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Huang TC, Chang CH, Hsiao PF, Hsu CK, Lin CY, Wu CS, Yeh SP, Tsai TF. Cutaneous T-cell lymphoma: Consensus on diagnosis and management in Taiwan. J Formos Med Assoc 2024:S0929-6646(24)00517-5. [PMID: 39496538 DOI: 10.1016/j.jfma.2024.11.001] [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: 08/26/2024] [Revised: 10/17/2024] [Accepted: 11/01/2024] [Indexed: 11/06/2024] Open
Abstract
Cutaneous T cell lymphomas (CTCLs), with mycosis fungoides (MF) and Sézary syndrome (SS) as the classic types, are the commonest group of primary cutaneous lymphomas. The diverse clinical manifestation and non-specific histologic findings of early lesions in CTCLs render diagnosis challenging. Treatment modalities also vary and include topical and oral medications, chemotherapy, phototherapy, and radiation therapies. Local dermatological, hemato-oncologic and radiotherapeutical experts in Taiwan convened meetings in 2023 to review and discuss the latest evidence and updates regarding diagnosis and management of CTCLs. A consensus was developed with the aim to raise awareness and understanding, provide practical guidance for early diagnosis and appropriate management, and ultimately optimize care to maximize benefits of patients.
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Affiliation(s)
- Tai-Chung Huang
- Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chung-Hsing Chang
- Skin Institute, Department of Dermatology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Institute of Medical Sciences, College of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Pa-Fan Hsiao
- Department of Dermatology, MacKay Memorial Hospital, Taipei, Taiwan; Department of Medicine, MacKay Medical College, New Taipei City, Taiwan; MacKay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
| | - Chao-Kai Hsu
- Department of Dermatology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chien-Yio Lin
- Department of Dermatology, Chang Gung Memorial Hospital, Linkou and Taipei, Taiwan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chien-Shan Wu
- Department of Dermatology, Pingtung Veterans General Hospital, Pingtung, Taiwan
| | - Su-Peng Yeh
- Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan; China Medical University, Taichung, Taiwan
| | - Tsen-Fang Tsai
- Department of Dermatology, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
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Identification of Skin Lesions by Using Single-Step Multiframe Detector. J Clin Med 2021; 10:jcm10010144. [PMID: 33406761 PMCID: PMC7796252 DOI: 10.3390/jcm10010144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/23/2020] [Accepted: 12/30/2020] [Indexed: 01/09/2023] Open
Abstract
An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.
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