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一种深度学习算法作为头颈部非黑色素瘤皮肤恶性肿瘤诊断支持工具的实用性和可靠性。

The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies.

作者信息

Medela Alfonso, Sabater Alberto, Montilla Ignacio Hernández, MacCarthy Taig, Aguilar Andy, Chiesa-Estomba Carlos Miguel

机构信息

Medical Data Science, Legit.Health, Bilbao, Spain.

Clinical Endpoint Innovation, Legit.Health, Bilbao, Spain.

出版信息

Eur Arch Otorhinolaryngol. 2025 Mar;282(3):1585-1592. doi: 10.1007/s00405-024-08951-z. Epub 2024 Sep 6.

Abstract

OBJECTIVE

The incidence of non-melanoma skin cancers, encompassing basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), is on the rise globally and new methods to improve skin malignancy diagnosis are necessary. This study aims to assess the performance of a CE-certified medical device as a diagnosis support tool in a head & neck (H&N) outpatient clinic, specifically focusing on the classification of three key diagnostics: BCC, cSCC, and non-malignant lesions (such as Actinic Cheilitis, Actinic Keratosis, and Seborrheic Keratosis).

METHODS

a prospective, longitudinal, non-randomized study was designed to evaluate the performance of a deep learning-based method as a diagnosis tool in a group of patients referred to the head & neck clinic for suspicious skin lesions.

RESULTS

135 patients were included, 92 (68.1%) were male and 43 (31.9%) were female. The median age was 71 years +/- 9 (Min: 56/Max: 91). Of those, 108 were malignant pathologies (54 basal cell carcinoma and 54 squamous cell carcinoma) and 27 benign pathologies (14 seborrheic keratoses, 2 actinic keratoses, and 11 actinic cheilitis). Of special significance is the remarkable performance of the medical device in identifying malignant lesions (basal cell carcinoma and squamous cell carcinoma) within the top-5 most likely diagnoses in above 90% of cases, underscoring its potential utility for early diagnosis and treatment.

CONCLUSION

In this study, the effectiveness of deep learning methods, with a particular focus on vision transformers, as a diagnostic aid for H&N cutaneous non-melanoma skin cancers was demonstrated, highlighting its potential value for early detection and treatment of non-melanoma skin cancers. In this vein, further research is needed in the future to elucidate the role of this technology, because of its potential in the primary care clinic, dermatology, and head & neck surgery clinic as well as in patients with suspicious lesions, as a self-exploration tool.

摘要

目的

非黑色素瘤皮肤癌(包括基底细胞癌(BCC)和皮肤鳞状细胞癌(cSCC))的发病率在全球范围内呈上升趋势,因此需要新的方法来改善皮肤恶性肿瘤的诊断。本研究旨在评估一种获得CE认证的医疗设备在头颈(H&N)门诊作为诊断支持工具的性能,特别关注三种关键诊断的分类:BCC、cSCC和非恶性病变(如光化性唇炎、光化性角化病和脂溢性角化病)。

方法

设计了一项前瞻性、纵向、非随机研究,以评估基于深度学习的方法在一组因可疑皮肤病变转诊到头颈诊所的患者中作为诊断工具的性能。

结果

纳入135例患者,其中男性92例(68.1%),女性43例(31.9%)。中位年龄为71岁±9岁(最小:56岁/最大:91岁)。其中,108例为恶性病变(54例基底细胞癌和54例鳞状细胞癌),27例为良性病变(14例脂溢性角化病、2例光化性角化病和11例光化性唇炎)。特别值得注意的是,该医疗设备在90%以上的病例中,在前5个最可能的诊断中识别出恶性病变(基底细胞癌和鳞状细胞癌)的表现出色,突出了其在早期诊断和治疗中的潜在效用。

结论

在本研究中,证明了深度学习方法,特别是视觉Transformer,作为头颈皮肤非黑色素瘤皮肤癌诊断辅助工具的有效性,突出了其在非黑色素瘤皮肤癌早期检测和治疗中的潜在价值。鉴于其在初级保健诊所、皮肤科、头颈外科诊所以及可疑病变患者中作为自我检测工具的潜力,未来需要进一步研究以阐明该技术的作用。

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