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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.00367 |
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| author | Saeed, Numan Hassan, Salma Hardan, Shahad Aly, Ahmed Taratynova, Darya Nawaz, Umair Khan, Ufaq Ridzuan, Muhammad Andrearczyk, Vincent Depeursinge, Adrien Xie, Yutong Eugene, Thomas Metz, Raphaël Dore, Mélanie Delpon, Gregory Papineni, Vijay Ram Kumar Wahid, Kareem Dede, Cem Ali, Alaa Mohamed Shawky Sjogreen, Carlos Naser, Mohamed Fuller, Clifton D. Oreiller, Valentin Jreige, Mario Prior, John O. Rest, Catherine Cheze Le Tankyevych, Olena Decazes, Pierre Ruan, Su Tanadini-Lang, Stephanie Vallières, Martin Elhalawani, Hesham Abgral, Ronan Floch, Romain Kerleguer, Kevin Schick, Ulrike Mauguen, Maelle Bourhis, David Leclere, Jean-Christophe Sambourg, Amandine Rahmim, Arman Hatt, Mathieu Yaqub, Mohammad |
| author_facet | Saeed, Numan Hassan, Salma Hardan, Shahad Aly, Ahmed Taratynova, Darya Nawaz, Umair Khan, Ufaq Ridzuan, Muhammad Andrearczyk, Vincent Depeursinge, Adrien Xie, Yutong Eugene, Thomas Metz, Raphaël Dore, Mélanie Delpon, Gregory Papineni, Vijay Ram Kumar Wahid, Kareem Dede, Cem Ali, Alaa Mohamed Shawky Sjogreen, Carlos Naser, Mohamed Fuller, Clifton D. Oreiller, Valentin Jreige, Mario Prior, John O. Rest, Catherine Cheze Le Tankyevych, Olena Decazes, Pierre Ruan, Su Tanadini-Lang, Stephanie Vallières, Martin Elhalawani, Hesham Abgral, Ronan Floch, Romain Kerleguer, Kevin Schick, Ulrike Mauguen, Maelle Bourhis, David Leclere, Jean-Christophe Sambourg, Amandine Rahmim, Arman Hatt, Mathieu Yaqub, Mohammad |
| contents | We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction Saeed, Numan Hassan, Salma Hardan, Shahad Aly, Ahmed Taratynova, Darya Nawaz, Umair Khan, Ufaq Ridzuan, Muhammad Andrearczyk, Vincent Depeursinge, Adrien Xie, Yutong Eugene, Thomas Metz, Raphaël Dore, Mélanie Delpon, Gregory Papineni, Vijay Ram Kumar Wahid, Kareem Dede, Cem Ali, Alaa Mohamed Shawky Sjogreen, Carlos Naser, Mohamed Fuller, Clifton D. Oreiller, Valentin Jreige, Mario Prior, John O. Rest, Catherine Cheze Le Tankyevych, Olena Decazes, Pierre Ruan, Su Tanadini-Lang, Stephanie Vallières, Martin Elhalawani, Hesham Abgral, Ronan Floch, Romain Kerleguer, Kevin Schick, Ulrike Mauguen, Maelle Bourhis, David Leclere, Jean-Christophe Sambourg, Amandine Rahmim, Arman Hatt, Mathieu Yaqub, Mohammad Computer Vision and Pattern Recognition We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks. |
| title | A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.00367 |