Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

Abstract

Background: Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. Methods: The PubMed database was queried, and a total of 168 articles (2016–2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. Results: The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. Conclusions: AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

“…Nevertheless, a recent systematic review by Franzese et al 10 found that AI research in radiotherapy is still in its infancy, and most of the studies performed to date have primarily focused on the contouring process. As those authors observed, more research is needed to better understand the potential use of AI for planning, optimization and delivery 10 …”

“…AI has the potential to improve head and neck surgery in many different ways, but especially by improving the analysis of medical imaging, voice samples, gene expression and clinical data 4 . AI can be applied in a wide range of different areas, including robotic surgery, 5–8 radiotherapy, 9,10 clinical oncology, 11 radiology, 12–17 nursing, 18–20 pathology, 6,21–23 phoniatrics 24 and molecular biology 25–28 . AI has been used to develop prediction models based on the analysis of existing data sets to predict cancer progression, 29 overall survival stratification, 30 lymph node metastases, 31,32 treatment‐related toxicity 11 and hospital stay length 33 .…”

“…15 Weissmann et al 9 developed a DL model capable of contouring lymph nodes in the head and neck region with a degree of accuracy comparable to that of an expert radiation oncologist. Nevertheless, a recent systematic review by Franzese et al 10 found that AI research in radiotherapy is still in its infancy, and most of the studies performed to date have primarily focused on the contouring process. As those authors observed, more research is needed to better understand the potential use of AI for planning, optimization and delivery.…”

“…As those authors observed, more research is needed to better understand the potential use of AI for planning, optimization and delivery. 10 AI could potentially play a complementary role in pathological diagnosis. Algorithms could be used to evaluate biopsy images, thus decreasing the risk of error and reducing the time needed to evaluate the images, and potentially improving diagnostic accuracy.…”