Computer-Aided Diagnosis and Characterization of Diseases

Dear Colleagues,

While traditional diagnosis remains a key component of clinical workup, many diseases can nowadays benefit from earlier and more refined characterization in order to make the most of increasingly advanced - often personalized - therapies and multimodality treatment regimens. In the modern multidisciplinary clinical context, such advances tend to rely on the collation and analysis of much varied information and data obtained through at various stages of a patient's workup. The extraction, quantity and variety of such data and their subsequent synthesis is often well beyond human capabilities and thus requires the use of advanced computed analysis. This is where the concept of Computer-Aided Diagnosis and related approaches arose from and, while those were initially developed for extracting and combining features derived from images (e.g., from modalities such as X-ray, CT, mammography, MR, US, PET and SPECT), they have subsequently and more recently been broadened to encompass all types of clinical data and biomarkers (from genomics to imaging, alongside etiology, environment, etc.), with the aim of providing an actionable understanding of disease to assist with its detailed characterization (e.g., type, stage) and optimize interventional strategies. This Special Issue will gather as comprehensive as possible a collection of relevant examples of such approaches at any stage of the clinical workup, across specialties, disciplines and applications.

Prof. Luc Bidaut
Prof. Kenji Suzuki
Guest Editors

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