Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and responses to treatment. Better estimations of prognosis would support treatment planning and patient support. Advances in machine learning have informed development of clinical predictive models, but their integration into clinical practice is almost non-existent. One reasons for this is the lack of interpretability of models. In this paper, we use a novel brain tumour dataset to compare two interpretable rule list models against popular machine learning approaches for brain tumour survival prediction. All models are quantitatively evaluated using standard performance metrics. The rule lists are also qualitatively assessed for their interpretability and clinical utility. The interpretability of the black box machine learning models is evaluated using two post-hoc explanation techniques, LIME and SHAP. Our results show that the rule lists were only slightly outperformed by the black box models. We demonstrate that rule list algorithms produced simple decision lists that align with clinical expertise. By comparison, post-hoc interpretability methods applied to black box models may produce unreliable explanations of local model predictions. Model interpretability is essential for understanding differences in predictive performance and for integration into clinical practice.
Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
Centre for Doctoral Training in AI for Medical Diagnosis and Care（人工智能辅助医疗诊断与护理博士培训中心）；School of Computing, University of Leeds（利兹大学计算机学院）；School of Computer Science, University of Leeds（利兹大学计算机科学学院）；Leeds Cancer Centre, St James’s University Hospital, Leeds, UK（利兹癌症中心，圣詹姆斯大学医院，利兹，英国）
A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth
A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma
University of Warwick（华威大学）；Shaukat Khanum Memorial Cancer Hospital Research Centre（肖卡特·汗姆纪念癌症医院研究中心）；University of Birmingham（伯明翰大学）；Newcastle University（纽卡斯尔大学）；University of Sheffield（谢菲尔德大学）；The Alan Turing Institute（阿兰·图灵研究所）；University Hospitals Coventry & Warwickshire NHS Trust（考文垂与华威郡大学医院NHS信托）
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Otto von Guericke University Magdeburg（奥托·冯·格里克马格德堡大学）；Center for Behavioral Brain Sciences（行为脑科学中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
University of Minho（米尼奥大学）；Institute for Artificial Intelligence in Medicine（人工智能医学研究所）；Graz University of Technology（格拉茨工业大学）；University Medicine Essen（埃森大学医学中心）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；University Hospital Essen（埃森大学医院）；German Cancer Consortium (DKTK)（德国癌症联盟）
Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance
University College London（伦敦大学学院）；National Hospital for Neurology and Neurosurgery（国立神经病学和神经外科医院）；NVIDIA（英伟达）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）；Royal National Orthopaedic Hospital（皇家国立骨科医院）；University College Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Royal Free Hospital（皇家自由医院）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民保健服务信托）；Imperial College London（帝国理工学院）
Brain Tumour Removing and Missing Modality Generation using 3D WDM
Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心 / LASI）；Computer Algorithms for Medicine Laboratory（计算机医学算法实验室）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学医院医学人工智能研究所）；Institute of Medical Informatics, University Hospital RWTH Aachen（亚琛工业大学医学院医学信息学研究所）；Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen（亚琛工业大学医学院口腔颌面外科）；Institute of Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形学与视觉研究所）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学医院科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；Department of Physics, TU Dortmund University（多特蒙德工业大学物理系）
An AI based Digital Score of Tumour-Immune Microenvironment Predicts Benefit to Maintenance Immunotherapy in Advanced Oesophagogastric Adenocarcinoma
University of Warwick（华威大学）；The Royal Marsden Hospital NHS Foundation Trust（皇家马斯登医院 NHS 基金会信托）；The Institute of Cancer Reseach（癌症研究所）；The Alan Turing Institute（艾伦·图灵研究所）；Histofy Ltd（Histofy 有限公司）
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany；Biomedical Magnetic Resonance, Faculty of Nature Sciences, Otto von Guericke University Magdeburg, Germany；Genomics Research Centre, Human Technopole, Milan, Italy；Institute for Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Germany；Digital Engineering Faculty, University of Potsdam, Germany；Machine Learning, Hasso-Plattner-Institute, Potsdam, Germany；Department of Biomedical Data Science, Stanford University, Stanford, CA, United States；Centre for Behavioural Brain Sciences, Magdeburg, Germany；German Centre for Neurodegenerative Disease, Magdeburg, Germany
What neurosurgeons need to see: synthetic intra-operative MRI from ultrasound for brain-shift compensation in brain tumour surgery
Department of Neurosurgery, Neurovascular Unit, Río Hortega University Hospital, Valladolid, Spain（西班牙巴利亚多利德里奥·奥尔特加大学医院神经外科神经血管科）；Specialized Group in Biomedical Imaging and Computational Analysis (GEIBAC), Instituto de Investigación Biosanitaria de Valladolid (IBioVALL), Valladolid, Spain（西班牙巴利亚多利德生物医学研究与计算分析专业组(GEIBAC)，巴利亚多利德生物健康研究所(IBioVALL)）
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Center for Behavioral Brain Sciences（行为脑科学研究中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病研究中心）
Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation
Center Algoritmi / LASI, University of Minho（米尼奥大学 Center Algoritmi / LASI）；Computer Algorithms for Medicine Laboratory（计算机算法医学实验室）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学中心医学人工智能研究所）；Institute of Medical Informatics, University Hospital RWTH Aachen（亚琛工业大学附属医院医学信息学研究所）；Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen（亚琛工业大学附属医院口腔颌面外科）；Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho（米尼奥大学医学院生命与健康科学研究所）；ICVS/3B’s – PT Government Associate Laboratory（ICVS/3B’s – 葡萄牙政府附属实验室）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学中心科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；Department of Physics, TU Dortmund University（多特蒙德工业大学物理系）
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Institute for Surgical Technology and Biomechanics, University of Bern（伯尔尼大学外科技术与生物力学研究所）；University Clinic for Radio-oncology, Inselspital, Bern University Hospital, University of Bern（伯尔尼大学伯尔尼大学医院因塞尔医院放射肿瘤学大学诊所）；Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern（伯尔尼大学因塞尔医院诊断与介入神经放射学大学研究所高级神经成像支持中心）
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Inception Institute of Artificial Intelligence（人工智能起源研究所）；Lehigh University（利哈伊大学）；Tianjin University（天津大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
