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

On the catch trend of mechanised gill netters landed at Madras fisheries harbour

An average of about 7 drift gill nets and 3 seasonally operating bottom set gill nets land at the Madras Fisheries Harbour by the Pablo type mechanised boats. These mechanised boats in the length range of 7 - 8 m are fitted with 24 – 30 Hp engines and operate in area off Madras coast in 20 - 50 m depth range throughout the year except the southeast monsoon period, October- December. The catch trend of the gill netters with special reference to the seasonal abundance of the different groups caught during the period, 1988 - '89 are dealt with in the present study

Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Impact of stream impurities on compressor power requirements for CO2 pipeline transportation

The economic viability of Carbon Capture and Sequestration (CCS) as a means of mitigating CO2 emissions is significantly dependent on the minimisation of costs associated with the compression and transportation of the captured CO2. This paper describes the development and application of a rigorous thermodynamic model to compute and compare power requirements for various multistage compression strategies for CO2 streams containing typical impurities originating from various capture technologies associated with industrial and power emission sectors. The compression options examined include conventional multistage integrally geared centrifugal compressors, supersonic shockwave compressors and multistage compression combined with subcritical liquefaction and pumping. The study shows that for all the compression options examined, the compression power reduces with the increase in the purity of the CO2 stream, while the inter-stage cooling duty is predicted to be significantly higher than the compression power demand. For CO2 streams carrying less than 5% impurities, multistage compression combined with liquefaction and subsequent pumping from ca 62 bar pressure can offer higher efficiency than conventional gas-phase compression. In the case of a raw/dehumidified oxy-fuel CO2 stream of ca 85% purity, subcritical liquefaction at 62 bar pressure is shown to increase the cooling duty by ca 50% as compared to pure CO2

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

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers