Ex vivo-expanded highly pure ABCB5+ mesenchymal stromal cells as good manufacturing practice-compliant autologous advanced therapy medicinal product for clinical use: Process validation and first in-human data

© 2020 International Society for Cell & Gene Therapy Background aim: Mesenchymal stromal cells (MSCs) hold promise for the treatment of tissue damage and injury. However, MSCs comprise multiple subpopulations with diverse properties, which could explain inconsistent therapeutic outcomes seen among therapeutic attempts. Recently, the adenosine triphosphate-binding cassette transporter ABCB5 has been shown to identify a novel dermal immunomodulatory MSC subpopulation. Methods: The authors have established a validated Good Manufacturing Practice (GMP)-compliant expansion and manufacturing process by which ABCB5+ MSCs can be isolated from skin tissue and processed to generate a highly functional homogeneous cell population manufactured as an advanced therapy medicinal product (ATMP). This product has been approved by the German competent regulatory authority to be tested in a clinical trial to treat therapy-resistant chronic venous ulcers. Results: As of now, 12 wounds in nine patients have been treated with 5 × 105 autologous ABCB5+ MSCs per cm2 wound area, eliciting a median wound size reduction of 63% (range, 32–100%) at 12 weeks and early relief of pain. Conclusions: The authors describe here their GMP- and European Pharmacopoeia-compliant production and quality control process, report on a pre-clinical dose selection study and present the first in-human results. Together, these data substantiate the idea that ABCB5+ MSCs manufactured as ATMPs could deliver a clinically relevant wound closure strategy for patients with chronic therapy-resistant wounds

Outcome of a de-labelling algorithm compared with results of penicillin (β-lactam) allergy testing

Background Penicillin allergy labels frequently impede guideline-directed treatment with a penicillin or other β-lactam antibiotics. Despite presumed allergy, targeted questioning may indicate a low probability of sensitization and permit reasonably safe administration of the antibiotic in question. In this study, we evaluated a standardized algorithm aiming to differentiate non-allergic patients from those with true allergic β-lactam hypersensitivity. Methods We retrospectively applied a de-labelling algorithm in 800 consecutive patients with suspected β-lactam hypersensitivity. All had undergone complete allergy work-up permitting to definitely exclude or diagnose β-lactam allergy between 2009 and 2019. Results In 595 (74.4%) out of 800 cases evaluated, β-lactam allergy could be excluded by negative challenge testing. IgE-mediated anaphylaxis was diagnosed in 70 (8.7%) patients, delayed-type hypersensitivity in 135 (16.9%). In 62 (88.6%) anaphylaxis cases, the algorithm correctly advised to use an alternative antibiotic. Accuracy was higher in patients with moderate to severe anaphylaxis (97.7%) compared to those with a history of mild reactions (73.1%). The algorithm correctly identified 122 (90.4%) patients with proven delayed-type hypersensitivity. It permitted de-labelling in 330 (55.5%) out of 595 patients with diagnostic exclusion of penicillin hypersensitivity, but failed to identify the remaining 265 (44.5%) as low-risk cases. Conclusions The algorithm detected 89.8% of cases with penicillin (β-lactam) allergy, sensitivity was optimal for moderate to severe anaphylaxis. Study data justify the implementation of a standardized de-labelling algorithm under close supervision in order to permit guideline-directed treatment and reduce the use of broad-spectrum antibiotics as part of an antibiotic stewardship program

Predominant patterns of beta-lactam hypersensitivity in a single German Allergy Center: exanthem induced by aminopenicillins, anaphylaxis by cephalosporins

Background Penicillins and other beta-lactam antibiotics are the most common elicitors of allergic drug reaction. However, data on the pattern of clinical reaction types elicited by specific beta-lactams are scarce and inconsistent. We aimed to determine patterns of beta-latam allergy, i.e. the association of a clinical reaction type with a specific beta-lactam antibiotic. Methods We retrospectively evaluated data from 800 consecutive patients with suspected beta-lactam hypersensitivity over a period of 11 years in a single German Allergy Center. Results beta-lactam hypersensitivity was definitely excluded in 595 patients, immediate-type (presumably IgE-mediated) hypersensitivity was diagnosed in 70 and delayed-type hypersensitivity in 135 cases. Most (59 out of 70, 84.3%) immediate-type anaphylactic reactions were induced by a limited number of cephalosporins. Delayed reactions were regularly caused by an aminopenicillin (127 out of 135, 94.1%) and usually manifested as a measles-like exanthem (117 out of 135, 86.7%). Intradermal testing proved to be the most useful method for diagnosing beta-lactam allergy, but prick testing was already positive in 24 out of 70 patients with immediate-type hypersensitivity (34.3%). Patch testing in addition to intradermal testing did not provide additional information for the diagnosis of delayed-type hypersensitivity. Almost all beta-lactam allergic patients tolerated at least one, usually several alternative substances out of the beta-lactam group. Conclusions We identified two patterns of beta-lactam hypersensitivity: aminopenicillin-induced exanthem and anaphylaxis triggered by certain cephalosporins. Intradermal skin testing was the most useful method to detect both IgE-mediated and delayed-type beta-lactam hypersensitivity

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd

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