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Note that for some images, they may be in TCIA, found at , while some are in TCGA, which are located at the GDC Data Portal (GDC (cancer.gov)). This tutorial explains how to get data from the GDC Data Portal.

NBIA Data Retriever: You may have seen online that you need the NBIA Data Retriever. This is only required if your data are in TCIA. CHECK TCIA first to see if there are data you need there. Most of the data will be on the GDC Data Portal, not TCIA. MRI images are more likely be in TCIA, while pathology are more likely to be in GDC Data Portal.

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Deep learning and neural networks have produced strong results for challenging computer vision tasks such as image classification and object detection, and it is easier than ever to get started with deep learning with just a rudimentary knowledge of Python, thanks to libraries such as PyTorch. Of course, the applications of deep learning are not just limited to basic natural image tasks; many other fields have benefited from neural networks, such as the key application area of medical imaging and computer-assisted diagnosis. In this tutorial, I will show how to use deep learning for a medical imaging classification task on a real dataset of breast cancer MR (magnetic resonance) scans, from start to finish, using Python.

For the first part of this blog series, I will introduce the standard data type of medical images, DICOM files (Digital Imaging and Communications in Medicine), and show how to obtain the MRI DICOM data. Next, I will show how to use Python to interact with the data, and convert it to a format easily usable for deep learning.

The second part of this series here describes how to use PyTorch to create a neural network for image classification and create a model training and evaluation pipeline that uses this data for the cancer detection task.

Next, we are ready to write some code to automatically extract .png files from the raw DICOM data, as .png files work nicely with PyTorch (and take up much less space than DICOMs). First, however, we need to define our classification task, as the labels that we will assign to each extracted .png image are based on this task definition.

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Does this means that the negative images do not contains any sort of tumor (even benign) , and that all the tumors in the annotations boxes are considered to be malignant? In that case, is there any benign tumor in the dataset that we can use?

Abstract:Low-grade gliomas are a heterogeneous group of infiltrative neoplasms. Radiomics allows the characterization of phenotypes with high-throughput extraction of quantitative imaging features from radiologic images. Deep learning models, such as convolutional neural networks (CNNs), offer well-performing models and a simplified pipeline by automatic feature learning. In our study, MRI data were retrospectively obtained from The Cancer Imaging Archive (TCIA), which contains MR images for a subset of the LGG patients in The Cancer Genome Atlas (TCGA). Corresponding molecular genetics and clinical information were obtained from TCGA. Three genes included in the genetic signatures were WEE1, CRTAC1, and SEMA4G. A CNN-based deep learning model was used to classify patients into low and high-risk groups, with the median gene signature risk score as the cut-off value. The data were randomly split into training and test sets, with 61 patients in the training set and 20 in the test set. In the test set, models using T1 and T2 weighted images had an area under the receiver operating characteristic curve of 73% and 79%, respectively. In conclusion, we developed a CNN-based model to predict non-invasively the risk stratification provided by the prognostic gene signature in LGGs. Numerous previously discovered gene signatures and novel genetic identifiers that will be developed in the future may be utilized with this method.Keywords: deep learning; radiomics; neural networks; gliomas; LGG; gene signatures

Healthcare and life sciences organizations use machine learning (ML) to enable precision medicine, anticipate patient preferences, detect disease, improve care quality, and understand inequities. Rapid growth in health information technologies has made patient-level data available from an increasingly diverse set of data modalities. Further, research has shown that the utility and accuracy of ML models can be improved by incorporating data from multiple data domains [1]. Intuitively, this is understandable, as we are providing our models a more complete view of the individuals and settings we look to describe.

Applying ML to diverse health datasets, known as Multimodal Machine Learning (Multimodal ML), is an active area of research and development. Analyzing linked patient-level data from diverse data modalities, such as genomics and medical imaging, promises to accelerate improvements in patient care. However, performing analysis of a single modality at scale has been challenging in on-premises environments. On-premises processing of multiple modalities of unstructured data has commonly been intractable due to the distinct infrastructure requirements of different modalities (such as FPG and GPU requirements). Yet, with AWS, you can readily deploy purpose-built pipelines and scale them to meet your needs, paying only for what you use.

In this two-part blog post, we demonstrate how to build a scalable, cloud architecture for Multimodal ML on health data. As an example, we will deploy a Multimodal ML pipeline to analyze the Non-Small Cell Lung Cancer (NSCLC) Radiogenomics data set, which consists of RNA sequence data, clinical data (reflective of EHR data), medical images, and human annotations of those images [2]. Although for the given use case, we predict survival outcome of patients diagnosed with NSCLC, Multimodal ML models can be applied to other applications, including, but not limited to, personalized treatment, clinical decision support, and drug response prediction.

In this first blog post, we step through data acquisition, data processing, and feature construction for each data modality. In the next blog post, we train a model to predict patient survival using the pooled set of features drawn from all of the data modalities, and contrast the results with models trained on one modality. While we present an application to genomic, clinical, and medical imaging data, the approach and architecture are applicable to a broad set of health data ML use-cases and frameworks.

Our architecture uses Amazon S3 to store the input datasets, and specialized pipelines to processes and transform each data modality, yielding features suitable for model training, as shown in Figure 1.

For genomic data, we leverage secondary analysis workflows, such as Illumina DRAGEN (Dynamic Read Analysis for GENomics) Bio-IT platform [3], that support commonly-used analytic techniques, like mapping and alignment of DNA and RNA fragments, detection of variants, assessment of quality, and quantification of RNA expression. The NSCLC Radiogenomic data consists of paired-end RNA-sequenced data from samples of surgically excised tumor tissue. S3 provides a secure, durable, and scalable storage location, suitable for analysis of large-scale genomic data. S3 can be used to store input data, including sequenced reads and reference genomes, intermediate files, and output data. We conduct secondary analysis of sequenced reads to quantify gene expression level. The output is in tabular form with expression level for each gene per sample. The subject-level, quantitative expression of genes can then be used as features for model training.

Medical imaging data is commonly stored in the DICOM file format, a standard that combines metadata and pixel data in a single object. For a volumetric scan, such as a lung CT scan, each cross-section slice is typically stored as an individual DICOM file. However, for ML purposes, analyzing 3-dimensional data provides a more wholistic view of the region of interest (ROI), thus providing better predictive values. We convert the scans in DICOM format into NIfTI format. Thus, we download the DICOM files, store them to S3, then use using Amazon SageMaker Processing to perform the transformation. Specifically, for each subject and study, we launch a SageMaker Processing job with a custom container to read the 2D DICOM slice files for both the CT scan and tumor segmentation, combine them to 3D volumes, save the volumes in NIfTI format, and write the NIfTI object back to S3.

With the medical imaging data in volumetric format, we compute radiomic features describing the tumor region in the same SageMaker Processing job. We use AWS Step Functions to orchestrate the processing for entire imaging dataset in a scalable and fault-tolerant fashion.

Finally, the features engineered from each data modality are written to Amazon SageMaker Feature Store, a purpose-built repository for storing ML features. Feature preparation and model training is performed using Amazon SageMaker Studio.

By default, SageMaker Studio notebooks, local data, and model artifacts are all encrypted with AWS managed customer master keys (CMKs). In the present example, we are working with deidentified data. However, when working with Protected Health Information (PHI), it is important to encrypt all data at rest and in transit, apply narrowly defined roles, and limit access in accordance with the principles of least privilege. You can find further guidance on best practices in the white paper Architecting for HIPAA Security and Compliance.

For this demonstration, we are using Illumina DRAGEN platform for secondary analysis. DRAGEN offers accurate and ultra-fast next generation sequencing secondary analysis pipelines for whole genome, RNA-seq, exome, methylome, and cancer data. It uses FPGA-based Amazon EC2 F1 instances to provide hardware-accelerated implementations of genomic analysis algorithms. We run DRAGEN RNA pipeline to map the reads to the human hg19 reference genome [2]. We also use gene annotation files (GTF/GFF format) to align reads to splice junctions and quantify gene expression level. The GTF file for hg19 genome can be downloaded from GENCODE project. The steps to execute the DRAGEN RNA pipeline are as follows: e24fc04721