Python technology and its applications in radiomics

Abstract

Python, developed by Guido van Rossum, is favored for its simplicity and extensive ecosystem of libraries, which facilitate efficient coding and integration with other programming languages. Here, we aim to explore and summarize the role of Python in radiomics, a field focused on extracting and analyzing quantitative features from medical imaging to improve disease characterization and treatment evaluation. Radiomics addresses the complexities of tumor heterogeneity by transforming imaging data from modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) into actionable insights, often using statistical methods and machine learning techniques. Its primary applications include differentiating between benign and malignant tumors and predicting treatment outcomes, etc. Python is integral to several stages of radiomics, including image acquisition, region of interest (ROI) segmentation, feature extraction, and statistical analysis. By utilizing libraries such as PyRadiomics and Scikit-learn, researchers can significantly enhance the accuracy and efficiency of their analyses. Looking forward, Python holds considerable promise in radiomics, especially with ongoing advancements in medical imaging and big data. However, challenges such as data standardization, model interpretability, and patient privacy protection must be addressed to fully unlock its potential for improving diagnostic precision and patient outcomes.