Rectal MRI radiomics for predicting pathological complete response: Where we are.

Area of Science:

• Oncologic Imaging
• Radiology
• Medical Informatics

Background:

• Pathological complete response (pCR) is a key endpoint in rectal cancer treatment.
• Predicting pCR non-invasively can guide treatment decisions and improve patient outcomes.
• Rectal MRI radiomics offers a novel approach for quantitative image analysis.

Purpose of the Study:

• To present a typical radiomics analysis pipeline for rectal MRI.
• To review recent studies on radiomics for predicting pCR in rectal cancer.
• To discuss future directions and next steps for rectal MRI radiomics in pCR prediction.

Main Methods:

• Review of existing literature on rectal MRI radiomics for pCR prediction.
• Description of a standard radiomics workflow, including image acquisition, segmentation, feature extraction, and model building.
• Analysis of methodologies for developing robust predictive models.

Main Results:

• Radiomics analysis of rectal MRI is a feasible and emerging technique for pCR prediction.
• Various radiomics features and machine learning models have been explored for their predictive capabilities.
• The field is rapidly developing with ongoing research into advanced methodologies.

Conclusions:

• Rectal MRI radiomics is a promising tool for non-invasively predicting pCR in rectal cancer.
• Further research is needed to standardize methodologies and validate models for clinical implementation.
• Future work should focus on prospective studies and integration with clinical data.