Anando Sen is a quantitative expert with particular interest in healthcare-related applications. He is currently part of the IDEAS National Evaluation Team at ϲ. Before joining ϲ, he was a Data Scientist at Newcastle University (John Walton Muscular Dystrophy Research Centre) working on the standardisation of paediatric clinical trial data. He has held previous positions at University of Texas MD Anderson Cancer Center (Imaging Physics), Columbia University (Biomedical Informatics), and University of Houston (Biomedical Engineering). He was awarded his PhD (in Mathematics) from University of Houston in 2012.
Anando's research interests are diverse and his primary projects have included a wide variety of applications in healthcare that are summarised below.
IDEAS National Evaluation Team -IDEAS-NET is the interdisciplinary evaluation of complex innovations in health and social care National Evaluation Team, based at ϲ, Newcastle upon Tyne, UK, funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research (HSDR) Programme, to undertake evaluations in the organisation and delivery of complex innovations in health and care services. Adopting a bespoke co-design approach within each evaluation, the team produces rigorous, timely and useful evidence to inform the transformation of services, innovations and outcomes, for all relevant stakeholders across health and social care.
conect4children - conect4children (c4c) was an Innovative Medicines Initiative funded project that addressed the barriers to conducting paediatric research by creating a pan-European clinical trial network. A strand of c4c focussed on the standardisation of data in paediatric clinical trials. Tasks included (1) Implementation of the FAIR data principles; (2) Building a Cross Cutting Paediatric Data Dictionary to be used in proof of viability paediatric trials; (3) Developing a new global set of standards for paediatric data in collaboration with CDISC; (4) Exploring ways to use tools to standardise disease specfic data; and (5) Research the linkage between real world data and the data in clinical trials. These tasks would result in data that is more standardised, and interoperable. This potentially poolable data can then be used (and re-used) to allow for better planning of clinical trials and increased knowledge about paediatric medicines development.
Radiology-pathology correlation for tumor delineations -Despite tremendous advancement of in vivo imaging modalities, there remains substantial uncertainty in tumor delineations in these images. Histopathology is the gold-standard for determining the extent of cancerous tumors. Correlating in vivo imaging with histopathology has the potential to provide a direct spatial comparison between the two. We use clinical and pre-clinical data to develop and validate correlative pathology methods.
Deformable image registration for liver cancer radiation therapy -Radiation therapy is often used to treat inoperable cholangiocarcinomas (bile-duct cancer). Due to the vast changes the liver can undergo during irradiation, deformable image registration an attractive option for comparing pre and post-treatment images, hence assessing the treatment. We develop and compare deformable registration techniques for this purpose.
Quantitative assessment of population representativeness in individual clinical trials -An apriori estimate for the population representativeness of a clinical trial can be a major aid in patient selection strategies. We develop a metric GIST 2.0, to compute the population representativeness based on eligibility criteria. The iterative use of this metric during the design of eligibility criteria can potentially optimize population representativeness within the constraints of patient safety.
Tumor localization tasks in prostate nuclear medicine images -Model Observers are mathematical models used for tumor localization tasks in medical imaging. Since medical images are analyzed by radiologists, it is crucial for model observers to be relevant for humans. We develop a visual-search observer and implement inefficiency models within it to account for human perceptions.
CT reconstructions with truncated projection data -CT scans have tremendous diagnostic value in the detection of tumors but are accompanied by risks of radiation-induced carcinomas. Restricting CT scans to a small region of interest can subtantially reduce the exposure to irradiation. We present a new algorithm to reconstruct anatomical images from such restricted scans that can maintain image quality comparable to full CT scans. The performance of the algorithm and its robustness to noise are validated on both synthetic and experimental data.
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