T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915015293927424 |
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| author | Yi, Weixi Wang, Yipei Thorley, Natasha Ng, Alexander Punwani, Shonit Kasivisvanathan, Veeru Barratt, Dean C. Saeed, Shaheer Ullah Hu, Yipeng |
| author_facet | Yi, Weixi Wang, Yipei Thorley, Natasha Ng, Alexander Punwani, Shonit Kasivisvanathan, Veeru Barratt, Dean C. Saeed, Shaheer Ullah Hu, Yipeng |
| contents | Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. While machine learning (ML) models have demonstrated radiologist-level accuracy in detecting prostate cancer from these two sequences, this study investigates the potential of ML-enabled methods using only the T2w sequence as input during inference time. We first discuss the technical feasibility of such a T2-only approach, and then propose a novel ML formulation, where DWI sequences - readily available for training purposes - are only used to train a meta-learning model, which subsequently only uses T2w sequences at inference. Using multiple datasets from more than 3,000 prostate cancer patients, we report superior or comparable performance in localising radiologist-identified prostate cancer using our proposed T2-only models, compared with alternative models using T2-only or both sequences as input. Real patient cases are presented and discussed to demonstrate, for the first time, the exclusively true-positive cases from models with different input sequences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07416 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging Yi, Weixi Wang, Yipei Thorley, Natasha Ng, Alexander Punwani, Shonit Kasivisvanathan, Veeru Barratt, Dean C. Saeed, Shaheer Ullah Hu, Yipeng Image and Video Processing Computer Vision and Pattern Recognition Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. While machine learning (ML) models have demonstrated radiologist-level accuracy in detecting prostate cancer from these two sequences, this study investigates the potential of ML-enabled methods using only the T2w sequence as input during inference time. We first discuss the technical feasibility of such a T2-only approach, and then propose a novel ML formulation, where DWI sequences - readily available for training purposes - are only used to train a meta-learning model, which subsequently only uses T2w sequences at inference. Using multiple datasets from more than 3,000 prostate cancer patients, we report superior or comparable performance in localising radiologist-identified prostate cancer using our proposed T2-only models, compared with alternative models using T2-only or both sequences as input. Real patient cases are presented and discussed to demonstrate, for the first time, the exclusively true-positive cases from models with different input sequences. |
| title | T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.07416 |