Research Topic

State of the Art Body Composition Profiling: Advances in Imaging Modalities and Patient Outcomes.

About this Research Topic

Body composition assessment may rely on different modalities, such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT), magnetic resonance (MR), bioelectrical impedance assay, each one with its peculiarities. Imaging quantification of lean muscle mass and fat distribution is a potentially valuable tool to assess the functional status of cancer patients at diagnosis, during treatments and during follow-up. In recent years, there has been increasing interest towards the influence of body composition on oncological patients' outcomes. Visceral obesity, sarcopenia and sarcopenic obesity have been identified as factors that may affect the efficacy and toxicity of chemotherapy, the advent of surgical complication rates as well as the prognosis in terms of survival in patients with various malignancies. Factors causing changes of body composition are multifactorial, including but not limited to immobility, endocrine function, insulin metabolism, and nutritional deficiencies.

This Research Topic will have two goals: on one hand to enlarge knowledge about the imaging techniques to assess body composition profiling, including the most traditional ones, as well as artificial intelligence-aided techniques, if provided. On the other, to expand knowledge about the effects of body composition profiling on different outcomes including, but not limited to, chemotherapy-related complications, surgical-related complication, overall survival, progression-free survival, disease-free survival and so on.

For this Research Topic, we aim to collect papers that cover the state of the art of body composition profiling (assessed by any imaging modality) and its relationship on cancer patients' outcomes. We encourage submissions of Original Research as well as comprehensive Systematic Reviews and meta analysis.

Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in Frontiers in Oncology.


Keywords: Cancer Imaging, Muscle, Sarcopenia, Body Composition, Artificial Intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Body composition assessment may rely on different modalities, such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT), magnetic resonance (MR), bioelectrical impedance assay, each one with its peculiarities. Imaging quantification of lean muscle mass and fat distribution is a potentially valuable tool to assess the functional status of cancer patients at diagnosis, during treatments and during follow-up. In recent years, there has been increasing interest towards the influence of body composition on oncological patients' outcomes. Visceral obesity, sarcopenia and sarcopenic obesity have been identified as factors that may affect the efficacy and toxicity of chemotherapy, the advent of surgical complication rates as well as the prognosis in terms of survival in patients with various malignancies. Factors causing changes of body composition are multifactorial, including but not limited to immobility, endocrine function, insulin metabolism, and nutritional deficiencies.

This Research Topic will have two goals: on one hand to enlarge knowledge about the imaging techniques to assess body composition profiling, including the most traditional ones, as well as artificial intelligence-aided techniques, if provided. On the other, to expand knowledge about the effects of body composition profiling on different outcomes including, but not limited to, chemotherapy-related complications, surgical-related complication, overall survival, progression-free survival, disease-free survival and so on.

For this Research Topic, we aim to collect papers that cover the state of the art of body composition profiling (assessed by any imaging modality) and its relationship on cancer patients' outcomes. We encourage submissions of Original Research as well as comprehensive Systematic Reviews and meta analysis.

Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in Frontiers in Oncology.


Keywords: Cancer Imaging, Muscle, Sarcopenia, Body Composition, Artificial Intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 July 2021 Abstract
17 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 July 2021 Abstract
17 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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