detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2405.07861
Tema
Electrical Engineering and Systems... Computer Science - Computer Vision...Autor
Tai, Chi-en Amy Wong, AlexanderCategoría
Computer Science
Año
2024
fecha de cotización
15/5/2024
Palabras clave
imaging grading using cdi$^s$ breast cancerResumen
Breast cancer was diagnosed for over 7.8 million women between 2015 to 2020.
Grading plays a vital role in breast cancer treatment planning.
However, the current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs.
A recent paper leveraging volumetric deep radiomic features from synthetic correlated diffusion imaging (CDI$^s$) for breast cancer grade prediction showed immense promise for noninvasive methods for grading.
Motivated by the impact of CDI$^s$ optimization for prostate cancer delineation, this paper examines using optimized CDI$^s$ to improve breast cancer grade prediction.
We fuse the optimized CDI$^s$ signal with diffusion-weighted imaging (DWI) to create a multiparametric MRI for each patient.
Using a larger patient cohort and training across all the layers of a pretrained MONAI model, we achieve a leave-one-out cross-validation accuracy of 95.79%, over 8% higher compared to that previously reported.
Tai, Chi-en Amy,Wong, Alexander, 2024, Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
High-Frequency Repetitive Magnetic Stimulation at the Sacrum Alleviates Chronic Constipation in Parkinson’s Patients
magnetic stimulation parkinson’s significant patients scale sacrum pd hf-rms chronic constipation scores
