scholarly journals A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis

2019 ◽  
Vol 46 (9) ◽  
pp. 1878-1888
Author(s):  
Doris Leithner ◽  
Joao V. Horvat ◽  
Blanca Bernard-Davila ◽  
Thomas H. Helbich ◽  
R. Elena Ochoa-Albiztegui ◽  
...  
2015 ◽  
Author(s):  
He N. Xu ◽  
Julia Tchou ◽  
Min Feng ◽  
Huaqing Zhao ◽  
Nannan Sun ◽  
...  

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6531-6531 ◽  
Author(s):  
Kathryn Jean Ruddy ◽  
Lindsey R. Sangaralingham ◽  
Heather B. Neuman ◽  
Caprice Christian Greenberg ◽  
Rachel A. Freedman ◽  
...  

6531 Background: Annual mammography is recommended to screen residual breast tissue for new cancers and recurrent disease after treatment for early stage breast cancer. This study aimed to assess mammography rates over time in breast cancer survivors. Methods: We used administrative claims data from a large U.S. commercial insurance database, OptumLabs, to retrospectively identify privately- and Medicare Advantage-insured women with operable breast cancer who had residual breast tissue after definitive breast surgery between 2006 and 2015. We required coverage for at least 13 months following surgery. For each subsequent 13-month time period, we only included women without a loss of coverage, bilateral mastectomy, metastatic breast cancer diagnosis, or non-breast cancer diagnosis. We calculated the proportion of patients who had a mammogram during each 13-month period following breast surgery. We used multivariable logistic regression to test for factors associated with mammography in the first 13 months. Results: The cohort included 26,011 women followed for a median of 2.9 years (IQR 1.9-4.6) after surgery; 63.1% were less than 65 years of age, and 74.4% were white. In their first year of follow-up, 86% underwent mammography, but by year 7, this decreased to 73%. Fewer than 1% underwent MRI instead of mammography. In multivariable analysis, mammograms were less likely during the first year after surgery among women aged < 50 years (odds ratio [OR], 0.7; 95% confidence interval [CI], 0.6 to 0.8), African Americans (OR, 0.7; 95% CI, 0.7 to 0.8), patients who underwent mastectomy (OR, 0.7; 95% CI, 0.6 to 0.7), and patients residing in the Western part of the country (OR, 0.9; 95% CI, 0.7 to 0.9). Those with 1-2 comorbidities were more likely (OR, 1.1; 95% CI 1.1-1.2) than those with none to have a mammogram during that period. Mammography use did not differ significantly by year of diagnosis (2006-2015). Conclusions: Even in an insured cohort, a substantial proportion of breast cancer survivors do not undergo annual surveillance mammography. Mammography use falls as the time from the early stage breast cancer diagnosis increases. Understanding factors associated with lack of mammographic screening may help improve survivorship care.


2020 ◽  
Author(s):  
Elena Tsolaki ◽  
William Doran ◽  
Luca Magnani ◽  
Alessandro Olivo ◽  
Inge K. Herrmann ◽  
...  

The presence of calcification in tumours has been known for decades1. Indeed, calcified breast tissue is a fundamental criterion for early breast cancer diagnosis, indicative of malignancies2, and their appearance is used to distinguish between benign and malignant in breast biopsies3,4. However, an in-depth characterization of the nature and origin of tumour tissue calcification remains elusive5–8. Here, we report the presence of nano and micron-sized spherical particles made of highly crystalline whitlockite that are exclusively found in the arterial wall of malignant invasive tumours. By applying nanoanalytical methods to healthy, benign and malignant tumour breast tissue biopsies from patients, we show that poorly crystalline apatite can be found in all breast tissue samples, whereas spherical crystalline whitlockite particles are present only in invasive cancers, mainly in areas close to the lumen of the arterial wall. Moreover, we demonstrate that the concentration of these spherical crystalline particles increases with the grade of disease, and that their size can be related to tumour type. Therefore, our results not only provide new insight into calcification of tumour tissue, but also enable a precise, yet simple route of breast cancer diagnosis and staging.


2021 ◽  
Author(s):  
Valeria Romeo ◽  
Paola Clauser ◽  
Sazan Rasul ◽  
Panagiotis Kapetas ◽  
Peter Gibbs ◽  
...  

Abstract Purpose: to assess whether a radiomics and machine learning (ML) model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can differentiate benign and malignant breast lesions.Methods: 102 consecutive patients with 120 BI-RADS 0, 4 and 5 breast lesions (101 malignant, 19 benign) detected by ultrasound and/or mammography were prospectively enrolled and underwent hybrid 18F-FDG PET/MRI for diagnostic purposes. Quantitative parameters and radiomics features were extracted from dynamic contrast-enhanced (MTT, VD, PF), diffusion (ADCmean of breast lesions and contralateral breast parenchyma), PET (SUVmax, mean and minimum of breast lesions, SUVmean of uni- and contralateral breast parenchyma) and T2-w images. Different diagnostic models were developed using a fine gaussian support vector machine algorithm and exploring different combinations of quantitative parameters and radiomics features to obtain the highest accuracy in discriminating benign from malignant breast lesions using a 5-fold cross validation. The performance of the best radiomics and ML model was compared with that of expert readers review physician using the McNemar test.Results: Eight radiomics models were developed. The integrated model combining MTT and ADC with radiomics features extracted from PET and ADC images obtained the highest accuracy for breast cancer diagnosis (AUC 0.983) and was higher (AUC 0.868) yet not significant to expert readers review (p=0.508).Conclusion: A radiomics and ML model combining quantitative parameters and radiomics features extracted from synchronized multiparametric 18F-FDG PET/MRI images can accurately discriminate benign from malignant breast lesions.


2010 ◽  
Vol 44 (4) ◽  
pp. 300-303 ◽  
Author(s):  
Ji Sun Park ◽  
Ah Young Lee ◽  
Sang Gyun Bae ◽  
Seok Mo Lee

Oncotarget ◽  
2018 ◽  
Vol 9 (56) ◽  
pp. 30855-30868 ◽  
Author(s):  
Sarah Boughdad ◽  
Christophe Nioche ◽  
Fanny Orlhac ◽  
Laurine Jehl ◽  
Laurence Champion ◽  
...  

2019 ◽  
Vol 61 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Doris Leithner ◽  
Thomas H. Helbich ◽  
Blanca Bernard-Davila ◽  
Maria Adele Marino ◽  
Daly Avendano ◽  
...  

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