What question did this study set out to answer?

To review the evolution of medical imaging from qualitative interpretation to quantitative data extraction using handcrafted and deep radiomics, and to evaluate their clinical utility in oncology.

February 26, 2020British Journal of RadiologyOpen Access

Radiomics: from qualitative to quantitative imaging

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Authors

WRWilliam RogersUnited States Department of Veterans Affairs SSSithin Thulasi SeethaNational Center for Oncological Hadrontherapy TRTurkey RefaeeJazan University

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Overview

Narrative review demonstrates the conversion of medical scans into quantitative data via radiomics in oncology, highlighting improved diagnostic profiling and clinical decision support.

Key Points

To review the evolution of medical imaging from qualitative interpretation to quantitative data extraction using handcrafted and deep radiomics, and to evaluate their clinical utility in oncology.
Narrative review examining the multistage pipeline of handcrafted radiomics, including feature extraction based on shape, pixel intensity, and texture.
Methodological comparison evaluating handcrafted feature engineering alongside end-to-end deep learning architectures within radiomic workflows.
Synthesis of clinical translational literature focusing on predictive oncology models, diagnostic decision support, and technological limitations.
Handcrafted and deep radiomic workflows reliably decode covert imaging phenotypes into objective data to forecast clinical outcomes such as patient survival and therapy response.
Deep learning automates high-dimensional feature extraction but requires trade-offs between predictive capacity and biological interpretability relative to handcrafted methods.
Clinical adoption in oncology remains constrained by challenges in data standardization, model generalizability, and workflow integration.

Cite This Study

Rogers et al. (2020) studied this question.

synapsesocial.com/papers/69dccbdf481b6ebcb5e52da1 https://doi.org/10.1259/bjr.20190948

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February 26, 2020British Journal of RadiologyOpen Access

Radiomics: from qualitative to quantitative imaging

View Full Paper

Ask AI

Bookmark

Authors

WRWilliam RogersUnited States Department of Veterans Affairs SSSithin Thulasi SeethaNational Center for Oncological Hadrontherapy TRTurkey RefaeeJazan University

Discussion

Member takes

Overview

Narrative review demonstrates the conversion of medical scans into quantitative data via radiomics in oncology, highlighting improved diagnostic profiling and clinical decision support.

Key Points

To review the evolution of medical imaging from qualitative interpretation to quantitative data extraction using handcrafted and deep radiomics, and to evaluate their clinical utility in oncology.
Narrative review examining the multistage pipeline of handcrafted radiomics, including feature extraction based on shape, pixel intensity, and texture.
Methodological comparison evaluating handcrafted feature engineering alongside end-to-end deep learning architectures within radiomic workflows.
Synthesis of clinical translational literature focusing on predictive oncology models, diagnostic decision support, and technological limitations.
Handcrafted and deep radiomic workflows reliably decode covert imaging phenotypes into objective data to forecast clinical outcomes such as patient survival and therapy response.
Deep learning automates high-dimensional feature extraction but requires trade-offs between predictive capacity and biological interpretability relative to handcrafted methods.
Clinical adoption in oncology remains constrained by challenges in data standardization, model generalizability, and workflow integration.

Cite This Study

Rogers et al. (2020) studied this question.

synapsesocial.com/papers/69dccbdf481b6ebcb5e52da1 https://doi.org/10.1259/bjr.20190948

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