Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature—for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers—specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns

Increasing the Brønsted acidity of Ph2PO2H by the Lewis acid B(C6F5)3. Formation of an eight-membered boraphosphinate ring [Ph2POB(C6F5)2O]2

The Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged for financial support. The theoretical part of this work was supported by the Russian Science Foundation (Project 14-13-00832).Autoprotolysis of the metastable acid (C6F5)3BOPPh2OH, prepared in situ by the reaction of the rather weak Brønsted acid Ph2PO2H with the strong Lewis acid B(C6F5)3, gave rise to the formation of the eight-membered ring [Ph2POB(C6F5)2O]2 and C6F5H. The conjugate base was isolated as stable sodium crown ether salt [Na(15-crown-5)][Ph2PO2B(C6F5)3].Publisher PDFPeer reviewe

Large-scale database mining reveals hidden trends and future directions for cancer immunotherapy

LLC Cancer immunotherapy has fundamentally changed the landscape of oncology in recent years and significant resources are invested into immunotherapy research. It is in the interests of researchers and clinicians to identify promising and less promising trends in this field in order to rationally allocate resources. This requires a quantitative large-scale analysis of cancer immunotherapy related databases. We developed a novel tool for text mining, statistical analysis and data visualization of scientific literature data. We used this tool to analyze 72002 cancer immunotherapy publications and 1469 clinical trials from public databases. All source codes are available under an open access license. The contribution of specific topics within the cancer immunotherapy field has markedly shifted over the years. We show that the focus is moving from cell-based therapy and vaccination towards checkpoint inhibitors, with these trends reaching statistical significance. Rapidly growing subfields include the combination of chemotherapy with checkpoint blockade. Translational studies have shifted from hematological and skin neoplasms to gastrointestinal and lung cancer and from tumor antigens and angiogenesis to tumor stroma and apoptosis. This work highlights the importance of unbiased large-scale database mining to assess trends in cancer research and cancer immunotherapy in particular. Researchers, clinicians and funding agencies should be aware of quantitative trends in the immunotherapy field, allocate resources to the most promising areas and find new approaches for currently immature topics

Uncertainty driven pooling network for microvessel segmentation in routine histology images

Lymphovascular invasion (LVI) and tumor angiogenesis are correlated with metastasis, cancer recurrence and poor patient survival. In most of the cases, the LVI quantification and angiogenic analysis is based on microvessel segmentation and density estimation in immunohistochemically (IHC) stained tissues. However, in routine H&E stained images, the microvessels display a high level of heterogeneity in terms of size, shape, morphology and texture which makes microvessel segmentation a non-trivial task. Manual delineation of microvessels for biomarker analysis is labor-intensive, time consuming, irreproducible and can suffer from subjectivity among pathologists. Moreover, it is often beneficial to account for the uncertainty of a prediction when making a diagnosis. To address these challenges, we proposed a framework for microvessel segmentation in H&E stained histology images. The framework extends DeepLabV3+ by using an improved dice coefficient based custom loss function and also incorporating an uncertainty prediction mechanism. The proposed method uses an aligned Xception model, followed by atrous spatial pyramid pooling for feature extraction at multiple scales. This architecture counters the challenge of segmenting blood vessels of varying morphological appearance. To incorporate uncertainty, random transformations are introduced at test time for a superior segmentation result and simultaneous uncertainty map generation, highlighting ambiguous regions. The method is evaluated using 1167 images of size 512×512 pixels, extracted from 13 WSIs of oral squamous cell carcinoma (OSCC) tissue at 20x magnification. The proposed net-work achieves state-of-the-art performance compared to current semantic segmentation deep neural networks (FCN-8, U-Net, SegNet and DeepLabV3+)

Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome

Background: Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples. Methods: This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort. Results: Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86). Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved. Conclusions: Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data

Overview of marine fisheries in India during 2007

Fisheries sector in India plays an important role in the country’s economy and it supports the livelihood of millions of people. India is having 8,129 km of coastal length with 2.02 million sq. km of Exclusive Economic Zone (upto 200 m depth) and 0.452 million sq. km of continental shelf area

ESMO Guidance for Reporting Oncology real-World evidence (GROW)

Clinical epidemiolog

Sources of variation in cuticular hydrocarbons in the ant formica exsecta

Phenotypic variation arises from interactions between genotype and environment, although how variation is produced and then maintained remains unclear. The discovery of the nest-mate recognition system in Formica exsecta ants has allowed phenotypic variation in chemical profiles to be quantified across a natural population of 83 colonies. We investigated if this variation was correlated or not with intrinsic (genetic relatedness), extrinsic (location, light, temperature) or social (queen number) factors. (Z)-9-Alkenes and n-alkanes showed different patterns of variance: island (location) explained only 0.2% of the variation in (Z)-9-alkenes, but 21¬–29% in n-alkanes, whereas colony of origin explained 96% and 45–49% of the variation in (Z)-9-alkenes and n-alkanes, respectively. By contrast, within-colony variance of (Z)-9-alkenes was 4%, and 23–34% in n-alkanes, supporting the function of the former as recognition cues. (Z)-9-Alkene and n-alkane profiles were correlated with the genetic distance between colonies. Only n-alkane profiles diverged with increasing spatial distance. Sampling year explained a small (5%), but significant, amount of the variation in the (Z)-9-alkenes, but there was no consistent directional trend. Polygynous colonies and populous monogynous colonies were dominated by a rich C23:1 profile. We found no associations between worker size, mound exposure, or humidity, although effect sizes for the latter two factors were considerable. The results support the conjecture that genetic factors are the most likely source of between-colony variation in cuticular hydrocarbons