Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and is composed of tumors with significant molecular heterogeneity resulting from differences in intrinsic oncogenic signaling pathways [1]. Enabling precision medicine, anticipating patient preferences, detecting disease, and improving care quality for NSCLC patients are important topics among healthcare and life sciences (HCLS) communities.
Applying machine learning (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 analyses of multiple modalities at scale has been challenging in on-premises or cloud environments due to the distinct infrastructure requirements of each modality. With Amazon SageMaker, you can create purpose-built pipelines and scale them to meet your needs easily, paying only for what you use.
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