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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.11535 |
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| _version_ | 1866915112474902528 |
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| author | Wang, Binwu Rodriguez, Isaac Breitinger, Leon Tollens, Fabian Itzel, Timo Grimm, Dennis Sirazitdinov, Andrei Frölich, Matthias Schönberg, Stefan Teufel, Andreas Hesser, Jürgen Zhao, Wenzhao |
| author_facet | Wang, Binwu Rodriguez, Isaac Breitinger, Leon Tollens, Fabian Itzel, Timo Grimm, Dennis Sirazitdinov, Andrei Frölich, Matthias Schönberg, Stefan Teufel, Andreas Hesser, Jürgen Zhao, Wenzhao |
| contents | The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory test data as well as case report forms). TNM staging is the classification task. Features from the vectorized preprocessed tabular data and radiomics features from contrast-enhanced CT and MRI images are collected. Feature selection is performed based on mutual information. An XGBoost classifier predicts the TNM staging and it shows a prediction accuracy of $0.89 \pm 0.05$ and an AUC of $0.93 \pm 0.03$. The classifier shows that this high level of prediction accuracy can only be obtained by combining image and clinical laboratory data and therefore is a good example case where multi-model classification is mandatory to achieve accurate results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11535 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data Wang, Binwu Rodriguez, Isaac Breitinger, Leon Tollens, Fabian Itzel, Timo Grimm, Dennis Sirazitdinov, Andrei Frölich, Matthias Schönberg, Stefan Teufel, Andreas Hesser, Jürgen Zhao, Wenzhao Computer Vision and Pattern Recognition The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory test data as well as case report forms). TNM staging is the classification task. Features from the vectorized preprocessed tabular data and radiomics features from contrast-enhanced CT and MRI images are collected. Feature selection is performed based on mutual information. An XGBoost classifier predicts the TNM staging and it shows a prediction accuracy of $0.89 \pm 0.05$ and an AUC of $0.93 \pm 0.03$. The classifier shows that this high level of prediction accuracy can only be obtained by combining image and clinical laboratory data and therefore is a good example case where multi-model classification is mandatory to achieve accurate results. |
| title | A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.11535 |