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CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors

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Abstract

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon–Mann–Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.

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Availability of Data and Materials

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

Abbreviations

CT:

Computed tomography

BeOTs:

Benign ovarian tumors

BOTs:

Borderline ovarian tumors

eMOTs:

Early-stage malignant ovarian tumors

LASSO:

Least absolute shrinkage and selection operator

SVM:

Support vector machine

LOOCV:

Leave-one-out cross-validation

ML:

Five machine learning

ROC:

Receiver-operating characteristics

AUC:

Average area under the ROC curve

AI:

Artificial intelligence

ML:

Machine learning

PACS:

Picture archiving and communication system

PVP:

Portal-venous phase

DICOM:

Digital Imaging and Communications in Medicine

VOI:

Volume of interest

ICC:

Intraclass correlation coefficient

IBSI:

Image biomarker standardization initiative

WMW:

Wilcoxon-Mann-Whitney

LR:

Logistic regression

SNN:

Standard neutral network

RF:

Random forest

KNN:

k-nearest neighbors

DT:

Decision tree

FIGO:

Federation International of Gynecology and Obstetrics

CAD:

Computer-aided diagnosis

3D:

Three-dimentional

2D:

Two-dimentional

AI:

Artificial intelligence

IBSI:

Image biomarker standardization initiative

ICC:

Interclass correlation coefficient

Pre:

Precision

SENS:

Sensitivity

SPE:

Specificity

PVP:

Portal-venous phase

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Acknowledgements

We appreciate Dong Xie and Zheng Wang for providing medical guidance to the manuscript.

Funding

This study was funded in part by grants from the National Natural Science Foundation of China (Grant No. 81560425), Guangxi Clinical Research Center for Medical Imaging Construction (Grant No. Guike AD 20238096), and Beijing Medical Award Foundation (Grant No. YXJL-2022–0665-0210).

Author information

Author notes

  1. Jia Chen and Lei Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People’s Republic of China

    Jia Chen & Danke Su

  2. Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People’s Republic of China

    Jia Chen & Danke Su

  3. Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People’s Republic of China

    Jia Chen & Danke Su

  4. Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People’s Republic of China

    Jia Chen & Danke Su

  5. School of Computer Science and Engineering, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, Guangxi, People’s Republic of China

    Lei Liu

  6. Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People’s Republic of China

    Ziying He & Chanzhen Liu

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Contributions

Conception and design: DS and CL Development of methodology: JC, LL, DS, and CL Acquisition of data: LL and ZH Analysis and interpretation of data: JC, LL, and ZH Writing, review, and/or revision of the manuscript: JC Administrative, technical, or material support: DS and CL Study supervision: DS and CL.

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Correspondence to Danke Su or Chanzhen Liu.

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The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Guangxi Medical University Cancer Hospital (LW2022153). Written informed consent was obtained from individual or guardian participants.

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Chen, J., Liu, L., He, Z. et al. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors. J Digit Imaging. Inform. med. 37, 180–195 (2024). https://doi.org/10.1007/s10278-023-00903-z

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