Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (<scp>TORS</scp>)

Costantino, Andrea; Sampieri, Claudio; Pirola, Francesca; Virgilio, Armando De; Kim, Se‐Heon

doi:10.1002/hed.27283

Cited by 13 publications

(6 citation statements)

References 54 publications

(88 reference statements)

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“…Costantino et al [ 13 ] developed and validated six ML prediction models to predict the risk of surgical positive margins in patients who underwent transoral robotic surgery (TORS). In particular, the authors found that tumour classification and tumour site are the most important predictors of positive surgical margins.…”

Section: Resultsmentioning

confidence: 99%

“…The author conducted this systematic review following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [12] as reported in Figure 1. The authors searched all papers in the three major medical databases, such as Scopus (Elsevier), PubMed [National Institutes of Health's National Library of Medicine (NLM NIH)], and Cochrane Library (Wiley).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature

Loperfido,

Celebrini,

Marzetti

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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Aim: Artificial intelligence (AI) is a new field of science in which computers will provide decisions-supporting tools to help doctors make difficult clinical choices. Recent AI applications in otolaryngology include head and neck oncology, rhinology, neurotology, and laryngology. The aim of this systematic review is to describe the potential uses of AI in head and neck oncology with a special focus on the surgical field. Methods: The authors performed a systematic review, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, in the main medical databases, including PubMed, Scopus, and Cochrane Library, considering all original studies published until February 2023 about the role of AI in head and neck cancer surgery. The search strategy included a combination of the following terms: “artificial intelligence” or “machine learning” and “head and neck cancer”. Results: Overall, 303 papers were identified and after duplicate removal (12 papers) and excluding papers not written in English (1 paper) and off-topic (4 papers), papers were assessed for eligibility; finally, only 12 papers were included. Three main fields of clinical interest were identified: the most widely investigated included the role of AI in surgical margins assessment (7 papers); the second most frequently evaluated topic was complications assessment (4 papers); finally, only one paper dealt with the indication of salvage laryngectomy after primary radiotherapy. Conclusions: The authors report the first systematic review in the literature concerning the role of AI in head and neck cancer surgery. An increasing influx of AI applications to clinical problems in otolaryngology is expected, so specialists should be increasingly prepared to manage the constant changes. It will always remain critical for clinicians to use their skills and knowledge to critically evaluate the additional information provided by AI and make the final decisions on each patient.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature

Loperfido,

Celebrini,

Marzetti

et al. 2023

Exploration of Targeted Anti-Tumor Therapy

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“…To date, however, relatively few studies have investigated the role of AI in TORS. For example, only two studies have evaluated the value of AI (machine learning) to assess surgical margins following tumour resection with TORS 6,7 . Nevertheless, given the promising findings of those studies, more research is clearly warranted.…”

Section: Robotic Surgerymentioning

confidence: 99%

“…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 .…”

Section: Introductionmentioning

confidence: 99%

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

Wojtera,

Szewczyk,

Pieńkowski

et al. 2024

Journal of Surgical Oncology

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Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

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“…It provides improved precision, dexterity, and visualization, enabling surgeons to perform complex operations with greater accuracy and control. Andrea et al [150] propose the utilization of machine learning models to tackle the challenge associated with the prediction of positive surgical margins in patients subjected to transoral robotic surgery (TORS). Positive surgical margins have been linked to unfavorable clinical outcomes, emphasizing the importance of preoperative identification of patients at risk.…”

Section: ) Roboticsmentioning

confidence: 99%

Empowering Healthcare With Cyber-Physical System—A Systematic Literature Review

Khater,

Sallabi,

Serhani

et al. 2024

IEEE Access

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Cyber-physical systems (CPS) are recognized for their intelligence as they seamlessly interact with humans, enhancing the physical world through computation, communication, and control. Over the last few years, the evolution of CPS, including IoT components, has significantly impacted many facets of people's lifestyles. It has been immersed in a wide range of services and applications in various areas, including manufacturing, healthcare, and energy. However, the interrelationship between the cyber and physical worlds gives rise to a multitude of research problems and challenges. In the healthcare field, for instance, CPS introduces complexities related to interoperability, privacy, data security, and real-time data processing with critical implications for the reliability and safety of medical processes. To address these challenges and harness the full potential of CPS in healthcare, this paper, through a comprehensive literature review, aims to discuss cutting-edge CPS technologies and solutions that hold promise for healthcare applications. To this end, we propose a comprehensive architectural model that can serve as a benchmark for implementing CPS in healthcare applications. This model thoroughly details how services, components, and technologies can be integrated to transform massive raw data collected from the physical world into valuable information for an enhanced decision-making process. Finally, a use case on healthcare CPS is presented, outlining its characteristics, the role that different technologies have played in its development, and the major challenges in implementing such systems successfully. This study provides a cohesive understanding of the role CPS can play in empowering healthcare while offering insights into the challenges, and future research trends.INDEX TERMS Cyber-physical systems, Healthcare, Real-Time processing, IoT.

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Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS)

Cited by 13 publications

References 54 publications

Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature

Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature

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

Empowering Healthcare With Cyber-Physical System—A Systematic Literature Review

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