Radiomics Improves Cancer Screening and Early Detection

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Medical imaging is crucial for early cancer detection, historically relying on qualitative assessments.
• Recent advancements include quantitative image analysis (radiomics) and AI for improved diagnostic accuracy.

Purpose of the Study:

• To review various imaging modalities used in cancer screening.
• To illustrate the benefits of quantitative image analytics and radiomics in early cancer detection.
• To discuss the advancements and limitations in radiomics for building robust cancer classifier models.

Main Methods:

• Review of current imaging modalities (mammography, CT, MRI, PET, ultrasound) for cancer screening.
• Description of radiomics for converting medical images into quantitative data.
• Application of artificial intelligence (AI) for analyzing radiomic data.

Main Results:

• Radiomics and AI enhance diagnostic accuracy for early cancer detection.
• Quantitative image analysis improves the classification of malignant versus benign lesions.
• Diverse imaging modalities show promise across various cancer types.

Conclusions:

• Radiomics and AI are transforming early cancer detection, improving risk stratification and diagnostic precision.
• Further development requires access to well-annotated databases and biological descriptors.
• Continued research is essential to overcome limitations and enhance classifier model robustness.