Artificial intelligence in head and neck cancer diagnosis

Bassani, Sara; Nicola, Santonicco; Eccher, Albino; Scarpa, Aldo; Matteo, Vianini; Brunelli, Matteo; Bisi, Nicola; Nocini, Riccardo; Sacchetto, Luca; Pantanowitz, Liron; Girolami, Ilaria; Molteni, Gabriele

doi:10.1016/j.jpi.2022.100153

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…It has been reported that early diagnosis of HNC can improve treatment and survival outcomes remarkably [ 7 ]. With the current advancements in computational capacity and improvements in various subfields of AI, digital pathology has significantly evolved from using static images to whole slide images (WSI) [ 39 ], thus enhancing pathological workflow and quantifying a number of parameters for defining the tumor and its microenvironment [ 39 ]. A high-resolution of WSI of human tissue is isolated into regions of clinical significance.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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Introduction Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management. Methods Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines. Results Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks. Conclusion At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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“…Based on developments achieved to date, AI has shown great effectiveness in making diagnoses from imaging analysis, 8 although less insight is available on the ability of AI to interpret more complex, text‐based depictions of clinical scenarios. Regarding the head and neck setting, in particular, AI has been found to be reliable in analyzing maxillofacial radiological imaging for dental bone pathology, 9 in addition to the differential diagnosis of tumors of the head and neck 10,11 . Moreover, the effectiveness of AI in the diagnosis of tumors has also been demonstrated in the recognition of precancerous and cancerous lesions based on specimen images 12 .…”

Section: Discussionmentioning

confidence: 99%

“…Regarding the head and neck setting, in particular, AI has been found to be reliable in analyzing maxillofacial radiological imaging for dental bone pathology, 9 in addition to the differential diagnosis of tumors of the head and neck. 10 , 11 Moreover, the effectiveness of AI in the diagnosis of tumors has also been demonstrated in the recognition of precancerous and cancerous lesions based on specimen images. 12 Thus, the growing role of AI in performing repetitive analytic tasks 13 and assisting with complex forecasts 14 , 15 has been confirmed.…”

Section: Discussionmentioning

confidence: 99%

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

Pugliese,

Maccari,

Felisati

et al. 2023

Clinical Case Reports

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Key Clinical MessageLarge language models have made artificial intelligence readily available to the general public and potentially have a role in healthcare; however, their use in difficult differential diagnosis is still limited, as demonstrated by a case of necrotizing otitis externa.AbstractThis case report presents a peculiar case of necrotizing otitis externa (NOE) with skull base involvement which proved diagnostically challenging. The initial patient presentation and the imaging performed on the 78‐year‐old patient suggested a neoplastic rhinopharyngeal lesion and only after several unsuccessful biopsies the patient was transferred to our unit. Upon re‐evaluation of the clinical picture, a clinical hypothesis of NOE with skull base erosion was made and confirmed by identifying Pseudomonas aeruginosa in biopsy specimens of skull base bone and external auditory canal skin. Upon diagnosis confirmation, the patient was treated with culture‐oriented long‐term antibiotics with complete resolution of the disease. Given the complex clinical presentation, we chose to submit a posteriori this NOE case to two large language models (LLM) to test their ability to handle difficult differential diagnoses. LLMs are easily approachable artificial intelligence tools that enable human‐like interaction with the user relying upon large information databases for analyzing queries. The LLMs of choice were ChatGPT‐3 and ChatGPT‐4 and they were requested to analyze the case being provided with only objective clinical and imaging data.

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“…Several algorithms can be used to provide real-world clinical recommendations [50][51][52][53][54]. However, the performance of the model is hindered by the small size and poor quality of the training datasets [55]. The current medical utilizations of AI models comprise cancer diagnosis, integration of genomic data, clinical trial design, readmission analysis, and appropriate antibiotic treatment for infectious diseases [11,[56][57][58][59].…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

Chen,

Chin,

Chan

et al. 2023

Diagnostics

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Background: Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis. Methods: Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients’ clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared. Results: In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively. Conclusions: Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

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Artificial intelligence in head and neck cancer diagnosis

Cited by 11 publications

References 35 publications

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Are artificial intelligence large language models a reliable tool for difficult differential diagnosis? An a posteriori analysis of a peculiar case of necrotizing otitis externa

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

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