MUC4 and MUC5AC are highly specific tumour-associated mucins in biliary tract cancer

Alterations in epithelial mucin expression are associated with carcinogenesis, but there are few data in biliary tract cancer (BTC). In pancreatic malignancy, MUC4 is a diagnostic and prognostic tumour marker, whereas MUC5AC has been proposed as a sensitive serological marker for BTC. We assessed MUC4 and MUC5AC expression in (i) prospectively collected bile and serum specimens from 72 patients with biliary obstruction (39 BTC) by real-time reverse transcriptase–PCR (qPCR) and western blot analysis, and (ii) 79 archived biliary tissues (69 BTC) by immunohistochemistry. In bile, MUC4 protein was detected in 27% of BTC and 29% of primary sclerosing cholangitis (PSC) cases, but not in other benign and malignant biliary diseases (P<0.01 and P=0.06). qPCR revealed a 1.9-fold increased MUC4 mRNA expression in BTC patients' bile compared with benign disease. In archived tissues, MUC4 protein was detected in 37% of BTC but in none of the benign samples (P=0.03). In serum, MUC5AC was found exclusively in BTC and PSC sera (44% and 13%, respectively; P<0.001 for BTC vs non-BTC) and correlated negatively with BTC survival. Biliary MUC4 and serum MUC5AC are highly specific tumour-associated mucins that may be useful in the diagnosis and formulation of therapeutic strategies in BTC

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&nbsp;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