External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables.

Background And Purpose: While the inclusion of spatial dose information in deep learning (DL)-based normal-tissue complication probability (NTCP) models has been the focus of recent research studies, external validation is still lacking. This study aimed to externally validate a DL-based NTCP model for mandibular osteoradionecrosis (ORN) trained on 3D radiation dose distribution maps and clinical variables.

Methods And Materials: A 3D DenseNet-40 convolutional neural network (3D-mDN40) was trained on clinical and radiation dose distribution maps on a retrospective class-balanced matched cohort of 184 subjects.

Correlation of the treatment sensitivity of patient-derived organoids with treatment outcomes in patients with head and neck cancer (SOTO): protocol for a prospective observational study.

Introduction: Organoids have been successfully used in several areas of cancer research and large living biobanks of patient-derived organoids (PDOs) have been developed from various malignancies. The characteristics of the original tumour tissue such as mutation signatures, phenotype and genetic diversity are well preserved in organoids, thus showing promising results for the use of this model in translational research. In this study, we aim to assess whether we can generate PDOs from head and neck squamous cell carcinoma (HNSCC) samples and whether PDOs can be used to predict treatment sensitivity in HNSCC patients as well as to explore potential biomarkers.

Comparison of deep-learning multimodality data fusion strategies in mandibular osteoradionecrosis NTCP modelling using clinical variables and radiation dose distribution volumes.

Normal tissue complication probability (NTCP) modelling is rapidly embracing deep learning (DL) methods, acknowledging the importance of spatial dose information. Finding effective ways to combine information from radiation dose distribution maps (dosiomics) and clinical data involves technical challenges and requires domain knowledge. We propose different multi-modality data fusion strategies to facilitate future DL-based NTCP studies.

Is there a role for [F]-FMISO PET to guide dose adaptive radiotherapy in head and neck cancer? A review of the literature.

Purpose: Hypoxia is a major cause of radioresistance in head and neck cancer (HNC), resulting in treatment failure and disease recurrence. F-fluoromisonidazole [F]FMISO PET has been proposed as a means of localising intratumoural hypoxia in HNC so that radiotherapy can be specifically escalated in hypoxic regions. This concept may not be deliverable in routine clinical practice, however, given that [F]FMISO PET is costly, time consuming and difficult to access.

Fully automated radiotherapy treatment planning: A scan to plan challenge.

Background And Purpose: Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible.

Materials And Methods: A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up.

[F]FSPG-PET provides an early marker of radiotherapy response in head and neck squamous cell cancer.

The ability to image early treatment response to radiotherapy in head and neck squamous cell carcinoma (HNSCC) will enable the identification of radioresistant tumor volumes suitable for treatment intensification. Here, we propose the system x radiotracer (4)-4-(3-[F]fluoropropyl)-L-glutamate ([F]FSPG) as a non-invasive method to monitor radiation response in HNSCC. We assessed temporal changes in cell death, antioxidant status, and [F]FSPG retention following a single dose of 10 Gy irradiation in FaDU HNSCC cells.

Effect of synthetic CT on dose-derived toxicity predictors for MR-only prostate radiotherapy.

Objectives: Toxicity-driven adaptive radiotherapy (RT) is enhanced by the superior soft tissue contrast of magnetic resonance (MR) imaging compared with conventional computed tomography (CT). However, in an MR-only RT pathway synthetic CTs (sCT) are required for dose calculation. This study evaluates 3 sCT approaches for accurate rectal toxicity prediction in prostate RT.

Assessing the quality of patient-reported outcome measurements for gynecological cancers: a systematic review.

To provide perspective on patient-reported outcome measurement (PROM) instruments to adopt in patients diagnosed with gynecological cancers. A systematic search was conducted to identify PROMs developed for or applied in gynecological cancer populations. PROMs identified in more than one study subsequently underwent assessment according to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) criteria.

Salivary Electrostimulation in the Treatment of Radiation Therapy-Induced Xerostomia (LEONIDAS-2): A Multicenter, Randomized, Double-Masked, Sham-Controlled, Phase 3 Trial.

Purpose: Radiation therapy-induced xerostomia significantly affects quality of life in head and neck cancer survivors. Neuro-electrostimulation of the salivary glands may safely increase natural salivation and reduce dry mouth symptoms.

Methods And Materials: This multicenter, double-masked, randomized, sham-controlled clinical trial assessed the long-term effects of a commercially available intraoral neuro-electrostimulating device in lessening xerostomia symptoms, increasing salivary flow, and improving quality of life in individuals with radiation therapy-induced xerostomia.

The microbiota and radiotherapy for head and neck cancer: What should clinical oncologists know?

Radiotherapy is a linchpin in head and neck squamous cell carcinoma (HN-SCC) treatment. Modulating tumour and/or normal tissue biology offers opportunities to further develop HN-SCC radiotherapy. The microbiota, which can exhibit homeostatic properties and be a modulator of immunity, has recently received considerable interest from the Oncology community.

Neural network dose prediction for rectal spacer stratification in dose-escalated prostate radiotherapy.

Purpose: To develop a knowledge-based decision-support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time- and resource-intensive radiotherapy (RT) planning.

Methods: Forty-four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose-escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions.

Should the HPV positive oropharyngeal cancer patient be considered for a two-stage dental assessment for their radiation treatment?

Patients due to commence head and neck radiation treatment are expected to undergo a dental assessment and be deemed 'dentally fit'. Though this intervention is welcomed by the dental fraternity it is not without its challenges especially in human papilloma virus (HPV) related oropharyngeal cancer (OPC) which has seen a phenomenal rise over the past decade. This perspective piece presents these challenges and proposes a potential adaption of the dental assessment for HPV OPC patients though not necessarily exclusive to this tumour sub-site.

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging-Based Radiation Therapy Planning of Patients With Head and Neck Cancer.

Purpose: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI.

Methods And Materials: Six patients from an external site and 17 patients from a local cohort were analyzed separately.

Clinical outcomes in relapsed oropharyngeal cancer after definitive (chemo) radiotherapy.

Objectives: To report clinical outcomes of relapsed oropharyngeal squamous cell carcinoma (OPSCC) after definitive intensity-modulated (chemo)radiotherapy [(C)RT].

Materials And Methods: Data for all relapsed patients treated for OPSCC with definitive (C)RT between 2010 and 2016 were collected. Primary end-point was post-failure survival (PFS).

Dose-escalated intensity-modulated radiotherapy in patients with locally advanced laryngeal and hypopharyngeal cancers: ART DECO, a phase III randomised controlled trial.

Background: Radical (chemo)radiotherapy offers potentially curative treatment for patients with locally advanced laryngeal or hypopharyngeal cancer. We aimed to show that dose-escalated intensity-modulated radiotherapy (DE-IMRT) improved locoregional control.

Methods: We performed a phase III open-label randomised controlled trial in patients with laryngeal or hypopharyngeal cancer (AJCC III-IVa/b, TNM 7).

The impact of Human Papilloma Virus status on the prediction of head and neck cancer chemoradiotherapy outcomes using the pre-treatment apparent diffusion coefficient.

Objective: To determine the impact of Human Papilloma Virus (HPV) oropharyngeal cancer (OPC) status on the prediction of head and neck squamous cell cancer (HNSCC) chemoradiotherapy (CRT) outcomes with pre-treatment quantitative diffusion-weighted magnetic resonance imaging (DW-MRI).

Methods: Following ethical approval, 65 participants (53 male, age 59.9 ± 7.