Inorganic arsenic and human prostate cancer Arsênico inorgânico e câncer de próstata humano

We critically evaluated the etiologic role of inorganic arsenic in human prostate cancer. We assessed data from relevant epidemiologic studies concerning environmental inorganic arsenic exposure. Whole animal studies were evaluated as were in vitro model systems of inorganic arsenic carcinogenesis in the prostate. Multiple studies in humans reveal an association between environmental inorganic arsenic exposure and prostate cancer mortality or incidence. Many of these human studies provide clear evidence of a dose-response relationship. Relevant whole animal models showing a relationship between inorganic arsenic and prostate cancer are not available. However, cellular model systems indicate arsenic can induce malignant transformation of human prostate epithelial cells in vitro. Arsenic also appears to impact prostate cancer cell progression by precipitating events leading to androgen independence in vitro. Available evidence in human populations and human cells in vitro indicates that the prostate is a target for inorganic arsenic carcinogenesis. A role for this common environmental contaminant in human prostate cancer initiation and/or progression would be very important.<br>Realizamos uma avaliação crítica do papel etiológico do arsênico inorgânico no câncer de próstata humano. Avaliamos dados de estudos epidemiológicos relevantes referentes à exposição ao arsênico inorgânico ambiental. Foram avaliados estudos com animais completos, bem como sistemas de modelo in vitro de carcinogênese decorrente de arsênico inorgânico na próstata. Estudos múltiplos em seres humanos revelaram uma associação entre exposição ao arsênico inorgânico ambiental e mortalidade por ou incidência de câncer de próstata. Muitos desses estudos em seres humanos oferecem indícios claros de uma relação dose-resposta. Não se encontram disponíveis modelos animais completos relevantes que mostrem uma relação entre arsênico inorgânico e câncer de próstata. Contudo, os sistemas de modelos celulares indicam que o arsênico é capaz de levar a transformações malignas de células epiteliais da próstata humana in vitro. Aparentemente, o arsênico também tem um impacto na progressão do câncer de próstata ao precipitar eventos que levam à independência de andrógeno in vitro. Os indícios disponíveis em populações humanas e células humanas in vitro indicam que a próstata é alvo da carcinogênese de arsênico inorgânico. Um papel para esse contaminante ambiental comum na iniciação e/ou progressão do câncer de próstata humano seria de suma importância

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd