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Alapati R, Renslo B, Wagoner SF, Karadaghy O, Serpedin A, Kim YE, Feucht M, Wang N, Ramesh U, Bon Nieves A, Lawrence A, Virgen C, Sawaf T, Rameau A, Bur AM. Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology. Laryngoscope 2025; 135:687-694. [PMID: 39258420 DOI: 10.1002/lary.31756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 07/25/2024] [Accepted: 08/23/2024] [Indexed: 09/12/2024]
Abstract
OBJECTIVE This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria. DATA SOURCES A comprehensive search was conducted using PubMed, Scopus, Embase, and Cochrane Database of Systematic Reviews, incorporating search terms related to "artificial intelligence," "machine learning," "deep learning," "neural network," and various head and neck neoplasms. REVIEW METHODS Two independent reviewers analyzed each published study for adherence to the 65-point TRIPOD-AI criteria. Items were classified as "Yes," "No," or "NA" for each publication. The proportion of studies satisfying each TRIPOD-AI criterion was calculated. Additionally, the evidence level for each study was evaluated independently by two reviewers using the Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence. Discrepancies were reconciled through discussion until consensus was reached. RESULTS The study highlights the need for improvements in ML algorithm reporting in head and neck oncology. This includes more comprehensive descriptions of datasets, standardization of model performance reporting, and increased sharing of ML models, data, and code with the research community. Adoption of TRIPOD-AI is necessary for achieving standardized ML research reporting in head and neck oncology. CONCLUSION Current reporting of ML algorithms hinders clinical application, reproducibility, and understanding of the data used for model training. To overcome these limitations and improve patient and clinician trust, ML developers should provide open access to models, code, and source data, fostering iterative progress through community critique, thus enhancing model accuracy and mitigating biases. LEVEL OF EVIDENCE NA Laryngoscope, 135:687-694, 2025.
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Affiliation(s)
- Rahul Alapati
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Bryan Renslo
- Department of Otolaryngology-Head & Neck Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
| | - Sarah F Wagoner
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Omar Karadaghy
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Aisha Serpedin
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Yeo Eun Kim
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Maria Feucht
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Naomi Wang
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Uma Ramesh
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Antonio Bon Nieves
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Amelia Lawrence
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Celina Virgen
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
| | - Tuleen Sawaf
- Department of Otolaryngology-Head & Neck Surgery, University of Maryland, Baltimore, Maryland, U.S.A
| | - Anaïs Rameau
- Department of Otolaryngology-Head & Neck Surgery, Weill Cornell, New York City, New York, U.S.A
| | - Andrés M Bur
- Department of Otolaryngology-Head & Neck Surgery, University of Kansas Medical Center, Kansas City, Kansas, U.S.A
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Migliorelli A, Manuelli M, Ciorba A, Stomeo F, Pelucchi S, Bianchini C. Role of Artificial Intelligence in Human Papillomavirus Status Prediction for Oropharyngeal Cancer: A Scoping Review. Cancers (Basel) 2024; 16:4040. [PMID: 39682226 PMCID: PMC11640028 DOI: 10.3390/cancers16234040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 11/21/2024] [Accepted: 11/30/2024] [Indexed: 12/18/2024] Open
Abstract
Human papillomavirus (HPV) infection is sexually transmitted and commonly widespread in the head and neck region; however, its role in tumor development and prognosis has only been demonstrated for oropharyngeal squamous cell carcinoma (HPV-OPSCC). The aim of this review is to analyze the results of the most recent literature that has investigated the use of artificial intelligence (AI) as a method for discerning HPV-positive from HPV-negative OPSCC tumors. A review of the literature was performed using PubMed/MEDLINE, EMBASE, and Cochrane Library databases, according to PRISMA for scoping review criteria (from 2017 to July 2024). A total of 15 articles and 4063 patients have been included. Eleven studies analyzed the role of radiomics, and four analyzed the role of AI in determining HPV histological positivity. The results of this scoping review indicate that AI has the potential to play a role in predicting HPV positivity or negativity in OPSCC. Further studies are required to confirm these results.
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Affiliation(s)
- Andrea Migliorelli
- ENT & Audiology Unit, Department of Neurosciences, University Hospital of Ferrara, 44100 Ferrara, Italy
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Hernandez-Herrera GA, Calcano GA, Nagelschneider AA, Routman DM, Van Abel KM. Imaging Modalities for Head and Neck Cancer: Present and Future. Surg Oncol Clin N Am 2024; 33:617-649. [PMID: 39244284 DOI: 10.1016/j.soc.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2024]
Abstract
Several imaging modalities are utilized in the diagnosis, treatment, and surveillance of head and neck cancer. First-line imaging remains computed tomography (CT); however, MRI, PET with CT (PET/CT), and ultrasound are often used. In the last decade, several new imaging modalities have been developed that have the potential to improve early detection, modify treatment, decrease treatment morbidity, and augment surveillance. Among these, molecular imaging, lymph node mapping, and adjustments to endoscopic techniques are promising. The present review focuses on existing imaging, novel techniques, and the recent changes to imaging practices within the field.
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Jhang H, Park SJ, Sul AR, Jang HY, Park SH. Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes. Korean J Radiol 2024; 25:414-425. [PMID: 38627874 PMCID: PMC11058425 DOI: 10.3348/kjr.2023.1281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/27/2024] [Accepted: 02/04/2024] [Indexed: 05/01/2024] Open
Abstract
OBJECTIVE This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. MATERIALS AND METHODS We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1-10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. RESULTS Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%-93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements (P ≤ 0.036). CONCLUSION There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.
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Affiliation(s)
- Hoyol Jhang
- Division of Healthcare Research, National Evidence-Based Healthcare Collaborating Agency, Seoul, Republic of Korea
| | - So Jin Park
- Division of Healthcare Research, National Evidence-Based Healthcare Collaborating Agency, Seoul, Republic of Korea
| | - Ah-Ram Sul
- Division of Healthcare Research, National Evidence-Based Healthcare Collaborating Agency, Seoul, Republic of Korea.
| | - Hye Young Jang
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Park SH. New Oncologic Imaging Section in the Korean Journal of Radiology. Korean J Radiol 2024; 25:6-7. [PMID: 38184763 PMCID: PMC10788602 DOI: 10.3348/kjr.2023.1165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 11/21/2023] [Indexed: 01/08/2024] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Jo KH, Kim J, Cho H, Kang WJ, Lee SK, Sohn B. 18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma. Yonsei Med J 2023; 64:738-744. [PMID: 37992746 PMCID: PMC10681825 DOI: 10.3349/ymj.2023.0187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/26/2023] [Accepted: 08/17/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC. MATERIALS AND METHODS Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. RESULTS A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. CONCLUSION When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.
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Affiliation(s)
- Kwan Hyeong Jo
- Department of Nuclear Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Jinna Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hojin Cho
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Won Jun Kang
- Department of Nuclear Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Yao H, Zhang X. A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples. Front Microbiol 2023; 14:1232295. [PMID: 37529327 PMCID: PMC10387549 DOI: 10.3389/fmicb.2023.1232295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning.
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Affiliation(s)
- Huanchun Yao
- Department of Cancer, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xinglong Zhang
- Department of Hematology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
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