Role of MRI in the staging of breast cancer patients: does histological type and molecular subtype matter?

Bitencourt, Almir Galvão Vieira; Pereira, Nara P.; França, Luciana Karla Lira; Silva, Caroline B; Paludo, Jociana; Paiva, Hugo; Graziano, Luciana; Guatelli, Camila Souza; Souza, Juliana Alves de; Marques, Elvira Ferreira

doi:10.1259/bjr.20150458

Cited by 15 publications

(7 citation statements)

References 26 publications

(33 reference statements)

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“…Our sample of HER2 overexpressing breast carcinomas was composed of large tumours, with high rates of multifocality as well as suspicious axillary lymph nodes, which is consistent with the literature [ [17] , [18] , [19] ]. In our study, the pCR rate among patients determined to be HER2+ by IHC was 65%, compared to 22% among patients in the FISH group, similar to the results of other investigators [ 4 , 7 ].…”

Section: Discussionsupporting

confidence: 90%

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

et al. 2020

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Background To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). Methods This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). Findings The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). Interpretation The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. Funding NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

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Section: Discussionsupporting

confidence: 90%

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

et al. 2020

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“…PR‐positivity is usually associated with a luminal A molecular subtype. This is consistent with a study looking at MRI size and histological and molecular subtype, which found that correlation between primary tumor size on MRI and size as determined on surgical pathology was better for luminal A and triple‐negative molecular subtypes than for luminal B and HER 2 molecular subtypes 26 . This may explain the association for PR‐positivity and biopsy‐proven contralateral cancers in the current study.…”

Section: Discussionsupporting

confidence: 92%

“…This is consistent with a study looking at MRI size and histological and molecular subtype, which found that correlation between primary tumor size on MRI and size as determined on surgical pathology was better for luminal A and triple-negative molecular subtypes than for luminal B and HER 2 molecular subtypes. 26 This may explain the association for PR-positivity and biopsy-proven contralateral cancers in the current study. Although we did not find a similar association in the triplenegative patients, this may be due to the overall small percentage of triple-negative patients included in our patient population.…”

Section: Factors Associated With Detection Of Occult Cancermentioning

confidence: 58%

Factors associated with MRI detection of occult lesions in newly diagnosed breast cancers

Wecsler

Jeong

Raghavendra

et al. 2020

Journal of Surgical Oncology

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Background The use of preoperative magnetic resonance imaging (MRI) for newly diagnosed breast cancer remains controversial. We examined factors associated with detection of occult multicentric, multifocal, and contralateral malignant lesions only seen by MRI. Methods We performed a retrospective analysis of consecutive patients undergoing preoperative MRI for breast cancer. Clinicopathologic data were assessed regarding the findings of multifocality, multicentricity, and the presence of contralateral lesions. We analyzed the association of factors with these findings on MRI. Results Of 857 patients undergoing MRI, 770 patients met inclusion criteria. Mean age was 54.7 years. Biopsy‐proven detection rates by MRI for multifocal, multicentric, and contralateral cancers were 6.2% (48 of 770), 1.9% (15 of 770) and 3.1% (24 of 770), respectively. African American race and heterogeneously or extremely dense mammographic density were associated with multifocal cancers on MRI. Larger lesion size and mammographic density were associated with multicentric cancers. Invasive lobular carcinoma (ILC) and progesterone receptor (PR)‐positivity were associated with contralateral cancers. Conclusions African American race, heterogeneously or extremely dense mammographic density, ILC, and PR‐positivity were associated with additional biopsy‐proven cancers based on MRI. These factors should be considered when assessing the clinical utility of preoperative breast MRI.

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“…And the four major subtypes luminal A, luminal B, HER2-overexpressed and triple-negative breast cancer (TNBC) were defined. Magnetic resonance imaging (MRI), a noninvasive and high sensitive examination, has been increasingly used in the assessment of breast disease, including the differential diagnosis of benign and malignant lesions, preoperative evaluation, pretreatment planning and efficacy prediction 5 , 6 . The correlation between the MRI morphology as well as dynamic features and the molecular subtypes of breast cancer have been reported 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

“…But most previous studies were based on describing the findings using a lexicon by the radiologist subjectivly. Although most of lexicon is accepted generally, it turned to be variable with different observer 6 , 9 . Quantitative analysis of the MRI dynamic features in breast cancer had been reported 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

Computer-aided evaluation of the correlation between MRI morphology and immunohistochemical biomarkers or molecular subtypes in breast cancer

Jiang

Hong

Zhang

et al. 2017

Sci Rep

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Studies using tumor circularity (TC), a quantitative MRI morphologic index, to evaluate breast cancer are scarce. The purpose of this study is to evaluate the correlation between TC and immunohistochemical biomarkers or molecular subtypes in breast cancer. 146 patients with 150 breast cancers were selected. All tumors were confirmed by histopathology and examined by 3.0T MRI. TC was calculated by computer-aided software. The associations between TC and patient age, tumor size, histological grade, molecular subtypes, and immunohistochemical biomarkers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed. TC correlated inversely with tumor size (r = −0.224, P < 0.001), ER (r = −0.490, P < 0.001) and PR (r = −0.484, P < 0.001). However, TC correlated positively with Ki67 (r = 0.332, P < 0.001) and histological grade (r = 0.309, P < 0.001). In multiple linear regression analysis, tumor size, ER, PR and Ki67 were independent influential factors of TC. Compared with HER2-overexpressed (61.6%), luminal A (54.7%) and luminal B (52.3%) subtypes, triple-negative breast cancer (TNBC) showed the highest score of TC (70.8%, P < 0.001). Our study suggests that TC can be used as an imaging biomarker to predict the aggressiveness of newly diagnosed breast cancers. TNBC seems to present as an orbicular appearance when comparing with other subtypes.

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Role of MRI in the staging of breast cancer patients: does histological type and molecular subtype matter?

Abstract: The real benefit of MRI for treatment planning in patients with breast cancer may be different according to the histological type and molecular subtype.

Cited by 15 publications

References 26 publications

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer

Factors associated with MRI detection of occult lesions in newly diagnosed breast cancers

Computer-aided evaluation of the correlation between MRI morphology and immunohistochemical biomarkers or molecular subtypes in breast cancer

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