
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39297299
10.1080/07853890.2024.2405075
2405075
Version of Record
Research Article
Oncology
Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules
L. Jiang et al.
https://orcid.org/0000-0002-6860-755X
Jiang Long a*
Zhou Yang b*
Miao Wang c*
Zhu Hongda a
Zou Ningyuan a
Tian Yu a
Pan Hanbo a
Jin Weiqiu a
Huang Jia a
Luo Qingquan a
a Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
b Department of Purchasing Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
c Department of Thoracic Surgery, The Third People’s Hospital of Zhengzhou, Zhengzhou, China
* These authors contributed equally.

CONTACT Qingquan Luo luoqingquan@hotmail.com
Long Jiang dylan919@139.com Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
19 9 2024
2024
19 9 2024
56 1 240507511 12 2023
16 5 2024
17 5 2024
KnowledgeWorks Global Ltd.18 9 2024
published online in a building issue18 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Introduction

Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction.

Methods

Patients with stage 0–IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN.

Results

Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules.

Conclusions

Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.

Keywords

Artificial intelligence
prediction
lung cancer
National Natural Science Foundation of China 10.13039/501100001809 81702251 81972176 Natural Science Foundation of Shanghai 18ZR1435100 Nurture Projects for Basic Research of Shanghai Chest Hospital 2021YNJCQ3 Shanghai Municipal Health Commission 10.13039/100017950 20214Y0418 Shanghai Talent Development Fund 2021068 Talent Training Plan of Shanghai Chest Hospital Open Project Program of Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education 22X010201609-003 Fundamental Research Funds for the Central Universities 10.13039/501100012226 YG2023QNB24 This study has received funding by National Natural Science Foundation of China (81702251, 81972176), Natural Science Foundation of Shanghai (18ZR1435100), Nurture Projects for Basic Research of Shanghai Chest Hospital (2021YNJCQ3), Shanghai Municipal Health Commission (20214Y0418), Shanghai Talent Development Fund (2021068), Talent Training Plan of Shanghai Chest Hospital, Open Project Program of Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education (22X010201609-003) and Fundamental Research Funds for the Central Universities (YG2023QNB24).
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pmcIntroduction

Lung cancer remains the leading cause of cancer mortality worldwide, resulting in over 1.7 million deaths per year [1]. The high mortality rate is largely attributed to the fact that many patients present at later stages when curative treatment is no longer possible [2]. This underscores the critical importance of early detection and diagnosis of lung cancer for improving patient outcomes. Screening high risk individuals with low-dose computed tomography (CT) has been demonstrated to reduce lung cancer mortality by enabling detection at earlier more treatable stages [3]. However, CT screening also identifies large numbers of indeterminate pulmonary nodules, requiring strategies to accurately determine which nodules warrant more invasive workup versus continued imaging surveillance [3].

Solitary pulmonary nodules (SPNs) are spherical lesions in the lung measuring less than 3 cm, representing one of the most common incidental findings on chest CTs [4]. The first step in evaluation is differentiating benign nodules, which can simply be ­monitored, from malignant nodules that require timely diagnosis and treatment. While certain benign-appearing nodules such as calcified granulomas can be definitively diagnosed radiologically, others present a diagnostic challenge [5]. The likelihood of malignancy is related to nodule size, smoking history and other ­clinical risk factors, but imaging features play an important role [5].

Previous studies have demonstrated the usefulness of CT parameters in determining the degree of invasiveness of lung cancer [5]. However, the irregular shape of SPN makes the measurement of CT values difficult, leading to potential inaccuracies. The use of artificial intelligence (AI) in analysing CT images can provide more accurate and consistent measurements of the nodule’s CT parameters, owing to the automated segmentation and detection of the nodule boundary [6]. Therefore, we aim to investigate the value of AI-assisted quantitative CT parameters in predicting the degree of invasiveness of lung cancer manifested as SPNs.

Increasingly, AI applications using computer vision and machine learning are being developed to analyse radiological images and extract data to assist in medical decision-making [7]. These tools show tremendous promise for improving lung cancer screening and the evaluation of SPNs detected on CT. AI assessment of pulmonary nodule size, shape, texture, growth rate and densitometry can help predict the probability of malignancy and need for further workup [8].

Once a nodule is determined to be malignant, accurate staging is critical for optimal treatment planning and prognosis determination. Non-small cell lung cancer (NSCLC) encompasses a heterogeneous group of histologic subtypes with varying degrees of invasiveness [9]. The ability to determine tumour invasiveness non-invasively based on CT imaging could have major clinical impact by guiding surgical approaches. More invasive NSCLC subtypes may warrant lobectomy, while localized non-invasive lesions can be treated with sublobar resection [10]. However, the relationship between CT characteristics and pathologic invasiveness remains incompletely understood.

Both visual qualitative features as well as quantitative densitometry measures on CT have been studied for assessing invasiveness [7]. Soft ground glass opacities are associated with less invasive histologic patterns such as lepidic growth, whereas solid nodules suggest more invasive subtypes [11]. Quantitatively, previous studies have identified associations between higher mean Hounsfield unit (HU) values and tumour invasiveness [12]. However, small irregular nodules make accurate visual and quantitative CT assessment difficult. Recently, AI tools have been applied to lung cancer CT analysis with promising results for precision medicine [8].

The purpose of this study is to investigate the potential of using AI-assisted quantitative CT metrics to predict the degree of invasiveness of lung cancer manifesting as SPNs. We hypothesize that AI-based segmentation and densitometric analysis can enhance prediction of tumour invasiveness based on preoperative chest CTs. Accurate non-invasive risk stratification could optimize treatment decisions, particularly selection between limited sublobar resection versus lobectomy.

Methods

Study design and data source

This retrospective single centre study analysed a cohort of patients who underwent surgical resection for early-stage NSCLC at Shanghai Chest Hospital between January 2020 and December 2022 (Figure 1). The aim was to assess the utility of quantitative CT metrics from computer-aided diagnosis (CAD) software for predicting tumour invasiveness. Patients were identified by searching the institutional electronic medical record system and pathology database. The study was approved by the Institutional Review Board of Shanghai Chest Hospital (KS20256), and written informed consent for the use and analysis of clinical data was obtained preoperatively from each patient.

Figure 1. Consort diagram of patients included in the study. Numbers in parentheses are numbers of patients.

Study population

Patients were included if they met the following criteria: (1) clinical stage 0, IA or IB NSCLC based on the 8th edition TNM staging system; (2) surgical resection with curative intent performed at the study institution; (3) preoperative thin slice CT chest performed at the study institution; (4) SPN identified on CT interpreted as suspicious for lung cancer; (5) pathological diagnosis of NSCLC made on the resected specimen. Patients were excluded if they had multiple pulmonary nodules or if histology demonstrated small cell lung cancer or carcinoid tumour.

After applying inclusion and exclusion criteria, the final study population consisted of 355 patients with clinical early-stage NSCLC who underwent surgical resection during the study period. All patients had preoperative CT chest scans available for retrospective analysis.

CT image acquisition

As part of routine preoperative evaluation, patients underwent CT chest imaging on one of several CT scanners at Shanghai Chest Hospital. CTs were performed with a slice thickness of 1 or 1.25 mm on scanners from multiple vendors including Siemens (Munich, Germany), GE (Boston, MA), Philips (Amsterdam, Netherlands) and Toshiba (Minato City, Japan). Scan parameters were: tube voltage 120 kVp, tube current modulated with range 100–500 mA, convolution kernel sharp. Images were reconstructed at 0.625–1.25 mm slice thickness in the axial plane, along with coronal and sagittal reconstructions.

CT image analysis

The radiographic assessment was performed by using Dr. Wise Lung Analyser (MIDS-PNAS, V1.3.0.1), an AI software that automatically segments lung nodules and measures quantitative CT parameters. The software employs deep learning algorithms to detect nodule boundaries and measure size, density, texture and other quantitative features. The system operates as a comprehensive AI-based solution that analyses multiple signs and diagnoses multiple diseases using chest CT scans. It facilitates an all-inclusive automatic analysis for lung diseases, aiding in both the management of diagnosis and the coordination of subsequent treatment. First, the SPN was manually localized by a trained research assistant. Next, the software automatically segmented the nodule and extracted quantitative CT features. Our experiment utilized the Dr. Wise system – an advanced intelligent imaging solution. This system was employed to safely store the raw DICOM image data on the cloud, facilitating effortless retrieval, viewing and intelligent diagnostic assistance. In order to assess the effectiveness of the algorithm, it is vital to gauge the real-world performance of the predictive model by considering various key performance indicators stemming from the acquired results. The existing AI model within the Dr. Wise system harnesses vast quantities of data, thereby optimizing model performance and overall generalizability.

The primary CT metrics used in this study were mean HUs, maximum HU and minimum HU. HU values reflect the X-ray attenuation coefficient of tissues on CT, related to density. The software calculates HU statistics by analysing the distribution of pixel values within the nodule segmentation mask. Values were recorded for analysis.

Histopathology assessment

Resected NSCLC specimens were processed at the study institution per routine pathology protocols. Slides were analysed by two experienced thoracic pathologists to determine histologic subtype based on WHO criteria as well as presence of lymph node metastases [13]. For this study, nodules were classified as high risk invasive if they demonstrated micropapillary or solid subtype or had nodal metastases. Low risk non-invasive subtypes included lepidic, papillary and acinar.

Statistical analysis

Descriptive statistics were used to characterize the cohort. Univariate logistic regression was performed to identify CT metrics associated with high-risk invasive nodules. Odds ratios and 95% confidence intervals were calculated. Receiver operating characteristic (ROC) curves were generated to assess the predictive performance of CT parameters. The area under the curve (AUC) was calculated. Multivariate logistic regression incorporated significant univariate predictors to identify independent factors associated with invasiveness. Two tailed p values <.05 were considered statistically significant for all analyses. Statistics were performed using SPSS v27.0 (IBM, Armonk, NY).

Results

The clinical and radiological characteristics of the 355 patients with early-stage NSCLC who underwent surgical resection are summarized in Table 1. The cohort had a mean age of 57.4 ± 10.2 years and was predominantly female (228 patients, 64.2%). Among the SPNs, 24.5% (87) were located in the left upper lobe, 14.6% (52) in the left lower lobe, 34.4% (122) in the right upper lobe, 7.3% (26) in the right middle lobe and 19.2% (68) in the right lower lobe.

Table 1. Clinical characteristics of SPN.

Variable	Histological high risk SPN (N = 74)	Histological low risk SPN (N = 281)	p Value	
Age, years	57.4	57.3	.552	
Sex (%)	 	 	.343	
 Male	30 (34.5%)	97 (40.5%)	 	
 Female	44 (65.5%)	184 (59.5%)	 	
Location (%)	 	 	.097	
 LUL	14 (18.9%)	73 (26.0%)	 	
 LLL	12 (16.2%)	40 (14.2%)	 	
 RUL	22 (29.7%)	100 (35.6%)	 	
 RML	4 (5.5%)	22 (7.8%)	 	
 RLL	22 (29.7%)	46 (16.4%)	 	
CT value	 	 	 	
 CT value max (HU)a	85	 18	<.001	
 CT value min (HU)a	−349	−652	<.001	
 CT value mean (HU)a	10	−303	<.001	
 CT findings (%)	 	 	<.001	
 Pure GGO	3 (4.1%)	68 (24.2%)	 	
 Part solid	32 (43.2%)	174 (61.9%)	 	
 Solid	39 (52.7%)	39 (13.9%)	 	
Clinical stage (%)	 	 	<.001	
 0	3 (4.1%)	68 (24.2%)	 	
 IA	61 (82.4%)	201 (71.5%)	 	
 IB	10 (13.5%)	12 (4.3%)	 	
Pathology stage (%)	 	 	<.001	
 IA	39 (52.7%)	270 (96.1%)	 	
 IB	5 (6.8%)	11 (3.9%)	 	
 IIB	8 (10.8%)	0 (0%)	 	
 IIIA	22 (29.7%)	0 (0%)	 	
a CT value was measured and recorded by Dr. Wise Lung Analyzer (MIDS-PNAS, V1.3.0.1).

All patients underwent preoperative thin section CT chest performed at Shanghai Chest Hospital on one of several multidetector CT scanners, including models from Siemens (Munich, Germany), GE (Boston, MA), Philips (Amsterdam, Netherlands) and Toshiba (Minato City, Japan). Scanning was done at 120 kVp with tube current modulation ranging from 100 to 500 mA. Images were reconstructed with sharp convolution kernel at 0.625–1.25 mm slice thickness.

Quantitative CT analysis was performed by a trained analyst using a dedicated computer aided diagnosis software Dr. Wise Lung Analyzer version 1.3 (MIDS-PNAS, Shenzhen, China). This Food and Drug Administration cleared software allows semi-automated segmentation and quantitative feature analysis of lung nodules. The SPNs were manually localized on the CT dataset, then automatic segmentation was applied to define a volume of interest around the nodule. Quantitative CT metrics were extracted from the voxel data within this volume of interest.

The primary measures used for this study were mean HUs, maximum HU and minimum HU. The HU reflects the X-ray attenuation coefficient of tissues on CT proportional to density. Summary statistics of the HU values within the nodule segmentation were calculated by the software. Quantitative CT analysis showed the nodules had mean HU of −240.5 (range −950 to 184.3), maximum HU of 39.0 (range −774 to 493) and minimum HU of −607.5 (range −1024 to 147) (Table 1).

In terms of visual characteristics on CT, 71 (20.0%) nodules were pure ground glass, 206 (58.0%) were part solid and 78 (22.0%) were solid (Table 1). Histopathological analysis demonstrated a predominant acinar subtype in 185 (52.1%) nodules, papillary in 106 (30.0%), lepidic in 38 (10.7%), solid in 22 (6.2%) and micropapillary in 4 (1.1%) (Table 2). The minor subtype was acinar in 121 (34.1%), papillary in 127 (35.8%), lepidic in 49 (13.8%), solid in 15 (4.2%) and micropapillary in 18 (5.1%). Lymph node metastases were present in 30 (8.5%) patients.

Table 2. Pathologic characteristics of SPN.

Variable	Histological high risk SPN (N = 74)	Histological low risk SPN (N = 281)	p Value	
Histological components (predominant)	 	 	<.001	
 Acinar	25	160	 	
 Papillary	23	83	 	
 Lepidic	0	38	 	
 Solid	22	0	 	
 Micropapillary	4	0	 	
 Spread through air spaces	45	0	 	
Histological components (minor)	 	 	<.001	
 Acinar	26	95	 	
 Papillary	9	118	 	
 Lepidic	1	48	 	
 Solid	15	0	 	
 Micropapillary	18	0	 	
Pathologic LN status	 	 	<.001	
 N0	44	281	 	
 N1	8	0	 	
 N2	22	0	 	

The primary outcome was pathological invasiveness, classified as high risk or low risk. High risk invasive nodules were defined as those with micropapillary or solid predominant subtype on histology or presence of nodal metastases. Low risk non-invasive subtypes included acinar, papillary and lepidic. Based on these criteria, there were 74 (20.8%) high risk invasive nodules and 281 (79.2%) low risk non-invasive nodules (Table 2).

Figure 2 compares the representative CT scans of low- and high-risk SPNs. Univariate logistic regression was performed to assess associations between clinical and imaging variables and pathological invasive status (Table 3). The CT value max, CT value min and CT value mean were significantly higher in the high-risk SPN group compared to the low-risk group (mean: 85 vs. 18 HU, p < .001; −349 vs. −652 HU, p < .001; 10 vs. −303 HU, p < .001, respectively). Categorical CT characteristic of solid versus part-solid versus pure ground glass nodule was also significantly associated with invasive status (p < .001). Of note, clinical stage (0 vs. IA vs. IB) likewise showed a significant relationship (p < .001).

Figure 2. Representative case images of computed tomography (CT) analysis. (A) Histological low risk SPN. (B) Histological high risk SPN. Measurement of GGO proportion was conducted by the software.

Table 3. Univariate and multivariate analyses to identify factors associated with high-risk SPN.

 	Univariate analysis	Multivariate analysis	
Variable	OR (95%CI)	p Value	OR (95%CI)	p Value	
CT value max (HU)	1.007 (1.004–1.010)	<.001	0.996 (0.991–1.001)	.124	
CT value min (HU)	1.007 (1.001–1.002)	<.001	0.999 (0.998–1.000)	.168	
CT value mean (HU)	1.006 (1.004–1.008)	<.001	1.007 (1.003–1.010)	<.001	
CT findings	5.133 (3.149–8.368)	<.001	2.209 (1.191–4.100)	.012	
Clinical stage	4.006 (2.143–7.486)	<.001	1.511 (0.630–3.625)	.355	

Receiver operating characteristic curve analysis was applied to evaluate the diagnostic performance of quantitative CT metrics for distinguishing invasive from non-invasive nodules. The area under the ROC curve for mean HU was 0.811 (95%CI 0.754–0.867, p < .001). An AUC of 0.811 indicates good discriminative ability (Figure 3).

Figure 3. Receiver-operating characteristics area under the curve (AUC) (0.811, 95% confidence interval (CI), 0.754–0.867, p < .001) for CT value mean and CT findings used to identify low or high risk SPN.

Multivariate logistic regression was performed incorporating significant univariate clinical and imaging predictors. On multivariate analysis, mean HU (OR 1.007 per unit, 95%CI 1.003–1.010, p < .001) and nodule type on CT (OR 2.209 solid vs. part-solid vs. ground glass, 95%CI 1.191–4.100, p = .012) remained as independent statistically significant predictors of pathological invasiveness (Table 3).

Box plots were created to visually demonstrate the distribution of quantitative CT metrics across invasive and non-invasive groups (Figures 4 and 5). For mean HU, maximum HU and minimum HU, the distribution was shifted to higher values in the high-risk invasive nodules compared to low-risk non-invasive nodules. Similarly, the proportion of solid nodules was higher in the invasive group, while non-invasive nodules were more likely to be part-solid or pure ground glass.

Figure 4. Box plots for the comparison of distribution of (A) CT value max, (B) CT value min and (C) CT value mean for identifying low or high risk SPN.

Figure 5. Distribution of CT findings for identifying low or high risk SPN. (A) Pure GGO, (B) part solid and (C) solid.

Discussion

This preliminary study found that quantitative CT measures of SPN obtained through specialized software can predict degree of invasiveness. The CT value mean was the most significant independent predictor on multivariate regression, with an AUC of 0.811 for identifying high-risk SPNs. These novel findings suggest potential clinical utility for noninvasive preoperative assessment of cancer aggressiveness. However, as an initial retrospective single-centre analysis, there are significant limitations requiring careful consideration.

A key strength of this study was the use of a large dataset of 355 pathologically confirmed malignant SPNs with rigorous central histopathology review based on current WHO criteria [13]. This substantial sample size enabled multivariate regression to account for potential confounders and identify the most significant quantitative CT predictors of invasiveness from among the candidate variables. State-of-the-art commercially available software was leveraged for efficient, precise segmentation and quantification of CT parameters. This addressed a key challenge in quantitative analysis of lung nodules, allowing reliable density measures even for small, irregularly shaped SPNs barely over 5 mm in diameter.

However, several limitations stem from the retrospective single-centre design and warrant discussion. Most importantly, as a preliminary study at a single high-volume academic medical centre, the generalizability and reproducibility of the findings remain unknown. Additional prospective and multicentre studies are critically needed to validate the utility of quantitative CT measures for predicting SPN invasiveness across more diverse patient populations and practice settings. Furthermore, comparison with a sizable cohort of confirmed benign nodules will be essential for evaluating real-world clinical performance if this approach is to be applied for preoperative risk stratification. Without a comparator benign group, the sensitivity and specificity of CT metrics for predicting invasiveness cannot be assessed. Incorporating automated quantitative CT analysis into prospectively acquired multi-institutional data with centralized blinded histopathology review will significantly strengthen the evidence base.

Another limitation is the lack of direct comparison to other quantitative imaging approaches for SPN evaluation, particularly radiomic machine learning techniques which have shown promise in recent studies. Comparing the performance of software-based CT metrics to radiomic predictors derived from deep learning algorithms trained on large annotated SPN datasets could help benchmark the results and determine optimal methods. Furthermore, combining quantitative CT measures with clinical and radiomic variables in a predictive model may improve performance.

Should our findings be validated, the clinical implications are exciting. Preoperative CT examination of quantitative nodule features can guide surgical planning and determine the appropriate extent of resection based on the anticipated invasiveness and risk [14]. However, the impact on actual clinical decision making and patient outcomes remains speculative. Further research is critically needed to determine how these metrics can be effectively translated into personalized treatment planning for early-stage lung cancer. Will quantitative CT measures provide sufficient accuracy for clinical utility? What thresholds are appropriate for guiding sublobar versus lobectomy resection? How does this approach compare to invasive mediastinal staging? Therefore, extensive research is paramount for understanding how these metrics can be successfully incorporated into personalized treatment planning for early-stage lung cancer.

The major strengths of the current study include the utilization of AI software to enable accurate quantification of CT parameters, as well as identification of CT value mean as an independent predictor for high-risk SPN. The results could guide individualized treatment strategies based on preoperative CT assessment. Future directions include expanding the sample size for validation and developing predictive models incorporating clinical and radiomic features.

Furthermore, this study underscores the potential of AI-driven applications in the realm of quantitative analysis of medical imaging. The viable usage of specialized software for precise segmentation and quantification could have far-reaching implications not only for lung nodules but potentially for a broader array of healthcare applications.

Our findings also present an important foundation upon which future research can build. Benchmarked results from our study employing CT metrics can be an instrumental comparative tool for evaluating newer methods. Specifically, radiomic machine learning techniques, which are being increasingly explored in the field of medical imaging analysis, could benefit from a comparative analysis with our CT metrics.

Finally, this study establishes a stepping stone towards larger, multicentric prospective studies. Gathering data across different demographics and clinical settings would support the validation and generalization of our findings. Interestingly, our study also implicates the expansion of comparison groups to benign nodules, drawing attention towards the use of quantitative CT analysis for differential diagnosis.

It is intriguing to envision a transformation in clinical decision making and personalized treatment planning fuelled by these additions to our understanding of quantitative CT measures in SPN invasiveness. However, further research is critical to materialize these implications and answer imminent questions.

In summary, this timely study provides initial evidence that quantitative CT measures could serve as noninvasive predictors of SPN invasiveness. The use of dedicated SPN analysis software represents an important step towards AI-driven decision support in lung cancer care. However, robust external validation and comparison to other emerging technologies are required to fully determine clinical utility. With further rigorous evaluation, quantitative preoperative CT assessment may eventually help enable personalized risk stratification and treatment strategies. But significant work remains to demonstrate reproducibility, optimize predictive models, and prospectively evaluate impacts on clinical care and outcomes.

Acknowledgements

Previous communication to a society or meeting: A part of the current study was accepted for Presentation at WCLC 2023 in Singapore.

Author contributions

Study concepts: Long Jiang. Study design: Long Jiang, Yang Zhou and Wang Miao. Data acquisition: Long Jiang, Hongda Zhu and Ningyuan Zou. Quality control of data and algorithms: Yu Tian and Hanbo Pan. Data analysis and interpretation: Weiqiu Jin. Statistical analysis: Long Jiang and Jia Huang. Manuscript preparation: Long Jiang. Manuscript editing: Long Jiang, Jia Huang and Qingquan Luo. Manuscript review: all authors.

Ethical approval

The study was approved by the Institutional Review Board of Shanghai Chest Hospital (KS20256).

Consent form

Written informed consent for the use and analysis of clinical data was obtained preoperatively from each patient.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.
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