NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912990724358144 |
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| author | Han, Youngung Cha, Minkyung Kim, Kyeonghun Um, Induk Sho, Myeongbin Bae, Joo Young Jung, Jaewon Park, Jung Hyeok Lee, Seojun Kim, Nam-Joon Jeong, Woo Kyoung Lee, Won Jae Hong, Pa Liao, Ken Ying-Kai Lee, Hyuk-Jae |
| author_facet | Han, Youngung Cha, Minkyung Kim, Kyeonghun Um, Induk Sho, Myeongbin Bae, Joo Young Jung, Jaewon Park, Jung Hyeok Lee, Seojun Kim, Nam-Joon Jeong, Woo Kyoung Lee, Won Jae Hong, Pa Liao, Ken Ying-Kai Lee, Hyuk-Jae |
| contents | Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_29449 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification Han, Youngung Cha, Minkyung Kim, Kyeonghun Um, Induk Sho, Myeongbin Bae, Joo Young Jung, Jaewon Park, Jung Hyeok Lee, Seojun Kim, Nam-Joon Jeong, Woo Kyoung Lee, Won Jae Hong, Pa Liao, Ken Ying-Kai Lee, Hyuk-Jae Computer Vision and Pattern Recognition Artificial Intelligence Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903. |
| title | NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.29449 |