Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning

Jiaqi Li, Wei Wang, Wang W, Lejian Liao, Liao L, Liu X, Xin Liu
Physics in medicine and biology · 2020 · vol. 66(4) , pp. 045019 · doi:10.1088/1361-6560/abd66b · PMID:33361557 · W3117624494
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A few-shot learning framework was developed to predict the nonperfused volume ratio of adenomyosis from MRI images, achieving an area under the curve of 0.8375 for NPV ratio classification.

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This study developed a few-shot learning framework using convolutional neural networks to predict the nonperfused volume ratio in adenomyosis patients undergoing high-intensity focused ultrasound ablation. The researchers analyzed MRI data from 208 individuals to address the lack of qualitative interpretation standards and limited training data associated with this novel treatment approach. Results indicated that the model achieved varying levels of sensitivity and area under the curve across different prediction intervals, demonstrating its potential for selecting eligible patients and judging clinical efficacy. This paper is centrally about adenomyosis — specifically focusing on pre-treatment imaging analysis to optimize outcomes for HIFU ablation therapy.

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Abstract

The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using magnetic resonance imaging (MRI) before HIFU ablation treatment, which leading to inter-reader variability. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in the automatic disease diagnosis of MRI. Since the use of HIFU to treat adenomyosis is a novel treatment, there is not enough MRI data to support CNNs. We proposed a novel few-shot learning framework that extends CNNs to predict NPV ratio of HIFU ablation treatment for adenomyosis. We collected a dataset from 208 patients with adenomyosis who underwent MRI examination before and after HIFU treatment. Our proposed method was trained and evaluated by fourfold cross validation. This framework obtained sensitivity of 85.6%, 89.6% and 92.8% at 0.799, 0.980 and 1.180 FPs per patient. In the receiver operating characteristics analysis for NPV ratio of adenomyosis, our proposed method received the area under the curve of 0.8233, 0.8289, 0.8412, 0.8319, 0.7010, 0.7637, 0.8375, 0.8219, 0.8207, 0.9812 for the classifications of NPV ratio interval [0%-10%), [10%-20%), …, [90%-100%], respectively. The present study demonstrated that few-shot learning on NPV ratio prediction of HIFU ablation treatment for adenomyosis may contribute to the selection of eligible patients and the pre-judgment of clinical efficacy.
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Abstract The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, there are no qualitative interpretation standards for predicting the NPV ratio of adenomyosis using magnetic resonance imaging (MRI) before HIFU ablation treatment, which leading to inter-reader variability. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in the automatic disease diagnosis of MRI. Since the use of HIFU to treat adenomyosis is a novel treatment, there is not enough MRI data to support CNNs. We proposed a novel few-shot learning framework that extends CNNs to predict NPV ratio of HIFU ablation treatment for adenomyosis. We collected a dataset from 208 patients with adenomyosis who underwent MRI examination before and after HIFU treatment. Our proposed method was trained and evaluated by fourfold cross validation. This framework obtained sensitivity of 85.6%, 89.6% and 92.8% at 0.799, 0.980 and 1.180 FPs per patient. In the receiver operating characteristics analysis for NPV ratio of adenomyosis, our proposed method received the area under the curve of 0.8233, 0.8289, 0.8412, 0.8319, 0.7010, 0.7637, 0.8375, 0.8219, 0.8207, 0.9812 for the classifications of NPV ratio interval [0%–10%), [10%–20%), …, [90%–100%], respectively. The present study demonstrated that few-shot learning on NPV ratio prediction of HIFU ablation treatment for adenomyosis may contribute to the selection of eligible patients and the pre-judgment of clinical efficacy. Access this article The computer you are using is not registered by an institution with a subscription to this article. Please choose one of the options below.

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Condition tags

adenomyosis

MeSH descriptors

Adenomyosis Image Processing, Computer-Assisted Machine Learning Machine Learning Magnetic Resonance Imaging Magnetic Resonance Imaging Adenomyosis Adenomyosis Adenomyosis Adult Adult Female Female High-Intensity Focused Ultrasound Ablation High-Intensity Focused Ultrasound Ablation Humans Humans Image Processing, Computer-Assisted Image Processing, Computer-Assisted Middle Aged

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