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sylvana Hassanieh

University of Oxford - MRC Oxford Institute for Radiation Oncology

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Scholarly Papers (1)

1.

A Machine Learning Model of Complete Response to Radiation in Rectal Cancer Reveals Immune Infiltrate and TGFβ Signalling as Key Predictors

Number of pages: 25 Posted: 17 Nov 2022
University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Aberdeen - School of Medicine, University of Aberdeen - School of Medicine, University of Leeds - Institute of Medical Research, Besti Cadwaladr University - North Wales Cancer Treatment Centre, University of Leeds - Leeds Institute of Medical Research at St. James’s, University College London - MRC Clinical Trials Unit, University of Leeds - Institute of Medical Research, University of Leeds - Institute of Medical Research, Queen's University Belfast - Patrick G Johnston Centre for Cancer Research, Wellcome Sanger Institute, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology, Wellcome Sanger Institute - Department of Human Genetics, University Hospital Zurich - Department of Pathology and Molecular Pathology, University of Oxford - Wellcome Trust Centre for Human Genetics, University College London - MRC Clinical Trials Unit, University of Edinburgh - Cancer Research UK Edinburgh Centre, University of Oxford - MRC Oxford Institute for Radiation Oncology, University of Oxford - MRC Oxford Institute for Radiation Oncology and Independent
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Abstract:

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Rectal Neoplasms, Radiotherapy, Precision Medicine, prediction, TGF-beta, immune response, Genes.