Assessing the need for adaptive radiotherapy in head and neck cancer patients using an automatic planning tool

AURKAPS1, HERC2P2 and SDHAP1 pseudogenes: molecular role in development and progression of head and neck squamous cell carcinomas and their diagnostic utility

Background

Pseudogenes are epigenetic elements whose function is mostly unknown in cancer including head and neck cancers (HNSCCs). In our study we analyzed selected three pseudogenes, aurora kinase A pseudogene 1 (AURKAPS1), hect domain and RLD 2 pseudogene 2 (HERC2P2) and succinate dehydrogenase complex flavoprotein subunit A pseudogene 1 (SDHAP1), in the context of molecular function, biological role and potential utility as a biomarker in HNSCCs.

Materials and methods

Based on The Cancer Genome Atlas (TCGA) data we checked potential association of pseudogenes with pathological and clinical features, survival, cellular phenotype and involvement in pathways and cellular processes, and association with patients' response to radiotherapy.

Results

Only AURKAPS1 pseudogene has significant upregulation in cancer than in normal samples and could be used as a diagnostic biomarker. Expression levels of all pseudogenes are dependent on cancer localization. SDHAP1 are the most differentiated and associated with tumor subtypes, expressions of AURKAPS1 do not depend on this tumor classification. Higher expression levels of AURKAPS1, HERC2P2 and SDHAP1 were associated with more aggressive phenotypes and associated with important cellular pathways and biological processes. Moreover, we observed that the expression of all pseudogenes were higher in human papilloma virus (HPV)(+) than in HPV(-) patients. Only AURKAPS1 was associated with higher genome instability and worse response to radiotherapy. Patients with higher expression levels of AURKAPS1 and HERC2P2 displayed better survival.

Conclusions

AURKAPS1 is a potential biomarker for HNSCC patients. This pseudogene is associated with changes in DNA repair, which should be more deeply analyzed in the future.

TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy

Background

Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is of importance to optimize patient clinical benefit and treatment resources.

Purpose

The purpose of this study is to assess the feasibility of using a vision-transformer (ViT) based neural network to predict radiotherapy induced anatomic change of HNC patients.

Methods

We retrospectively included 121 HNC patients treated with definitive RT/CRT. We collected the planning CT (pCT), planned dose, CBCTs acquired at the initial treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model construction and evaluation. A UNet-style ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn and patient body for volumetric change evaluation. We used data from 100 patients for training and validation, and the remaining 21 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), structural similarity index (SSIM), dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT.

Results

The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE and SSIM between the normalized predicted CBCT to CBCT21 are 0.009 and 0.933, while the average dice coefficient between body mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively.

Conclusions

The proposed method showed promising performance for predicting radiotherapy induced anatomic change, which has the potential to assist in the decision making of HNC Adaptive RT.

Emerging pharmacotherapy trends in preventing and managing oral mucositis induced by chemoradiotherapy and targeted agents

INTRODUCTION

The introduction of targeted therapy and immunotherapy has tremendously changed the clinical outcomes and prognosis of cancer patients. Despite innovative pharmacological therapies and improved radiotherapy (RT) techniques, patients continue to suffer from side effects, of which oral mucositis (OM) is still the most impactful, especially for quality of life.

AREAS COVERED

We provide an overview of current advances in cancer pharmacotherapy and RT, in relation to their potential to cause OM, and of the less explored and more recent literature reports related to the best management of OM. We have analyzed natural/antioxidant agents, probiotics, mucosal protectants and healing coadjuvants, pharmacotherapies, immunomodulatory and anticancer agents, photobiomodulation and the impact of technology.

EXPERT OPINION

The discovery of more precise pathophysiologic mechanisms of CT and RT-induced OM has outlined that OM has a multifactorial origin, including direct effects, oxidative damage, upregulation of immunologic factors, and effects on oral flora. A persistent upregulated immune response, associated with factors related to patients' characteristics, may contribute to more severe and long-lasting OM. The goal is strategies to conjugate individual patient, disease, and therapy-related factors to guide OM prevention or treatment. Despite further high-quality research is warranted, the issue of prevention is paramount in future strategies.

A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy

Early regression—the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)—is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.