| 1. |
- Bokhorst, John-Melle, et al.
(författare)
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Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images
- 2023
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Ingår i: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- 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 ) 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 .
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| 2. |
- Bokhorst, John-Melle, et al.
(författare)
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Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer
- 2023
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Ingår i: Modern Pathology. - : ELSEVIER SCIENCE INC. - 0893-3952 .- 1530-0285. ; 36:9
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H & E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H & E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n 1/4 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H & E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials. & COPY; 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
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| 3. |
- Bokhorst, John-Melle, et al.
(författare)
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Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer
- 2023
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Ingår i: Cancers. - : MDPI. - 2072-6694. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.
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| 4. |
- Fiehn, Anne-Marie Kanstrup, et al.
(författare)
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Distribution of histopathological features along the colon in microscopic colitis
- 2021
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Ingår i: International Journal of Colorectal Disease. - : Springer. - 0179-1958 .- 1432-1262. ; 36, s. 151-159
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The diagnosis microscopic colitis (MC) consisting of collagenous colitis (CC) and lymphocytic colitis (LC) relies on histological assessment of mucosal biopsies from the colon. The optimal biopsy strategy for reliable diagnosis of MC is controversial. The aim of this study was to evaluate the distribution of histopathological features of MC throughout the colon. Methods Mucosal biopsies from multiple colonic segments of patients with MC who participated in one of the three prospective European multicenter trials were analyzed. Histological slides were stained with hematoxylin-and-eosin, a connective tissue stain, and CD3 in selected cases. Results In total, 255 patients were included, 199 and 56 patients with CC and LC, respectively. Both groups exhibited a gradient with more pronounced inflammation in the lamina propria in the proximal colon compared with the distal colon. Similarly, the thickness of the subepithelial collagenous band in CC showed a gradient with higher values in the proximal colon. The mean number of intraepithelial lymphocytes was > 20 in all colonic segments in patients within both subgroups. Biopsies from 86 to 94% of individual segments were diagnostic, rectum excluded. Biopsies from non-diagnostic segments often showed features of another subgroup of MC. Conclusion Conclusively, although the severity of the histological changes in MC differed in the colonic mucosa, the minimum criteria required for the diagnosis were present in the random biopsies from the majority of segments. Thus, our findings show MC to be a pancolitis, rectum excluded, questioning previously proclaimed patchiness throughout the colon.
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| 5. |
- Haddad, Tariq Sami, et al.
(författare)
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Improving tumor budding reporting in colorectal cancer: a Delphi consensus study
- 2021
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Ingår i: Virchows Archiv. - : SPRINGER. - 0945-6317 .- 1432-2307. ; 479:3, s. 459-469
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.
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