Histologic grading systems are used to guide diagnosis, therapy, and audit on an international basis. The reproducibility of grading systems is usually tested within small groups of pathologists who have previously worked or trained together. This may underestimate the international variation of scoring systems. We therefore evaluated the reproducibility of an established system, the Banff classification of renal allograft pathology, throughout Europe. We also sought to improve reproducibility by providing individual feedback after each of 14 small groups of cases. Kappa values for all features studied were lower than any previously published, confirming that international variation is greater than interobserver variation as previously assessed. A prolonged attempt to improve reproducibility, using numeric or graphical feedback, failed to produce any detectable improvement. We then asked participants to grade selected photographs, to eliminate variation induced by pathologists viewing different areas of the slide. This produced improved kappa values only for some features. Improvement was influenced by the nature of the grade definitions. Definitions based on “area affected” by a process were not improved. The results indicate the danger of basing decisions on grading systems that may be applied very differently in different institutions.

Over the last decade DLW employing ultrafast pulsed lasers has become a well-established technique for the creation of custom-made free-form three-dimensional (3D) microscaffolds out of a variety of materials ranging from proteins to biocompatible glasses. Its potential applications for manufacturing a patient's specific scaffold seem unlimited in terms of spatial resolution and geometry complexity. However, despite few exceptions in which live cells or primitive organisms were encapsulated into a polymer matrix, no demonstration of an in vivo study case of scaffolds generated with the use of such a method was performed. Here, we report a preclinical study of 3D artificial microstructured scaffolds out of hybrid organic-inorganic (HOI) material SZ2080 fabricated using the DLW technique. The created 2.1 × 2.1 × 0.21 mm(3) membrane constructs are tested both in vitro by growing isolated allogeneic rabbit chondrocytes (Cho) and in vivo by implanting them into rabbit organisms for one, three and six months. An ex vivo histological examination shows that certain pore geometry and the pre-growing of Cho prior to implantation significantly improves the performance of the created 3D scaffolds. The achieved biocompatibility is comparable to the commercially available collagen membranes. The successful outcome of this study supports the idea that hexagonal-pore-shaped HOI microstructured scaffolds in combination with Cho seeding may be successfully implemented for cartilage tissue engineering.

Introduction: Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach. Methods: Tissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently. Results: ANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists’ VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particular for the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction. Conclusions: Our experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.

Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.