Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods.

ABSTRACT:

Background

Deep learning methods have great potential to predict tumor characterization, such as histological diagnosis and genetic aberration. The objective of this study was to evaluate and validate the predictive performance of multimodality imaging-derived models using computer-aided diagnostic (CAD) methods for prediction of MDM2 gene amplification to identify well-differentiated liposarcoma (WDLPS) and lipoma.

Materials and methods

All 127 patients from two institutions were included with 89 patients in one institution for model training and 38 patients in the other institution for external validation between January 2012 and December 2018. For each modality, handcrafted radiomics analysis with manual segmentation was applied to extract 851 features for each modality,

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8991509 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature