Asset Details
Do you wish to reserve the book?
MRI-based deep learning and radiomics pipeline for myxoid liposarcoma: a feasibility study in a rare sarcoma
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
MRI-based deep learning and radiomics pipeline for myxoid liposarcoma: a feasibility study in a rare sarcoma
Do you wish to request the book?
MRI-based deep learning and radiomics pipeline for myxoid liposarcoma: a feasibility study in a rare sarcoma
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
MRI-based deep learning and radiomics pipeline for myxoid liposarcoma: a feasibility study in a rare sarcoma
2025
Overview
Myxoid liposarcoma (MLPS) is a rare soft tissue sarcoma characterized by histopathological variability, which poses challenges for accurate grading and treatment planning. This study evaluated the feasibility of an automated MRI-based pipeline that combines deep learning and radiomics for non-invasive tumor assessment. In a retrospective multicenter cohort of 48 patients with histologically confirmed MLPS, a 3D U-Net convolutional neural network was trained to perform automatic tumor segmentation on axial T2-weighted MR images. Radiomics features were subsequently extracted from the segmented volumes and used to train a Random Forest classifier for predicting tumor grade, defined by centralized histopathological review according to WHO criteria. The segmentation model achieved a median Dice similarity coefficient of 0.892. The radiomics-based grading classifier reached a mean area under the curve of 0.745, with an F1-score of 0.729 and a balanced accuracy of 0.723 in distinguishing high-grade from low-grade tumors. Most classification errors occurred in borderline or histologically heterogeneous cases. These findings suggest that automated segmentation and radiomics analysis may offer valuable support for MLPS grading and complement histopathology, particularly in diagnostically complex cases. Further prospective validation in larger cohorts is warranted.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
