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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Computer Vision-Radiomics & Pathognomics.

Alexandra T Bourdillon¹

¹Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.

Otolaryngologic Clinics of North America

|June 23, 2024

Summary

Computer vision extracts radiographic (radiomics) and histopathologic (pathognomics) features for head and neck disorders. Machine learning advances tumor and nodal characterization, guiding prognosis and clinical management.

Keywords:

Computer vision Deep learning Histopathology Machine learning Pathognomics Radiology Radiomics

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Area of Science:

Medical imaging analysis
Computational pathology
Biomarker discovery

Background:

Molecular biomarkers are crucial for understanding head and neck disorders.
Computer vision extends this by extracting radiographic (radiomics) and histopathologic (pathognomics) features.
Machine learning (ML) and deep learning (DL) show promise in head and neck cancer research.

Purpose of the Study:

To review the current landscape of radiomic and pathognomic applications in head and neck disorders.
To identify gaps in current methodologies and inform future research directions.
To highlight the potential of integrating multidimensional data for improved clinical management.

Main Methods:

Review of existing literature on radiomics and pathognomics in head and neck cancers.
Analysis of machine learning and deep learning applications for feature characterization.
Synthesis of findings to identify areas for methodological advancement.

Main Results:

ML and DL have significantly advanced the characterization of tumor and nodal features in head and neck cancers.
Radiomics and pathognomics offer complementary information to molecular biomarkers.
Current applications show potential for improving outcome prediction.

Conclusions:

Further development of novel methodologies is essential for integrating diverse data inputs.
Comprehensive examination of disease features through advanced computational techniques can guide prognosis.
Enhanced data integration will ultimately improve clinical management strategies for head and neck disorders.