Abstract 6391: Scalable colorectal cancer (CRC) diagnosis model with arterial and unenhanced phases of KRAS mutational status using Apache Spark

Abstract

Abstract The exponential increase in Medical data and Computer-aided diagnostic tools has made it easier for Machine Learning (ML) Researchers to extract valuable insights from medical images resulting in better patient outcomes during Medical Summarization. According to current statistics, 8.8 million people died every year due to cancer death. It is therefore imperative to use these available image datasets for better diagnosis, increased patient outcomes and recovery from deadly diseases. As a result, this study proposes a Colorectal Cancer classification model for detecting KRAS mutation status of Patients through their Radiomic images using the transfer learning of a deep learning pipeline on Apache Spark. The datasets will be curated from National Cancer Institute (NCI), features extraction or classification of Computed tomography(CT) images into CT- based handcrafted radiomic signatures with a Convolutional Neural Network(CNN) using the Apache Spark framework, the system uploads the segmented scanning images to the High Distributed File System (HDFS), Confidence Interval(CI), Area Under Curve(AUC), Precision and Recall for Validation cohort, Docker for app deployment and containerization of the model developed for reproducibility, transfer learning and model reuse. The result would be a real-time model using Deep learning pipelines with Apache Spark and Keras Tensor flow for KRAS Mutation detection using Colorectal Cancer images. In conclusion, this research project would produce a scalable and reproducible model for the faster diagnosis of Colorectal Cancer through the KRAS mutation status of CRC Patients. Citation Format: Mary Adetutu Adewunmi. Scalable colorectal cancer (CRC) diagnosis model with arterial and unenhanced phases of KRAS mutational status using Apache Spark [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6391.