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Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study

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Abstract

Purpose

To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification.

Methods

Two hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility.

Results

The DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896–0.992) and 0.885 (95% confidence interval 0.834–0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit.

Conclusion

A DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment.

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Abbreviations

DLRN:

Deep learning radiomics nomogram

ROI:

Region of interest

T2WI:

T2-weighted MRI

DWI:

Diffusion-weighted imaging

LNM:

Lymph node metastasis

PMI:

Parametrial invasion

LVSI:

Lymphovascular space invasion

AUC:

Area under curve

DL:

Deep learning

CNN:

Convolutional neural network

ICC:

Intraclass correlation coefficient

DCA:

Decision curve analysis

SCC-Ag:

Squamous cell carcinoma antigen

LASSO:

Least absolute shrinkage and selection operator

ROC:

Receiver operating characteristic

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Acknowledgements

The authors would like to thank all staff from the college of Informatics and the department of radiology and gynecology for their hard work and invaluable support for this study. The authors also express gratitude to all the patients for contribution.

Funding

This work was supported by the project supported by the Cultivation plan of ‘top-notch’ postgraduate of Chongqing Medical University in 2021 (BJRC202111), and the program of Intelligence Medicine Special Research and Development of Chongqing Medical University in 2021 (YJSZHYX202101).

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Authors and Affiliations

  1. College of Medical Informatics, Chongqing Medical University, No.1 Medical College Road, Chongqing, China

    Yajiao Zhang, Jinglong Du, Zhibo Xiao & Yanbing Liu

  2. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Chao Wu & Furong Lv

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Contributions

Study conception and design: YJZ and YBL; All authors contributed to the development of methodology; Acquisition of data (acquired and managed patients, provided facilities, etc.): YJZ, CW, JLD and ZBX; Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): YJZ, FRL and YBL; Writing, reviewing, and/ or revision of the manuscript: YJZ and YBL; Administrative, technical, or material support: YJZ, CW, JLD, ZBX, FRL and YBL; Study supervision and guarantors: FRL and YBL.

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Correspondence to Yanbing Liu.

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The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the First Affiliated Hospital of Chongqing Medical University. Due to the retrospective and noninterventional nature of the study, the written informed consent was waived by The First Affiliated Hospital of Chongqing Medical University Review Board.

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Zhang, Y., Wu, C., Du, J. et al. Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study. Abdom Radiol 49, 258–270 (2024). https://doi.org/10.1007/s00261-023-04125-3

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