Browsing by Author "Pellacani, Giovanni"

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    Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy
    (2022) Campanella, Gabriele; Navarrete-Dechent, Cristian; Liopyris, Konstantinos; Monnier, Jilliana; Aleissa, Saud; Minhas, Brahmteg; Scope, Alon; Longo, Caterina; Guitera, Pascale; Pellacani, Giovanni; Kose, Kivanc; Halpern, Allan C.; Fuchs, Thomas J.; Jain, Manu
    Basal cell carcinoma (BCC) is the most common skin cancer, with over 2 million cases diagnosed annually in the UnitedStates. Conventionally, BCC is diagnosed by naked eye examination and dermoscopy. Suspicious lesions are either removed or biopsied for histopathological confirmation, thus lowering the specificity of noninvasive BCC diagnosis. Recently, reflectance confocal microscopy, a noninvasive diagnostic technique that can image skin lesions at cellular level resolution, has shown to improve specificity in BCC diagnosis and reduced the number needed to biopsy by 2-3 times. In this study, we developed and evaluated a deep learning-based artificial intelligence model to automatically detect BCC in reflectance confocal microscopy images. The proposed model achieved an area under the curve for the receiver operator characteristic curve of 89.7%(stack level) and 88.3%(lesion level), a performance on par with that of reflectance confocal microscopy experts. Furthermore, themodel achieved an area under the curve of 86.1% on a held-out test set from international collaborators, demonstrating the reproducibility and generalizability of the proposed automated diagnostic approach. These results provide a clear indication that the clinical deployment of decision support systems for the detection of BCC in reflectance confocal microscopy images has the potential for optimizing the evaluation and diagnosis of patients with skin cancer.
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    Reflectance confocal microscopy terminology for non-melanocytic skin lesions: A Delphi consensus of experts
    (Elsevier Inc., 2025) Navarrete Dechent, Cristian Patricio; Longo, Caterina; Liopyris, Konstantinos; Ardigo, Marco; Ahlgrimm-Siess, Verena; Bahadoran, Phillipe; Carrera, Cristina; Braga, Juliana Casagrande Tavoloni; Chen, Chih-Shan J.; Correa, Lilia; Carvahlo, Nathalie de; Durkin, John; Farnetani, Francesca; Grant-Kels, Jane M.; Gill, Melissa; Gonzalez, Salvador; Hartmann, Daniela; Hoffman-Wellenhof, Rainer; Huho, Albert; Ludzik, Joanna; Malvehy, Josep; Marghoob, Ashfaq A.; Moscarella, Elvira; Oliviero, Margaret; Puig, Susana; Rabinovitz, Harold; Rao, Babar; Rezze, Gisele G.; Rossi, Anthony M.; Rubinstein, Gene; Ruini, Cristel; Sattler, Elke; Soyer, H. Peter; Schwartz, Rodrigo; Thng, Steven; Ulrich, Martina; Witkowski, Alexander; Dusza, Stephen W.; Guitera, Pascale; Pellacani, Giovanni; Scope, Alon; Jain, Manu
    Background There is lack of uniformity in reflectance confocal microscopy (RCM) terminology. Objective To establish expert consensus on a standardized set of RCM terms that describe non-melanocytic lesions (NML). Methods We invited RCM experts to participate in a Delphi-consensus study. Fifty-nine RCM descriptors were extracted from a previous systematic review on RCM terminology for describing NML. Of these, 35 items were presented as 4 groups of synonymous terms and 24 items as single, non-synonymous terms. For the first round, an agreement threshold was set at >70%. Participants could also propose new terms. Terms with ≤70% agreement and newly proposed terms were carried over to the next round. For subsequent rounds, agreement threshold was set at >50%. Results The study was conducted between June 2021 and May 2023. Forty-two of 44 (95%) invited experts participated. Three iterative Delphi rounds were completed, resulting in a consensus list of 36 terms, including 32 synonymous- and 4 non-synonymous- terms for describing NML. Limitations Only experts were included. We did not evaluate definitions of terms in the study. Conclusions We propose a simplified list of RCM terms, vetted by RCM experts, for describing and diagnosing NML. Uniform terminology could benefit clinical practice, research, and education.