MRI-based machine learning nomograms assess cervical cancer risk

MRI-based machine learning-based nomograms can evaluate postoperative risk factors in cervical cancer patients, a study published October 25 in Academic Radiology has found. Researchers led by Zhang Yu from the First Affiliated Hospital of Anhui Medical University in China reported that nomograms based on clinical and imaging parameters performed significantly better than MRI alone in assessing cervical cancer risk factors. “Machine learning-based radiomics have a greater advantage in the prediction of cervical cancer ..., and the combination with incoherent motion diffusion-weighted imaging [IVIM-DWI] and clinical parameters can complement and improve the predictive performance,” Yu and co-authors wrote. Several risk factors are involved in cervical cancer, which can impact decision-making on treatment strategies. MRI is the go-to imaging method for assessing cervical cancer, but the researchers noted that it can only clarify macroscopic changes. Tissue biopsy and surgical pathology are other methods for assessing this cancer, but these are invasive. IVIM-DWI is a multi-parametric MRI scanning technique that can reflect the water molecular motion and microcirculatory perfusion of tumors. This shows the pathophysiological state of tumors. Previous research suggests that IVIM-DWI can be applied to a variety of tumors for benign and malignant identification, proliferative potential revelation, recurrence prediction, and prognostic assessment. Radiomics meanwhile all...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Womens Imaging Source Type: news