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- Bokhorst, John-Melle, et al.
(author)
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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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In: Scientific Reports. - : NATURE PORTFOLIO. - 2045-2322. ; 13:1
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Journal article (peer-reviewed)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. |
- Geessink, Oscar G. F., et al.
(author)
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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
- 2019
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In: Cellular Oncology. - : SPRINGER. - 2211-3428 .- 2211-3436. ; 42:3, s. 331-341
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Journal article (peer-reviewed)abstract
- PurposeTumor-stroma ratio (TSR) serves as an independent prognostic factor in colorectal cancer and other solid malignancies. The recent introduction of digital pathology in routine tissue diagnostics holds opportunities for automated TSR analysis. We investigated the potential of computer-aided quantification of intratumoral stroma in rectal cancer whole-slide images.MethodsHistological slides from 129 rectal adenocarcinoma patients were analyzed by two experts who selected a suitable stroma hot-spot and visually assessed TSR. A semi-automatic method based on deep learning was trained to segment all relevant tissue types in rectal cancer histology and subsequently applied to the hot-spots provided by the experts. Patients were assigned to a stroma-high or stroma-low group by both TSR methods (visual and automated). This allowed for prognostic comparison between the two methods in terms of disease-specific and disease-free survival times.ResultsWith stroma-low as baseline, automated TSR was found to be prognostic independent of age, gender, pT-stage, lymph node status, tumor grade, and whether adjuvant therapy was given, both for disease-specific survival (hazard ratio=2.48 (95% confidence interval 1.29-4.78)) and for disease-free survival (hazard ratio=2.05 (95% confidence interval 1.11-3.78)). Visually assessed TSR did not serve as an independent prognostic factor in multivariate analysis.ConclusionsThis work shows that TSR is an independent prognosticator in rectal cancer when assessed automatically in user-provided stroma hot-spots. The deep learning-based technology presented here may be a significant aid to pathologists in routine diagnostics.
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| 3. |
- Li, Yihao, et al.
(author)
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c-Myb Enhances Breast Cancer Invasion and Metastasis through the Wnt/beta-Catenin/Axin2 Pathway
- 2016
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In: Cancer Research. - 0008-5472 .- 1538-7445. ; 76:11, s. 3364-3375
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Journal article (peer-reviewed)abstract
- The molecular underpinnings of aggressive breast cancers remain mainly obscure. Here we demonstrate that activation of the transcription factor c-Myb is required for the prometastatic character of basal breast cancers. An analysis of breast cancer patients led us to identify c-Myb as an activator of Wnt/beta-catenin signaling. c-Myb interacted with the intracellular Wnt effector beta-catenin and coactivated the Wnt/beta-catenin target genes Cyclin D1 and Axin2. Moreover, c-Myb controlled metastasis in an Axin2-dependent manner. Expression microarray analyses revealed a positive association between Axin2 and c-Myb, a target of the proinflammatory cytokine IL1 beta that was found to be required for IL1 beta-induced breast cancer cell invasion. Overall, our results identified c-Myb as a promoter of breast cancer invasion and metastasis through its ability to activate Wnt/beta-catenin/Axin2 signaling. (C) 2016 AACR.
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| 4. |
- Smit, Marloes A., et al.
(author)
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Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
- 2023
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In: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 14
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Journal article (peer-reviewed)abstract
- 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. © 2023 The Authors
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