Expression of p53 up-regulated modulator of apoptosis (PUMA) in non-melanoma skin cancer of long-term immunosuppressed solid organ transplant recipients compared to immunocompetent patients.

The risk of UV radiation (UVR)-induced non-melanoma skin cancer (NMSC) is dramatically increased in immunosuppressed organ transplant recipients compared to immunocompetent patients. In the skin, p53 up-regulated modulator of apoptosis (PUMA) is a central regulator of apoptosis in response to UVR damage and immune response regulation. Data on the expression of PUMA in patients with NMSC relative to immune status is limited To study differences in the expression and distribution of PUMA in cutaneous SCC and BCC by immunohistochemistry between immunocompetent patients and organ transplant recipients, and the effect of CsA-containing immunosuppressive maintenance regimens on this expression. PUMA expression in SCC (n = 34) and BCC (n = 20) was analysed comparatively by immunohistochemical staining in matched cohorts of 27 immunocompetent patients and 27 organ transplant recipients SCC and BCC showed unequivocal positive PUMA expression, however, there was no significant difference in NMSC between organ transplant recipients and immunocompetent patients. A 17% reduction in staining score for PUMA in SCC, but not in BCC, of organ transplant recipients treated with a cyclosporin (CsA)-containing regimen was noted compared to organ transplant recipients without chronic CsA intake (p = 0.0381) PUMA expression in SCC, but not BCC, is significantly reduced by CsA-containing therapy, suggesting a disturbance of apoptosis by iatrogenic immunosuppression with a divergent impact on SCC and BCC.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).