Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification
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arXiv
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| Format: | Preprint |
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2024
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| author | Perlo, Daniele Berton, Luca Delpiano, Alessia Menchini, Francesca Tibaldi, Stefano Grosso, Marco Fonio, Paolo |
| author_facet | Perlo, Daniele Berton, Luca Delpiano, Alessia Menchini, Francesca Tibaldi, Stefano Grosso, Marco Fonio, Paolo |
| contents | The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14206 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification Perlo, Daniele Berton, Luca Delpiano, Alessia Menchini, Francesca Tibaldi, Stefano Grosso, Marco Fonio, Paolo Image and Video Processing Artificial Intelligence J.3; I.1.2 The liver is the most involved organ by distant metastasis in colon-rectal cancer (CRC) patients and it comes necessary to be aware of the mutational status of the lesions to correctly design the best individual treatment. So far, efforts have been made in order to develop non-invasive and real-time methods that permit the analysis of the whole tumor, using new artificial intelligence tools to analyze the tumor's image obtained by Computed Tomography (CT) scan. In order to address the current medical workflow, that is biopsy analysis-based, we propose the first DeepLearning-based exploration, to our knowledge, of such classification approach from the patient medical imaging. We propose i) a solid pipeline for managing undersized datasets of available CT scans and ii) a baseline study for genomics mutation diagnosis support for preemptive patient follow-up. Our method is able to identify CRC RAS mutation family from CT images with 0.73 F1 score. |
| title | Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification |
| topic | Image and Video Processing Artificial Intelligence J.3; I.1.2 |
| url | https://arxiv.org/abs/2401.14206 |