
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

63415
10.1038/s41598-024-63415-y
Article
Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer
Jin Hua 1
Wang Yuchao 38231142@qq.com

2
Li Xushuo 3
Yang Ying 3
Qi Ruixue qiruixue@126.com

4
1 grid.508387.1 0000 0005 0231 8677 Department of Respiratory Medicine, Jinshan Hospital, Fudan University, Shanghai, 201508 China
2 https://ror.org/05x1ptx12 grid.412068.9 0000 0004 1759 8782 Department of Medical Imaging, Third Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150030 China
3 https://ror.org/013q1eq08 grid.8547.e 0000 0001 0125 2443 Department of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508 China
4 grid.508387.1 0000 0005 0231 8677 Department of Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508 China
6 9 2024
6 9 2024
2024
14 207885 9 2023
28 5 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78–0.92), 0.76 (95% CI 0.68–0.84), and 0.89 (95% CI 0.84–0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86–1.00), 0.79 (95% CI 0.68–0.91), and 0.96 (95% CI 0.90–1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72–0.96), 0.75 (95% CI 0.62–0.87), and 0.86 (95% CI 0.75–0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.

Keywords

Chemo-immunotherapy
Non-small cell lung cancer
Radiomics
Nomogram
Subject terms

Oncology
Cancer
Shanghai Science and Technology Committee20JC1418200 Qi Ruixue issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Non-small cell lung cancer (NSCLC) accounts for approximately 80–85% of all lung cancers. The treatment of advanced NSCLC depends on tumor histology, pathological subtype, disease stage, and clinical characteristic of the patient1. Chemotherapy and immunotherapy play crucial roles in the treatment of advanced NSCLC2. However, the prognosis of advanced NSCLC remains poor, with a 5-year survival rate of less than 15%3.

Currently, combining chemo-immunotherapy is purposed to improve the survival of advanced NSCLC patients. The mechanism of standard chemotherapy is to induce tumor cell lethality4. Combining standard chemotherapy and immune checkpoint inhibitors could strengthen the immune system against tumor cells5. Clinical trials have revealed that chemo-immunotherapy has the potential benefit in the treatment of advanced NSCLC patients6. However, chemo-immunotherapy may lead to enhanced treatment-related toxicities. Thus, selecting eligible candidates for chemo-immunotherapy should be done carefully. Identifying biomarkers to predict responders of chemo-immunotherapy is one of the major challenges7.

Previous studies have demonstrated that image biomarkers of non-invasive CT scans could be used to predict the pathological subtypes of lung tumor8. Medical imaging biomarkers have also been reported to be feasible for predicting responders to different therapies for advanced NSCLC9. Radiomics is a method used to extract quantitative medical image features, providing imaging biomarkers for diagnostic and prognostic purposes10. Additionally, radiomics features and clinical predictors can be combined to build a radiomics nomogram model, which is useful for individual management11.

However, to our knowledge, no study has investigated potential radiomics biomarkers to predict responders of chemo-immunotherapy in advanced NSCLC patients. We hypothesized that CT scan-based radiomics could be a useful tool to achieve this goal. To explore this hypothesis, CT scan-based radiomics was used to identify potential image biomarkers. A radiomics nomogram model was build by combining the image biomarkers and clinical predictors to predict responders to chemo-immunotherapy in advanced NSCLC patients.

Results

Patients’ clinical characteristics

The work flow of this study is illustrated in Fig. 1. Most patients achieved disease control after chemo-immunotherapy, including complete response (CR, n = 4), partial response (PR, n = 28) and stable disease (SD, n = 114). The clinical characteristics of all the patients are presented in Table 1. No significant differences in gender, primary tumor position, metastases site (lung, brain, and bone), pathological subtype, smoke habits and ECOG performance status were observed between progressive disease (PD) and disease control (DC) cases. However, older age and positive liver metastases were found more frequently in PD cases compared to DC cases (Table 1). Two representative case scenario are depicted in Fig. 2.Figure 1 Workflow of the study illustrating the process followed in this investigation. DC disease control, PD progressive disease.

Table 1 The clinical characteristics of the patients in all cohorts.

	Primary cohort	Internal validation cohort	External validation cohort	
DC (N = 77)	PD (N = 61)	P	DC (N = 34)	PD (N = 24)	P	DC (N = 35)	PD (N = 25)	P	
Radscore	0.27 (0.21)	0.65 (0.27)	< 0.001	0.17 (0.14)	0.75 (0.34)	< 0.001	0.26 (0.19)	0.59 (0.29)	< 0.001	
Gender			0.442			0.299			0.308	
 Female	26 (33.8%)	16 (26.2%)		11 (32.4%)	4 (16.7%)		14 (40.0%)	6 (24.0%)		
 Male	51 (66.2%)	45 (73.8%)		23 (67.6%)	20 (83.3%)		21 (60.0%)	19 (76.0%)		
Age	61 (10)	69 (8)	< 0.001	59 (10)	69 (7)	< 0.001	58 (9)	67 (8)	< 0.001	
Pathology sub-type			0.905			0.427			0.541	
 Adenocarcinoma	59 (76.6%)	46(75.4%)		27 (79.4%)	20 (83.3%)		31 (88.6%)	22 (88.0%)		
 Large cell carcinoma	6 (7.8%)	4 (6.6%)		1 (2.9%)	2 (8.3%)		1 (2.9%)	2 (8.0%)		
 Squamous cell carcinoma	12 (15.6%)	11 (18.0%)		6 (17.6%)	2 (8.3%)		3 (8.6%)	1 (4.0%)		
Primary tumor portion			0.954			0.299			0.262	
 Center	49 (63.6%)	40 (65.6%)		23 (67.6%)	20 (83.3%)		19 (54.3%)	18 (72.0%)		
 Peripheral	28 (36.4%)	21 (34.4%)		11 (32.4%)	4 (16.7%)		16 (45.7%)	7 (28.0%)		
Lung metastases			0.526			1.00			0.891	
 Negative	44 (57.1%)	39 (63.9%)		19 (55.9%)	13 (54.2%)		22 (62.9%)	17 (68.0%)		
 Positive	33 (42.9%)	22 (36.1%)		15 (44.1%)	11 (45.8%)		13 (37.1%)	8 (32.0%)		
Brain metastases			0.886			0.933			0.964	
 Negative	51 (66.2%)	42 (68.9%)		20 (58.8%)	13 (54.2%)		21 (60.0%)	16 (64.0%)		
 Positive	26 (33.8%)	19 (31.1%)		14 (41.2%)	11 (45.8%)		14 (40.0%)	9 (36.0%)		
Liver metastases			< 0.001			0.020			0.044	
 Negative	62 (80.5%)	29 (47.5%)		28 (82.4%)	12 (50.0%)		28 (80.0%)	13 (52.0%)		
 Positive	15 (19.5%)	32 (52.5%)		6 (17.6%)	12 (50.0%)		7 (20.0%)	12 (48.0%)		
Bone metastases			0.163			0.336			1.000	
 Negative	57 (74.0%)	52 (85.2%)		30 (88.2%)	18 (75.0%)		29 (82.9%)	20 (80.0%)		
 Positive	20 (26.0%)	9 (14.8%)		4 (11.8%)	6 (25.0%)		6 (17.1%)	5 (20.0%)		
Other sites metastases			0.195			0.988			0.382	
 Negative	70 (90.9%)	50 (82.0%)		31 (91.2%)	21 (87.5%)		34 (97.1%)	22 (88.0%)		
 Positive	7 (9.1%)	11 (18.0%)		3 (8.8%)	3 (12.5%)		1 (2.9%)	3 (12.0%)		
Outcome			< 0.001			< 0.001			< 0.001	
 CR	3 (3.9%)	0 (0%)		0 (0%)	0 (0%)		1 (2.9%)	0 (0%)		
 PR	16 (20.8%)	0 (0%)		6 (17.6%)	0 (0%)		6 (17.1%)	0 (0%)		
 SD	58 (75.3%)	0 (0%)		28 (82.4%)	0 (0%)		28 (80.0%)	0 (0%)		
 PD	0 (0%)	61 (100%)		0 (0%)	24 (100%)		0 (0%)	25 (100%)		
Smoke			0.658			0.870			1.000	
 Negative	6 (7.8%)	7 (11.5%)		3 (8.8%)	1 (4.2%)		5 (14.3%)	3 (12.0%)		
 Positive	71 (92.2%)	54 (88.5%)		31 (91.2%)	23 (95.8%)		30 (85.7%)	22 (88.0%)		
 ECOG performance status	1.9 (0.5)	1.9 (0.6)	0.800	2.0 (0.4)	1.9 (0.4)	0.744	1.9 (0.5)	2.0 (0.5)	0.674	
CR complete response, DC disease control, PD progressive disease. Data presented as mean (SD) or N (ratio).

Figure 2 Chest CT images of two cases. (A) Intrapulmonary nodular metastasis (arrow) is seen in a partial response (PR) case (male, aged 74 years) before chemo-immunotherapy, (B) The nodular metastasis disappears 6 month later after chemo-immunotherapy, (C) Lung tumor (arrow) is seen in a progressive disease (PD) case (male, aged 66 years) before chemo-immunotherapy, (D) The tumor significantly progresses 6 month later after chemo-immunotherapy.

Radiomics features selection and radiomics model construction

A total of 107 radiomics features were extracted from the imaging of each patient. After eliminating the unstable and redundant features, 18 radiomics features were retained. Following radiomics feature selection, 12 radiomics features were identified as the radiomics signatures for predicting responders to chemo-immunotherapy. The radscore calculation formula was as follows: Radscore = 0.40316 + 5e−05 × shape_SurfaceArea + 0.00335 × firstorder_90Percentile + 0.00166 × firstorder_InterquartileRange + 0.00019 × firstorder_Range + 0 × glcm_ClusterProminence + − 2.08444 × glcm_MCC + − 0.63278 × glcm_SumEntropy + 80.74644 × glrlm_RunVariance + − 263.23819 × glszm_GrayLevelNonUniformityNormalized + − 1.52064 × glszm_LargeAreaLowGrayLevelEmphasis + − 51.55987 × ngtdm_Busyness + 0 × ngtdm_Complexity. The radscore of each patient is shown in Fig. 3.Figure 3 Radscore distribution of each patient. Distribution of radscores in the (A) primary cohort, (B) internal validation cohort, and (C) external validation cohort.

Clinical features selection and development of a radiomics nomogram model

Multivariate logistic regression analysis showed that age and liver metastases were the two independently significant clinical predictors for responders to chemo-immunotherapy in advanced NSCLC. The radscore was combined with the selected clinical predictors to form a radiomics nomogram model for predicting responders to chemo-immunotherapy in advanced NSCLC (Fig. 4).Figure 4 Radiomics nomogram and calibration curves for predicting responders to chemo-immunotherapy. (A) The radiomics nomogram model integrates the radscore, age, and presence of liver metastases. Calibration curves demonstrate the agreement of the radiomics nomogram model for predicting progressive disease (PD) in advanced NSCLC in the (B) primary cohort, (C) internal validation cohort, and (D) external validation cohort.

Performance evaluation and validation of the radiomics model, clinical model and radiomics nomogram model

The area under the receiver operating characteristic (ROC) curves (AUC) of the radiomics model, clinical model, and radiomics nomogram model were 0.85 (95% confidence interval [CI] 0.78–0.92), 0.76 (95% CI 0.68–0.84), and 0.89 (95% CI 0.84–0.94), respectively, in the primary cohort, 0.93 (95% CI 0.86–1.00), 0.79 (95% CI 0.68–0.91), and 0.96 (95% CI 0.90–1.00), in the internal validation cohort and 0.84 (95% CI 0.72–0.96), 0.75 (95% CI 0.62–0.87), and 0.86 (95% CI 0.75–0.96), in the external validation cohort, respectively (Table 2). The calibration curves demonstrated good agreement between the predicted and observed probability for predicting responders to chemo-immunotherapy in advanced NSCLC patients (Fig. 4). The radiomics nomogram model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.96 (95% CI 0.89–1.00), 1.00 (95% CI 1.00–1.00), and 0.86 (95% CI 0.80–0.92) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively (Table 3).Table 2 The predict performance of radscore, clinical predictors and radiomics nomogram model in all cohorts.

		AUC	95%CI	SPE	SEN	NPV	PPV	#	*	
Primary cohort	Radiomics model	0.85	0.78–0.92	0.84	0.79	0.83	0.80	–	0.084	
Clinical model	0.76	0.68–0.84	0.74	0.70	0.76	0.68	0.084	–	
Radiomics nomogram model	0.89	0.84–0.94	0.69	0.92	0.91	0.7	0.069	< 0.001	
Internal validation cohort	Radiomics model	0.93	0.86–1.00	0.97	0.79	0.87	0.95	–	0.039	
Clinical model	0.79	0.68–0.91	0.94	0.54	0.74	0.87	0.039	–	
Radiomics nomogram model	0.96	0.90–1.00	0.88	0.96	0.97	0.85	0.293	0.002	
External validation cohort	Radiomics model	0.84	0.72–0.96	0.86	0.84	0.88	0.81	–	0.263	
Clinical model	0.75	0.62–0.87	0.51	0.96	0.94	0.57	0.263	–	
Radiomics nomogram model	0.86	0.75–0.96	0.74	0.92	0.93	0.72	0.719	0.060	
#Compared with radiomics model; *compared with radiomics model; –: not applied; AUC area under the curve, CI confidence interval, NPV negative predictive value, PPV positive predictive value, SEN sensitivity, SPE specificity.

Table 3 The predict performance of radiomics nomogram model in all histological subtypes.

	AUC	95% CI	SPE	SEN	NPV	PPV	
Squamous cell carcinoma	0.96	0.89–1.00	1.00	0.85	0.9	1.00	
Large cell carcinoma	1.00	1.00–1.00	1.00	1.00	1.00	1.00	
Adenocarcinoma	0.86	0.80–0.92	0.76	0.86	0.88	0.73	

Clinical usefulness

Comparing with the treat-all or treat-none scheme, the radiomics nomogram model provided a net benefit for clinical decisions, as indicated by the clinical decision curve analysis in all the cohorts. A better net benefit of the radiomics nomogram model compared to the clinical model was observed in most threshold probability areas (Fig. 5). By comparing these strategies, DCA helps in determining the threshold probabilities at which a predictive model is beneficial for clinical use. This is crucial for models predicting the efficacy of treatments, such as chemo-immunotherapy in cancer patients, where treatment decisions have significant implications on patient outcomes and healthcare resources.Figure 5 Clinical decision curves of the clinical model and radiomics nomogram model for predicting responders to chemo-immunotherapy. Clinical decision curves in the (A) primary cohort, (B) internal validation cohort, and (C) external validation cohort depict the net benefit at different threshold probabilities.

Discussion

In this study, a CT scan-based radiomics nomogram model was developed and validated, demonstrating good ability to predict responders to chemo-immunotherapy in advanced NSCLC patients.

Previously, trials such as KEYNOTE-024 have shown improved outcomes with immunotherapy compared to standard chemotherapy in NSCLC patients without targetable drivers12. Similarly, KEYNOTE-189 and KEYNOTE-407 trials have shown the benefit of chemo-immunotherapy in NSCLC patients regardless of PD-L1 expression13,14. Gelsomino et al. reported that the overall survival and progression-free survival were significantly longer in NSCLC patients treated with chemo-immunotherapy regardless of PD-L1 expression, although the highest benefit was found in NSCLC patients with strong PD-L1 expression/positivity15. In accordance with the previous reports, recent study showed that benefits were granted irrespectively from the PD-L1 expression levels16.

Clinical parameters and peripheral blood examination have been explored as potential biomarkers for predicting responders to chemo-immunotherapy in NSCLC patients. Factors such as neutrophil-to-lymphocyte ratio, reflecting systemic inflammation, and the presence of bone or liver metastases have been associated with resistance to immunotherapy17–19. Additionally, baseline number of metastatic sites and serum albumin levels have been reported to be associated with immunotherapy efficiency20. In our study, elder age and liver metastases were identified as independent risk factors for predicting responders to chemo-immunotherapy in advanced NSCLC.

Radiomics has emerged as a promising approach for predicting immunotherapy response in NSCLC patients21. Studies have shown associations between radiomics signatures and immunotherapy response, with features such as tumor volume, invasion of tumor boundaries, and tumor spatial heterogeneity being linked to survival outcomes22. In our study, the radiomics feature of SurfaceArea was found to be related to therapy resistance in chemo-immunotherapy, reflecting tumor boundary and surface heterogeneity. Furthermore, GLCM features (including ClusterProminence, MCC, and SumEntropy) were associated with therapy resistance, consistent with previous findings linking hypoxia to therapy resistance in NSCLC patients20,23. Our study was consistent with previous reports.

Strengths of our study include its prospective design and validation using a separate cohort. However, limitations exist, such as the lack of consideration of pathological subtypes in radiomics model building. Nonetheless, recent evidence suggests that histology may not significantly affect chemo-immunotherapy outcomes24. The radiomics nomogram model also showed good predictive performances in different histological subtypes. Additionally high-order wavelet or Guess features were not included due to their instability and lack of clinical interpretation. Moreover, pseudoprogressions (with proportion about 2–6%) were not specifically addressed in this study, which might lead to potential bias.

In conclusion, our study presents a radiomics nomogram model combining radiomics signatures and clinical predictors to predict the responders to chemo-immunotherapy in advanced NSCLC patients.

Methods

Study design and patient population

This study was reviewed and approved by the Institutional Review Board of Jinshan Hospital, Fudan University (No.JIEC2021S47 and No. JIEC2023S84). All patients provided written informed consent, and the methods conducted in this study adhered to relevant guidelines and regulations.

Between December 2021 to August 2022, 221 consecutive advanced NSCLC patients were enrolled prospectively from Jinshan Hospital, Fudan University (Center 1). Inclusion criteria were as follows: (1) clinical confirmed advanced NSCLC (stage IV); (2) absence of targetable drivers (EGFR, ALK, ROS1 or MET mutation or RET fusions) or previous surgical treatment. Exclusion criteria were: (1) presence of infection; (2) failure to complete at lest 4 cycles of chemo-immunotherapy; (3) lost to follow-up. Ultimately, 196 patients were included for further analysis. All patients received cisplatin-paclitaxel plus toripalimab for 4–6 cycles as the first-line treatment with a regular intervals of 21 days. All the patients underwent lung CT scanning before chemo-immunotherapy within 7 days.

Between December 2021 to October 2023, 95 consecutive advanced NSCLC patients were enrolled retrospectively from the Third Affiliated Hospital, Heilongjiang University of Chinese Medicine (Center 2). Inclusion criteria were as follows: (1) clinical confirmed advanced NSCLC (stage IV); (2) receipt of chemo-immunotherapy as the first-line treatment with atleast 4 cycles; (3) patients underwent lung CT scanning before chemo-immunotherapy within 30 days. Exclusion criteria were: (1) Poor CT image quality; (2) lost to follow-up. Ultimately, 60 patients were included for further analysis. These patients received chemo-immunotherapy (cisplatin-paclitaxel plus toripalimab or sintilimab) as the first-line treatment with a regular intervals of 21 days.

Tumor responses to chemo-immunotherapy were evaluated according to Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1)25. Based on the final recorded assessment represented the best response achieved by each patient, the responses were categorized as: complete response (CR), partial response (PR) and stable disease (SD), or progressive disease (PD). Patients achieving CR, PR, or SD were categorized into the disease control (DC) group, while those with PD were categorized into the PD group. The patients from Center 1 were randomly divided into a primary cohort (n = 138) and an internal validation cohort (n = 58) according to a 7:3 ratio. The patients from Center 2 were assigned into an external validation cohort (n = 60). Clinical data including age, gender, pathological subtype, primary tumor position, tumor metastasis site, smoke habits and Eastern Cooperative Oncology Group (ECOG) performance status of all the patients were recorded.

CT image acquisition and tumor segmentation

The lung CT scanning were using Siemens (Erlangen, Germany) and Philips (Amsterdam, Netherlands) equipments with the following parameters: tube voltage = 120 or 100 kVp, tube current = 250 or 320 mA, collimation = 0.625 mm section thickness = 5.0 mm. The primary tumor sites (region of interests [ROIs]) were manually delineated by reader 1 using ITK-SNAP (version 4.0.0, http://www.itksnap.org). Thirty days later, ROIs (data from primary cohort) were redrawn by reader 1 and by another reader to assess intraobserver and interobserver reproducibility of radiomics features. All the readers were blinded to the patients' clinical information.

Radiomics feature extraction, selection and radiomics model construction

CT images were normalized, and radiomics features were extract using Pyradiomics (https://pypi.org/project/pyradiomics/) following the recommendations of Imaging Biomarkers Standardization Initiative (https://arxiv.org/abs/1612.07003). Unstable features (intra- or interclass correlation coefficients < 0.75) and redundant features (Pearson correlation coefficient > 0.9) were excluded from further analysis. Radiomics signatures for predicting responders to chemo-immunotherapy were selected using least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. A radiomics model was constructed using logistic regression, and the radscore of each patient was calculated by linear fitting of the radiomics signatures.

Radiomics nomogram model construction, discrimination, calibration, and validation

Multivariate logistic regression analysis was performed to select clinical independent predictors of age, gender, primary tumor position, metastases site (lung, brain, liver and bone), and pathological subtype for responders of chemo-immunotherapy (clinical model). A radiomics nomogram model integrating the radiomics model and clinical model was constructed using logistic regression analysis. The goodness of fit of the radiomics nomogram model was evaluated by calibration curves and the Hosmer–Lemeshow tests in the primary, internal validation, and external validation cohorts. The predictive performances of the radiomics model, clinical model, and radiomics nomogram model were assessed using the AUC in all cohorts. The predictive performances of the radiomics nomogram model in different histological subtypes were also performed (data from Center 1).

Clinical usefulness

The clinical usefulness of the clinical model and radiomics nomogram model was evaluated by clinical decision curve analysis (DCA) with net benefit at different threshold probabilities in all cohorts. The DCA is a method used to evaluate the clinical usefulness of diagnostic tests or prediction models. It specifically examines the net benefit of using these models across a range of threshold probabilities, compared to default strategies such as "treat-all" or "treat-none.

Statistical analysis

Statistical analysis was conducted using R (version 4.3.0; http://www.Rproject.org). Independent sample t-test (with normality and variance homogeneity) or Mann–Whitney U test (without normality and variance homogeneity) were used for continuous variables, and Chi-squared test was used to for categorical variables. Association between radiomics signatures and clinical information was calculated by Spearman's correlation. The "glmnet" package, "rms" package, "pROC" package, "dca.R" package, "survival" and "survminer" packages were utilized. Statistically significant was defined as P < 0.05.

Author contributions

R.Q. and Y.W. designed the research study. X.L., Y.W. and Y.Y. provided help and advice on acquisition of data. Y.W. analyzed the data. H.J. and R.Q. wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.

Data availability

All data generated or analysed during this study are included in this published article.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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