From Radical Resection to Precision Surgery: Integrating Diagnostic Biomarkers, Radiomics-Based Predictive Models, and Perioperative Systemic Therapy in Head and Neck Oncology

Head and neck cancer surgery has evolved from radical organ-sacrificing procedures to function-preserving approaches integrated within multidisciplinary frameworks. This comprehensive literature review, concentrating on studies from the past five years while incorporating relevant publications from the last three decades and landmark historical papers, examines the evolving role of surgery emphasizing diagnostic methodologies including comprehensive genomic profiling, validated imaging biomarkers, and their clinical integration for treatment selection and response prediction. Modern surgical practice demonstrates a paradigm shift toward precision medicine through validated diagnostic technologies. Comprehensive genomic profiling identifies clinically actionable alterations in over 90% of head and neck squamous cell carcinomas, with tumor mutational burden serving as a validated predictive biomarker for immunotherapy response. Programmed death-ligand 1 (PD-L1) combined positive score functions as a validated diagnostic biomarker for immunotherapy efficacy, demonstrating significant clinical benefit in biomarker-selected populations. Radiomics-based predictive models utilizing machine learning algorithms achieve diagnostic accuracies exceeding 85% for treatment response prediction when validated across independent cohorts. Quantitative ultrasound spectroscopy combined with magnetic resonance imaging radiomics demonstrates high sensitivity and specificity for radiation response prediction. Habitat imaging techniques characterizing tumor microenvironmental heterogeneity predict pathologic complete response to neoadjuvant chemoimmunotherapy with area under the curve values approaching 0.90 in validation studies. Integration of these diagnostic methodologies enables response-adaptive treatment strategies, with neoadjuvant chemotherapy facilitating mandibular preservation and adjuvant therapy omission in over half of human papillomavirus (HPV)-associated cases following surgical downstaging. Clinical validation of these diagnostic platforms enables accurate treatment response prediction and informed surgical decision-making, though standardization across institutions and demonstration of survival benefits through prospective trials remain essential for broader implementation.

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Journal
Diagnostics
Published
2025-12-23
DOI
https://doi.org/10.3390/diagnostics16010049
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

From Radical Resection to Precision Surgery: Integrating Diagnostic Biomarkers, Radiomics-Based Predictive Models, and Perioperative Systemic Therapy in Head and Neck Oncology

Juan Pablo Rodrigo, K. Thomas Robbins, Alfio Ferlito, Arlene A. Forastiere et al.
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Radiomics and Machine Learning in Medical Imaging
article

From Radical Resection to Precision Surgery: Integrating Diagnostic Biomarkers, Radiomics-Based Predictive Models, and Perioperative Systemic Therapy in Head and Neck Oncology

Juan Pablo Rodrigo, K. Thomas Robbins, Alfio Ferlito, Arlene A. Forastiere, Carol R. Bradford, Primož Strojan, J J Beitler, Marc Hamoir, Karthik Nagaraja Rao, Petra Ambrosch, Nabil Saba, Alvaro Sanabria, Luiz P. Kowalski, Orlando Guntinas-Lichius
article en

Abstract

Head and neck cancer surgery has evolved from radical organ-sacrificing procedures to function-preserving approaches integrated within multidisciplinary frameworks. This comprehensive literature review, concentrating on studies from the past five years while incorporating relevant publications from the last three decades and landmark historical papers, examines the evolving role of surgery emphasizing diagnostic methodologies including comprehensive genomic profiling, validated imaging biomarkers, and their clinical integration for treatment selection and response prediction. Modern surgical practice demonstrates a paradigm shift toward precision medicine through validated diagnostic technologies. Comprehensive genomic profiling identifies clinically actionable alterations in over 90% of head and neck squamous cell carcinomas, with tumor mutational burden serving as a validated predictive biomarker for immunotherapy response. Programmed death-ligand 1 (PD-L1) combined positive score functions as a validated diagnostic biomarker for immunotherapy efficacy, demonstrating significant clinical benefit in biomarker-selected populations. Radiomics-based predictive models utilizing machine learning algorithms achieve diagnostic accuracies exceeding 85% for treatment response prediction when validated across independent cohorts. Quantitative ultrasound spectroscopy combined with magnetic resonance imaging radiomics demonstrates high sensitivity and specificity for radiation response prediction. Habitat imaging techniques characterizing tumor microenvironmental heterogeneity predict pathologic complete response to neoadjuvant chemoimmunotherapy with area under the curve values approaching 0.90 in validation studies. Integration of these diagnostic methodologies enables response-adaptive treatment strategies, with neoadjuvant chemotherapy facilitating mandibular preservation and adjuvant therapy omission in over half of human papillomavirus (HPV)-associated cases following surgical downstaging. Clinical validation of these diagnostic platforms enables accurate treatment response prediction and informed surgical decision-making, though standardization across institutions and demonstration of survival benefits through prospective trials remain essential for broader implementation.

DiagnosticsVol. 16(1)
Cliniques Universitaires Saint-Luc (BE), University of Padua (IT), Johns Hopkins University (US), Emory University (US), University of Ljubljana (SI), Southern Illinois University School of Medicine (US), Johns Hopkins Medicine (US), AC Camargo Hospital (BR), Institute of Oncology Ljubljana (SI), Universidad de Antioquia (CO), University Hospital Schleswig-Holstein (DE), HCG Cancer Hospital (IN), Cancer Hospital and Research Institute (IN), Hospital Universitario Central de Asturias (ES), Jena University Hospital (DE), Atlanta VA Medical Center (US), The Ohio State University (US), University of Lübeck (DE)
Peace, Justice and strong institutions
Openalex Percentile: Top 31%
Radiomics and Machine Learning in Medical Imaging
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