Browsing by Author "Rotemberg, Veronica"

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    Accuracy in anatomical location on dermatological surgery: a multi‐centre retrospective study
    (2023) Donoso, Francisca; Hidalgo, Leonel; Cowen, Emily A.; Villagran, Sofía; Villablanca, Paula; Puerto, Constanza del; Silva‐Valenzuela, Sergio; Galimany, Lucas; Majerson, Daniela; Andino, Romina; Uribe, Pablo; Droppelmann, Katherine; Cárdenas, Consuelo; Abarzúa‐Araya, Álvaro; Castro‐Ayala, Juan C.; Kurtansky, Nicholas R.; Halpern, Allan C.; Molenda, Matthew A.; Rotemberg, Veronica; Navarrete-Dechent, Cristian
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    Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy
    (2024) Liopyris, Konstantinos; Navarrete-Dechent, Cristian; Marchetti, Michael A.; Rotemberg, Veronica; Apalla, Zoe; Argenziano, Giuseppe; Blum, Andreas; Braun, Ralph P.; Carrera, Cristina; Codella, Noel C. F.; Combalia, Marc; Dusza, Stephen W.; Gutman, David A.; Helba, Brian; Hofmann-Wellenhof, Rainer; Jaimes, Natalia; Kittler, Harald; Kose, Kivanc; Lallas, Aimilios; Longo, Caterina; Malvehy, Josep; Menzies, Scott; Nelson, Kelly C.; Paoli, John; Puig, Susana; Rabinovitz, Harold S.; Rishpon, Ayelet; Russo, Teresa; Scope, Alon; Soyer, H. Peter; Stein, Jennifer A.; Stolz, Willhelm; Sgouros, Dimitrios; Stratigos, Alexander J.; Swanson, David L.; Thomas, Luc; Tschandl, Philipp; Zalaudek, Iris; Weber, Jochen; Halpern, Allan C.; Marghoob, Ashfaq A.
    Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanomaspecific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.
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    In vivo optical imaging-guided targeted sampling for precise diagnosis and molecular pathology
    (2021) Sahu, Aditi; Oh, Yuna; Peterson, Gary; Cordova, Miguel; Navarrete-Dechent, Cristian; Gill, Melissa; Alessi-Fox, Christi; Gonzalez, Salvador; Phillips, William; Wilson, Steven; Afzalneia, Reza; Rose, Raven; Mohsere, Abu-Akeel; Bello, Danielle; Marghoob, Ashfaq; Rossi, Anthony; Wolchok, Jedd D.; Merghoub, Taha; Rotemberg, Veronica; Chen, Chih-Shan Jason; Rajadhyaksha, Milind
    Conventional tissue sampling can lead to misdiagnoses and repeated biopsies. Additionally, tissue processed for histopathology suffers from poor nucleic acid quality and/or quantity for downstream molecular profiling. Targeted micro-sampling of tissue can ensure accurate diagnosis and molecular profiling in the presence of spatial heterogeneity, especially in tumors, and facilitate acquisition of fresh tissue for molecular analysis. In this study, we explored the feasibility of performing 1-2 mm precision biopsies guided by high-resolution reflectance confocal microscopy (RCM) and optical coherence tomography (OCT), and reflective metallic grids for accurate spatial targeting. Accurate sampling was confirmed with either histopathology or molecular profiling through next generation sequencing (NGS) in 9 skin cancers in 7 patients. Imaging-guided 1-2 mm biopsies enabled spatial targeting for in vivo diagnosis, feature correlation and depth assessment, which were confirmed with histopathology. In vivo 1-mm targeted biopsies achieved adequate quantity and high quality of DNA for next-generation sequencing. Subsequent mutational profiling was confirmed on 1 melanoma in situ and 2 invasive melanomas, using a 505-gene mutational panel called Memorial Sloan Kettering-Integrated mutational profiling of actionable cancer targets (MSK-IMPACT). Differential mutational landscapes, in terms of number and types of mutations, were found between invasive and in situ melanomas in a single patient. Our findings demonstrate feasibility of accurate sampling of regions of interest for downstream histopathological diagnoses and molecular pathology in both in vivo and ex vivo settings with broad diagnostic, therapeutic and research potential in cutaneous diseases accessible by RCM-OCT imaging.
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    International Skin Imaging Collaboration-Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling
    (2024) Scope, Alon; Liopyris, Konstantinos; Weber, Jochen; Barnhill, Raymond L.; Braun, Ralph P.; Curiel-Lewandrowski, Clara N.; Elder, David E.; Ferrara, Gerardo; Grant-Kels, Jane M.; Jeunon, Thiago; Lallas, Aimilios; Lin, Jennifer Y.; Marchetti, Michael A.; Marghoob, Ashfaq A.; Navarrete-Dechent, Cristian; Pellacani, Giovanni; Soyer, Hans Peter; Stratigos, Alexander; Thomas, Luc; Kittler, Harald; Rotemberg, Veronica; Halpern, Allan C.
    Background: A common terminology for diagnosis is critically important for clinical communication, education, research and artificial intelligence. Prevailing lexicons are limited in fully representing skin neoplasms. Objectives: To achieve expert consensus on diagnostic terms for skin neoplasms and their hierarchical mapping. Methods: Diagnostic terms were extracted from textbooks, publications and extant diagnostic codes. Terms were hierarchically mapped to super-categories (e.g. 'benign') and cellular/tissue-differentiation categories (e.g. 'melanocytic'), and appended with pertinent-modifiers and synonyms. These terms were evaluated using a modified-Delphi consensus approach. Experts from the International-Skin-Imaging-Collaboration (ISIC) were surveyed on agreement with terms and their hierarchical mapping; they could suggest modifying, deleting or adding terms. Consensus threshold was >75% for the initial rounds and >50% for the final round. Results: Eighteen experts completed all Delphi rounds. Of 379 terms, 356 (94%) reached consensus in round one. Eleven of 226 (5%) benign-category terms, 6/140 (4%) malignant-category terms and 6/13 (46%) indeterminate-category terms did not reach initial agreement. Following three rounds, final consensus consisted of 362 terms mapped to 3 super-categories and 41 cellular/tissue-differentiation categories. Conclusions: We have created, agreed upon, and made public a taxonomy for skin neoplasms and their hierarchical mapping. Further study will be needed to evaluate the utility and completeness of the lexicon.
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    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.