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Contains fulltext : 293879.pdf (Publisher’s version ) (Open Access) AIM: Reliably diagnosing or safely excluding serous tubal intraepithelial carcinoma (STIC), a precursor lesion of tubo-ovarian high-grade serous carcinoma (HGSC), is crucial for individual patient care, for better understanding the oncogenesis of HGSC, and for safely investigating novel strategies to prevent tubo-ovarian carcinoma. To optimize STIC diagnosis and increase its reproducibility, we set up a three-round Delphi study. METHODS AND RESULTS: In round 1, an international expert panel of 34 gynecologic pathologists, from 11 countries, was assembled to provide input regarding STIC diagnosis, which was used to develop a set of statements. In round 2, the panel rated their level of agreement with those statements on a 9-point Likert scale. In round 3, statements without previous consensus were rated again by the panel while anonymously disclosing the responses of the other panel members. Finally, each expert was asked to approve or disapprove the complete set of consensus statements. The panel indicated their level of agreement with 64 statements. A total of 27 statements (42%) reached consensus after three rounds. These statements reflect the entire diagnostic work-up for pathologists, regarding processing and macroscopy (three statements); microscopy (eight statements); immunohistochemistry (nine statements); interpretation and reporting (four statements); and miscellaneous (three statements). The final set of consensus statements was approved by 85%. CONCLUSION: This study provides an overview of current clinical practice regarding STIC diagnosis amongst expert gynecopathologists. The experts' consensus statements form the basis for a set of recommendations, which may help towards more consistent STIC diagnosis. 01 juli 2023

Contains fulltext : 291079.pdf (Publisher’s version ) (Open Access) BACKGROUND: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. METHODS: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. RESULTS: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. CONCLUSION: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ($$n=14$$ n = 14 ) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/.

Contains fulltext : 232938.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 170535.pdf (Publisher’s version ) (Open Access) PURPOSE: In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs. METHODS: To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms. RESULTS: The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT). CONCLUSIONS: Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.

Contains fulltext : 153279.pdf (Publisher’s version ) (Open Access) BACKGROUND: Mechanical ventilation (MV) induces an inflammatory response that may result in (acute) lung injury. Lidocaine, an amide local anesthetic, has anti-inflammatory properties in vitro and in vivo, possibly due to an attenuation of pro-inflammatory cytokines, intracellular adhesion molecule-1 (ICAM-1), and reduction of neutrophils influx. We hypothesized an attenuation of MV-induced inflammatory response with intravenously administered lidocaine. METHODS: Lidocaine (Lido) (2, 4, and 8 mg/kg/h) was intravenously administered during 4 h of MV with a tidal volume of 8 ml/kg, positive end expiratory pressure 1,5 cmH2O and FiO2 0.4. We used one ventilated control (CON) group receiving vehicle. After MV, mice were euthanized, and lungs and blood were immediately harvested, and cytokine levels and ICAM-1 levels were measured in plasma and lung homogenates. Pulmonary neutrophils influx was determined in LEDER-stained slices of lungs. Anesthetic need was determined by painful hind paw stimulation. RESULTS: Lidocaine-treated animals (Lido 2, 4 and 8 mg/kg/h) showed higher interleukin (IL)-10 plasma levels compared to control animals. Lidocaine treatment with 8 mg/kg/h (Lido 8) resulted in higher IL-10 in lung homogenates. No differences were observed in pro-inflammatory cytokines, ICAM-1, and pulmonary influx between the different ventilated groups. CONCLUSIONS: Intravenously administered lidocaine increases levels of plasma IL-10 with infusion from 2, 4, and 8 mg/kg/h and pulmonary levels of IL-10 with 8 mg/kg/h in a murine mechanical ventilation model. Intravenously administered lidocaine appears to reduce anesthetic need in mice.

Contains fulltext : 174693.pdf (Publisher’s version ) (Open Access) Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The Drosophila larval neuromuscular junction (NMJ), a well-established model for glutamatergic synapses, has been extensively studied for decades. Identification of mutations causing NMJ morphological defects revealed a repertoire of genes that regulate synapse development and function. Many of these were identified in large-scale studies that focused on qualitative approaches to detect morphological abnormalities of the Drosophila NMJ. A drawback of qualitative analyses is that many subtle players contributing to NMJ morphology likely remain unnoticed. Whereas quantitative analyses are required to detect the subtler morphological differences, such analyses are not yet commonly performed because they are laborious. This protocol describes in detail two image analysis algorithms "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics", available as Fiji-compatible macros, for quantitative, accurate and objective morphometric analysis of the Drosophila NMJ. This methodology is developed to analyze NMJ terminals immunolabeled with the commonly used markers Dlg-1 and Brp. Additionally, its wider application to other markers such as Hrp, Csp and Syt is presented in this protocol. The macros are able to assess nine morphological NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the NMJ terminal.

Contains fulltext : 241550.pdf (Publisher’s version ) (Open Access) Radboud University, 28 januari 2022 Promotores : Laak, J.A.W.M. van der, Ginneken, B. van Co-promotores : Litjens, G.J.S., Hulsbergen-van de Kaa, C.A. 188 p.

Contains fulltext : 220344.pdf (Publisher’s version ) (Open Access) Radboud University, 15 september 2020 Promotores : Laak, J.A.W.M. van der, Karssemeijer, N. Co-promotores : Bult, P., Ciompi, F.

Contains fulltext : 178907.pdf (Publisher’s version ) (Open Access) Radboud University, 20 december 2017 Promotor : Karssemeijer, N. Co-promotores : Litjens, G.J.S., Laak, J.A.W.M. van der