Applying multi-modal magnetic resonance imaging radiomics in differentiating benign and malignant ovarian tumors

Fengzhi Cui; Jianhua Liu; Nan Zhang; Junfeng Lv

doi:10.1117/12.3006200

10 October 2023 Applying multi-modal magnetic resonance imaging radiomics in differentiating benign and malignant ovarian tumors

Fengzhi Cui, Jianhua Liu, Nan Zhang, Junfeng Lv

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279958 (2023) https://doi.org/10.1117/12.3006200
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

OBJECTIVE: To explore the clinical application valuation of prediction model on the basis of multi-modal magnetic resonance imaging (MRI) radiomics for discriminating ovarian malignant and benign tumors. METHODS: Totally 93 patients with 110 ovarian tumors confirmed by operation and histopathology were collected, including the training cohort (n=76) and testing cohort (n=34). Radiomics features are extracted from the DWI, axial T2WI lipid pressure, T1 LAVA enhancement, sagittal T2WI of MRI images by A.K. software. Based on the above features, a Logistic model was constructed to classify benign and malignant ovarian tumors. Additionally, diagnostic performance of the prediction model is assessed and compared. RESULTS: The multi-modal MRI radiomics prediction model exhibits excellent diagnostic performance. In the training cohorts, the accuracy, AUC, sensitivity and specificity of the multimodal MRI radiomic prediction model in differentiating benign and malignant ovarian tumors were 0.816, 0.887, 0.744 and 0.909 respectively, while in the testing cohorts were 0.794, 0.839, 0.684 and 0.933. CONCLUSION: The Logistic model MRIbased DWI, axial T2WI lipid pressure, T1 LAVA enhancement, sagittal T2WI achieved promising diagnostic performance, expected to be regarded as a preoperative imaging tool in classifying benign and malignant ovarian tumors.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Fengzhi Cui, Jianhua Liu, Nan Zhang, and Junfeng Lv "Applying multi-modal magnetic resonance imaging radiomics in differentiating benign and malignant ovarian tumors", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279958 (10 October 2023); https://doi.org/10.1117/12.3006200

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KEYWORDS

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Magnetic resonance imaging

Radiomics

Performance modeling

Tumor growth modeling

Education and training

Diffusion weighted imaging

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