Correlation between dynamic contrast-enhanced MRI characteristics and apparent diffusion coefficient with Ki-67-positive expression in non-mass enhancement of breast cancer

As a remarkably specific characteristic of breast cancer observed on magnetic resonance imaging (MRI), the association between the NME type breast cancer and prognosis, including Ki-67, necessitates comprehensive exploration. To investigate the correlation between dynamic contrast-enhanced MRI (DCE-MRI) characteristics and apparent diffusion coefficient (ADC) values with Ki-67-positive expression in NME type breast cancer. A total of 63 NME type breast cancer patients were retrospectively reviewed. Malignancies were confirmed by surgical pathology. All patients underwent DCE and diffusion-weighted imaging (DWI) before surgery. DCE-MRI characteristics, including tumor distribution, internal enhancement pattern, axillary adenopathy, and time-intensity curve types were observed. ADC values and lesion sizes were also measured. The correlation between these features and Ki-67 expression were assessed using Chi-square test, Fisher's exact test, and Spearman rank analysis. The receiver operating characteristic curve and area under the curve (AUC) was used to evaluate the diagnostic performance of Ki-67-positive expression. Regional distribution, TIC type, and ipsilateral axillary lymph node enlargement were correlated with Ki-67-positive expression (χ2 = 0.397, 0.357, and 0.357, respectively; P < 0.01). ADC value and lesion size were positively correlated with Ki-67-positive expression (rs = 0.295, 0.392; P < 0.05). The optimal threshold values for lesion size and ADC value to assess Ki-67 expression were determined to be 5.05 (AUC = 0.759) cm and 0.403 × 10-3 s/mm2 (AUC = 0.695), respectively. The best diagnosis performance was the ADC combined with lesion size (AUC = 0.791). The ADC value, lesion size, regional distribution, and TIC type in NME type breast cancer were correlated with Ki-67-positive expression. These features will aid diagnosis and treatment of NME type breast cancer.

Molecular Subtypes of Breast Cancer: A Review for Breast Radiologists

Gene expression profiling has reshaped our understanding of breast cancer by identifying four molecular subtypes: (1) luminal A, (2) luminal B, (3) human epidermal growth factor receptor 2 (HER2)-enriched, and (4) basal-like, which have critical differences in incidence, response to treatment, disease progression, survival, and imaging features. Luminal tumors are most common (60%-70%), characterized by estrogen receptor (ER) expression. Luminal A tumors have the best prognosis of all subtypes, whereas patients with luminal B tumors have significantly shorter overall and disease-free survival. Distinguishing between these tumors is important because luminal B tumors require more aggressive treatment. Both commonly present as irregular masses without associated calcifications at mammography; however, luminal B tumors more commonly demonstrate axillary involvement at diagnosis. HER2-enriched tumors are characterized by overexpression of the HER2 oncogene and low-to-absent ER expression. HER2+ disease carries a poor prognosis, but the development of anti-HER2 therapies has greatly improved outcomes for women with HER2+ breast cancer. HER2+ tumors most commonly present as spiculated masses with pleomorphic calcifications or as calcifications alone. Basal-like cancers (15% of all invasive breast cancers) predominate among "triple negative" cancers, which lack ER, progesterone receptor (PR), and HER2 expression. Basal-like cancers are frequently high-grade, large at diagnosis, with high rates of recurrence. Although imaging commonly reveals irregular masses with ill-defined or spiculated margins, some circumscribed basal-like tumors can be mistaken for benign lesions. Incorporating biomarker data (histologic grade, ER/PR/HER2 status, and multigene assays) into classic anatomic tumor, node, metastasis (TNM) staging can better inform clinical management of this heterogeneous disease.

The spectrum of pathological diagnoses in non-sentinel axillary lymph node biopsy: A single institution's experience

Although axillary lymphadenopathy is a common clinical encounter, systemic evaluation of non-sentinel lymph node biopsy is sparse. We reviewed our institution's 15-year experience to delineate the spectrum of diagnoses in non-sentinel axillary lymph nodes. 1165 non-sentinel axillary lymph node biopsies were retrieved and the diagnosis and relevant clinical information was reviewed. This spectrum of diagnoses was further stratified by gender, age, and oncologic history. The spectrum of diagnoses included: breast carcinoma (27.6%), lymphoma (29.2%), melanoma (3.5%), other carcinoma (2.9%), sarcoma (0.4%), and benign changes (36.3%). The most common diagnoses in men were lymphoma (61.8%) and benign changes (23.6%); while in women they were benign change (41.2%), breast carcinoma (37.8%) and lymphoma (16.7%). Besides benign changes, lymphoma and breast carcinoma were most common in women younger and older than 30 years, respectively. In patients with a history of malignancy, the most common diagnoses were metastasis from the known tumor and benign change; while in patients with a negative oncologic history and female patients without a history of breast cancer, the diagnosis was generally either lymphoma or benign change. Anaplastic large cell lymphoma was rare but may be mistaken as metastatic carcinoma thus a high index of suspicion is warranted. Thus through retrospective review of a large cohort of non-sentinel axillary lymph node biopsies, we described the spectrum of pathological entities based on the gender, age, and clinical history, which could provide valuable information for further work-up of axillary lymph node biopsy.

Performance of preoperative breast MRI based on breast cancer molecular subtype

PURPOSE

To assess the performance of preoperative breast MRI biopsy recommendations based on breast cancer molecular subtype.

METHODS

All preoperative breast MRIs at a single academic medical center from May 2010 to March 2014 were identified. Reports were reviewed for biopsy recommendations. All pathology reports were reviewed to determine biopsy recommendation outcomes. Molecular subtypes were defined as Luminal A (ER/PR+ and HER2-), Luminal B (ER/PR+ and HER2+), HER2 (ER-, PR- and HER2+), and Basal (ER-, PR-, and HER2-). Logistic regression assessed the probability of true positive versus false positive biopsy and mastectomy versus lumpectomy.

RESULTS

There were 383 patients included with a molecular subtype distribution of 253 Luminal A, 44 Luminal B, 20 HER2, and 66 Basal. Two hundred and thirteen (56%) patients and 319 sites were recommended for biopsy. Molecular subtype did not influence the recommendation for biopsy (p = 0.69) or the number of biopsy site recommendations (p = 0.30). The positive predictive value for a biopsy recommendation was 42% overall and 46% for Luminal A, 43% for Luminal B, 36% for HER2, and 29% for Basal subtype cancers. The multivariate logistic regression model showed no difference in true positive biopsy rate based on molecular subtype (p = 0.78). Fifty-one percent of patients underwent mastectomy and the multivariate model demonstrated that only a true positive biopsy (odds ratio: 5.3) was associated with higher mastectomy rates.

CONCLUSION

Breast cancer molecular subtype did not influence biopsy recommendations, positive predictive values, or surgical approaches. Only true positive biopsies increased the mastectomy rate.

Practical Considerations for the Use of Breast MRI for Breast Cancer Evaluation in the Preoperative Setting

Preoperative contrast-enhanced (CE) breast magnetic resonance imaging (MRI) remains controversial in the newly diagnosed breast cancer patient. Additional lesions are frequently discovered in these patients with CE breast MRI. As staging and treatment planning evolve to include more information on tumor biology and aggression, so should our consideration of extent of disease. Directing CE breast MRI to those patients most likely to have additional disease may be beneficial. We sought to develop practical guidance for the use of preoperative CE breast MRI in the newly diagnosed breast cancer patient based on recent scientific data. Our review suggests several populations for whom preoperative breast MRI is most likely to find additional disease beyond that seen on conventional imaging. These can be viewed in three categories: (1) tumor biology-patients with invasive lobular carcinoma or aggressive tumors such as triple negative breast cancer (estrogen receptor negative, progesterone receptor negative, and human epidermal growth factor receptor 2 (HER2) negative) and HER2 positive tumors; (2) patient characteristics-dense breast tissue or younger age, especially those age <60; and (3) clinical scenarios-patients with more sonographic disease than expected or those who are node positive at initial diagnosis. Focusing breast MRI on patients with any of the aforementioned characteristics may help utilize preoperative breast MRI where it is likely to have the most impact.