The recurrence rate is less than that previously observed in historical materials, but current, commonly used risk factors are still useful in evaluating recurrence risks. Stratification by pT and pN classification and the number of risk factors enables the identification of large patient groups characterized by such a low recurrence rate that it is questionable whether adjuvant treatment is motivated. See Video Abstract at http://links.lww.com/DCR/A663.

Deep learning is transforming the analysis of biological images but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10 6 1-megapixel images in ~5.5 h for ~$250, with a sub-$100 cost achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org. Main Text While deep learning is an increasingly popular approach to extracting quantitative information from biological images, its limitations significantly hinder its widespread adoption. Chief among these limitations are the requirements for expansive sets of training data and computational resources. Here, we sought to overcome the latter limitation. While deep learning methods have remarkable accuracy for a range of image-analysis tasks including classification 1 , segmentation 2-4 , and object tracking 5,6 , they have limited throughput even with GPU acceleration. For example, even when running segmentation models on a GPU, typical inference speeds on megapixel-scale images are in the range of 5-10 frames per second, limiting the scope of analyses that can be performed on images in a timely fashion. The necessary domain knowledge and associated costs of GPUs pose further barriers to entry, although recent software packages 7-11 have attempted to solve these two issues. While cloud computing has proven effective for other data types 12-15 , scaling analyses to large imaging datasets in the cloud while constraining costs is a considerable challenge. To meet this need, here we have developed the DeepCell Kiosk (Fig. 1a). This software package takes in configuration details (user authentication, GPU type, etc.) and creates a cluster in the cloud that runs predefined deep learningenabled image-analysis pipelines. This cluster is managed by Kubernetes, an open-source framework for running software containers (software that is bundled with its dependencies so it can be run as an isolated process) across a group of servers. An alternative way to view Kubernetes is as an operating system for cloud computing. Data is submitted to the cluster through either a web-based front-end, a command line tool, or an ImageJ plugin. Once submitted, it is placed in a database where the specified image-analysis pipeline can pick up the dataset, perform the desired analysis, and make the results available for download. Results can be visualized by a variety of visualization software tools 16,17. To ensure that image-analysis pipelines can be run efficiently on this cluster, we made two software design choices. First, image-analysis pipelines access trained deep learning models through a centralized model server in the cluster. This strategy enables the cluster to efficiently allocate resources, as the various co...

Adjuvant chemotherapy aims at eradicating tumour cells sometimes present after radical surgery for a colorectal cancer (CRC) and thereby diminish the recurrence rate and prolong time to recurrence (TTR). Remaining tumour cells will lead to recurrent disease that is usually fatal. Adjuvant therapy is administered based upon the estimated recurrence risk, which in turn defines the need for this treatment. This systematic overview aims at describing whether the need has decreased since trials showing that adjuvant chemotherapy provides benefits in colon cancer were performed decades ago. Thanks to other improvements than the administration of adjuvant chemotherapy, such as better staging, improved surgery, the use of radiotherapy and more careful pathology, recurrence risks have decreased. Methodological difficulties including intertrial comparisons decades apart and the present selective use of adjuvant therapy prevent an accurate estimate of the magnitude of the decreased need. Furthermore, most trials do not report recurrence rates or TTR, only disease-free and overall survival (DFS/OS). Fewer colon cancer patients, particularly in stage II but also in stage III, today display a sufficient need for adjuvant treatment considering the burden of treatment, especially when oxaliplatin is added. In rectal cancer, neo-adjuvant treatment will be increasingly used, diminishing the need for adjuvant treatment.

Deep learning is transforming the ability of life scientists to extract information from images. These techniques have better accuracy than conventional approaches and enable previously impossible analyses. As the capability of deep learning methods expands, they are increasingly being applied to large imaging datasets. The computational demands of deep learning present a significant barrier to large-scale image analysis. To meet this challenge, we have developed DeepCell 2.0, a platform for deploying deep learning models on large imaging datasets (>10 5megapixel images) in the cloud. This software enables the turnkey deployment of a Kubernetes cluster on all commonly used operating systems. By using a microservice architecture, our platform matches computational operations with their hardware requirements to reduce operating costs. Further, it scales computational resources to meet demand, drastically reducing the time necessary for analysis of large datasets. A thorough analysis of costs demonstrates that cloud computing is economically competitive for this application. By treating hardware infrastructure as software, this work foreshadows a new generation of software packages for biology in which computational resources are a dynamically allocated resource.