
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-09835
00020
10.1097/MD.0000000000039608
3
6800
Research Article
Observational Study
The value analysis of high-resolution thin-layer CT in the identification of early lung adenocarcinoma: An observation study
Zhao Zhiwei MM a
Yang Hao MB b
https://orcid.org/0009-0004-9903-8814
Wang Wenxuan MM b*
a Medical Imaging Center, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, Xinjiang, China
b The Diagnostic Radiology Department, The 964th Hospital of PLA Joint Logistic Support Force, Changchun, Jilin, China.
* Correspondence: Wenxuan Wang, No. 4799, Lvyuan District, Changchun 130000, Jilin, China (e-mail: wangwenxuan783@sina.com).
13 9 2024
13 9 2024
103 37 e3960806 11 2023
27 7 2024
16 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

The aim of this study was to explore the clinical value of high-resolution thin-layer computed tomography (CT) for the identification of early lung adenocarcinoma. Ninety patients with early lung adenocarcinoma who were diagnosed and treated in our hospital were selected as study subjects and divided into noninvasive (NIG, n = 51) and invasive (IG, n = 39) groups according to their pathological findings. Both groups underwent high-resolution target scanning. Differences in lesion size, density, and distribution between the 2 groups were compared. Intergroup differences in the CT signs were examined. A receiver-operating characteristic curve was established to calculate the diagnostic efficacy of high-resolution, thin-layer CT for early lung adenocarcinoma infiltration. The maximum diameter and density of the tumors were significantly higher in the IG than in the NIG (P < .05). The proportions of CT signs of lobulation, spicule, and vessel convergence were higher in the IG patients compared to the NIG (P < .05). High-resolution thin-layer CT for the diagnosis of lung adenocarcinoma infiltration had an AUC of 0.6702 (P < .05), a diagnostic sensitivity of 64.10%, and a diagnostic specificity of 60.78%. High-resolution thin-layer CT had certain differential diagnostic efficacy for early lung adenocarcinoma, which clearly presents various CT signs of early lung adenocarcinoma lesions.

CT signs
diagnostic efficacy
early lung adenocarcinoma
high-resolution thin-layer CT
ROC curve
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

According to the latest Global Cancer Burden Data, up to 1.8 million patients died of lung cancer worldwide that year.[1] In 2020, lung cancer caused 710,000 deaths in China, consistently ranking among the leading causes of cancer-related mortality.[2] Lung cancer is currently recognized as the most common malignant tumor worldwide. Patients with lung cancer tend to have a poorer prognosis and a higher mortality rate, with a shorter progression-free survival even with timely and aggressive treatment, thus posing a greater clinical risk.[3] Lung adenocarcinoma is a non-small cell carcinoma and one of the main pathological types of lung cancer.[4] Moreover, lung adenocarcinoma mostly originates from small bronchitis, and the early clinical symptoms are not obvious and can often be detected when patients undergo chest radiography.[5] Lung adenocarcinoma lesions typically manifest as round or oval shapes and generally grow and proliferate slowly; however, a minority of patients might experience hematogenous metastasis at the early stage of the disease, and lymphatic metastasis mostly occurs in advanced patients.[6,7]

The traditional diagnosis of lung adenocarcinoma mostly relies on X-ray detection, laboratory testing, or histopathological examination. However, pathological examination has the disadvantages of a long detection cycle, invasive operation, and poor reproducibility, which limits the clinical promotion of this modality. Although laboratory testing is reproducible, the results are susceptible to a variety of influencing factors, which leads to a notable reduction in specificity.[6] Imaging tests have the characteristics of good reproducibility and strong generalizability, among which computed tomography (CT) technology has been proven to have the advantages of high-density resolution, small interference from extradimensional structures, and superior spatial structural imaging.[8] A study of 89 patients with lung adenocarcinoma revealed a significant difference in the quantitative indicators of high-resolution thin-layer CT between patients with lung adenocarcinoma and those with benign lung lesions. The sensitivity and specificity of high-resolution thin-layer CT for the diagnosis of lung adenocarcinoma were 89.36% and 71.43%, respectively.[9]

The authors of this study discovered in their practical work that high-resolution thin-layer CT can clearly display fine structures within the lung tissue, such as blood vessels, interlobular septa, and millimeter-sized nodules. Furthermore, it does not require contrast enhancement during scanning, making it the preferred method for diffuse pulmonary lesions. However, the authors of this study, through a review of the literature, discovered that there are currently few studies on the application of high-resolution thin-layer CT in the diagnosis of lung adenocarcinoma, particularly on whether high-resolution thin-layer CT can be used to distinguish the presence of infiltration in lung adenocarcinoma.

Therefore, the aim of this study was to explore the value of high-resolution thin-layer CT in distinguishing lung adenocarcinoma and assessing infiltration by means of a comparative group approach, providing a reference for the formulation of clinical intervention strategies for lung adenocarcinoma patients.

2. Methods

2.1. General data

Ninety patients with early lung adenocarcinoma who were diagnosed and treated in our hospital from August 2021 to March 2023 were selected as study subjects and divided into noninvasive (NIG, n = 51) and invasive (IG, n = 39) groups according to their pathological findings. In the NIG, there were 31 males and 20 females, with an average age of (46.32 ± 5.13) years; in the IG, there were 23 males and 16 females, with an average age of 47.02 ± 4.69 years. There were no statistically significant differences in the general clinical data between the 2 groups. This study was approved by the 964th Hospital of the PLA Joint Logistic Support Force Ethics Committee. All patients provided written informed consent before enrollment in the study.

The inclusion criteria were as follows: (1) diagnosis of early lung adenocarcinoma by pathological diagnosis, (2) patients with pleural effusion and other related clinical symptoms, (3) single lesion with a maximum diameter < 10 mm, (4) patients who did not receive chemoradiotherapy before CT detection, and (5) patients with complete data of high-resolution thin-layer CT detection.

The exclusion criteria were as follows: (1) patients with mental disorders, (2) patients with other malignant tumors, (3) patients with multiple pulmonary nodules, (4) patients with combined systemic infections, (5) patients with solid components in the lesion, (6) patients undergoing other incomplete clinical investigations, (7) patients with congenital thoracic malformations, and (8) patients with hilar and mediastinal lymph node metastases.

2.2. Intervention methods

All enrolled subjects underwent high-resolution thin-layer CT. A Siemens 64-slice CT scanner was used as diagnostic equipment. During the scan, the patients were placed in a supine position with their hands elevated. Following the application of shielding to the thyroid and genital areas, patients were instructed to inhale deeply and hold their breath during the scanning procedure. First, a routine chest scan was performed from above the apex pulmonis to below the bilateral costophrenic angles using the following scanning parameters: tube voltage of 120 kV, layer thickness of 5 mm, a high-resolution algorithm to reconstruct the layer thickness of 0.625 mm, a matrix of 512 × 512, and an automatic exposure control of 0.5 sec/r. Pitch factor and iterative reconstructions were applied to control radiation dose and image quality, enabling high-resolution target scan at the lesion site. After the scan was completed, the acquired parameters were transmitted to the workstation, and the volumetric reproduction technique was used to reconstruct the 3-dimensional pulmonary images of the patients. The lesion sites were observed in detail, and the lesion location, maximum tumor diameter, CT detection density, and CT signs (lobulation sign, spicule sign, and vessel convergence sign) were recorded. The maximum diameter of the tumor was the largest cross-sectional length of the lesion in each direction, and the CT detection density was the density of the parenchymal site of the lesion. The indicators were averaged over 3 consecutive measurements.

2.3. Observation indicators and evaluation standards

The enrolled subjects were grouped according to pathological findings, and the following indicators were compared: (1) the difference in baseline clinical data (such as gender, age, diagnosis method, comorbidities, etc); (2) the difference in CT quantitative indicators (the maximum diameter of the tumor and the density); (3) the difference in CT signs (lobulation sign, spicule sign, vessel convergence sign, etc) between the 2 groups; (4) the analysis of CT signs of typical cases; (5) the diagnostic efficiency of high-resolution thin-layer CT in lung adenocarcinoma was calculated by plotting receiver-operating characteristic (ROC) curve. The flow diagram of the study design is displayed in Figure 1.

Figure 1. Flow diagram of the study design.

2.4. Statistical methods

Data were collected using EXCEL 2021 and data analysis was performed using SPSS 21.0. The measurement data were expressed by mean ± standard deviation and analyzed using the t test, and counting data were expressed by ratio and analyzed using the chi-square test. The diagnostic efficiency of high-resolution, thin-layer CT for lung adenocarcinoma was calculated by plotting ROC curves. Differences were considered statistically significant at P < .05.

3. Results

3.1. General clinical data analysis of patients in the IG and NIG

General clinical data such as sex, average age, average body mass index, comorbidities, diagnostic methods, and lesion location were compared between the 2 groups, and the results showed that there were no significant differences in the above data between the 2 groups (P > .05), as indicated in Table 1.

Table 1 General clinical data analysis of patients in the IG and NIG (mean ± SD)/[n (%)].

Clinical data	Noninvasive group (n = 51)	Invasive group (n = 39)	P	
Gender	Male	31 (60.78)	23 (58.97)	.862	
Female	20 (39.22)	16 (41.03)	
Average age (years)	46.32 ± 5.13	47.02 ± 4.69	.669	
Body mass index (kg/m2)	23.65 ± 4.16	24.01 ± 3.98	.416	
Comorbidities	Hyperlipidemia	12 (23.53)	10 (25.64)	.817	
Diabetes mellitus	6 (11.76)	5 (12.82)	.880	
Chronic kidney disease	2 (3.92)	1 (2.56)	.722	
Diagnostic methods	Bronchoscopic biopsy	26 (50.98)	20 (51.28)	.219	
Surgical pathology	15 (29.41)	16 (41.03)	
Percutaneous puncture biopsy	10 (19.61)	3 (7.69)	
Lesion location	Superior lobe of left lung	15 (29.41)	10 (25.64)	.626	
Inferior lobe of left lung	9 (17.65)	5 (12.82)	
Superior lobe of right lung	11 (21.57)	6 (15.38)	
Middle lobe of right lung	13 (25.49)	10 (25.64)	
Inferior lobe of right lung	3 (5.88)	8 (20.51)	
Smoking	Yes	20 (39.22)	15 (38.46)	.265	
No	31 (60.78)	24 (61.54)	
Ionizing radiation exposure	Yes	6 (11.76)	3 (7.69)	.971	
No	45 (88.24)	36 (92.31)	
IG: invasive group; NIG: noninvasive group.

3.2. Comparison of quantitative indicators of high-resolution CT between the 2 groups

The intergroup comparison showed that the maximum tumor diameter and density in the IG were significantly higher than those in the NIG (P < .05), as shown in Table 2 and Figure 2.

Table 2 Comparison of quantitative indicators of high-end high-resolution CT between the 2 groups.

Quantitative indicators	Noninvasive group (n = 51)	Invasive group (n = 39)	t	P	
Maximum tumor diameter (mm)	8.11 ± 1.23	8.86 ± 1.23	2.867	.005	
Density (Hu)	−506.32 ± 169.51	−423.26 ± 186.51	2.205	.030	
CT: computed tomography.

Figure 2. Comparison of quantitative indicators of high-resolution CT between the 2 groups. The maximum tumor diameter and density in the IG were significantly higher than those in the NIG (P < .05). * represents a statistically significant difference between groups. CT: computed tomography; IG: invasive group; NIG: noninvasive group.

3.3. Comparison of CT signs of the lesion area between the 2 groups

The proportions of CT signs of lobulation, spicule, and vessel convergence were significantly higher in the IG than in the NIG (P < .05). There was no significant difference in the pleural indentation sign and air bronchogram between the 2 groups (P > .05), as indicated in Table 3 and Figure 3. The high-resolution thin-layer CT signs of typical patients and those with benign lesions are shown in Figures 4–7.

Table 3 Comparison of CT signs of the lesion area between the 2 groups [n (%)].

CT signs	Noninvasive group (n = 51)	Invasive group (n = 39)	χ2	P	
Lobulation sign	3 (5.88)	8 (20.51)	4.409	.036	
Spicule sign	8 (15.68)	10 (25.64)	5.326	.021	
Pleural indentation sign	7 (13.73)	5 (12.82)	1.165	.083	
Air bronchogram	5 (9.80)	6 (15.38)	2.665	.136	
Vessel convergence sign	5 (9.80)	8 (20.51)	3.569	.021	
CT: computed tomography.

Figure 3. Comparison of CT signs of the lesion area between the 2 groups. The proportions of CT signs of lobulation, spicule, and vessel convergence were significantly higher in the IG than in the NIG (P < .05). * represents a statistically significant difference between groups. CT: computed tomography; IG: invasive group; NIG: noninvasive group.

Figure 4. High-resolution thin-layer CT sign in a typical case. The patient presented with microinvasive adenocarcinoma, and a high-resolution, thin-layer CT scan showed lobulation signs, spicule signs, and pleural retraction. CT: computed tomography.

Figure 5. High-resolution thin-layer CT sign in a typical case. The patient presented with lung adenocarcinoma, and a high-resolution, thin-layer CT scan showed lobulation and spicule signs. CT: computed tomography.

Figure 6. High-resolution thin-layer CT sign in a typical case. The patient presented with lung adenocarcinoma, and a high-resolution, thin-layer CT scan showed spicule signs, vessel convergence signs, and pleural retraction. CT: computed tomography.

Figure 7. High-resolution thin-layer CT sign in benign pulmonary hamartoma. CT: computed tomography.

3.4. The diagnostic efficiency analysis of high-resolution thin-layer CT in lung adenocarcinoma

A high-resolution thin-layer CT ROC curve for the diagnosis of lung adenocarcinoma infiltration was plotted, and the density value of −448.0 Hu at the site of the lesion was determined to be the threshold. The AUC was calculated to be 0.6702, 95% CI = 0.5577–0.7826, SE = 0.0574, P = .0058, the sensitivity was 64.10%, and the specificity was 60.78%, as indicated in Figure 8.

Figure 8. The diagnostic efficiency analysis of high-resolution thin-layer CT in lung adenocarcinoma. CT: computed tomography.

4. Discussion

Lung adenocarcinoma is a relatively common pathological type of lung cancer,[10] which belongs to non-small cell lung cancer. An epidemiological study indicated that the incidence of lung adenocarcinoma has steadily increased annually in recent years, and the overall number of cases accounts for approximately 40% of all lung cancer cases.[11] Early clinical symptoms of lung adenocarcinoma include chest tightness, cough, and chest pain, which are similar to those of respiratory diseases such as pneumonia and colds, and thus easily ignored by patients. Generally, lung adenocarcinoma is diagnosed in the middle and late stages and is characterized by poor prognosis and accelerated disease progression. Consequently, an early and accurate diagnosis is an important prerequisite for prolonging patient survival and improving prognosis.[12,13]

This study analyzed the clinical value of high-resolution, thin-layer CT for the identification of lung adenocarcinoma by adopting grouping and comparison methods. The results showed that in terms of quantitative indicators (maximum tumor diameter and lesion density), there were significant differences between the NIG and IG, and the maximum tumor diameter and tumor density in the IG were significantly higher than those in the NIG. In a study by Wang et al,[14] CT imaging was conducted on 317 patients with lung adenocarcinoma, and it was found that there was a significant difference in CT signs between patients with infiltrating lung adenocarcinoma and those without infiltrating lung adenocarcinoma; multivariate analysis showed that the tumor density was closely linked to the prognosis of the patients, which was an independent factor influencing the prognosis of the patients (P = .008). Zhang et al[15] analyzed 3 cases of lung adenocarcinoma and discovered that high-resolution thin-layer CT had better diagnostic efficacy for lung adenocarcinoma and that there was a significant difference in terms of CT signs between patients with infiltrating lung adenocarcinoma and those without infiltrating lung adenocarcinoma, suggesting that the imaging test has a certain predictive ability for lung adenocarcinoma infiltration. According to the authors of this study, CT is the most widely used examination modality in the clinic, and high-resolution thin-layer CT is more effective in planar reconstruction and volumetric representation and can obtain images with higher clarity and lower noise through the processing of patients’ raw data, which can significantly reduce the interference of external structures on the detection results and improve diagnostic accuracy.[16,17] In this study, the maximum diameter of the tumor in the IG was higher than that in the NIG, and the density of the lesions was higher, suggesting that the above quantitative indicators may be of value in the identification of lung adenocarcinoma infiltration.

To verify the feasibility of the above theory, the diagnostic ROC curve of CT density for lung adenocarcinoma was further plotted in this study, and the calculation showed that the diagnostic AUC was 0.6702 (P = .0058), suggesting that CT density is valuable in the diagnosis of lung adenocarcinoma infiltration, which is in line with the results of other studies. Shao et al[18] found that high-resolution CT has good application value in predicting early lung adenocarcinoma pathological subtypes as well as its growth patterns; patients with infiltrating lung adenocarcinomas had higher diameters and greater densities, and application of foci density to the analysis of lung adenocarcinoma pathological subtypes was of value, but it had no significance in predicting the invasive growth patterns. Similar results were found in a study by Nakamura et al,[19] which noted that there was a significant difference in alveolar wall thickness between the normal alveolar wall and the cancerous area (0.034 mm vs 0.084 mm) and that the healthy lung area and lung cancer area can be correctly differentiated based on quantitative indicators of CT signs.

In this study, the differences in CT signs between patients with infiltrating lung adenocarcinoma and those with non-infiltrating lung adenocarcinoma were also compared, and the results showed that patients with infiltrating lung adenocarcinoma had a higher rate of lobulation, spicule, and vessel convergence signs than patients with non-infiltrating lung adenocarcinoma. Similar to the results of the present study, Dong et al[20] revealed that the detection rates of spicule, lobulation, vessel convergence, and vacuole signs were higher in patients with infiltrating lung adenocarcinoma than in those with non-infiltrating lung adenocarcinoma. The authors of this study found that the appearance of the vessel convergence sign is mostly related to the need for blood supply and oxygenation of blood vessels during the growth of lung adenocarcinoma and that greater numbers of neovascularization signify a poorer prognosis for the patient[21]; spicule sign represents the needle-like streaks radiating from the lesion along the interlobular septa, lymphatics, and blood vessels, which is prevalent in patients with infiltrating lung adenocarcinoma[22]; lobulation sign represents the result of the adenocarcinoma obstructed and constrained by the surrounding tissues, resulting in an uneven change in the margins of the lesion.[23] These lesions are closely associated with the condition of lung adenocarcinoma patients and identifying them as early CT signs of lung adenocarcinoma can assist physicians in making more accurate diagnoses, thereby providing a reference for subsequent clinical treatment plans.

High-resolution thin-layer CT demonstrates good application efficacy in the differential diagnosis of early lung adenocarcinoma, which clearly presents various CT signs of early lesions of lung adenocarcinoma, provides clinical references for subsequent treatment, and has a certain value in terms of widespread applicability. Although this study provides a reference for the identification of lung adenocarcinoma in terms of quantitative indicators, it also has shortcomings such as a small sample size and lack of follow-up data. If multicenter, large-sample studies can be conducted in the later stages and the analysis of pathological characteristics of patients can be introduced appropriately, it will facilitate the establishment of a foundation for the clinical promotion of high-resolution thin-layer CT.

Author contributions

Conceptualization: Zhiwei Zhao, Wenxuan Wang.

Data curation: Hao Yang, Wenxuan Wang.

Formal analysis: Zhiwei Zhao, Hao Yang, Wenxuan Wang.

Investigation: Hao Yang.

Methodology: Zhiwei Zhao, Wenxuan Wang.

Project administration: Zhiwei Zhao, Wenxuan Wang.

Resources: Wenxuan Wang.

Software: Zhiwei Zhao.

Validation: Hao Yang.

Writing – original draft: Zhiwei Zhao, Wenxuan Wang.

Writing – review & editing: Wenxuan Wang.

Abbreviations:

CT computed tomography

IG invasive group

NIG noninvasive group

ROC receiver-operating characteristic.

The authors have no funding and conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

How to cite this article: Zhao Z, Yang H, Wang W. The value analysis of high-resolution thin-layer CT in the identification of early lung adenocarcinoma: An observation study. Medicine 2024;103:37(e39608).
==== Refs
References

[1] Wang Q Li M Yang M . Analysis of immune-related signatures of lung adenocarcinoma identified two distinct subtypes: implications for immune checkpoint blockade therapy. Aging (Albany NY). 2020;12 :3312–39.32091408
[2] Wu J Li L Zhang H . A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma. Oncogene. 2021;40 :4413–24.34108619
[3] Tonyali O Gonullu O Ozturk MA Kosif A Civi OG . Hepatoid adenocarcinoma of the lung and the review of the literature. J Oncol Pharm Pract. 2020;26 :1505–10.32041468
[4] Shen Y Li D Liang Q Yang M Pan Y Li H . Cross-talk between cuproptosis and ferroptosis regulators defines the tumor microenvironment for the prediction of prognosis and therapies in lung adenocarcinoma. Front Immunol. 2022;13 :1029092.36733399
[5] Fujikawa R Muraoka Y Kashima J . Clinicopathologic and genotypic features of lung adenocarcinoma characterized by the international association for the study of lung cancer grading system. J Thorac Oncol. 2022;17 :700–7.35227909
[6] Chen Y Tang L Huang W Zhang Y Abisola FH Li L . Identification and validation of a novel cuproptosis-related signature as a prognostic model for lung adenocarcinoma. Front Endocrinol (Lausanne). 2022;13 :963220.36353226
[7] Yu Y Wang Z Zheng Q Li J . FAM72 serves as a biomarker of poor prognosis in human lung adenocarcinoma. Aging (Albany NY). 2021;13 :8155–76.33686947
[8] Zuo S Wei M Wang S Dong J Wei J . Pan-cancer analysis of immune cell infiltration identifies a prognostic immune-cell characteristic score (ICCS) in lung adenocarcinoma. Front Immunol. 2020;11 :1218.32714316
[9] Jin CY Du L Nuerlan AH Wang XL Yang YW Guo R . High expression of RRM2 as an independent predictive factor of poor prognosis in patients with lung adenocarcinoma. Aging (Albany NY). 2020;13 :3518–35.33411689
[10] Yi M Li A Zhou L Chu Q Luo S Wu K . Immune signature-based risk stratification and prediction of immune checkpoint inhibitor’s efficacy for lung adenocarcinoma. Cancer Immunol Immunother. 2021;70 :1705–19.33386920
[11] Zhuansun Y Bian L Zhao Z . Clinical characteristics of hepatoid adenocarcinoma of the lung: four case reports and literature review. Cancer Treat Res Commun. 2021;29 :100474.34656923
[12] Toki MI Harrington K Syrigos KN . The role of spread through air spaces (STAS) in lung adenocarcinoma prognosis and therapeutic decision making. Lung Cancer. 2020;146 :127–33.32534331
[13] Peng SL Wang R Zhou YL . Insight of a metabolic prognostic model to identify tumor environment and drug vulnerability for lung adenocarcinoma. Front Immunol. 2022;13 :872910.35812404
[14] Wang T Yang Y Liu X . Primary invasive mucinous adenocarcinoma of the lung: prognostic value of CT imaging features combined with clinical factors. Korean J Radiol. 2021;22 :652–62.33236544
[15] Zhang T Li X Liu J . Prediction of the invasiveness of ground-glass nodules in lung adenocarcinoma by radiomics analysis using high-resolution computed tomography imaging. Cancer Control. 2022;29 :10732748221089408.35848489
[16] Zhang J Jiang YM Xu AD Lin S Fang N Wang YL . Characteristics of fibrotic-foci-like lung adenocarcinoma on 18 F-FDG PET/computed tomography and HRCT. Nucl Med Commun. 2023;44 :502–8.37036299
[17] Chen X Li P Zhang M Wang X Wang D . Value of preoperative (18)F-FDG PET/CT and HRCT in predicting the differentiation degree of lung adenocarcinoma dominated by solid density. PeerJ. 2023;11 :e15242.37138817
[18] Shao X Niu R Jiang Z Shao X Wang Y . Role of PET/CT in management of early lung adenocarcinoma. AJR Am J Roentgenol. 2020;214 :437–45.31714848
[19] Nakamura S Mori K Iwano S . Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns. Nagoya J Med Sci. 2020;82 :25–31.32273629
[20] Dong H Yin LK Qiu YG . Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol. 2023;33 :3931–40.36600124
[21] Cai Y Chen T Zhang S Tan M Wang J . Correlation exploration among CT imaging, pathology and genotype of pulmonary ground-glass opacity. J Cell Mol Med. 2023;27 :2021–31.37340599
[22] Bhagat S Gupta V Jain SK Aaggarwal S Khanduri S Batra S . The diagnostic accuracy of a novel scoring system using multi-detector computed tomography to diagnose lung cancer. Cureus. 2023;15 :e35848.37033527
[23] Peng L Shang QW Deng HY Liu ZK Li W Wang Y . Lobe-specific lymph node dissection in early-stage non-small-cell lung cancer: an overview. Asian J Surg. 2023;46 :683–7.35918226
