Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains

Bringing an end to diabetes stigma and discrimination: an international consensus statement on evidence and recommendations

To accelerate an end to diabetes stigma and discrimination an international multi-disciplinary expert panel (N=51 members, 18 countries) conducted rapid reviews and participated in a three-round Delphi survey process. They achieved Consensus on 25 Statements of Evidence and 24 Statements of Recommendations. The Consensus is that diabetes stigma is driven primarily by blame, perceptions of burden/sickness, (in)visibility, and fear/disgust. People with diabetes often encounter sigma (negative social judgments, stereotypes, prejudice), which can adversely affect emotional, mental and physical health, self-care, access to optimal healthcare, and social and professional opportunities. Up to one-in-three experience discrimination (unfair and prejudicial treatment) due to diabetes, e.g., in healthcare, education and employment. Diabetes stigma and discrimination are harmful, unacceptable, unethical and counterproductive. Collective leadership is needed to pro-actively challenge, and bring an end to, diabetes stigma and discrimination. Consequently, the panel achieved unanimous consensus on a pledge to end diabetes stigma and discrimination