Marcus, Hani JJ

Williams, Simon CC

English

UCL Discovery

Frontiers in OncologyOPEN ACCESSEDITED AND REVIEWED BYAali Jan Sheen,Manchester Royal Infirmary,United Kingdom*CORRESPONDENCESimon C. Williamssimon.williams32@nhs.netRECEIVED 20 June 2023ACCEPTED 28 June 2023PUBLISHED 11 July 2023CITATIONWilliams SC and Marcus HJ (2023)Editorial: Investigations into the potentialbenefits of artificial intelligence and deeplearning to surgical oncologists.Front. Oncol. 13:1243305.doi: 10.3389/fonc.2023.1243305COPYRIGHT© 2023 Williams and Marcus. This is anopen-access article distributed under theterms of the Creative Commons AttributionLicense (CC BY). The use, distribution orreproduction in other forums is permitted,provided the original author(s) and thecopyright owner(s) are credited and thatthe original publication in this journal iscited, in accordance with acceptedacademic practice. No use, distribution orreproduction is permitted which does notcomply with these terms.TYPE EditorialPUBLISHED 11 July 2023DOI 10.3389/fonc.2023.1243305Editorial: Investigations into thepotential benefits of artificialintelligence and deep learningto surgical oncologistsSimon C. Williams1,2* and Hani J. Marcus1,31Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London,London, United Kingdom, 2Department of Neurosurgery, St George’s University Hospital,London, United Kingdom, 3Division of Neurosurgery, National Hospital for Neurology andNeurosurgery, London, United KingdomKEYWORDSartificial intelligence, machine learning, surgery, oncology, computer vision,deep learningEditorial on the Research TopicInvestigations into the potential benefits of artificial intelligence anddeep learning to surgical oncologists‘Any sufficiently advanced technology is indistinguishable from magic.”So said science fiction writer Arthur C. Clarke, though few could have predicted thevast advances made in healthcare technology realised over the past decades. In the realm ofsurgical oncology, this sentiment holds true as we witness the rise of artificial intelligence(AI) and deep learning. These transformative technologies have the potential torevolutionise the field, enabling surgical oncologists to achieve remarkableadvancements. This Research Topic aims to explore the potential benefits of AI anddeep learning in assisting surgical oncologists and enhancing their decision-makingcapabilities. By harnessing the power of these technologies, we can aspire to achievemore accurate diagnoses, personalised treatment plans, optimised surgical interventions,and improved patient care (1).This Research Topic presents nine publications spanning the surgical landscape,featuring research applied to Hepatobiliary Surgery (Jeong et al.; Li et al.; Huang et al.;Wang et al.), Orthopaedics (Yan et al.), Urology (Zhang et al.), Otolaryngology andNeurosurgery (Hill et al.; CRANIAL consortium). Each article is a demonstration of theunique opportunity AI poses to modern surgical oncologists. Here, we summarise the keyinsights presented in these publications, highlighting the advancements that AI presents inthe field of surgical oncology.First, the marriage of AI and deep learning with surgical oncology allows for intelligentdata analysis on an unprecedented scale. These transformative technologies excel at swiftlyand comprehensively processing vast amounts of complex data, as exemplified by the workof the CRANIAL Consortium et al. Their publication showcases machine learning-drivenidentification of predictors of cerebrospinal fluid rhinorrhoea following endonasal skullfrontiersin.org01Williams and Marcus 10.3389/fonc.2023.1243305base surgery, utilising a vast corpus of surgical data. Yet AI drivendata analysis is not confined to text data - within the domain ofmachine learning is computer vision, a branch of AI that givescomputer platforms understanding of image and video data (2).Advances in this field, particularly in radiomics, have garneredglobal attention due to their remarkable progress; however, theirapplication in the operating theatre has yet to be fully realised (3).Within this Research Topic, Wang et al. describe the construction ofa survival prediction model that integrates multimodal imagingdata with clinical data, showcasing how AI can lead tocomprehensive insights and personalised treatment planning,optimising patient outcomes.Second, the work published in this Research Topic presents howAI and deep learning techniques have the potential to enhancesurgical guidance and assistance. Zhang et al. demonstrate theutility of computer vision in pre-operative planning andintraoperative decision making through their generation of amodel able to predict renal perfusion regions based on automatedsegmentation of renovascular imaging. Such advancement stands toincrease patient safety through ever-more precise surgical planning.The application of surgical AI is not confined to the pre- and post-operative stages; by analysing intraoperative data, such as liveimaging, physiological signals, and surgical instrument tracking,AI algorithms can provide surgical oncologists with valuableguidance, assisting them in navigating critical structures,optimizing surgical margins, and ensuring precise tumourresection (4). Moreover, AI-powered systems can detect andpredict potential complications, alerting surgeons in advance andallowing for timely interventions, ultimately leading to improvedpatient safety and outcomes (5).Perhaps most valuable, however, is the promise of AI to offerpredictive models that shape treatment decisions and outcomes,as demonstrated by numerous articles within this Research Topic.By leveraging large datasets, these models identify prognosticfactors, predict treatment responses, and stratify patients intorisk groups (6). Jeong et al. describe the creation survivalpred ic t ion pla t form for pat i ents wi th intrahepat iccholangiocarcinoma (ICC), enabling categorisation of patientsinto risk groups to guide clinical interventions. A similarprognostic approach is adopted by Yan et al., who describe thecreation of a deep learning model to predict overall survival inchondrosarcoma patients. Notably, their DeepSurv modeloutperformed traditional models of survival prediction,highlighting the unique capability of machine learning methodsto identify subtle relationships between variables in large,complex datasets. Applications such as this give an insight intohow AI will individualise treatment decisions based on patient-specific data, replacing crude and generic risk prediction systems.Surgical oncologists can harness these insights to tailor treatmentplans to individual patients, optimize the sequencing of therapies,and explore alternative strategies.The integration of AI and deep learning transcends clinicalpractice and extends to surgical training and education. Virtualreality (VR) and augmented reality (AR) platforms, combined withFrontiers in Oncology 02AI algorithms, can simulate realistic surgical scenarios, providingsurgical trainees with a safe and controlled environment forpracticing complex procedures, such as tumour resections. Byanalysing trainee performance and offering real-time feedback,AI-powered systems may accelerate the learning curve, enhancingsurgical skills acquisition (7). The scope of AI to benefit traineesdoes not stop there, however – AI algorithms may aid in curatingeducational resources, extracting key insights from scientificliterature, and delivering personalised learning experiences tosurgical oncologists. Hill et al. demonstrate the ability of AI toaid disease classification, through their use of AI to subclassifyglioblastoma, a disease with a profoundly poor prognosis, enablinga clear taxonomy and better prediction of patient outcomes.The articles presented within this Research Topic showcase therich field of AI research in surgical oncology, yet whilst the potentialbenefits of AI and deep learning in surgical oncology are vast,ethical considerations and challenges must be navigated. Issues suchas data privacy, algorithm bias, transparency, and accountabilitymust be addressed to ensure patient safety, maintain trust in thehealthcare system, and mitigate potential risks (2). Closecollaboration between clinicians, researchers, policymakers, andregulatory bodies is crucial to establish guidelines and frameworksthat uphold ethical standards and govern the integration of AI insurgical oncology (6).We sincerely believe that the contents of this Research Topicwill be of interest to surgeons, oncologists, and members of thewider healthcare team alike. AI and deep learning have the potentialto revolutionise the field of surgical oncology, the true benefits ofwhich are yet to be fully realised. By embracing the future, weembark on a journey to redefine healthcare.Author contributionsSW - writing - original draft. HM - writing - reviewing andediting; conceptualization. All authors contributed to the article andapproved the submitted version.Conflict of interestThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliatedorganizations, or those of the publisher, the editors and thereviewers. Any product that may be evaluated in this article, orclaim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.frontiersin.orgWilliams and Marcus 10.3389/fonc.2023.1243305References1. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare.Future Healthc J (2019) 6:94–8. doi: 10.7861/futurehosp.6-2-942. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery:promises and perils. Ann Surg (2018) 268:70–6. doi: 10.1097/SLA.00000000000026933. Hashimoto DA, Rosman G, Rus D, Meireles OR. Surgical video in the age of bigdata. Ann Surg (2018) 268:e47–8. doi: 10.1097/SLA.00000000000024934. Starup-Hansen J, Williams SC, Funnell JP, Hanrahan JG, Islam S, Al-MohammadA, et al. Optimising trajectory planning for stereotactic brain tumour biopsy usingartificial intelligence: a systematic review of the literature. Br J Neurosurg (2023), 1–10.doi: 10.1080/02688697.2023.2210225Frontiers in Oncology 035. Stam WT, Goedknegt LK, Ingwersen EW, Schoonmade LJ, Bruns ERJ, Daams F.The prediction of surgical complications using artificial intelligence in patientsundergoing major abdominal surgery: a systematic review. Surgery (2022) 171:1014–21. doi: 10.1016/j.surg.2021.10.0026. Williams S, Layard Horsfall H, Funnell JP, Hanrahan JG, Khan DZ, Muirhead W,et al. Artificial intelligence in brain tumour surgery-an emerging paradigm. Cancers(Basel) (2021) 13:5010. doi: 10.3390/cancers131950107. Lam K, Chen J, Wang Z, Iqbal FM, Darzi A, Lo B, et al. Machine learning fortechnical skill assessment in surgery: a systematic review. NPJ Digit Med (2022) 5:1–16.doi: 10.1038/s41746-022-00566-0frontiersin.org

Editorial: Investigations into the potential benefits of artificial intelligence and deep learning to surgical oncologists

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Editorial: Investigations into the potential benefits of artificial intelligence and deep learning to surgical oncologists

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