JMIR Publications

ABSTRACT

Head and neck squamous cell carcinoma (HNSCC) represents a significant public health challenge. its treatment is characterized by high anatomical complexity and a critical need for functional preservation of speech, swallowing, and respiration. While multimodality care, including surgery, radiotherapy, and systemic therapy, has substantially improved patients’ survival, there are still high rates of life-altering toxicities and low response rates to immunotherapy. Current clinical decision-making largely relies on "digital snapshots"—static representations derived from population-based statistics and time-point-specific imaging—which usually fail to account for the rapid anatomical and biological change during treatment course. Digital twins hold great promise in accelerating scientific breakthroughs and transforming oncology treatment and precision medicine by the creations of high‑fidelity virtual patient representations that integrate real‑time biological, anatomical, and clinical data. In this viewpoint, we propose three interconnected digital twins to establish a conceptual framework for the HNSCC digital twin. Those digital twins include the anatomical twin, utilizing virtual surgical planning and augmented reality for geometric precision; the dosimetric twin, employing daily imaging and synthetic CT generation for automated adaptive radiotherapy; and the biological twin, integrating deep phenotyping, radiomics, and mechanistic omics to simulate individualized therapeutic trajectories. Beyond acute care, we explore the clinical utility of digital twins in navigating the "de-escalation dilemma" for HPV-associated disease and improving survive through digital phenotyping. With the advent of longitudinal data from medical-purpose Internet of Things (IoT) sensors and circulating tumor DNA (ctDNA) kinetics, digital twins can serve as active sentinels, detecting functional decline or subclinical events months before clinical manifestation. Furthermore, the implementation of synthetic control arms via virtual populations offers a venue to accelerate clinical trials and address ethical issues in rare molecular subtypes. Despite this potential, substantial barriers to implementation remain, including profound anatomical instability during treatment, the "black box" nature of deep learning algorithms, and the challenges of multiscale data integration. We provide a technical roadmap for the development of "morphing" digital twins—dynamic systems capable of continuous geometric and biological auditing. By bridging the gap between the macroscopic world of radiation physics and the microscopic world of cellular evolution, digital twins promise to shift the management of HNSCC from statistical probability toward personalized biological determinism, optimizing HNSCC management while maintaining the essential human functions of the patients.