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Background & Aims: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Method: We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Results: Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%). Conclusion: Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA. Impact and implications: The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA.

Only a fraction of specimens under study are usually selected for quantification in histology. Multilevel sampling or tissue probes, slides and fields of view (FOVs) in the regions of interest (ROIs) are required. In general, all parts of the organs under study should be given the same probability to be taken into account; that is, the sampling should be unbiased on all levels. The objective of our study was to provide an overview of the use of virtual microscopy in the context of developing sampling strategies of FOVs for stereological quantification. We elaborated this idea on 18 examples from multiple fields of histology, including quantification of extracellular matrix and muscle tissue, quantification of organ and tumour microvessels and tumour‐infiltrating lymphocytes, assessing osseointegration of bone implants, healing of intestine anastomoses and osteochondral defects, counting brain neurons, counting nuclei in vitro cell cultures and others. We provided practical implications for the most common situations, such as exhaustive sampling of ROIs, sampling ROIs of different sizes, sampling the same ROIs for multiple histological methods, sampling more ROIs with variable intensities or using various objectives, multistage sampling and virtual sampling. Recommendations were provided for pilot studies on systematic uniform random sampling of FOVs as a part of optimizing the efficiency of histological quantification to prevent over‐ or undersampling. We critically discussed the pros and cons of using virtual sections for sampling FOVs from whole scanned sections. Our review demonstrated that whole slide scans of histological sections facilitate the design of sampling strategies for quantitative histology. [ABSTRACT FROM AUTHOR]

Background & Aims: The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method., Method: We selected 166 PLC biopsies divided into training, internal and external validation sets: 90, 29 and 47 samples, respectively. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, tiles of 256x256 pixels were extracted from the WSIs and used to train a ResNet18 neural network. The tumour/non-tumour annotations served as labels during training, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm., Results: Pathological review classified the training and validation sets into hepatocellular carcinoma (HCC, 33/90, 11/29 and 26/47), intrahepatic cholangiocarcinoma (iCCA, 28/90, 9/29 and 15/47), and cHCC-CCA (29/90, 9/29 and 6/47). In the two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the two-cluster model predictions (major contingent) in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (cluster 0: 5-97%; cluster 1: 2-94%)., Conclusion: Our method applied to PLC HES biopsy could identify specific morphological features of HCC and iCCA. Although no specific features of cHCC-CCA were recognized, assessing the proportion of HCC and iCCA tiles within a slide could facilitate the identification of cHCC-CCA., Impact and Implications: The diagnosis of primary liver cancers can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified primary liver cancers on routine-stained biopsies using a weakly supervised learning method. Our model identified specific features of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Despite no specific features of cHCC-CCA being recognized, the identification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma tiles within a slide could facilitate the diagnosis of primary liver cancers, and particularly cHCC-CCA., Competing Interests: The authors declare no conflicts of interest that pertain to this work. Please refer to the accompanying ICMJE disclosure forms for further details., (© 2024 The Author(s).)

Congenital talipes equinovarus, or clubfoot, remains one of the commonest congenital limb deformities. In most cases of clubfoot are corrected after five to six cast changes and, in many cases, an Achilles tendon tenotomy. This technique results in feet that are strong, flexible, and plantigrade. The aim of this study is to quantify the histological modifications that occur early in the healing process of lesions made on the Achilles tendons. We made sagittal sections (without suture, with absorbable suture, with nonabsorbable suture), frontal section (without suture, with absorbable suture, with non-absorbable suture), crosssection, dentate section and section into Z. For 1 month (day 7, 14, 21, 28) we studied the following histological characteristics grading it semi quantitatively: nuclear density, the amount of interstitial collagen, tendency to the parallel orientation of the nuclei of fibroblasts, granulomatous reactions. In all evaluated sections we observed a predominantly lymphocytic and plasma cell inflammatory infiltrate, with maximum intensity on day 7 of postintervention. The intensity of the inflammatory reaction gradually decreased to day 28. On day 7, post-intervention, we identified the presence of granulation tissue at the site of injury. From day 14 we identified a maturation of granulation tissue: the intensity of chronic inflammatory infiltrate decreases, fibroblasts becoming prevalent and producing a more obvious collagen matrix. We observed the tendency of the collagen fibers toward parallel orientation evident from day 14 for sagittal section and 21 for the frontal. By day 28, the distance between the cut ends gradually decreased with reducing the amount of fat interposed, which is absent on day 28. [ABSTRACT FROM AUTHOR]

Histological procedures aim at providing good-quality sections that can be used for a light microscopic evaluation of tissue. These are applicable to identify either spontaneous or diseases-induced changes. Routinely, tissues are fixed with neutral formalin 10%, embedded in paraffin, and manually sectioned with a microtome to obtain 4-5 μm thick paraffin sections. Dewaxed sections are then stained with HE&S (hematoxylin-eosin and saffron) or can be used for other purposes (special stains, immunohistochemistry, in situ hybridization, etc.). During this processing, many steps and procedures are critical to ensure standard and interpretable sections. This chapter provides key recommendations to efficiently achieve this objective.