Katja Pinker
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Explore the profile of Katja Pinker including associated specialties, affiliations and a list of published articles.
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205
Citations
3748
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Recent Articles
1.
Basukala D, Mikheev A, Li X, Goldberg J, Gilani N, Moy L, et al.
Front Oncol
. 2025 Mar;
15:1524634.
PMID: 40066090
Introduction: The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals...
2.
Cozzi A, Di Leo G, Houssami N, Gilbert F, Helbich T, Alvarez Benito M, et al.
Eur Radiol
. 2025 Feb;
PMID: 40016317
Objectives: To investigate the surgical impact of preoperative breast MRI in patients diagnosed with invasive lobular breast cancer (ILC) in a prospective observational study. Methods: The prospective MIPA observational study...
3.
Panagiotis K, Gullo R, Resch D, Pinker K
Radiologie (Heidelb)
. 2025 Feb;
65(3):170-177.
PMID: 39900666
Background: Molecular imaging has been introduced into breast imaging in recent years, in order to improve breast cancer (BC) depiction as well as our understanding of cancer-associated processes at a ...
4.
Prinz D, Bartsch S, Ehret V, Friske J, Pinker K, Helbich T
Radiologie (Heidelb)
. 2024 Nov;
65(3):162-169.
PMID: 39611894
Background: The combination of different MRI methods is described as multiparametric MRI (mpMRI) and plays a significant role in breast cancer diagnostics. Currently, mpMRI includes contrast-enhanced and diffusion-weighted MRI. For...
5.
Simmons A, Han L, Reiner J, Mango V, Sevilimedu V, Pinker K, et al.
Radiol Imaging Cancer
. 2024 Nov;
6(6):e240010.
PMID: 39513941
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...
6.
Hirsch L, Huang Y, Makse H, Martinez D, Hughes M, Eskreis-Winkler S, et al.
Acad Radiol
. 2024 Oct;
32(3):1218-1225.
PMID: 39482209
Rationale 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...
7.
Gullo R, Ochoa-Albiztegui R, Chakraborty J, Thakur S, Robson M, Jochelson M, et al.
Cancers (Basel)
. 2024 Oct;
16(20).
PMID: 39456574
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...
8.
Avendano D, Marino M, Bosques-Palomo B, Davila-Zablah Y, Zapata P, Avalos-Montes P, et al.
Insights Imaging
. 2024 Oct;
15(1):244.
PMID: 39387984
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...
9.
Honda M, Sigmund E, Le Bihan D, Pinker K, Clauser P, Karampinos D, et al.
Eur Radiol
. 2024 Oct;
PMID: 39379708
Objectives: This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. Methods: A literature...
10.
Bitencourt A, Bhowmik A, Marcal Filho E, Gullo R, Mazaheri Y, Kapetas P, et al.
BJR Open
. 2024 Aug;
6(1):tzae019.
PMID: 39165295
Metabolic imaging in clinical practice has long relied on PET with fluorodeoxyglucose (FDG), a radioactive tracer. However, this conventional method presents inherent limitations such as exposure to ionizing radiation and...