| 1. |
- Balkenhol, Maschenka C. A., et al.
(författare)
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Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics
- 2021
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Ingår i: Breast. - : Elsevier. - 0960-9776 .- 1532-3080. ; 56, s. 78-87
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Tidskriftsartikel (refereegranskat)abstract
- The tumour microenvironment has been shown to be a valuable source of prognostic information for different cancer types. This holds in particular for triple negative breast cancer (TNBC), a breast cancer subtype for which currently no prognostic biomarkers are established. Although different methods to assess tumour infiltrating lymphocytes (TILs) have been published, it remains unclear which method (marker, region) yields the most optimal prognostic information. In addition, to date, no objective TILs assessment methods are available. For this proof of concept study, a subset of our previously described TNBC cohort (n = 94) was stained for CD3, CD8 and FOXP3 using multiplex immunohistochemistry and subsequently imaged by a multispectral imaging system. Advanced whole-slide image analysis algorithms, including convolutional neural networks (CNN) were used to register unmixed multispectral images and corresponding H&E sections, to segment the different tissue compartments (tumour, stroma) and to detect all individual positive lymphocytes. Densities of positive lymphocytes were analysed in different regions within the tumour and its neighbouring environment and correlated to relapse free survival (RFS) and overall survival (OS). We found that for all TILs markers the presence of a high density of positive cells correlated with an improved survival. None of the TILs markers was superior to the others. The results of TILs assessment in the various regions did not show marked differences between each other. The negative correlation between TILs and survival in our cohort are in line with previous studies. Our results provide directions for optimizing TILs assessment methodology. (C) 2021 The Author(s). Published by Elsevier Ltd.
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| 2. |
- Bulten, Wouter, et al.
(författare)
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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists
- 2021
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Ingår i: Modern Pathology. - : NATURE PUBLISHING GROUP. - 0893-3952 .- 1530-0285. ; 34, s. 660-671
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Tidskriftsartikel (refereegranskat)abstract
- The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohens kappa, 0.799 vs. 0.872;p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohens kappa, 0.733 vs. 0.786;p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
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| 3. |
- de Bel, Thomas, et al.
(författare)
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Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning
- 2022
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Ingår i: npj Breast Cancer. - : Nature Portfolio. - 2374-4677. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (+/- 0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted kappa = 0.747 +/- 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.
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| 4. |
- de Bel, Thomas, et al.
(författare)
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Residual cyclegan for robust domain transformation of histopathological tissue slides
- 2021
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- Variation between stains in histopathology is commonplace across different medical centers. This can have a significant effect on the reliability of machine learning algorithms. In this paper, we propose to reduce performance variability by using-consistent generative adversarial (CycleGAN) networks to remove staining variation. We improve upon the regular CycleGAN by incorporating residual learning. We comprehensively evaluate the performance of our stain transformation method and compare its usefulness in addition to extensive data augmentation to enhance the robustness of tissue segmentation algorithms. Our steps are as follows: first, we train a model to perform segmentation on tissue slides from a single source center, while heavily applying augmentations to increase robustness to unseen data. Second, we evaluate and compare the segmentation performance on data from other centers, both with and without applying our CycleGAN stain transformation. We compare segmentation performances in a colon tissue segmentation and kidney tissue segmentation task, covering data from 6 different centers. We show that our transformation method improves the overall Dice coefficient by 9% over the non-normalized target data and by 4% over traditional stain transformation in our colon tissue segmentation task. For kidney segmentation, our residual CycleGAN increases performance by 10% over no transformation and around 2% compared to the non-residual CycleGAN. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
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| 5. |
- Dooper, Stephan, et al.
(författare)
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Gigapixel end-to-end training using streaming and attention
- 2023
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weakly supervised learning, such as attention-gated multiple instance learning, have shown promising results, but often use multi-stage or patch-wise training strategies risking suboptimal feature extraction, which can negatively impact performance. In this paper, we propose to train a ResNet-34 encoder with an attention-gated classification head in an end-to-end fashion, which we call StreamingCLAM, using a streaming implementation of convolutional layers. This allows us to train end-to-end on 4-gigapixel microscopic images using only slide-level labels.We achieve a mean area under the receiver operating characteristic curve of 0.9757 for metastatic breast cancer detection (CAMELYON16), close to fully supervised approaches using pixel-level annotations. Our model can also detect MYC-gene translocation in histologic slides of diffuse large B-cell lymphoma, achieving a mean area under the ROC curve of 0.8259. Furthermore, we show that our model offers a degree of interpretability through the attention mechanism.
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| 6. |
- Faryna, Khrystyna, et al.
(författare)
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Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology
- 2024
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Ingår i: Computers in Biology and Medicine. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0010-4825 .- 1879-0534. ; 170
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Tidskriftsartikel (refereegranskat)abstract
- In histopathology practice, scanners, tissue processing, staining, and image acquisition protocols vary from center to center, resulting in subtle variations in images. Vanilla convolutional neural networks are sensitive to such domain shifts. Data augmentation is a popular way to improve domain generalization. Currently, state-of-the-art domain generalization in computational pathology is achieved using a manually curated set of augmentation transforms. However, manual tuning of augmentation parameters is time-consuming and can lead to sub-optimal generalization performance. Meta-learning frameworks can provide efficient ways to find optimal training hyper-parameters, including data augmentation. In this study, we hypothesize that an automated search of augmentation hyper-parameters can provide superior generalization performance and reduce experimental optimization time. We select four state-of-theart automatic augmentation methods from general computer vision and investigate their capacity to improve domain generalization in histopathology. We analyze their performance on data from 25 centers across two different tasks: tumor metastasis detection in lymph nodes and breast cancer tissue type classification. On tumor metastasis detection, most automatic augmentation methods achieve comparable performance to state-of-the-art manual augmentation. On breast cancer tissue type classification, the leading automatic augmentation method significantly outperforms state-of-the-art manual data augmentation.
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| 7. |
- Faryna, Khrystyna, et al.
(författare)
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Towards embedding stain-invariance in convolutional neural networks for H&E-stained histopathology
- 2024
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Ingår i: DIGITAL AND COMPUTATIONAL PATHOLOGY, MEDICAL IMAGING 2024. - : SPIE-INT SOC OPTICAL ENGINEERING. - 9781510671713 - 9781510671706
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Konferensbidrag (refereegranskat)abstract
- Convolutional neural networks (CNNs) are known to fail if a difference exists in the data they are trained and tested on, known as domain shifts. This sensitivity is particularly problematic in computational pathology, where various factors, such as different staining protocols and stain providers, introduce domain shifts. Many solutions have been proposed in the literature to address this issue, with data augmentation being one of the most popular approaches. While data augmentation can significantly enhance the performance of a CNN in the presence of domain shifts, it does not guarantee robustness. Therefore, it would be advantageous to integrate generalization to specific sources of domain shift directly into the network's capabilities when known to be present in the real world. In this study, we draw inspiration from roto-translation equivariant CNNs and propose a customized layer to enhance domain generalization and the CNN's ability to handle variations in staining. To evaluate our approach, we conduct experiments on two publicly available, multi-institutional datasets: CAMELYON17 and MIDOG.
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| 8. |
- Geessink, Oscar G. F., et al.
(författare)
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Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer
- 2019
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Ingår i: Cellular Oncology. - : SPRINGER. - 2211-3428 .- 2211-3436. ; 42:3, s. 331-341
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Jarkman, Sofia, et al.
(författare)
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Generalization of Deep Learning in Digital Pathology : Experience in Breast Cancer Metastasis Detection
- 2022
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Ingår i: Cancers. - : MDPI. - 2072-6694. ; 14:21
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Pathology is a cornerstone in cancer diagnostics, and digital pathology and artificial intelligence-driven image analysis could potentially save time and enhance diagnostic accuracy. For clinical implementation of artificial intelligence, a major question is whether the computer models maintain high performance when applied to new settings. We tested the generalizability of a highly accurate deep learning model for breast cancer metastasis detection in sentinel lymph nodes from, firstly, unseen sentinel node data and, secondly, data with a small change in surgical indication, in this case lymph nodes from axillary dissections. Model performance dropped in both settings, particularly on axillary dissection nodes. Retraining of the model was needed to mitigate the performance drop. The study highlights the generalization challenge of clinical implementation of AI models, and the possibility that retraining might be necessary. Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926-0.998) and FROC of 0.838 (95% CI 0.757-0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800-0.998) and FROC 0.744 (95% CI 0.566-0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201-0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model s performance.
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| 10. |
- Kartasalo, Kimmo, et al.
(författare)
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Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps
- 2021
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Ingår i: European Urology Focus. - : Elsevier. - 2405-4569. ; 7:4, s. 687-691
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Forskningsöversikt (refereegranskat)abstract
- Diagnosis and Gleason grading of prostate cancer in biopsies are critical for the clinical management of men with prostate cancer. Despite this, the high grading variability among pathologists leads to the potential for under-and overtreatment. Artificial intelligence (AI) systems have shown promise in assisting pathologists to perform Gleason grading, which could help address this problem. In this mini-review, we highlight studies reporting on the development of AI systems for cancer detection and Gleason grading, and discuss the progress needed for widespread clinical implementation, as well as anticipated future developments. Patient summary: This mini-review summarizes the evidence relating to the validation of artificial intelligence (AI)-assisted cancer detection and Gleason grading of prostate cancer in biopsies, and highlights the remaining steps required prior to its widespread clinical implementation. We found that, although there is strong evidence to show that AI is able to perform Gleason grading on par with experienced uropathologists, more work is needed to ensure the accuracy of results from AI systems in diverse settings across different patient populations, digitization platforms, and pathology laboratories.
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