Browsing by Author "Guitera, Pascale"

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    Delphi Consensus Among International Experts on the Diagnosis, Management, and Surveillance for Lentigo Maligna
    (2023) Longo, Caterina; Navarrete-Dechent, Cristian; Tschandl, Philipp; Apalla, Zoe; Argenziano, Giuseppe; Braun, Ralph P.; Bataille, Veronique; Cabo, Horacio; Hoffmann-Wellhenhof, Rainer; Forsea, Ana Maria; Garbe, Claus; Guitera, Pascale; Raimond, Karls; Marghoob, Ashfaq A.; Malvehy, Josep; Del Marmol, Veronique; Moreno, David; Nehal, Kishwer S.; Nagore, Eduardo; Paoli, John; Pellacani, Giovanni; Peris, Ketty; Puig, Susana; Soyer, H. Peter; Swetter, Susan; Stratigos, Alexander; Stolz, Wilhelm; Thomas, Luc; Tiodorovic, Danica; Zalaudek, Iris; Kittler, Harald; Lallas, Aimilios
    Introduction: Melanoma of the lentigo maligna (LM) type is challenging. There is lack of consensus on the optimal diagnosis, treatment, and follow-up.
  • No Thumbnail Available
    Item
    In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response
    (2022) Sahu, Aditi; Kose, Kivanc; Kraehenbuehl, Lukas; Byers, Candice; Holland, Aliya; Tembo, Teguru; Santella, Anthony; Alfonso, Anabel; Li, Madison; Cordova, Miguel; Gill, Melissa; Fox, Christi; Gonzalez, Salvador; Kumar, Piyush; Wang, Amber Weiching; Kurtansky, Nicholas; Chandrani, Pratik; Yin, Shen; Mehta, Paras; Navarrete-Dechent, Cristian; Peterson, Gary; King, Kimeil; Dusza, Stephen; Yang, Ning; Liu, Shuaitong; Phillips, William; Guitera, Pascale; Rossi, Anthony; Halpern, Allan; Deng, Liang; Pulitzer, Melissa; Marghoob, Ashfaq; Chen, Chih-Shan Jason; Merghoub, Taha; Rajadhyaksha, Milind
    Response to immunotherapies can be variable and unpredictable. Pathology-based phenotyping of tumors into 'hot' and 'cold' is static, relying solely on T-cell infiltration in single-time single-site biopsies, resulting in suboptimal treatment response prediction. Dynamic vascular events (tumor angiogenesis, leukocyte trafficking) within tumor immune microenvironment (TiME) also influence anti-tumor immunity and treatment response. Here, we report dynamic cellular-level TiME phenotyping in vivo that combines inflammation profiles with vascular features through non-invasive reflectance confocal microscopic imaging. In skin cancer patients, we demonstrate three main TiME phenotypes that correlate with gene and protein expression, and response to toll-like receptor agonist immune-therapy. Notably, phenotypes with high inflammation associate with immunostimulatory signatures and those with high vasculature with angiogenic and endothelial anergy signatures. Moreover, phenotypes with high inflammation and low vasculature demonstrate the best treatment response. This non-invasive in vivo phenotyping approach integrating dynamic vasculature with inflammation serves as a reliable predictor of response to topical immune-therapy in patients.
  • Thumbnail Image
    Item
    Lentigo maligna and lentigo maligna melanoma in patients younger than 50 years: a multicentre international clinical-dermoscopic study
    (2024) Longo, Caterina; Sticchi, Alberto; Curti, Alex; Kaleci, Shaniko; Moscarella, Elvira; Argenziano, Giuseppe; Thomas, Luc; Guitera, Pascale; Huang, Chen; Tiodorovic, Danica; Apalla, Zoe; Peris, Ketty; del Regno, Laura; Guida, Stefania; Lallas, Aimilios; Kittler, Harald; Pellacani, Giovanni; Navarrete-Dechent, Cristian
    Background Lentigo maligna/lentigo maligna melanoma (LM/LMM) is usually diagnosed in older patients, when lesions are larger. However, it is important to detect it at an earlier stage to minimize the area for surgical procedure.
  • Thumbnail Image
    Item
    Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease
    (2024) Sangers, Tobias E.; Kittler, Harald; Blum, Andreas; Braun, Ralph P.; Barata, Catarina; Cartocci, Alessandra; Combalia, Marc; Esdaile, Ben; Guitera, Pascale; Haenssle, Holger A.; Kvorning, Niels; Lallas, Aimilios; Navarrete-Dechent, Cristian; Navarini, Alexander A.; Podlipnik, Sebastian; Rotemberg, Veronica; Soyer, H. Peter; Tognetti, Linda; Tschandl, Philipp; Malvehy, Josep
    Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.Methods: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.Conclusions: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.
  • No Thumbnail Available
    Item
    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.