What question did this study set out to answer?

To evaluate the clinical applications, advantages, and limitations of radiomics, using hepatocellular carcinoma as a model disease.

March 30, 2023DiagnosticsOpen Access

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma

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Authors

TLThomas Lévi‐StraussHôpital l'Archet BTBettina TortoriciCentre Hospitalier Universitaire de Nice OLOlivier LopezCentre Georges François Leclerc

Discussion

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Implication

Review demonstrates the diagnostic and prognostic utility of radiomics in hepatocellular carcinoma, highlighting its emerging role in precision oncology.

Key Points

To evaluate the clinical applications, advantages, and limitations of radiomics, using hepatocellular carcinoma as a model disease.
Synthesized evidence on artificial intelligence algorithms applied to medical imaging for high-throughput textural feature extraction.
Assessed radiomic capabilities in diagnosing difficult liver tumors, predicting histological features, and estimating medical and surgical therapy responses.
Radiomics enables non-invasive, high-throughput profiling of entire tumor heterogeneity, surpassing the localized sampling limits of traditional biopsies.
Standalone radiomic models occasionally show reduced accuracy, indicating that combining imaging features with clinical and non-radiomic data is often necessary for optimal performance.

Cite This Study

Lévi‐Strauss et al. (2023) studied this question.

synapsesocial.com/papers/6a129eb473c5a8f747a583e3 https://doi.org/10.3390/diagnostics13071303

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March 30, 2023DiagnosticsOpen Access

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma

View Full Paper

Ask AI

Bookmark

Authors

TLThomas Lévi‐StraussHôpital l'Archet BTBettina TortoriciCentre Hospitalier Universitaire de Nice OLOlivier LopezCentre Georges François Leclerc

Discussion

Member takes

Implication

Review demonstrates the diagnostic and prognostic utility of radiomics in hepatocellular carcinoma, highlighting its emerging role in precision oncology.

Key Points

To evaluate the clinical applications, advantages, and limitations of radiomics, using hepatocellular carcinoma as a model disease.
Synthesized evidence on artificial intelligence algorithms applied to medical imaging for high-throughput textural feature extraction.
Assessed radiomic capabilities in diagnosing difficult liver tumors, predicting histological features, and estimating medical and surgical therapy responses.
Radiomics enables non-invasive, high-throughput profiling of entire tumor heterogeneity, surpassing the localized sampling limits of traditional biopsies.
Standalone radiomic models occasionally show reduced accuracy, indicating that combining imaging features with clinical and non-radiomic data is often necessary for optimal performance.

Cite This Study

Lévi‐Strauss et al. (2023) studied this question.

synapsesocial.com/papers/6a129eb473c5a8f747a583e3 https://doi.org/10.3390/diagnostics13071303

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