Inflammatory breast cancer after neoadjuvant chemotherapy: can magnetic resonance imaging precisely diagnose the final pathological response?

Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Assessment of Inflammatory Breast Cancer Prior to and After Neoadjuvant Treatment

BACKGROUND

The aim of this study was to describe the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features of inflammatory breast cancer (IBC) and to assess the value of DCE-MRI for the prediction of pathological complete response (pCR).

METHODS

Image analysis was performed in 15 patients with IBC (cT4d) and 12 patients with non-IBC (cT2), and included the assessment of BIRADS characteristics, skin alterations, enhancement characteristics, and changes post chemotherapy. Sensitivity and specificity of DCE-MRI for the presence of residual disease were obtained. Pearson's correlation coefficients were calculated comparing the (preoperative) tumor size with the histological size.

RESULTS

Skin thickening/enhancement (80%) and non-mass-like enhancement (66.7%) occurred more often in IBC (16.7 vs. 8.3% in non-IBC). In 2 of 3 cases of IBC, pCR was correctly predicted (sensitivity 92%, specificity 67%), compared to 3 of 5 cases in non-IBC (sensitivity 86%, specificity 40%). Lower peak enhancement might be associated with a higher likelihood of pCR in IBC. No other parameters predicted eventual pCR. In IBC, no correlation between preoperative tumor size and histological size was found (r = 0.22, p = 0.50), whereas in non-IBC, size estimations were more accurate (r = 0.75, p = 0.03).

CONCLUSION

IBC is characterized on MRI by skin changes and non-mass-like enhancement. Radiological complete response seems indicative of pCR in IBC and non-IBC. Size estimation of residual disease in IBC appears to be inaccurate.

Reliability of MRI in measuring the response to neoadjuvant chemotherapy in breast cancer patients and its therapeutic implications

Aim: Neoadjuvant chemotherapy (NAC) has recently been applied in treatment of operable breast cancers to enable breast conservation. We aimed to evaluate the accuracy of MRI in delineating residual tumor and pathological complete response (pCR). Patients & methods: 69 cases treated with NAC were monitored using breast MRI, findings were recorded and compared with histopathology. Results: MRI showed radiological complete response in 19 (27.5%), which correlated with pCR in 12 (63%) cases. However, five (7.3%) patients who achieved pCR were missed. Overall, the sensitivity was 70.6%, specificity 86.5%, positive predictive value 63.2% and negative predictive value of 90.0%. Conclusion: MRI showed promising results for evaluating response to NAC and predicting pCR, results need validation in larger trial.

(18)F-FDG PET/CT predicts survival in patients with inflammatory breast cancer undergoing neoadjuvant chemotherapy

PURPOSE

The objective of this study was to evaluate the role of (18)F-FDG PET/CT in predicting overall survival in inflammatory breast cancer patients undergoing neoadjuvant chemotherapy.

METHODS

Included in this retrospective study were 53 patients with inflammatory breast cancer who had at least two PET/CT studies including a baseline study before the start of neoadjuvant chemotherapy. Univariate and multivariate analyses were performed to assess the effects on survival of the following factors: tumor maximum standardized uptake value (SUVmax) at baseline, preoperatively and at follow-up, decrease in tumor SUVmax, histological tumor type, grade, estrogen, progesterone, HER2/neu receptor status, and extent of disease at presentation including axillary nodal and distant metastases.

RESULTS

By univariate analysis, survival was significantly associated with decrease in tumor SUVmax and tumor receptor status. Patients with decrease in tumor SUVmax had better survival (P = 0.02). Patients with a triple-negative tumor (P = 0.0006), a Her2/neu-negative tumor (P = 0.038) or an ER-negative tumor (P = 0.039) had worse survival. Multivariate analysis confirmed decrease in tumor SUVmax and triple-negative receptor status as significant predictors of survival. Every 10% decrease in tumor SUVmax from baseline translated to a 15% lower probability of death, and complete resolution of tumor FDG uptake translated to 80% lower probability of death (P = 0.014). Patients with a triple-negative tumor had 4.11 times higher probability of death (P = 0.004).

CONCLUSION

Decrease in tumor SUVmax is an independent predictor of survival in patients with inflammatory breast cancer undergoing neoadjuvant chemotherapy. Further investigation with prospective studies is warranted to clarify its role in assessing response and altering therapy.

Meta-analysis of magnetic resonance imaging in detecting residual breast cancer after neoadjuvant therapy

BACKGROUND

It has been proposed that magnetic resonance imaging (MRI) be used to guide breast cancer surgery by differentiating residual tumor from pathologic complete response (pCR) after neoadjuvant chemotherapy. This meta-analysis examines MRI accuracy in detecting residual tumor, investigates variables potentially affecting MRI performance, and compares MRI with other tests.

METHODS

A systematic literature search was undertaken. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate (relative) diagnostic odds ratios ([R]DORs). Summary sensitivity (correct identification of residual tumor), specificity (correct identification of pCR), and areas under the SROC curves (AUCs) were derived. All statistical tests were two-sided.

RESULTS

Forty-four studies (2050 patients) were included. The overall AUC of MRI was 0.88. Accuracy was lower for "standard" pCR definitions (referent category) than "less clearly described" (RDOR = 2.41, 95% confidence interval [CI] = 1.11 to 5.23) or "near-pCR" definitions (RDOR = 2.60, 95% CI = 0.73 to 9.24; P = .03.) Corresponding AUCs were 0.83, 0.90, and 0.91. Specificity was higher when negative MRI was defined as contrast enhancement less than or equal to normal tissue (0.83, 95% CI = 0.64 to 0.93) vs no enhancement (0.54, 95% CI = 0.39 to 0.69; P = .02), with comparable sensitivity (0.83, 95% CI = 0.69 to 0.91; vs 0.87, 95% CI = 0.80 to 0.92; P = .45). MRI had higher accuracy than mammography (P = .02); there was only weak evidence that MRI had higher accuracy than clinical examination (P = .10). No difference in MRI and ultrasound accuracy was found (P = .15).

CONCLUSIONS

MRI accurately detects residual tumor after neoadjuvant chemotherapy. Accuracy was lower when pCR was more rigorously defined, and specificity was lower when test negativity thresholds were more stringent; these definitions require standardization. MRI is more accurate than mammography; however, studies comparing MRI and ultrasound are required.

Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer?

Clinical evidence regarding the value of MRI for therapy responses assessment in breast cancer is increasing. The objective of this study is to compare the diagnostic capability of diffusion-weighted MR imaging (DW-MRI) and contrast-enhanced MR imaging (CE-MRI) to evaluate and predict pathological response in breast cancer patients receiving neoadjuvant chemotherapy (NAC). We performed a meta-analysis of all available studies of the diagnostic performance of DW-MRI or CE-MRI to evaluate and predict pathological response to NAC in patients with breast cancer. We determined sensitivities and specificities across studies, calculated positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (DOR) and constructed summary receiver operating characteristic curves using hierarchical regression models. Methodological quality was assessed by QUADAS tool. Thirty-four studies met the inclusion criteria and involved 1,932 pathologically confirmed patients in total. Methodological quality was relatively high. DW-MRI sensitivity was 0.93 (95 % CI 0.82-0.97) and specificity was 0.82 (95 % CI 0.70-0.90). Overall LR+ was 5.09 (95 % CI 3.09-8.38), LR- was 0.09 (95 % CI 0.04-0.22), and DOR was 55.59 (95 % CI 21.80-141.80). CE-MRI sensitivity was 0.68 (95 % CI 0.57-0.77) and specificity was 0.91 (95 % CI 0.87-0.94). Overall LR+ was 7.48 (95 % CI 5.29-10.57), LR- was 0.36 (95 % CI 0.27-0.48), and DOR was 20.98 (95 % CI 13.24-33.24). Our study confirms that DW-MRI is a high sensitive and CE-MRI is a high specific modality in predicting pathological response to NAC in breast cancer patients. The combined use of DW-MRI and CE-MRI has the potential to improve the diagnostic performance in monitoring NAC. Further large prospective studies are warranted to assess the actual value of this combination in breast cancer preoperative treatment screening.

Accuracy of breast magnetic resonance imaging in predicting pathologic response in patients treated with neoadjuvant chemotherapy

BACKGROUND

Prior studies of the ability of magnetic resonance imaging (MRI) to predict pathologic response to neoadjuvant chemotherapy have shown conflicting results that vary depending on baseline molecular characteristics. This study examines the ability of MRI to predict pathologic complete response (pCR) and explores the influence of tumor molecular profiles on MRI sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

METHODS

Eighty-one patients with invasive breast cancer treated with neoadjuvantsystemic therapy between 2002 and 2009 who were imaged with breast MRI pre- and post-treatment were reviewed. Patient, tumor, and treatment characteristics were recorded. Comparisons of molecular subsets and their influence on MRI sensitivity, specificity, PPV, and NPV were made using χ(2)contingency tables.

RESULTS

The sensitivity, specificity, PPV, and NPV of MRI for predicting pCR for the total group were 92%, 50%, 74%, and 80%, respectively. Patients had the following molecular subtypes: 33/81 (41%) HR+Her2-, 23/81 (28%) HR+/-Her2 +, and 25/81(31%) triple receptor negative (TN). Molecular subtype did not demonstrate a significant correlation of radiographic and pathologic response, although MRI NPV was highest in the TN subset (100%) followed by those with HR+/-Her2+ disease (87.5%). Multivariate analysis did not show that tumor characteristics (estrogen receptor status, progesterone receptor status, HER2 status) or neoadjuvant treatment (doxorubicin, cyclophosphamide, paclitaxel versus other or trastuzumab) had any effect on MRI sensitivity or specificity.

CONCLUSIONS

In patients receiving neoadjuvant systemic therapy for invasive breast cancer, molecular subtype and systemic regimen administered did not significantly influence the sensitivity, specificity, PPV, or NPV of MRI in predicting pathologic response.

Comparison of mammography, sonography, MRI and clinical examination in patients with locally advanced or inflammatory breast cancer who underwent neoadjuvant chemotherapy

OBJECTIVES

The purpose of this study was to determine the relative accuracies of mammography, sonography, MRI and clinical examination in predicting residual tumour size and pathological response after neoadjuvant chemotherapy for locally advanced or inflammatory breast cancer. Each prediction method was compared with the gold standard of surgical pathology.

METHODS

43 patients (age range, 25-62 years; mean age, 42.7 years) with locally advanced or inflammatory breast cancer who had been treated by neoadjuvant chemotherapy were enrolled prospectively. We compared the predicted residual tumour size and the predicted response on imaging and clinical examination with residual tumour size and response on pathology. Statistical analysis was performed using weighted kappa statistics and intraclass correlation coefficients (ICC).

RESULTS

The ICC values between predicted tumour size and pathologically determined tumour size were 0.65 for clinical examination, 0.69 for mammography, 0.78 for sonography and 0.97 for MRI. Agreement between the response predictions at mid-treatment and the responses measured by pathology had kappa values of 0.28 for clinical examination, 0.32 for mammography, 0.46 for sonography and 0.68 for MRI. Agreement between the final response predictions and the responses measured by pathology had kappa values of 0.43 for clinical examination, 0.44 for mammography, 0.50 for sonography and 0.82 for MRI.

CONCLUSION

Predictions of response and residual tumour size made on MRI were better correlated with the assessments of response and residual tumour size made upon pathology than were predictions made on the basis of clinical examination, mammography or sonography. Thus, the evaluation of predicted response using MRI could provide a relatively sensitive early assessment of chemotherapy efficacy.

Inflammatory breast cancer: the disease, the biology, the treatment

Inflammatory breast cancer (IBC) is a rare and aggressive form of invasive breast cancer accounting for 2.5% of all breast cancer cases. It is characterized by rapid progression, local and distant metastases, younger age of onset, and lower overall survival compared with other breast cancers. Historically, IBC is a lethal disease with less than a 5% survival rate beyond 5 years when treated with surgery or radiation therapy. Because of its rarity, IBC is often misdiagnosed as mastitis or generalized dermatitis. This review examines IBC's unique clinical presentation, pathology, epidemiology, imaging, and biology and details current multidisciplinary management of the disease, which comprises systemic therapy, surgery, and radiation therapy.

Accuracy of MRI in prediction of pathologic complete remission in breast cancer after preoperative therapy: a meta-analysis

OBJECTIVE

Prediction of pathologic complete remission in breast cancer after preoperative therapy presents difficulties. We performed a meta-analysis to determine the ability of MRI to predict pathologic complete remission in patients with breast cancer after preoperative therapy.

MATERIALS AND METHODS

Medical subject heading terms ("MRI" and "Breast Neoplasm") and key words ("neoadjuvant" or "primary systemic" or "preoperative" or "presurgery") were used for a literature search in the MEDLINE database. A meta-analysis of pooled data from eligible studies was performed to estimate the accuracy of MRI in predicting pathologic complete remission after preoperative therapy in patients with breast cancer.

RESULTS

Twenty-five studies were included in this meta-analysis. Pooled weighted estimates of sensitivity and specificity were 0.63 (range, 0.56-0.70) and 0.91 (range, 90.89-0.92), respectively. Heterogeneity between studies was highly influenced by the pathologic complete remission rate, with a regression coefficient of -6.160 (p = 0.020). Subgroup analysis showed that the specificity of MRI in studies with a pathologic complete remission rate of > or = 20% was lower than that in studies with a pathologic complete remission rate of < 20% (p = 0.0003).

CONCLUSION

This meta-analysis indicates that MRI has high specificity and relatively lower sensitivity in predicting pathologic complete remission after preoperative therapy in patients with breast cancer. The pathologic complete remission rate may influence the performance of MRI accuracy in this setting, which deserves further investigation.