Clinical and pathological diagnosis and comparison of benign and malignant eyelid tumors

Sendul, Selam Yekta; Akpolat, Çetin; Yılmaz, Zerrin; Ton, Özlem; Güven, Dilek; Kabukçuoğlu, Fevziye

doi:10.1016/j.jfo.2020.07.019

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…The difference may be due to the different sample size and that difficult cases are more likely to visit our medical center. Supporting the literatures, malignant eyelid tumors developed at an elder age than the benign tumors [2,54,55]. Similar to some previous studies, we also observed a lower eyelid predominance for the malignant eyelid tumors [9,56].…”

Section: Et Al [49]supporting

confidence: 91%

“…Suspicions of malignancy should be aroused when clinical signs arise, such as loss of lashes, central ulceration, infiltration, gradual enlargement, loss of sensation, induration, irregular or 'pearly' borders, destruction of eyelid structure, telangiectasia. Risk factors such as smoking, history of previous skin cancer, excessive sun exposure, previous radiation and immunosuppression also contribute to the occurrence of eyelid tumors [52][53][54].…”

Section: Et Al [49]mentioning

confidence: 99%

“…In our study, 35.36% eyelid tumors are malignant tumors. According to epidemiological investigations, Sendul et al [54] found that 87.1% were benign tumor, the left (12.9%) composed of malignant tumors. Xu et al [55] revealed that 86.2% were benign tumors with data from a same medical center.…”

Section: Et Al [49]mentioning

confidence: 99%

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Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

Hui

Zhang

et al. 2022

J Big Data

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Eyelid tumors accounts for 5–10% of skin tumors. It is important but difficult to identify malignant eyelid tumors from benign lesions in a cost-effective way. Traditional screening methods for malignancy in eyelid tumors require laborious and time-consuming histopathological process. Therefore, we aimed to develop a deep learning (DL)-based image analysis system for automatic identification of benign and malignant eyelid tumors. Using a common digital camera, we collected clinical images from patients who were histopathologically diagnosed with eyelid tumors. We trained 8 convolutional neural network (CNN) models to identify benign and malignant eyelid tumors, including ResNet-50, ResNet-101, InceptionV3, and InceptionResNetV2. Another group of patients with eyelid tumors were also collected as the prospective validation dataset. Performance of DL models and human clinicians in prospective validation dataset were evaluated and compared. A total of 309 images from 209 patients were used for training DL system, all eight models reached an average accuracy greater than 0.958 in the internal cross-validation. 36 images from 36 patients were included for the prospective validation, the models reached the best performance in accuracy, sensitivity, specificity, and area under curve (AUC) of 0.889 (95% CI 0.747–0.956), 0.933 (95% CI 0.702–0.988), 0.857 (95% CI 0.654–0.950), and 0.966 (95% CI 0.850–0.993), respectively. DL system had a similar performance as the senior ophthalmologists, and outreached the performance of junior ophthalmologists and medical students. DL system can identify benign and malignant tumors through common clinical images, with a better performance than most ophthalmologists. Combining DL system with smartphone may enable patients’ self-monitoring for eyelid tumors and assist in doctors’ clinical decision making.

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Section: Et Al [49]supporting

confidence: 91%

Section: Et Al [49]mentioning

confidence: 99%

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Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

Hui

Zhang

et al. 2022

J Big Data

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“…Such differences in tumor types and sequence may be due to differences in skin type, genetic predispositions, Similar to some studies, malignant tumors and premalignant lesions tended to occur at older ages compared to benign tumors (6,8). This result may emphasize of the impact of aging and exposure to several traumatic factors such as high levels of solar radiation on malignant tumors.…”

Section: Discussionsupporting

confidence: 74%

Evaluation of the Eyelid and Periocular Tumors

Özdoğan

2022

Kırıkkale Üniversitesi Tıp Fakültesi Dergisi

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Amaç: Amacımız göz kapağı ve perioküler tümörlerin demografik ve histopatolojik özelliklerini belirlemek ve karşılaştırmaktır.Gereç ve Yöntemler: Göz hastalıkları kliniğinde göz kapağı ve perioküler tümör cerrahisi geçiren hastaların tıbbi kayıtları retrospektif olarak incelendi. Veriler yaş, cinsiyet, tümör yerleşimi ve histopatolojik sonuçları içeriyordu ve benign, malign ve premalign tümörler arasında karşılaştırmalı çalışma yapıldı. Tümörün ayrıntılı bölgesi sağ ve sol veya iki taraflı, üst ve alt olarak tanımlandı. Medial ve lateral kantal tümörler ve kaş tümörleri perioküler tümörler olarak tanımlandı. İstatistiksel analiz için SPSS bilgisayar istatistik yazılımı (versiyon Chicago) kullanıldı. Anlamlı farklılıklar için ki-kare testi kullanıldı.Bulgular: Histopatolojik doğrulaması yapılan toplam 190 hasta değerlendirildi, 160'ı (%84.2) göz kapağı lezyonu ve 30'u (%15.8) perioküler lezyondu. Göz kapağı lezyonlarının 140'ı (%87.5) benign, 17'si (%10.6) malign ve 3'ü (%1.8) premalign idi. Perioküler lezyonların 20'si (%66.7) benign, 9'u (%30) malign ve 1'i (%3.3) premalign idi. Benign tümörlerin malign (p=0.03) ve premalign lezyonlara (p=0.038) göre daha genç yaşlarda ortaya çıktığı bulundu. Benign tümörlerin 110’u (%68.7) kadınlarda 50’si (%31.3) erkeklerde görüldü. Benign tümörlerin aksine malign ve premalign tümörler erkeklerde daha sıktı (p=0.003). Sağ gözde malign tümörler anlamlı derecede yüksek bulundu. Perioküler ve göz kapağı tümörleri arasında malignite açısından istatistiksel olarak anlamlı bir fark vardı (χ(2)=8.488, p=0.014). Malign tümörler perioküler lezyonlarda anlamlı olarak daha yüksek bulundu. Epidermal kist (%17.5) en sık görülen benign tümördü. Bazal hücreli karsinom en sık görülen malign tipti (%73.1).Sonuç: Erkek cinsiyeti, alt kapak yerleşimi ve yaşlılık, göz kapağı ve perioküler tümörlerde dikkat edilmesi gereken risk faktörleridir.

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“…Malignant lesions often masquerade as benign lesions and thus can be easily misdiagnosed 43 , 72 , 73 . There are some clinical signs that are generally accepted indications of malignancy, therefore, it is important that every clinician should be familiar with them 74 .…”

Section: Clinical Signs Of Malignancymentioning

confidence: 99%

Clinical Signs for Differential Diagnosis of Eyelid Tumours

Kopecký

Rokohl

Gaca

et al. 2023

Klin Monbl Augenheilkd

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This article aims to present the differential diagnostics of benign and malignant eyelid tumours. The most common malignant eyelid tumour is basal cell carcinoma, followed by squamous cell carcinoma. The common signs of malignity are loss of lashes, ulceration, and infiltration of the lesion. Often the clinical appearance is various and therefore only a histological analysis gives the proper diagnosis. For most tumours, surgical resection is the gold standard of therapy. The reconstruction of the defects should be performed by an experienced oculoplastic surgeon. In malignant tumours that require large safety margins, the defect can be easily very large, and the reconstruction must then be performed with advanced ophthalmic plastic reconstruction techniques.

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Clinical and pathological diagnosis and comparison of benign and malignant eyelid tumors

Cited by 7 publications

References 13 publications

Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

Evaluation of the Eyelid and Periocular Tumors

Clinical Signs for Differential Diagnosis of Eyelid Tumours

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