Molecular subtypes of invasive breast cancer: correlation between PET/computed tomography and MRI findings

2020 ◽  
Vol 41 (8) ◽  
pp. 810-816
Author(s):  
Meliha Akin ◽  
Sebnem Orguc ◽  
Feray Aras ◽  
Ali Riza Kandiloglu
Breast Cancer ◽  
2021 ◽  
Author(s):  
Ken Yamaguchi ◽  
Yukiko Hara ◽  
Isao Kitano ◽  
Takahiro Hamamoto ◽  
Kazumitsu Kiyomatsu ◽  
...  

2020 ◽  
Vol 182 (3) ◽  
pp. 581-589
Author(s):  
Maryam Althobiti ◽  
Abir A. Muftah ◽  
Mohammed A. Aleskandarany ◽  
Chitra Joseph ◽  
Michael S. Toss ◽  
...  

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 610-610
Author(s):  
A. C. Schmitz ◽  
M. A. van den Bosch ◽  
C. Loo ◽  
J. L. Peterse ◽  
M. Gertenbach ◽  
...  

610 Background: Magnetic Resonance Imaging (MRI) of the breast shows superior ability to visualize the extent of invasive breast cancer compared to conventional breast imaging. Nonetheless, MRI may under- or overestimate the extent of invasive disease, and the ability of MRI to depict components of disease around the primary invasive tumor is not well established. The purpose of this study was to precisely correlate MRI findings with histopathologic findings in breast cancer patients and to establish the incidence and quantity of surrounding MRI occult disease in breast cancer patients that are scheduled for breast-conserving therapy (BCT). Methods: Patients were prospectively included if they had biopsy-proven invasive breast cancer and the choice of treatment was BCT after pre-operative MRI. Pathology findings were spatially reconstructed and correlated with preoperative MRI. Tumors were stratified by absence or presence of an extensive intraductal component (EIC- or EIC+). The largest diameter of the MRI-visible lesion was compared with the largest diameter of the primary invasive tumor at pathology. Distances (mm) between the edge of the MRI-visible lesion and surrounding subclinical tumor foci (i.e., DCIS, invasive foci) were measured. At various distances from the edge of the MRI-visible tumor, the incidence of disease was determined. Results: 53 patients with 54 breast tumors were included. 42 tumors were EIC- and 12 were EIC+. The mean size (± SD) of the primary invasive tumor was 18.1 ± 7.5 mm on MRI and 19.5 ± 8.2 mm on pathology (Pearson's correlation coefficient: 0.75). The MRI-visible lesion was larger than or equal to the primary invasive tumor on pathology in 21 (39%) cases. Underestimation of the primary invasive tumor occurred up to 7 mm from the edge of the MRI-visible lesion. Beyond 10 mm, subclinical tumor foci were found in 48% off all tumors, in 36% and 92% of EIC- and EIC+ tumors (p < 0.001). Beyond 20 mm these rates were 19%, 7% and 67%, respectively (p < 0.001). Conclusions: Disease around MRI-visible lesions may be more extensive than assumed prior to treatment, especially in EIC+ tumors. This may have consequences for treatment margins in MRI-guided therapy of localized breast cancer. No significant financial relationships to disclose.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Eun Kyung Park ◽  
Kwang-sig Lee ◽  
Bo Kyoung Seo ◽  
Kyu Ran Cho ◽  
Ok Hee Woo ◽  
...  

AbstractRadiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of machine learning approaches to radiogenomics using low-dose perfusion computed tomography (CT) to predict prognostic biomarkers and molecular subtypes of invasive breast cancer. This prospective study enrolled a total of 723 cases involving 241 patients with invasive breast cancer. The 18 CT parameters of cancers were analyzed using 5 machine learning models to predict lymph node status, tumor grade, tumor size, hormone receptors, HER2, Ki67, and the molecular subtypes. The random forest model was the best model in terms of accuracy and the area under the receiver-operating characteristic curve (AUC). On average, the random forest model had 13% higher accuracy and 0.17 higher AUC than the logistic regression. The most important CT parameters in the random forest model for prediction were peak enhancement intensity (Hounsfield units), time to peak (seconds), blood volume permeability (mL/100 g), and perfusion of tumor (mL/min per 100 mL). Machine learning approaches to radiogenomics using low-dose perfusion breast CT is a useful noninvasive tool for predicting prognostic biomarkers and molecular subtypes of invasive breast cancer.


Author(s):  
Yasemin DURUM POLAT ◽  
Veli Süha ÖZTÜRK ◽  
Recep ÖZGÜR ◽  
İbrahim Halil ERDOĞDU ◽  
Filiz ABACIGİL ◽  
...  

2016 ◽  
Vol 38 (2) ◽  
pp. 122-127 ◽  
Author(s):  
M Zavyalova ◽  
S Vtorushin ◽  
N Telegina ◽  
N Krakhmal ◽  
O Savelieva ◽  
...  

The aim of the present study was to investigate the clinical and morphological features of nonspecific invasive breast cancer according to its molecular subtypes. Materials and Methods: 163 women with nonspecific invasive breast cancer (T1–4N0–3M0) were included in the present study. Luminal A type of breast cancer was detected in 101 women, luminal B type — in 23 women, overexpression of HER2/neu was identified in 14 women and triple-negative cancer — in 25 women. Results: The study revealed that various molecular subtypes of breast cancer differ in the morphological structure, the expression characteristics of the primary tumor and the rate of lymphogenous and hematogenous metastasis. Lymphogenous metastases were more frequently (in 71%) detected in HER2/neu overexpressing breast cancer than in luminal A (41%), luminal B (39%) and triple-negative tumors (40%). Hematogenous metastasis did not depend on the morphological structure of carcinoma infiltrative component, the state of tumor stroma as well as the proliferative activity in all the investigated groups. Conclusion: The revealed clinicopathological characteristics of different molecular subtypes of invasive breast cancer allow to predict the possible outcome of the disease and select personalized treatment strategy for patients more reasonably.


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