Breast Cancer Imaging Pre- and Post-Neoadjuvant Systemic Therapy

2021 ◽  
pp. 185-206
Author(s):  
Stephanie Chung ◽  
Sughra Raza
Keyword(s):  
Breast Cancer ◽  
Systemic Therapy ◽  
Cancer Imaging ◽  
Breast Cancer Imaging ◽  
Neoadjuvant Systemic Therapy

Development of a Hampton University Program for Novel Breast Cancer Imaging and Therapy Research

2013 ◽  
Author(s):  
Nicholas J. Kenney ◽  
Drew Weisenberger ◽  
Richard Brittan ◽  
Benjamin Welch
Keyword(s):  
Breast Cancer ◽  
Cancer Imaging ◽  
Breast Cancer Imaging ◽  
Hampton University

scholarly journals MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2447
Author(s):  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey P. Primakov ◽  
Sanaz Samiei ◽  
Thiemo J. A. van Nijnatten ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Systemic Therapy ◽  
Tumor Response ◽  
Added Value ◽  
Breast Cancer Patients ◽  
Neoadjuvant Systemic Therapy ◽  
Clinical Models ◽  
Combined Models ◽  
Complete Tumor

This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.

68Ga-FAPI-04 PET/CT, a new step in breast cancer imaging: a comparative pilot study with the 18F-FDG PET/CT

2021 ◽  
Author(s):  
Halil Kömek ◽  
Canan Can ◽  
Yunus Güzel ◽  
Zeynep Oruç ◽  
Cihan Gündoğan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Pilot Study ◽  
Fdg Pet ◽  
Cancer Imaging ◽  
Breast Cancer Imaging ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Multi-mode biodegradable tumour-microenvironment sensitive nanoparticles for targeted breast cancer imaging

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Zhenhui Nie ◽  
Ningbin Luo ◽  
Junjie Liu ◽  
Xinyi Zeng ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Imaging ◽  
Tumour Microenvironment ◽  
Breast Cancer Imaging ◽  
Multi Mode

Biodistribution and dosimetry of iodine-123-labelled Z -MIVE: an oestrogen receptor radioligand for breast cancer imaging

1997 ◽  
Vol 25 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Leonie J. M. Rijks ◽  
Ellinor Busemann Sokole ◽  
Michael G. Stabin ◽  
Kora de Bruin ◽  
Anton G. M. Janssen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Oestrogen Receptor ◽  
Cancer Imaging ◽  
Breast Cancer Imaging ◽  
Iodine 123
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