1,720,972 research outputs found
ARTIFICIAL INTELLIGENCE-BASED INTEGRATED APPROACH FOR SKIN CANCER RECOGNITION
Full text linkIntroduzione: l'incidenza del melanoma è in continuo aumento ed è responsabile della maggior parte dei decessi per tumore della pelle. La diagnosi precoce e la rimozione completa del tumore prima che inizi la fase di invasione profonda costituiscono le principali armi per ridurne mortalità e morbidità. Per questo motivo sono state sviluppate diverse tecniche non invasive di imaging per consentire l'identificazione precoce e più accurata delle lesioni maligne.
Obiettivi dello studio: questo studio si propone di elaborare un modello basato sull’ utilizzo dell’intelligenza artificiale (AI), in grado di distinguere, partendo da immagini ottenute con la tomografia a coerenza ottica confocale (LC-OCT), lesioni melanocitiche sospette all’indagine dermoscopica in benigne, maligne o con significato prognostico incerto.
Materiali e metodi: è stato condotto uno studio retrospettivo per elaborare e valutare l'accuratezza di un modello AI nel distinguere le immagini di lesioni melanocitiche in tre categorie diagnostiche: nevi melanocitici, nevi melanocitici atipici/displastici e melanoma. Sono state analizzate sezioni verticali (DICOM standard) acquisite tramite LC-OCT. Il set di immagini LC-OCT è stato arricchito, per cercare di aumentare il rapporto segnale/rumore (SNR) con diversi filtri (RAW, Gaussiano, LOG e MERGED). Le immagini filtrate con filtro gaussiano e RAW sono state utilizzate per estrarre cluster di pixel, secondo l'algoritmo SLIC Superpixels e Affinity Propagation (AP) Clustering. Sono state estratte le regioni di interesse e i biomarcatori con una libreria in R, chiamata Moddicom.
Diversi modelli di machine learning (regressione logistica bivariata, albero decisionale e random forest) sono stati addestrati per capire se qualche biomarcatore fosse in grado di distinguere il melanoma dalle altre lesioni.
Discussione e conclusioni: In questo studio pilota abbiamo sviluppato e dimostrato, per la prima volta in letteratura, la possibilità di sviluppare un modello di intelligenza artificiale per la discriminazione in vivo tra lesioni melanocitiche benigne, lesioni a potenziale maligno incerto e melanoma, basato su immagini LC-OCT. Abbiamo identificato diversi biomarcatori potenzialmente utili a questo scopo.
Sebbene questo modello non abbia ancora dimostrato buoni risultati in termini di prestazioni, sono stati identificati e proposti diverse strade per migliorarlo.Introduction: the incidence of melanoma is continuously increasing and it is responsible for the majority of skin cancer deaths. Early diagnosis and complete removal of the tumor tissue before the onset of deep invasion are the main factors for the reduction of its mortality and morbidity. For this reason, different non-invasive imaging techniques to allow the early and more accurate identification of malignant lesions have been developed.
Objectives of the study: this study aims to elaborate an AI integrated approach, based on images made using Line-field confocal optical coherence tomography (LC-OCT), able to correctly identify dermoscopically suspicious melanocytic lesions as benign, malignant or at uncertain prognostic significance.
Materials and methods: A retrospective study was conducted to elaborate and evaluate the accuracy of an AI model in distinguishing images of melanocytic lesions into three diagnostic categories: melanocytic nevi, atypical/dysplastic melanocytic nevi and melanoma. The analyzed images consisted of vertical sections (DICOM) acquired via LC-OCT. The set of LC-OCT DICOM images was enriched, in order to try to increase the signal/noise ratio (SNR) with respect to the investigation outcome with different filters (RAW, Gaussian, LOG and MERGED). The images filtered with Gaussian and RAW filter were used to extract pixel clusters, according to the SLIC Superpixels and Affinity Propagation (AP) Clustering algorithm. Regions of interest were extracted and image biomarkers were extracted with a library in R, called Moddicom.
Different machine learning models (bivariate logistic regression, decision tree and random forest) were trained to understand if any biomarker was able to discern melanoma from benign moles.
Results: 127 variables showed a statistically significant p-value on univariate testing. The most promising bivariate regression models were extracted. The performances on the training set are high, while those on the testing set are lower: this means that apparently good results of this technique are probably due to overfitting. Similarly, the decision tree and the random forest also showed excellent levels of accuracy, positive predictive value (PPV) and negative predictive value (NPV) for the training test with a drop in performance on the independent internal testing set.
Discussion and conclusions: In this pilot study we developed and demonstrated, for the first time in the literature, the feasibility of an artificial intelligence model for the in-vivo discrimination between benign melanocytic lesions, uncertain malignant potential lesions and melanoma based on LC-OCT images. We have identified several biomarkers potentially useful for this purpose.
Although this model has not yet demonstrated good results in terms of performance, several ways for its improvement have been identified and proposed
A Case of Post-Scabetic Nodules Analyzed With Non-Invasive Diagnostic Tools: Dermoscopy, RCM, LC-OCT, and Ultrasound
No full textLine-Field Confocal Optical Coherence Tomography of a Suspected Case of Galli-Galli Disease
No full textManagement of adult patients with severe atopic dermatitis treated with dupilumab during COVID-19 pandemic: a single-center real-life experience
Full text linkTurnover flap variations in the reconstruction of full-thickness nasal ala defects
No full textThe nose is a vital organ and an important aesthetic unit, being placed in the middle of the face. It is also one of the most frequent site involved by skin cancer, and surgical reconstruction can be sometimes challenging. We present two cases of full thickness nasal ala defects, managed with turnover flaps with and without earlobe cartilage graft
Turnover flap variations in the reconstruction of full-thickness nasal ala defects
Full text linkThe nose is a vital organ and an important aesthetic unit, being placed in the middle of the face. It is also one of the most frequent site involved by skin cancer, and surgical reconstruction can be sometimes challenging. We present two cases of full thickness nasal ala defects, managed with turnover flaps with and without earlobe cartilage graft
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Characterization of Basosquamous Cell Carcinoma: A Distinct Type of Keratinizing Tumour
Full text linkBasosquamous carcinoma is a rare clinical entity, which comprises 1.7–2.7% of all skin carcinomas. It is described as a basal cell carcinoma with features of squamous differentiation. To date, studies of the epidemiology of basosquamous carcinoma have been few and small in size. We report here the most extensive series of basosquamous carcinomas published to date, highlighting the differences between basosquamous carcinoma and other keratinizing tumours. Patients undergoing surgical excision for keratinizing tumours were enrolled in this study. Age, sex and tumour characteristics were recorded. A total of 1,519 squamous cell carcinomas, 288 basosquamous carcinomas and 4,235 basal cell carcinomas were collected. Basosquamous features were compared with those of basal cell and squamous cell carcinomas. For basosquamous carcinomas, 70.5% were located on the head and neck, particularly on the nose, forehead and cheeks, and represented almost 10% of the keratinizing tumours on the ears. Significant differences were found between basosquamous carcinoma and basal cell or squamous cell carcinomas. Basosquamous carcinoma should be considered a distinct type of keratinizing tumour with different anatomical, sex and age distributions
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