FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++ based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.Comment: 12 pages, 4 figures, submitted to IEEE Acces

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

Molecular Subtypes of Breast Cancer: Long-term Incidence Trends and Prognostic Differences

Background: Secular trends in incidence and prognosis of molecular breast cancer subtypes are poorly described. We studied long-term trends in a population of Norwegian women born 1886–1977. Methods: A total of 52,949 women were followed for breast cancer incidence, and 1,423 tumors were reclassified into molecular subtypes using IHC and in situ hybridization. We compared incidence rates among women born 1886–1928 and 1929–1977, estimated age-specific incidence rate ratios (IRR), and performed multiple imputations to account for unknown subtype. Prognosis was compared for women diagnosed before 1995 and in 1995 or later, estimating cumulative risk of death and HRs. Results: Between 50 and 69 years of age, incidence rates of Luminal A and Luminal B (HER2−) were higher among women born in 1929 or later, compared with before 1929 [IRRs 50–54 years; after imputations: 3.5; 95% confidence interval (CI), 1.8–6.9 and 2.5; 95% CI, 1.2–5.2, respectively], with no clear differences for other subtypes. Rates of death were lower in women diagnosed in 1995 or later, compared to before 1995, for Luminal A (HR 0.4; 95% CI, 0.3–0.5), Luminal B (HER2−; HR 0.5; 95% CI, 0.3–0.7), and Basal phenotype (HR 0.4; 95% CI, 0.2–0.9). Conclusions: We found a strong secular incidence increase restricted to Luminal A and Luminal B (HER2−) subtypes, combined with a markedly improved prognosis for these subtypes and for the Basal phenotype.acceptedVersio

Basal markers and prognosis in luminal breast cancer

Purpose Basal marker expression in triple-negative breast cancers identifies basal-like tumours, and thus separates the TN group into two prognostic groups. However, the expression and prognostic significance of basal markers in luminal breast cancers are poorly described. The aim of this study was to investigate the expression and prognostic value of basal markers (CK5, CK14 and EGFR) in luminal breast cancer. Methods A total of 1423 formalin-fixed, paraffin-embedded breast cancer tumours from a well-characterized cohort of Norwegian women, previously reclassified into molecular subtypes using IHC and ISH, were included in the study. For the present study, tumours expressing at least one of the basal markers CK5, CK14 or EGFR were defined as basal marker positive. Cumulative incidence of death from breast cancer and hazard ratio analyses were used to assess prognosis according to basal marker expression. Results and conclusion In total, 470 cases (33.0%) were basal marker positive. A higher proportion of the basal marker-positive tumours were of histopathological grade 3 compared to basal marker negative. For hormone receptor-positive, HER2-negative cases, we found better prognosis for basal marker-positive breast cancer compared to basal marker negative. For all subtypes combined, poorer prognosis for basal marker-negative cases was found in histopathological grade 2 tumours but not among grade 1 and 3

DTX3 copy number increase in breast cancer: a study of associations to molecular subtype, proliferation and prognosis

Purpose The degree of cell proliferation is important for subclassifcation of breast cancers into prognostic and therapeutic groups. DTX3 has been identifed as a driver of proliferation in luminal breast cancer. In this study, we describe DTX3 copy number in breast cancer primary tumours and corresponding axillary lymph node metastases, and studied associations with molecular subtype, proliferation and prognosis. Methods Using fuorescence in situ hybridization, we assessed DTX3 and chromosome 12 centromere (CEP12) copy number in 542 primary breast cancers and 117 lymph node metastases, from a well-described cohort of Norwegian breast cancer patients. Proliferation was expressed as mitotic counts and Ki67 score. Associations between DTX3 copy number and molecular subtype and proliferation were assessed using Pearson’s χ2 test. We studied the efect of copy number increase on prognosis estimating cumulative incidence of breast cancer death and hazard ratios. Results Mean DTX3 copy number≥4 was found in 23 tumours (4%), and mean≥5 in 9 tumours (1.7%). Copy number increase was found within all molecular subtypes except the 5 negative phenotype and the Luminal B (HER2+) subtype. DTX3 copy number increase was not accompanied by an increase in CEP12. Point estimates showed that there were associations between DTX3 copy number increase and high proliferation and poor prognosis; however, precision depended on copy number cut-of. Conclusions DTX3 copy number increase was present in a small proportion of breast cancer cases. There was an associationbetween copy number increase and high tumour cell proliferation and poor prognosis

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.publishedVersio

FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/

MRPS23 amplification and gene expression in breast cancer; association with proliferation and the non-basal subtypes

Purpose MRPS23 is recognized as a driver of proliferation in luminal breast cancer. The aims of the present study were to describe MRPS23 copy number change in breast cancer, and to assess associations between MRPS23 copy number change and molecular subtype, proliferation and prognosis, and between MRPS23 gene expression and molecular subtype and prognosis. Methods Using fluorescence in situ hybridization (FISH), we examined MRPS23 and centromere 17 copy number in 590 formalin-fixed, paraffin-embedded primary tumours and 144 corresponding lymph node metastases from a cohort of Norwegian breast cancer patients. Furthermore, we analysed MRPS23 gene expression data in 1971 primary breast cancer tumours from the METABRIC dataset. We used Pearson’s χ2 test to assess associations between MRPS23 copy number and molecular subtype and proliferation, and between MRPS23 expression and molecular subtype. We studied prognosis by estimating hazard ratios and cumulative incidence of death from breast cancer according to MRPS23 copy number and MRPS23 expression status. Results We found MRPS23 amplification (mean MRPS23 copy number ≥ 6 and/or MRPS23/chromosome 17 ratio ≥ 2) in 8% of primary tumours. Copy number increase associated with non-basal subtypes and higher tumour cell proliferation (Ki67). Higher MRPS23 expression associated with the Luminal B subtype. We found no significant association between MRPS23 amplification or MRSP23 gene expression, and prognosis. Conclusion Amplification of MRPS23 is associated with higher proliferation and non-basal subtypes in breast cancer. High MRPS23 expression is associated with the Luminal B subtype

MRPS23 amplification and gene expression in breast cancer; association with proliferation and the non-basal subtypes

Purpose MRPS23 is recognized as a driver of proliferation in luminal breast cancer. The aims of the present study were to describe MRPS23 copy number change in breast cancer, and to assess associations between MRPS23 copy number change and molecular subtype, proliferation and prognosis, and between MRPS23 gene expression and molecular subtype and prognosis. Methods Using fluorescence in situ hybridization (FISH), we examined MRPS23 and centromere 17 copy number in 590 formalin-fixed, paraffin-embedded primary tumours and 144 corresponding lymph node metastases from a cohort of Norwegian breast cancer patients. Furthermore, we analysed MRPS23 gene expression data in 1971 primary breast cancer tumours from the METABRIC dataset. We used Pearson’s χ2 test to assess associations between MRPS23 copy number and molecular subtype and proliferation, and between MRPS23 expression and molecular subtype. We studied prognosis by estimating hazard ratios and cumulative incidence of death from breast cancer according to MRPS23 copy number and MRPS23 expression status. Results We found MRPS23 amplification (mean MRPS23 copy number ≥ 6 and/or MRPS23/chromosome 17 ratio ≥ 2) in 8% of primary tumours. Copy number increase associated with non-basal subtypes and higher tumour cell proliferation (Ki67). Higher MRPS23 expression associated with the Luminal B subtype. We found no significant association between MRPS23 amplification or MRSP23 gene expression, and prognosis. Conclusion Amplification of MRPS23 is associated with higher proliferation and non-basal subtypes in breast cancer. High MRPS23 expression is associated with the Luminal B subtype