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Santer M, Kloppenburg M, Gottfried TM, Runge A, Schmutzhard J, Vorbach SM, Mangesius J, Riedl D, Mangesius S, Widmann G, Riechelmann H, Dejaco D, Freysinger W. Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma-A Systematic Review. Cancers (Basel) 2022; 14:5397. [PMID: 36358815 PMCID: PMC9654953 DOI: 10.3390/cancers14215397] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 07/22/2023] Open
Abstract
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10-258) and of LNs was 340 (SD ± 268; range 21-791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43-99%) and for testing sets 86% (SD ± 5%; range 76-92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI's role in LN-classification in locally-advanced HNSCC.
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Affiliation(s)
- Matthias Santer
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Marcel Kloppenburg
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Timo Maria Gottfried
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Annette Runge
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Joachim Schmutzhard
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Samuel Moritz Vorbach
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Julian Mangesius
- Department of Radiation-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - David Riedl
- University Hospital of Psychiatry II, Medical University of Innsbruck, 6020 Innsbruck, Austria
- Ludwig-Boltzmann Institute for Rehabilitation Research, 1100 Vienna, Austria
| | - Stephanie Mangesius
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Herbert Riechelmann
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Daniel Dejaco
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Wolfgang Freysinger
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria
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Yu Z, He Y, Schomann T, Wu K, Hao Y, Suidgeest E, Zhang H, Eich C, Cruz LJ. Achieving Effective Multimodal Imaging with Rare-Earth Ion-Doped CaF 2 Nanoparticles. Pharmaceutics 2022; 14:840. [PMID: 35456674 PMCID: PMC9024546 DOI: 10.3390/pharmaceutics14040840] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/06/2022] [Accepted: 04/08/2022] [Indexed: 01/16/2023] Open
Abstract
Nowadays, cancer poses a significant hazard to humans. Limitations in early diagnosis techniques not only result in a waste of healthcare resources but can even lead to delays in diagnosis and treatment, consequently reducing cure rates. Therefore, it is crucial to develop an imaging probe that can provide diagnostic information precisely and rapidly. Here, we used a simple hydrothermal method to design a multimodal imaging probe based on the excellent properties of rare-earth ions. Calcium fluoride co-doped with ytterbium, gadolinium, and neodymium (CaF2:Y,Gd,Nd) nanoparticles (NPs) is highly crystalline, homogeneous in morphology, and displays a high biosafety profile. In addition, in vitro and ex vivo experiments explored the multimodal imaging capability of CaF2:Y,Gd,Nd and demonstrated the efficient performance of CaF2:Y,Gd,Nd during NIR-II fluorescence/photoacoustic/magnetic resonance imaging. Collectively, our novel diagnosis nanoparticle will generate new ideas for the development of multifunctional nanoplatforms for disease diagnosis and treatment.
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Affiliation(s)
- Zhenfeng Yu
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
| | - Yuanyuan He
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
| | - Timo Schomann
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
- Percuros B.V., Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Kefan Wu
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; (K.W.); (H.Z.)
| | - Yang Hao
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
| | - Ernst Suidgeest
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands;
| | - Hong Zhang
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; (K.W.); (H.Z.)
| | - Christina Eich
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
| | - Luis J. Cruz
- Translational Nanobiomaterials and Imaging Group, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (Z.Y.); (Y.H.); (T.S.); (Y.H.)
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