Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids

Zhang, J.; Yang, Chao; Gong, Chang; Zhou, Ye; Li, Chenghai; Li, Faqi

doi:10.1080/02656736.2022.2090622

Cited by 11 publications

(5 citation statements)

References 38 publications

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“… § Basal distribution of residual myomas quantified according to Zhang et al . 27 , based on the distribution across four equal quadrants of the original myoma distribution: 0, no residual myoma; 1, limited distribution; 2, small distribution; 3, obvious distribution; 4, extensive distribution. ¶ It was not always possible to count multiple myomas, so these were expressed as ≥ 5.…”

Section: Resultsmentioning

confidence: 99%

“…‡Myoma type according to signal intensity on pretreatment T2-weighted imaging (T2WI): Type I: hypointense, signal intensity equal to that of skeletal muscle; Type II: isointense, signal intensity lower than that of uterine myometrium but higher than that of skeletal muscle; Type III: heterogeneous hyperintense; Type IV, homogeneous hyperintense. §Basal distribution of residual myomas quantified according to Zhang et al 27 , based on the distribution across four equal quadrants of the original myoma distribution: 0, no residual myoma; 1, limited distribution; 2, small distribution; 3, obvious distribution; 4, extensive distribution. ¶It was not always possible to count multiple myomas, so these were expressed as ≥ 5.…”

Section: Radiomics Feature Selection and Radiomics Score (Rad-score) ...mentioning

confidence: 99%

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Prediction using T2‐weighted magnetic resonance imaging‐based radiomics of residual uterine myoma regrowth after high‐intensity focused ultrasound ablation

Zhou

Zhang

Chen

et al. 2022

Ultrasound in Obstet & Gyne

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What are the novel findings of this work? T2-weighted magnetic resonance imaging (T2WI)-based radiomics parameters can predict regrowth of residual uterine myomas within 1 year after high-intensity focused ultrasound (HIFU) treatment. What are the clinical implications of this work?Accurate prediction of residual myoma regrowth is critical for the early customization of chronic disease management plans after HIFU ablation in patients with uterine myomas. T2WI-based radiomics may have a role in this regard.

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Section: Resultsmentioning

confidence: 99%

Section: Radiomics Feature Selection and Radiomics Score (Rad-score) ...mentioning

confidence: 99%

Prediction using T2‐weighted magnetic resonance imaging‐based radiomics of residual uterine myoma regrowth after high‐intensity focused ultrasound ablation

Zhou

Zhang

Chen

et al. 2022

Ultrasound in Obstet & Gyne

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“…Dataset Accuracy [1] Uterine Fibroids Images Data 88% [3] Uterine Fibroids Images Data 89% [6] Uterine Fibroids Images Data 94% Proposed Uterine Fibroids Images Data 99.8%…”

Section: Referencementioning

confidence: 99%

“…The accuracy of DL-based methods for UF detection from ultrasound images has been the subject of many investigations. For instance, a CNN model was proposed and showed 91.3% accuracy in detecting UF from 3D ultrasound images in a study [3]. Another investigation found that a DL-based system could identify UF in 2D ultrasound images with an impressive accuracy of 98.8 percent [4].…”

Section: Introductionmentioning

confidence: 99%

“…UF is more common in older women and can lead to painful periods, infertility, and pelvic discomfort. The appropriate management and treatment of UF, which may involve medication, surgery, or other interventions [3,4], requires early identification. Because it is non-invasive and commonly available, ultrasound imaging is frequently utilized for UF detection [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Shahzad,

Mushtaq,

Sabeeh

et al. 2023

Healthcare

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Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results. In this research, we evaluated state-of-the-art DL architectures VGG16, ResNet50, InceptionV3, and our proposed innovative dual-path deep convolutional neural network (DPCNN) architecture for UF detection tasks. Using preprocessing methods including scaling, normalization, and data augmentation, an ultrasound image dataset from Kaggle is prepared for use. After the images are used to train and validate the DL models, the model performance is evaluated using different measures. When compared to existing DL models, our suggested DPCNN architecture achieved the highest accuracy of 99.8 percent. Findings show that pre-trained deep-learning model performance for UF diagnosis from medical images may significantly improve with the application of fine-tuning strategies. In particular, the InceptionV3 model achieved 90% accuracy, with the ResNet50 model achieving 89% accuracy. It should be noted that the VGG16 model was found to have a lower accuracy level of 85%. Our findings show that DL-based methods can be effectively utilized to facilitate automated UF detection from medical images. Further research in this area holds great potential and could lead to the creation of cutting-edge computer-aided diagnosis systems. To further advance the state-of-the-art in medical imaging analysis, the DL community is invited to investigate these lines of research. Although our proposed innovative DPCNN architecture performed best, fine-tuned versions of pre-trained models like InceptionV3 and ResNet50 also delivered strong results. This work lays the foundation for future studies and has the potential to enhance the precision and suitability with which UF is detected.

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Robust Radiomics Models for Predicting HIFU Prognosis in Uterine Fibroids Using SHAP Explanations: A Multicenter Cohort Study

Liu,

Zeng,

Jinyun

et al. 2024

J Digit Imaging. Inform. med.

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Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids

Cited by 11 publications

References 38 publications

Prediction using T2‐weighted magnetic resonance imaging‐based radiomics of residual uterine myoma regrowth after high‐intensity focused ultrasound ablation

Prediction using T2‐weighted magnetic resonance imaging‐based radiomics of residual uterine myoma regrowth after high‐intensity focused ultrasound ablation

Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Robust Radiomics Models for Predicting HIFU Prognosis in Uterine Fibroids Using SHAP Explanations: A Multicenter Cohort Study

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