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A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study

  • Chest Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation.

Methods

558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated.

Results

In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524–5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216–0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168–0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram.

Conclusion

A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.

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Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Abbreviations

SCLC:

Small cell lung cancer

EP:

Etoposide-cisplatin

EC:

Etoposide-carboplatin

CT:

Computed tomography

PFS:

Progression-free survival

PCI:

Prophylactic cranial irradiation

CEA:

Carcinoma embryonic antigen

NSE:

Neuron specific enolase

ROI:

Region of interest

ICC:

Inter-/intraclass correlation coefficient

IBSI:

Imaging biomarker standardization initiative

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

NGTDM:

Neighboring gray tone difference matrix

Rad-score:

Radiomics score

ROC:

Receiver operating characteristic

AUC:

Area under the curve

C-index:

Concordance index

NRI:

Net reclassification improvement

IDI:

Integrated discrimination improvement

HR:

Hazard ratio

CI:

Confidence interval

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Funding

Supported by the Research Funds for Academic and Technological Leaders in Anhui Province of China (2021D299).

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Author notes

  1. Xiaomin Zheng and Kaicai Liu have been contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People’s Republic of China

    Xiaomin Zheng, Kaicai Liu, Cuiping Li, Chao Zhu, Yankun Gao & Xingwang Wu

  2. CT Advanced Application, GE HealthCare China, Beijing, 100186, People’s Republic of China

    Jianying Li

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  1. Xiaomin Zheng
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Contributions

Each author has contributed to conception and design, literature search, drafting of the article, critical revision, and final approval.

Corresponding author

Correspondence to Xingwang Wu.

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The authors declare that they have no conflict of interest.

Ethical approval

This retrospective study received ethical approval from the Institutional Review Committee of the First Affiliated Hospital of Anhui Medical University and the requirement for informed consent for patients was waived.

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Zheng, X., Liu, K., Li, C. et al. A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study. Radiol med 128, 1386–1397 (2023). https://doi.org/10.1007/s11547-023-01702-w

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