The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on grand-challenge.org using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC.

The Evolving Role of Novel Imaging Techniques for Radiotherapy Planning

The ability to visualise cancer with imaging has been crucial to the evolution of modern radiotherapy (RT) planning and delivery. And as evolving RT technologies deliver increasingly precise treatment, the importance of accurate identification and delineation of disease assumes ever greater significance. However, innovation in imaging technology has matched that seen with RT delivery platforms, and novel imaging techniques are a focus of much research activity. How these imaging modalities may alter and improve the diagnosis and staging of cancer is an important question, but already well served by the literature. What is less clear is how novel imaging techniques may influence and improve practical and technical aspects of RT planning and delivery. In this review, current gold standard approaches to integration of imaging, and potential future applications of bleeding-edge imaging technology into RT planning pathways are explored.

Target volumes comparison between postoperative simulation magnetic resonance imaging and preoperative diagnostic magnetic resonance imaging for prone breast radiotherapy after breast-conserving surgery

BACKGROUND

This study investigated the differences in target volumes between preoperative magnetic resonance imaging (MRIpre) and postoperative MRI (MRIpost) for breast radiotherapy after breast-conserving surgery (BCS) using deformable image registration (DIR).

METHODS AND MATERIALS

Seventeen eligible patients who underwent whole-breast irradiation in the prone position after BCS were enrolled. On MRIpre, the gross tumor volume (GTV) was delineated as GTVpre, which was then expanded by 10 mm to represent the preoperative lumpectomy cavity (LC), denoted as LCpre. The LC was expanded to the clinical target volume (CTV) and planning target volume (PTV) on the MRIpre and MRIpost, denoted as CTVpre, CTVpost, PTVpre, and PTVpost, respectively. The MIM software system was used to register the MRIpre and MRIpost using DIR. Differences were evaluated regarding target volume, distance between the centers of mass (dCOM), conformity index (CI), and degree of inclusion (DI). The relationship between CILC /CIPTV and the clinical factors was also assessed.

RESULTS

Significant differences were observed in LC and PTV volumes between MRIpre and MRIpost (p < 0.0001). LCpre was 0.85 cm3 larger than LCpost, while PTVpre was 29.38 cm3 smaller than PTVpost. The dCOM between LCpre and LCpost was 1.371 cm, while that between PTVpre and PTVpost reduced to 1.348 cm. There were statistically significant increases in CI and DI for LCpost-LCpre and PTVpost-PTVpre (CI = 0.221, 0.470; DI = 0.472, 0.635). No obvious linear correlations (p > 0.05) were found between CI and GTV, primary tumor volume-to-breast volume ratio, distance from the primary tumor to the nipple and chest wall, and body mass index.

CONCLUSIONS

Despite using DIR technology, the spatial correspondence of target volumes between MRIpre and MRIpost was suboptimal. Therefore, relying solely on preoperative diagnostic MRI with DIR for postoperative LC delineation is not recommended.

Assessing the Impact of Charged Particle Radiation Therapy for Head and Neck Adenoid Cystic Carcinoma: A Systematic Review and Meta-Analysis

Purpose: Head and neck adenoid cystic carcinoma (HNACC) is a radioresistant tumor. Particle therapy, primarily proton beam therapy and carbon-ion radiation, is a potential radiotherapy treatment for radioresistant malignancies. This study aims to conduct a meta-analysis to evaluate the impact of charged particle radiation therapy on HNACC. Methods: A comprehensive search was conducted in Pubmed, Cochrane Library, Web of Science, Embase, and Medline until December 31, 2022. The primary endpoints were overall survival (OS), local control (LC), and progression-free survival (PFS), while secondary outcomes included treatment-related toxicity. Version 17.0 of STATA was used for all analyses. Results: A total of 14 studies, involving 1297 patients, were included in the analysis. The pooled 5-year OS and PFS rates for primary HNACC were 78% (95% confidence interval [CI] = 66-91%) and 62% (95% CI = 47-77%), respectively. For all patients included, the pooled 2-year and 5-year OS, LC, and PFS rates were as follows: 86.1% (95% CI = 95-100%) and 77% (95% CI = 73-82%), 92% (95% CI = 84-100%) and 73% (95% CI = 61-85%), and 76% (95% CI = 68-84%) and 55% (95% CI = 48-62%), respectively. The rates of grade 3 and above acute toxicity were 22% (95% CI = 13-32%), while late toxicity rates were 8% (95% CI = 3-13%). Conclusions: Particle therapy has the potential to improve treatment outcomes and raise the quality of life for HNACC patients. However, further research and optimization are needed due to the limited availability and cost considerations associated with this treatment modality.