Radiotherapy has long been the mainstay of treatment for patients with head and neck cancer and has traditionally involved a stage-dependent strategy whereby all patients with the same TNM stage receive the same therapy. We believe there is a substantial opportunity to improve radiotherapy delivery beyond just technological and anatomical precision. In this Series paper, we explore several new ideas that could improve understanding of the phenotypic and genotypic differences that exist between patients and their tumours. We discuss how exploiting these differences and taking advantage of precision medicine tools-such as genomics, radiomics, and mathematical modelling-could open new doors to personalised radiotherapy adaptation and treatment. We propose a new treatment shift that moves away from an era of empirical dosing and fractionation to an era focused on the development of evidence to guide personalisation and biological adaptation of radiotherapy. We believe these approaches offer the potential to improve outcomes and reduce toxicity.

No abstract

No abstract

Intensity-modulated radiotherapy for CUP site or T1-category BOT carcinoma had similar clinical outcomes. Identifying hidden BOT primary carcinoma with novel approaches (eg, transoral robotic surgery and transoral laser microsurgery) may lead to changes in the radiotherapy target volume and dose prescription. Studies are needed to investigate the effect of these differences on quality of life and functional outcomes.

Introduction To develop a radiomic‐based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer. Methods We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in‐house data‐chjmirocterization algorithm was used to extract 3D‐radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, Fc = α * FPET + (1 − α) * FCT, 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco‐regional control (LRC), recurrence‐free survival (RFS), metastasis‐free survival (MFS) and overall survival (OS) were estimated by Kaplan–Meier analysis, and compared using log‐rank test. Results Median follow‐up was 59 months. pCR was achieved in 34 (50%) patients. Five‐year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0–1 indicative of complete response or minimal residual disease was significantly associated with improved 5‐year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04–0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5‐year LRC favouring low‐score group (91.1% vs. 80%, 95% CI 0.09–1.77, P = 0.2). Conclusion The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.