CADM1 is a diagnostic marker in early-stage mycosis fungoides: Multicenter study of 58 cases

Yuki, Akihiko; Shinkuma, Satoru; Fujikawa, Hiroki; Kato, Taisuke; Homma, Erina; Hamade, Yohei; Onodera, Osamu; Matsuoka, Masao; Shimizu, Hiroshi; Iwata, Hiroaki; Abe, Riichiro

doi:10.1016/j.jaad.2018.06.025

Cited by 24 publications

(21 citation statements)

References 25 publications

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“…In our small series of SS patients, CADM1 expression was observed in patients with progressive SS: four of five SS patients harboring CADM1+ cells that made up over 20% of the CD3+CD4+ fraction died of progressive SS within 1.5 years after the appearance of more than 20% of CADM1+ cells. Similar to our observations, recent reports have described that CADM1 is expressed in the cutaneous infiltrates of MF, and its expression might be related to the poor prognosis . Therefore, further cohort study is required to address whether CADM1 expression is a marker of progressive MF and SS.…”

Section: Discussionsupporting

confidence: 88%

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The expression of cell adhesion molecule 1 and its splicing variants in Sézary cells and cell lines from cutaneous T‐cell lymphoma

Yamaguchi

Morizane

Hamada

et al. 2019

The Journal of Dermatology

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Cell adhesion molecule 1 (CADM1) is aberrantly expressed by T‐cell neoplasms such as adult T‐cell leukemia/lymphoma (ATLL) and mycosis fungoides (MF). We studied the expression of CADM1 and its splicing variants in Sézary syndrome (SS), MF, other cutaneous T‐cell lymphoma (CTCL), and cell lines derived from T‐ and B‐cell lymphomas. Soluble CADM1 was measured in the patients’ sera. CADM1+ cells in the blood and skin lesions were examined by flow cytometry and immunostaining, respectively. Soluble CADM1 was measured by ELISA, and the splicing variants of CADM1 transcripts were determined by reverse transcriptase‐polymerase chain reaction, followed by sequencing. As a result, circulating CADM1+ cells were significantly increased in seven out of 10 patients with SS, ranging from 7.9% to 74.5% of the CD3+CD4+ fractions (median 33.7%; cut‐off value 6.5%). The percentages of CADM1+ cells were usually less than those of circulating Sézary cells. CADM1 was expressed, to various degrees, in six of nine T‐cell lines derived from SS, MF, ATLL, and anaplastic large cell lymphoma (ALCL), but negative in B‐cell lymphoma‐derived cell lines. CADM1+ cells were present in the skin infiltrates of MF, SS, ATLL and ALCL. Serum levels of soluble CADM1 were not significantly elevated in SS/MF. Three major splicing variants of CADM1 expressed by neoplastic T‐cells contained different combinations of the exons 7, 8, 9 and 11, including a putative oncogenic variant composed of exons 7‐8‐9‐11. In conclusion, CADM1 is frequently expressed in Sézary cells and cell lines from CTCL.

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Section: Discussionsupporting

confidence: 88%

“…Similar to our observations, recent reports have described that CADM1 is expressed in the cutaneous infiltrates of MF, and its expression might be related to the poor prognosis. 14,15 Therefore, further cohort study is required to address whether CADM1 expression is a marker of progressive MF and SS.…”

Section: Discussionmentioning

confidence: 99%

The expression of cell adhesion molecule 1 and its splicing variants in Sézary cells and cell lines from cutaneous T‐cell lymphoma

Yamaguchi

Morizane

Hamada

et al. 2019

The Journal of Dermatology

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“…By using the visualization method, the diagnostic basis of the auxiliary diagnostic model can be clearly presented, which improve a certain degree of transparency to the black-box model. The AI diagnosis system proposed in this paper not only provided accurate and robust diagnosis results but also provided the diagnostic basis of 44 Their research results have achieved good performance. Artificial analysis of limited data (dozens of samples) has the characteristics of one-sidedness and long research time, which undoubtedly increases the difficulty of researching diagnostic markers.…”

Section: Discussionmentioning

confidence: 88%

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Liu¹,

Zhang²,

Zhao³

et al. 2020

DMSO

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Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

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“…18 CADM1'in erken evre MF için tanı belirteci olabileceği bildirilmiştir. 19 Bunların yanı sıra THR gen analizinde "high-throughput" sekanslamanın, PCR'ye göre daha sensitif ve spesifik olduğu tespit edilmiştir. 20 Fakat bunlar klinik pratikte uygulamaya geçirilebilmiş değildir.…”

Section: Mi̇kozi̇s Fungoi̇desunclassified

Diagnosis and Staging in Primary Cutaneous Lymphomas

Yüksel¹

2020

Turkiye Klinikleri J Dermatol

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Primer kutanöz lenfomalar, kutanöz T hücreli lenfoma (KTHL) ve kutanöz B hücreli lenfoma (KBHL)ların heterojen bir grubudur. Tanı aldıklarında çoğunlukla deriye lokalizedirler. KTHL'ler yaklaşık %80'ini, KBHL'ler ise %20'sini oluştur-maktadır. 1,2 Farklı klinik ve prognostik özelliklere sahip primer kutanöz lenfomaların tanısı için klinik, histopatoloji, immünfenotiplendirme ve gen rearranjman analizleri beraber değerlendirilmelidir. 2-4 Bu özellikleri göz önünde bulundurularak, primer kuta

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CADM1 is a diagnostic marker in early-stage mycosis fungoides: Multicenter study of 58 cases

Cited by 24 publications

References 25 publications

The expression of cell adhesion molecule 1 and its splicing variants in Sézary cells and cell lines from cutaneous T‐cell lymphoma

The expression of cell adhesion molecule 1 and its splicing variants in Sézary cells and cell lines from cutaneous T‐cell lymphoma

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Diagnosis and Staging in Primary Cutaneous Lymphomas

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