Publications by authors named "K Pinker"
Article Synopsis
- The text discusses the significance of multiparametric MRI (mpMRI) in breast cancer diagnostics, emphasizing the need for advanced MRI methods to capture cellular and molecular processes in cancer development.
- It highlights promising new MRI techniques like BOLD, IVIM, DMI, and CEST, which can noninvasively differentiate breast cancer subtypes and monitor treatment responses.
- The conclusion stresses that standardizing quantification methods and improving MRI techniques is essential for translating this preclinical research into effective clinical applications.
Purpose To evaluate racial disparities in preoperative breast MRI use and surgical margin outcomes among patients with recently diagnosed breast cancer. Materials and Methods This retrospective study included patients with breast cancer who presented to a single cancer center between 2008 and 2020, underwent breast surgery, and self-identified as White or Black. Patients were divided into MRI or no-MRI cohorts based on preoperative MRI use.
View Article and Find Full Text PDFRationale And Objectives: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women.
Materials And Methods: A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI.
Development of an MRI Radiomic Machine-Learning Model to Predict Triple-Negative Breast Cancer Based on Fibroglandular Tissue of the Contralateral Unaffected Breast in Breast Cancer Patients.
Cancers (Basel)
October 2024
Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast's fibroglandular tissue (FGT) in breast cancer patients.
Materials And Methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26-82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-naïve breast cancer. Patients were divided into training ( = 250) and validation ( = 291) sets.
Objectives: To validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1-5-year period in Mexican women.
Methods: This retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included.