Value Solid component area * (mm 2 ) 15.6 ± 15.0 34.6 ± 30.5 ＜0.001 & Solid component radio* (%) 12.4 ± 7.6 16.8 ± 9.6 0.015 & Density of Solid component * -164 ± 196 -99 ± 130 0.155 & Solid component number Solitary 41 (93.2) 34 (45.3) ＜0.001 ‡ Multiple 3 (6.8) 41 (54.7) Solid component shape Round 25 (56.8) 7 (9.3) ＜0.001 ‡ Irregular 19 (43.2) 68 (90.7) Solid component border Well-defined 6 (13.6) 35 (46.7) ＜0.001 ‡ Ill-defined 38 (86.4) 40 (53.3) margin of well-defined solid component Smooth 6 (100.0) 11 (31.4) 0.003 § Coarse 0 (0.0) 24 (68.6) Solid component distribution Concentrated 42 (95.5) 37 (49.3) ＜0.001 ‡ Scattered 2 (4.5) 38 (50.7) Solid component location Central 21 (47.7) 18 (24.0) 0.008 ‡ Eccentric 23 (52.3) 57 (76.0) Note: Data are presented as n (%) or Means ± SD. ‡ Calculated with the Pearson χ 2 test. § Calculated with the Fisher exact test. & Wilcoxon rank sum test. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-138228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":8410866,"identity":"0a51edb4-bf11-4b38-9a9c-26c241e88280","order_by":0,"name":"Zhi-gang Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCRBhYMNj397Y+PAD8Voq0mQMeA43G0sQr+XMYRsDifQ2AR5idPDP7jH++LUtjcdc8mEbUL+dnG4DIUvunDEwlm2z4bGcndj2oIAh2djsAAEtBhI5BsmSQFsYbie2G0gwHEjcRoyWw5Jth3kYbh5sk+AhUoth44czh3kMbjASqUXiRloxMzCQeSR7EoGBbECEX/hnJG/++MPAxp6f/fjDhx8q7OQIagEBZkR0GBChHAQYfxCpcBSMglEwCkYoAABr9UFOEdDYGAAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhi-gang","middleName":"","lastName":"Chu","suffix":""},{"id":8410867,"identity":"7f7e6060-72a0-45be-bb5a-60aa5c598739","order_by":1,"name":"Wang-jia Li","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wang-jia","middleName":"","lastName":"Li","suffix":""},{"id":8410868,"identity":"eeee2428-1de4-46be-a1a6-7aa56f73591a","order_by":2,"name":"Fa-jin Lv","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fa-jin","middleName":"","lastName":"Lv","suffix":""},{"id":8410869,"identity":"f42e37c4-e25a-4679-ab1c-1c06191f6458","order_by":3,"name":"Yi-wen Tan","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi-wen","middleName":"","lastName":"Tan","suffix":""},{"id":8410872,"identity":"a8dd18da-d51d-4724-b7cc-a27d3553d1ef","order_by":4,"name":"Bing-jie Fu","email":"","orcid":"","institution":"First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bing-jie","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2020-12-30 11:14:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-138228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-138228/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5095613,"identity":"fae292eb-1233-4fea-a764-9300cf395988","added_by":"auto","created_at":"2021-01-19 19:04:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83985,"visible":true,"origin":"","legend":"Flowchart of study population","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/8740901347961c424d22d54f.png"},{"id":5095922,"identity":"5f1fa877-0348-482c-8cbb-2102957beb09","added_by":"auto","created_at":"2021-01-19 19:10:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439402,"visible":true,"origin":"","legend":"A 55-year-old woman with an invasive adenocarcinoma. Axial (a), coronal (b) and sagittal (c) CT images show a 13-mm lobulated and well-defined PSN (arrows) in the right upper lobe. The internal solid components are multiple, scattered, and irregular, and the peripheral GGO is heterogeneous. (d) Photomicrograph (hematoxylin - eosin stain) of PSN reveals adenocarcinoma with invasive foci (asterisk).","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/50cb9bf2d35993f9c56716e3.png"},{"id":5095792,"identity":"1ed96a02-31d6-4a57-bf11-dc5f1d53981b","added_by":"auto","created_at":"2021-01-19 19:07:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157791,"visible":true,"origin":"","legend":"A 50-year-old woman with an invasive adenocarcinoma. Axial (a), coronal (b) and sagittal (c) CT images show a 17-mm irregular and well-defined PSN (arrows) in the right upper lobe, the internal solid component is eccentric, irregular, and well-defined, and the peripheral GGO is homogeneous. Photomicrograph (hematoxylin - eosin stain) of PSN reveals adenocarcinoma with invasive foci (asterisk).","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/922f7cef8ce86af1291ca66f.jpeg"},{"id":5095921,"identity":"fcdb2482-9078-4c33-add8-339f3ed893d1","added_by":"auto","created_at":"2021-01-19 19:10:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416125,"visible":true,"origin":"","legend":"A 63-year-old woman with a transient PSN in the left upper lobe. Axial (a) and sagittal (b) CT images show an 8-mm round PSN (arrows), the internal high-attenuation zone is centric, round, and ill-defined, and the peripheral GGO is ill-defined. VR image (c). At follow-up CT (d) obtained 3 months later, the PSN has disappeared.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/238fe384e046cc4a458c46e1.png"},{"id":5095790,"identity":"ce17fbdb-6563-4fad-a281-9bd25e7c43d7","added_by":"auto","created_at":"2021-01-19 19:07:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119958,"visible":true,"origin":"","legend":"Receiver operating characteristic Curve for lesion border, lobulation, solid component shape and distribution in part-solid nodule.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/5655e2702931f59e12ae2bbb.png"},{"id":13649821,"identity":"95976438-b296-4e0c-9bd3-f31981601bba","added_by":"auto","created_at":"2021-09-17 09:38:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1520462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-138228/v1/028c01af-bde1-4fc3-ac65-acdf34bdd199.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eBenign and Malignant Part-Solid Nodules: Differentiation Via Thin-Section Computed Tomography\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eWith the widespread application of CT screening for lung cancer, the increasing detection of ground-glass nodules (GGNs) attracts attention because of their high possibility of being early lung cancer [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. GGNs, based on the absence or presence of a solid component, can be further classified into pure GGNs and part-solid nodules (PSNs) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PSN has been reported to be associated with lung adenocarcinoma, and its malignant rate is higher than that of pure GGNs and solid nodules [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pathologically, PSNs were mainly confirmed as minimally invasive adenocarcinoma and invasive adenocarcinoma, while pure GGNs were mostly adenocarcinoma in situ and sometimes minimally adenocarcinoma [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, an aggressive approach to treatment is usually recommended for PSNs, especially for lesions with solid components larger than 5\u0026nbsp;mm [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, PSN is a nonspecific radiological manifestation, which could also be found in various benign conditions, such as inflammatory disease, focal interstitial fibrosis, or alveolar hemorrhage [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. And some of them could be spontaneously resolved during follow-up. Oh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] found that a significant portion (48.7%) of PSNs disappeared over follow-up, and most of them were resolved within 3 months after initial detection. Therefore, quite a few PSNs could be benign lesions (BLs), and it is important to differentiate BLs from malignant lesions (MLs) based on initial CT data for the further management of PSNs.\u003c/p\u003e \u003cp\u003ePrevious studies have investigated the differences in CT features of PSNs with different natures [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Yang et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported that large size, well-defined border, and spiculation favored the diagnosis of MLs, and Lee et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] revealed that lesion multiplicity, large solid component, and ill-defined lesion border indicated transient lesions. Although the differences in size and CT value of internal solid component were also studied [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], these studies mainly compared the overall CT features of nodules rather than comprehensively analyzed the differences between their various components. What\u0026rsquo;s more, some results were acquired based on the enrolled cases confirmed only by follow-up, which limited the clinical value of their findings. The current study enrolled patients with benign PSNs confirmed by follow-up or pathology and malignant lesions by pathology, and the CT features of whole nodule, internal solid component and peripheral GGO component were analyzed and compared, respectively. The results may provide more information for differentiating benign and malignant PSNs.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom March 2016 to January 2020, patients with pathologically confirmed benign GGNs or resolved GGNs were collected, and cases with pathologically confirmed malignant GGNs during the same period were also collected. Then cases that met the following criteria were enrolled in this study. Inclusion criteria: (1) patients with pulmonary GGNs manifesting as PSNs; (2) patients\u0026rsquo; clinical data were complete. Exclusion criteria: (1) CT images with the thickness more than 1 mm; (2) presence of artifacts on CT images affecting evaluation. Finally, 117 patients with 119 PSNs were included in this study. The patients\u0026rsquo; selection procedure was shown in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT examinations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients underwent non-contrast chest CT with a 128-slice multi-detector CT scanner (SOMATOM Definition Flash system, Siemens Medical Systems) while holding their breath after inspiration, and were scanned from thoracic inlet to lung base. CT examinations were performed with the following parameters: tube voltage, 110 \u0026ndash; 120 kV; tube current, 50 \u0026ndash; 150 mAs; beam pitch, 1.0; detector collimation, 0.6 mm; rotation time, 0.5 s; reconstruction thickness, 1.0 mm or 0.625 mm; reconstruction interval, 1.0 mm or 0.625 mm, and reconstruction kernel, medium-sharp algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of CT Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCT images of all patients were reviewed by two radiologists with more than ten-year experience of chest CT interpretation, who were blinded to clinical data and pathological results. Any divergences of the two radiologists during evaluation were resolved by consensus. CT images were analyzed with lung window setting (window level, -600 HU; width, 1600 HU) by using Picture Archiving and Communication System and multiplanar reconstruction.\u003c/p\u003e\n\u003cp\u003eThe overall CT features of each PSN were evaluated: \u003cem\u003e(a)\u003c/em\u003e lesion size (mean of the longest diameter and the perpendicular diameter on axial images), \u003cem\u003e(b)\u003c/em\u003e lesion area (the largest area of entire PSN on axial images), \u003cem\u003e(c)\u003c/em\u003e location, \u003cem\u003e(d)\u003c/em\u003e lesion shape (round, oval, or irregular), \u003cem\u003e(e)\u003c/em\u003e lesion border (well-defined or ill-defined), \u003cem\u003e(f)\u003c/em\u003e lesion margin (smooth or coarse), \u003cem\u003e(g)\u003c/em\u003e lobulation, \u003cem\u003e(h)\u003c/em\u003e spiculation, \u003cem\u003e(i)\u003c/em\u003e air bronchogram, \u003cem\u003e(j)\u003c/em\u003e bubble lucency, \u003cem\u003e(k)\u003c/em\u003e pleural indentation, \u003cem\u003e(l)\u003c/em\u003e pulmonary vessel changes (distorted, dilated, or both). For GGO component, its density and uniformity (homogeneous or heterogeneous) were also evaluated. CT features that were analyzed for solid component included \u003cem\u003e(a)\u003c/em\u003e area (the largest area of internal solid component on axial images), \u003cem\u003e(b) \u003c/em\u003esolid ratio (largest area of solid component divided by largest lesion area), \u003cem\u003e(c) \u003c/em\u003edensity, \u003cem\u003e(d) \u003c/em\u003enumber (solitary or multiple), \u003cem\u003e(e) \u003c/em\u003eshape (round, oval, or irregular), \u003cem\u003e(f) \u003c/em\u003eborder (well-defined or ill-defined), \u003cem\u003e(g) \u003c/em\u003emargin of well-defined solid component (smooth or coarse), \u003cem\u003e(h) \u003c/em\u003edistribution (concentrated or scattered), \u003cem\u003e(i) \u003c/em\u003elocation (central, eccentric). We did not record the size for multiple and irregular solid components because it would preclude reliable and accurate evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and pathological data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients\u0026rsquo; clinical and laboratory data were recorded through Electronic Medical Record System. Clinical data, including patient age, sex, smoking history (never-smoker, ex-smoker, or current smoker), smoking amount and history of cancer were recorded. Laboratory findings, such as white blood cell (WBC) count, blood eosinophil count, and presence of blood eosinophilia, were also recorded. These laboratory examinations were performed before operation and within a week after CT examination.\u003c/p\u003e\n\u003cp\u003eAll existing histopathologic slides were reviewed by two pathologists and histopathologic analysis was performed according to the 2015 World Health Organization classification of tumors of the lung, pleura, thymus, and heart [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous data are expressed as mean \u0026plusmn; standard deviation, whereas categorical variables are presented as numbers and percentages. Continuous data were analyzed by using the analysis of Variance or Wilcoxon rank sum test, and categorical data were analyzed by Pearson \u0026chi;\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e test or Fisher\u0026rsquo;s exact test. A \u003cem\u003eP\u003c/em\u003e value less than .05 was considered to indicate a statistically significant difference. All statistical analyses were performed by using SPSS 20.0 (SPSS, Chicago, Ill).\u003c/p\u003e\n\u003cp\u003eBinary logistic regression analysis was performed to identify variables that could be used in differentiating benign from malignant PSNs. Because of multi-collinearity in some clinical data and CT features, the least absolute shrinkage and selection operator (LASSO) was used to further select features. Characteristics with a \u003cem\u003eP\u003c/em\u003e value of less than .05 at univariate analysis were used as the independent variables for LASSO analysis, and then the selected variables were put into binary logistic regression analysis. Receiver operating characteristic analyses were conducted for the variables with statistically significant differences on logistic regression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committee of The First affiliated Hospital of Chongqing Medical University (IRB No: 2019-062). The recommendations of the Declaration of Helsinki for biomedical research involving human subjects were also followed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne hundred and seventeen patients (mean age, 56 years \u0026plusmn; 11; age range, 24 \u0026ndash;79 years) were enrolled in this study. Of the 117 patients, 70 (59.8%) were women (mean age, 56 years \u0026plusmn; 11; age range, 29 \u0026ndash; 84 years), and 47 (40.2%) were men (mean age, 55 years \u0026plusmn; 11; age range, 25 \u0026ndash; 76 years). Thirty-one (31/117, 26.5%) patients had a history of smoking with the mean number of 26 pack-years (range, 0.8 \u0026ndash; 80 pack-years) and 10 (8.5%) patients had a history of cancer. Only two patients had blood eosinophilia and were diagnosed with benign nodules.\u003c/p\u003e\n\u003cp\u003eThe clinical data of 117 patients with BLs and MLs were summarized in Table 1. Among 117 patients with 119 PSNs, there were 73 (62.4%) patients with 75 (63.0%) MLs, including 12 (16.0%) adenocarcinomas in situ (AISs), 22 (29.3%) minimally invasive adenocarcinomas (MIAs), and 41 (54.7%) invasive adenocarcinomas (IAs). Of 44 (37.6%) patients with 44 (37.0%) BLs, 17 (38.6%) were completely resolved during follow-up and 27 (61.4%) were confirmed by postoperative pathologic examination. The patients with MLs were older than patients with BLs (P=0.005), and most of them were nonsmokers (P=0.005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT features of benign and malignant PSNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall CT findings of benign and malignant PSNs were showed in Table 2. The MLs were significantly larger than BLs (P\u0026lt;0.001), and were more likely to have well-defined border (P\u0026lt;0.001) and heterogeneous GGO (P=0.004). Regarding the morphological features, MLs showed lobulation (P\u0026lt;0.001), bubble lucency (P=0.001) and pleural indentation (P\u0026lt;0.001) more frequently than did BLs (Fig. 2). And all PSNs with spiculation, air bronchogram and pulmonary vessel changes were MLs.\u003c/p\u003e\n\u003cp\u003eThe CT findings of solid components in BLs and MLs were summarized in Table 3. The solid components in MLs were significantly larger than those in BLs (P \u0026lt; .001), and were more likely to be multiple (P\u0026lt;0.001), irregular (P\u0026lt;0.001), scattered (P\u0026lt;0.001) and eccentric (P=0.008) (Fig. 2, Fig. 3). While solid components in BLs were usually single, round, and central (Fig. 4) and were more likely to be ill-defined (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eBinary 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\u0026lt;0.001) of lesion, 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 solid components were significant predictors distinguishing malignant PSNs. Receiver operating characteristic analysis revealed that the areas under the curve for the border and lobulation of nodule, and shape and distribution of solid components were 0.710 (95% CI, 0.613-0.808; P\u0026lt;0.001), 0.775 (95% CI, 0.691- 0.858; P\u0026lt;0.001), 0.737 (95% CI, 0.638- 0.837; P\u0026lt;0.001), and 0.731 (95% CI, 0.642- 0.820; P\u0026lt;0..001), respectively (Fig. 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost PSNs are confirmed as MLs with invasiveness, but there are still some benign PSNs and some of them could disappear spontaneously [15]. As for treatment, BLs just require follow up, while MLs need early surgical resection [16]. Therefore, it is important to differentiate benign from malignant PSNs. This study investigated the clinical data of patients and CT features of benign and malignant PSNs, especially the differences of solid components. It was found that patients with MLs were more likely to be older and nonsmokers, and BLs and MLs significantly differed in CT features of whole nodule, internal solid component and GGO. In summarize, well-defined border and lobulation of nodule, and irregular shape and scattered distribution of internal solid components were significant indicators of malignant PSNs.\u003c/p\u003e\n\u003cp\u003eMost previous studies [17-20] mainly investigated the overall CT features of benign and malignant GGNs, and found significant differences in lesion size, border, lobulation spiculation, bubble lucency, air bronchogram, vascular convergence sign, and pleural indentation between BLs and MLs. In addition, some researches [11, 12] investigated the overall CT features of BLs and MLs appearing as PSNs. Oh et al. [11] analyzed 86 histologically confirmed or transient PSNs (29 malignant and 57 transient lesions), and found that MLs were more likely to be larger and have spiculation than BLs. According to Yang et al. [12] a larger size, well-defined border, and spiculation had higher predictive value for malignant PSNs. Although there are some differences among these studies because of different inclusion criteria, most of present findings are consistent with the previous results.\u003c/p\u003e\n\u003cp\u003eCompared with pure GGNs, the solid components in PSNs make them more characteristic. Therefore, it deserves to be meticulously analyzed and may provide additional findings for differential diagnosis of BLs and MLs. Previous researches [12,13,18,21] only investigated the size or/and CT value of solid component. Yang et al. [12] found that MLs were more likely to have a larger solid component with higher CT value than BLs, but its morphological manifestations were not well studied. The present study conducted a comprehensive analysis of solid component, and revealed that PSNs with larger, irregular, multiple, scattered, and eccentric solid components had a higher probability of malignancy, while BLs usually had single, round, and ill-defined solid components. However, there was no significant difference in density between MLs and BLs. In addition to the size and CT value of solid component, the current study found more characteristics with predictive value, which could provide new information for more accurate diagnosis. Therefore, solid component is an important part of PSN and has great significance in diagnosis.\u003c/p\u003e\n\u003cp\u003ePathologically, solid components in MLs were confirmed as tumor invasion with or without alveolar collapse or/and fibroblastic proliferation [22]. Different cell differentiation degrees and growth rates result in irregular shape of solid components, and the multiple scattered solid components may be related to multiple origins of invasions. Regarding benign PSNs, most of them were inflammatory lesions. The internal solid components usually represent a large number of inflammatory cells, and GGO represents exudates and fibrotic changes [1]. This indicates that inflammation in solid component is more severe than that in peripheral GGO, and the peripheral GGO might be secondary to central inflammation. Thus, in benign PSNs, the border of solid component and its peripheral GGO were usually ill-defined.\u003c/p\u003e\n\u003cp\u003eOur study had two limitations. First, the sample size of benign PSNs is small. Therefore, more cases are needed in future studies to confirm our results' accuracy. Second, variations in nodule measurement and characterization of lesions might exist due to different radiologists. Thus, we measured relevant parameters for three times and evaluated CT features of PSNs using multiplanar reconstruction. The consistent results of radiologists were used to ensure the reliability of results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBenign and malignant PSNs had significant differences in CT features. Well-defined and lobulated PSNs with irregular, multiple, and scattered solid components have a high probability of being malignant. In contrast, PSNs with sole, round, and blurred solid component indicate benign lesions, and follow-up should be firstly considered for them.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePSN: Part-solid nodule; GGN: Ground-glass nodule; BL: Benign lesion; ML: Malignant lesion; GGO: Ground-glass opacity; OR: Odds ratio; CI: Confidence interval; ROC: Receiver operating characteristic; WBC: White blood cell.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This retrospective study was approved by the medical ethical committee of the First Affiliated Hospital of Chongqing Medical University and the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials: \u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (81601545) and Chongqing Health and Family Planning Commission Foundation (2016MSXM018) of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: \u003c/strong\u003eCZG designed the study, reviewed and edited the manuscript. WJL and FJL participated in data interpretation and writing-original draft preparation. YWT and BJF contributed to the statistical analysis. All authors have approved the final version of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People\u0026rsquo;s Republic of China.\u003c/p\u003e\n\u003cp\u003eWang-jia Li, Fa-jin Lv, Bin-jie Fu, Zhi-gang Chu.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Pathology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People\u0026rsquo;s Republic of China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePark CM, Goo JM, Lee HJ, Lee CH, Chun EJ, Im JG. Nodular ground-glass opacity at thin-section CT: histologic correlation and evaluation of change at follow-up. Radiographics. 2007,27(2):391-408.\u003c/li\u003e\n\u003cli\u003eMao R, She Y, Zhu E, Chen D, Dai C, Wu C, et al. Proposal for Restaging of Invasive Lung Adenocarcinoma Manifesting as Pure Ground Glass Opacity. Ann Thorac Surg. 2019,107(5):1523-1531.\u003c/li\u003e\n\u003cli\u003eShe Y, Zhao L, Dai C, Chen Y, Zha J, Xie H, et al. Preoperative nomogram for identifying invasive pulmonary adenocarcinoma in patients with pure ground-glass nodule: A multi-institutional study. Oncotarget. 2017,8(10):17229-17238.\u003c/li\u003e\n\u003cli\u003eKim HY, Shim YM, Lee KS, Han J, Yi CA, Kim YK. Persistent pulmonary nodular ground-glass opacity at thin-section CT: histopathologic comparisons. Radiology. 2007,245(1):267-275.\u003c/li\u003e\n\u003cli\u003eNaidich DP, Bankier AA, MacMahon H, Schaefer-Prokop CM, Massimo Pistolesi M, Goo JM, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 2013,266(1):304-17.\u003c/li\u003e\n\u003cli\u003eLee HJ, Goo JM, Lee CH, Yoo CG, Kim YT, Im JG. Nodular ground-glass opacities on thin-section CT: size change during follow-up and pathological results. Korean J Radiol. 2007,8(1):22-31.\u003c/li\u003e\n\u003cli\u003eHenschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS, et al. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002,178(5):1053-1057.\u003c/li\u003e\n\u003cli\u003eYe T, Deng L, Wang S; Chen H, Zhang Y, Hu W, et al. Lung Adenocarcinomas Manifesting as Radiological Part-Solid Nodules Define a Special Clinical Subtype. J Thorac Oncol. 2019,14(4):617-627.\u003c/li\u003e\n\u003cli\u003eNagajima R, Yokose T, Kakinuma R, Nagi K, Nishiwaki Y, Ochiai A. Localized pure ground-glass opacity on highresolution CT: histologic characteristics. J Comput Assist Tomogr. 2002,26:323-9.\u003c/li\u003e\n\u003cli\u003eYamada N, Kusumoto M, Maeshima A, Suzuki K, Matsuno Y. Correlation of the solid part on high-resolution computed tomography with pathological scar in small lung adenocarcinomas. Jpn Clin Oncol. 2007,37(12):913-7.\u003c/li\u003e\n\u003cli\u003eOh JY, Kwon SY, Yoon HI, Lee SM, Yim JJ, Lee JH, et al. Clinical significance of a solitary ground-glass opacity (GGO) lesion of the lung detected by chest CT. Lung Cancer. 2007,55(1):67-73.\u003c/li\u003e\n\u003cli\u003eYang W, Sun Y, Fang W, Qian F, Ye J, Chen Q, et al. High-resolution Computed Tomography Features Distinguishing Benign and Malignant Lesions Manifesting as Persistent Solitary Subsolid Nodules. Clin Lung Cancer. 2018,19(1):e75-e83.\u003c/li\u003e\n\u003cli\u003eLee SM, Park CM, Goo JM, Lee CH, Lee HJ, Kim KG, et al. Transient Part-Solid Nodules Detected at Screening Thin-Section CT for Lung Cancer: Comparison with Persistent Part-Solid Nodules. Radiology. 2010,255(1):242-51.\u003c/li\u003e\n\u003cli\u003eTravis WD, Brambilla E, Burke AP, Marx A, Nicholson AG. Introduction to the 2015 World Health Organization classification of tumors of the lung, pleura, thymus, and heart. J Thorac Oncol. 2015,10:1240-2.\u003c/li\u003e\n\u003cli\u003eChoi WS, Park CM, Song YS, Lee SM, Wi JY, Goo JM. Transient subsolid nodules in patients with extrapulmonary malignancies: their frequency and differential features. Acta Radiol. 2015,56:428-437.\u003c/li\u003e\n\u003cli\u003ePedersen JH, Saghir Z, Wille MM, Thomsen LH, Skov BG, Ashraf H. Ground-Glass Opacity Lung Nodules in the Era of Lung Cancer CT Screening: Radiology, Pathology, and Clinical Management. Oncology (Williston Park). 2016,30(3):266-74.\u003c/li\u003e\n\u003cli\u003eFan L, Liu SY, Li QC, Yu H, Xiao XS. Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant. Br J Radiol. 2012,85(1015):897-904.\u003c/li\u003e\n\u003cli\u003eHu H, Wang Q, Tang H, Xiong L, Lin Q. Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: Differences between malignant and benign. Thorac Cancer. 2016,7(1):80-7.\u003c/li\u003e\n\u003cli\u003eGao F, Sun Y, Zhang G, Zheng X, Li M, Hua Y. CT characterization of different pathological types of sub-centimeter pulmonary ground-glass nodular lesions. Br J Radiol. 2019,92(1094):20180204.\u003c/li\u003e\n\u003cli\u003eQiu ZX, Cheng Y, Liu D, Wang WY, Wu X, Wu WL, et al. Clinical, pathological, and radiological characteristics of solitary ground-glass opacity lung nodules on high-resolution computed tomography. Ther Clin Risk Manag. 2016,12:1445-1453.\u003c/li\u003e\n\u003cli\u003eChen ML, Li XT, Wei YY, Qi LP, Sun YS. Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules? Thorac Cancer. 2019,10(2):234-242.\u003c/li\u003e\n\u003cli\u003eLee HJ, Lee CH, Jeong YJ, et al. IASLC/ATS/ERS international multidisciplinary classification of lung adenocarcinoma: novel concepts and radiologic implications. J Thorac Imaging. 2012,27(6):340-53.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1. Clinical features of 117 patients \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical features\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with Benign PSNs (n=44)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with Malignant PSNs (n=73)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e Value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge (y) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e52 \u0026plusmn; 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e58 \u0026plusmn; 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e21 (47.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e26 (35.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.196\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e23 (52.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e47 (64.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking history\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eNever-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e25 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e61 (83.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eEx-smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e5 (11.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3 (4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.005\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e14 (31.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e9 (12.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking amount (pack-years) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e23 \u0026plusmn; 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e27 \u0026plusmn; 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.404\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eHistory of cancer\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003ePresent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e8 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.316\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eAbsent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e42 (95.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e65 (89.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eWBC count (uL\u003csup\u003e-1\u003c/sup\u003e) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6872 \u0026plusmn; 2931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5631 \u0026plusmn; 2214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.051\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlood eosinophil count (uL\u003csup\u003e-1\u003c/sup\u003e) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e154 \u0026plusmn; 223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e100 \u0026plusmn; 92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.930\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlood eosinophilia\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026ge;500 per microlite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.139\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026lt;500 per microliter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e42 (95.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e73 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Data are presented as n (%) or Means \u0026plusmn; SD.\u003c/p\u003e\n\u003cp\u003e* Analysis of Variance, ANOVA.\u003c/p\u003e\n\u003cp\u003e\u0026Dagger; Calculated with the Pearson \u0026chi;\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e\n\u003cp\u003e\u0026sect; Calculated with the Fisher exact test.\u003c/p\u003e\n\u003cp\u003e\u0026amp; Wilcoxon rank sum test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable2. The overall CT findings of benign and malignant PSNs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eCT features\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eBenign PSNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=44)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003eMalignant PSNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=75)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e Value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLesion size (mm)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e11.1 \u0026plusmn; 4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e15.2 \u0026plusmn; 4.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLesion area (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e118.4 \u0026plusmn; 89.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e199.4 \u0026plusmn; 116.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eDensity of GGO (HU)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-635 \u0026plusmn; 84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e-616 \u0026plusmn; 81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.241\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRight upper lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e16 (36.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e33 (44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRight middle lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2 (2.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRight lower lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e7 (15.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e6 (8.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.528\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eLeft upper lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e12 (27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e25 (33.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eLeft lower lobe\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e8 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e9 (12.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLesion shape\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRound/Oval\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e13 (29.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e17 (22.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.404\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eIrregular\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e31 (70.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e58 (77.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLesion border\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eWell-defined\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e12 (27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e52 (69.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eIll-defined\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e32 (72.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e23 (30.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eMargin of well-defined border\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eSmooth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3 (25.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5 (9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.164\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eCoarse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e9 (75.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e47 (90.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eUniformity of GGO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eHomogeneous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e28 (63.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e27 (36.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003e0.004\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eHeterogeneous\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e16 (36.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e48 (64.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eLobulation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e45 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpiculation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e22 (29.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eAir bronchogram\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e18 (24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eBubble lucency\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1 (2.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e19 (25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003ePleural indentation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3 (6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e35 (46.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003ePulmonary vessel changes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e33 (44.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. Data are presented as n (%) or Means \u0026plusmn; SD.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Analysis of Variance, ANOVA.\u003c/p\u003e\n\u003cp\u003e\u0026Dagger; Calculated with the Pearson \u0026chi;\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e\n\u003cp\u003e\u0026sect; Calculated with the Fisher exact test.\u003c/p\u003e\n\u003cp\u003e\u0026amp; Wilcoxon rank sum test.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable3. The CT findings of solid component within Benign and Malignant PSNs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eCT features\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with Benign PSNs (n=44)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with Malignant PSNs (n=75)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e Value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component area\u003csup\u003e*\u003c/sup\u003e (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e15.6 \u0026plusmn; 15.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e34.6 \u0026plusmn; 30.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component radio* (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e12.4 \u0026plusmn; 7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e16.8 \u0026plusmn; 9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e0.015\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eDensity of Solid component\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-164 \u0026plusmn; 196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e-99 \u0026plusmn; 130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e0.155\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component number\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eSolitary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e41 (93.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e34 (45.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eMultiple\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e3 (6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e41 (54.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component shape\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eRound\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e25 (56.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e7 (9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eIrregular\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e19 (43.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e68 (90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component border\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eWell-defined\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6 (13.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e35 (46.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eIll-defined\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e38 (86.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e40 (53.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003emargin of well-defined solid component\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eSmooth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6 (100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e11 (31.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e0.003\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eCoarse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0 (0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e24 (68.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component distribution\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eConcentrated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e42 (95.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e37 (49.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" rowspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e＜0.001\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eScattered\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2 (4.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e38 (50.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003e\u003cstrong\u003eSolid component location\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eCentral\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e21 (47.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e18 (24.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e0.008\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"161\"\u003e\n\u003cp\u003eEccentric\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e23 (52.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e57 (76.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are presented as n (%) or Means \u0026plusmn; SD.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e Calculated with the Pearson \u0026chi;\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e\n\u003cp\u003e\u0026sect; Calculated with the Fisher exact test.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026amp;\u003c/sup\u003e Wilcoxon rank sum test.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Part-solid nodules, CT, Malignancy, Benignity, Solid component","lastPublishedDoi":"10.21203/rs.3.rs-138228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-138228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePulmonary\u003cstrong\u003e \u003c/strong\u003epart-solid nodules (PSNs) are reported to have a high possibility of malignancy, while benign PSNs are not rare.\u003cstrong\u003e \u003c/strong\u003eThis study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section CT features.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 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. The binary logistic regression analysis was performed to reveal predictors of malignant PSNs.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 119 PSNs in 117 patients (mean age, 56 years ± 11 [standard deviation]; 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). Between benign and malignant PSNs, there were significant differences in patients’ age and smoking history. Regarding CT characteristics, malignant lesions and benign lesions significantly differed in CT features of 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;\u003cem\u003e \u003c/em\u003eP=0.001) of nodule, and 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 internal solid components were significant independent predictors distinguishing malignant PSNs.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Well-defined and lobulated PSNs with irregular and scattered solid components have a high probability of being malignant.\u003c/p\u003e","manuscriptTitle":"Benign and Malignant Part-Solid Nodules: Differentiation Via Thin-Section Computed Tomography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-19 19:04:50","doi":"10.21203/rs.3.rs-138228/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4182baec-ba85-4ccc-a3b9-04a2725f1dad","owner":[],"postedDate":"January 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1959838,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-02-01T08:44:28+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-19 19:04:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-138228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-138228","identity":"rs-138228","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}