Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction.

Development of a bioactive tunable hyaluronic-protein bioconjugate hydrogel for tissue regenerative applications.

Every year, there are approximately 500 000 peripheral nerve injury (PNI) procedures due to trauma in the US alone. Autologous and acellular nerve grafts are among current clinical repair options; however, they are limited largely by the high costs associated with donor nerve tissue harvesting and implant processing, respectively. Therefore, there is a clinical need for an off-the-shelf nerve graft that can recapitulate the native microenvironment of the nerve.

Small-diameter artery decellularization: Effects of anionic detergent concentration and treatment duration on porcine internal thoracic arteries.

Engineered replacement materials have tremendous potential for vascular applications where over 400,000 damaged and diseased blood vessels are replaced annually in the United States alone. Unlike large diameter blood vessels, which are effectively replaced by synthetic materials, prosthetic small-diameter vessels are prone to early failure, restenosis, and reintervention surgery. We investigated the differential response of varying 0%-6% sodium dodecyl sulfate and sodium deoxycholate anionic detergent concentrations after 24 and 72 h in the presence of DNase using biochemical, histological, and biaxial mechanical analyses to optimize the decellularization process for xenogeneic vascular tissue sources, specifically the porcine internal thoracic artery (ITA).

Roles of Exosomes in Cardiac Fibroblast Activation and Fibrosis.

Alterations in the accumulation and composition of the extracellular matrix are part of the normal tissue repair process. During fibrosis, this process becomes dysregulated and excessive extracellular matrix alters the biomechanical properties and function of tissues involved. Historically fibrosis was thought to be progressive and irreversible; however, studies suggest that fibrosis is a dynamic process whose progression can be stopped and even reversed.

Deep learning can predict lymph node status directly from histology in colorectal cancer.

Background: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).

Objectives: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM).

Methods: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM.

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.

Background: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision.

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

Background: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy.

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.

Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone.

Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques.