Assessing response in breast cancer with dynamic contrast-enhanced magnetic resonance imaging: are signal intensity-time curves adequate?

Quantitative DCE-MRI parameters including K(trans) (transfer constant min(-1)) can predict both response and outcome in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Quantitative methods are time-consuming to calculate, requiring expensive software and interpretive expertise. For diagnostic purposes, signal intensity-time curves (SITCs) are used for tissue characterisation. In this study, we compare the ability of NAC-related changes in SITCs with K(trans) to predict response and outcomes. 73 women with primary breast cancer underwent DCE-MRI studies before and after two cycles of NAC. Patients received anthracycline and/or docetaxel-based chemotherapy. At completion of NAC, patients had local treatment with surgery & radiotherapy and further systemic treatments. SITCs for paired DCE-MRI studies were visually scored using a five-curve type classification schema encompassing wash-in and wash-out phases and correlated with K(trans) values and to the endpoints of pathological response, OS and DFS. 58 paired patients studies were evaluable. The median size by MRI measurement for 52 tumours was 38 mm (range 17-86 mm) at baseline and 26 mm (range 10-85 mm) after two cycles of NAC. Median baseline K(trans) (min(-1)) was 0.214 (range 0.085-0.469), and post-two cycles of NAC was 0.128 (range 0.013-0.603). SITC shapes were significantly related to K(trans) values both before (χ (2) = 43.3, P = 0.000) and after two cycles of NAC (χ (2) = 60.5, P = 0.000). Changes in curve shapes were significantly related to changes in K(trans) (χ (2) = 53.5, P = 0.000). Changes in curve shape were significantly correlated with clinical (P = 0.005) and pathological response (P = 0.005). Reductions in curve shape of ≥1 point were significant for overall improved survival using Kaplan-Meier analysis with a 5-year OS of 80.9 versus 68.6 % (P = 0.048). SITCs require no special software to generate and provide a useful method of assessing the effectiveness of NAC for primary breast cancer.

Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy.

PURPOSE: To investigate whether early changes in vascular parameters determined with dynamic contrast material-enhanced magnetic resonance (MR) imaging after two cycles of neoadjuvant chemotherapy (NAC) are predictive of disease-free and overall survival in primary breast cancer. MATERIALS AND METHODS: Institutional ethics approval and informed consent were obtained. Patients with primary breast cancer (median age, 45 years; age range, 22-70 years) recruited from January 2001 to September 2008 underwent dynamic contrast-enhanced MR imaging before and after two cycles of NAC. Quantitative and semiquantitative kinetic parameters were calculated, including the volume transfer constant (K(trans)) and the initial area under the gadolinium concentration-time curve over 60 seconds (IAUGC(60)). Cut points optimized to the receiver operating characteristic curve were used to dichotomize MR imaging data for Kaplan-Meier survival analysis. MR imaging parameters and known prognostic indicators in primary breast cancer were correlated with disease-free and overall survival by using the Cox proportional hazards model for univariate and multivariate analyses. RESULTS: MR imaging was performed before (n = 62) and after (n = 58) two cycles of NAC. The median follow-up time was 43.9 months for disease-free survival and 60.3 months for overall survival. There were 28 recurrences; 26 patients had distant metastases (two had additional local recurrence) and two had local recurrence only. There were 20 deaths, all of which were related to breast cancer. At univariate analysis, progesterone receptor status, the type of surgery performed, higher posttreatment K(trans) (P = .048), and larger posttreatment IAUGC(60) (P = .035) were significant predictors of worse disease-free survival. At multivariate analysis, progesterone receptor status (P = .002) and mean transit time (P = .025) were significant predictors of disease-free survival. Univariate analysis showed that clinical tumor stage (P = .005), progesterone receptor status (P = .025), and type of surgery performed (P = .017) were significant predictors of overall survival. Higher posttreatment K(trans) (P = .043), larger IAUGC(60) (P = .029), and larger tumor size at posttreatment MR imaging were predictive of worse overall survival (P = .018). Of these variables, K(trans) remained an independent indicator of overall survival (P = .038). CONCLUSION: Higher posttreatment tumor vascularization as depicted with dynamic contrast-enhanced MR imaging may be associated with higher recurrence and lower survival rates. Dynamic contrast-enhanced MR imaging parameters, in conjunction with traditional prognostic factors, have the potential to be prognostic biomarkers for disease-free and overall survival in primary breast cancer.

Vascular characterisation of triple negative breast carcinomas using dynamic MRI.

OBJECTIVES: Triple-negative (ER-/PR-/HER2-) breast carcinomas (TNBC) are aggressive tumours with underexplored imaging features. This study investigates whether their vascular characteristics as assessed by dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast-enhanced (DSC) MRI are distinct from the prognostically more favourable ER+/PR+/HER2- cancers. METHODS: Patients with primary breast cancer underwent MRI before neoadjuvant chemotherapy and were identified as ER-/PR-/HER2- or ER+/PR+/HER2- from core biopsy specimens. MRI parameters reflecting tissue perfusion, permeability, and extracellular leakage space were measured. Values for inflow transfer constant (K(trans)), outflow rate constant (k(ep)), leakage space (v(e)), area under the gadolinium curve (IAUGC(60) ), relative blood volume (rBV) and flow (rBF), and Mean Transit Time (MTT) were compared across receptor status and with known prognostic variables. RESULTS: Thirty seven patients were assessable in total (16 ER-/PR-/HER2-, 21 ER+/PR+/HER2-). Lower v(e) (p = 0.001), shorter MTT (p = 0.007) and higher k(ep) values (p = 0.044) were observed in TNBC. v(e) was lower across all T stages, node-negative (p = 0.004) and low-grade TNBC (p = 0.037). v(e) was the best predictor of triple negativity (ROC AUC 0.80). CONCLUSIONS: TNBC possess characteristic features on imaging, with lower extracellular space (higher cell density) and higher contrast agent wash-out rate (higher vascular permeability) suggesting a distinctive phenotype detectable by MRI.

Primary human breast adenocarcinoma: imaging and histologic correlates of intrinsic susceptibility-weighted MR imaging before and during chemotherapy.

PURPOSE: To investigate the histopathologic and dynamic magnetic resonance (MR) imaging correlates of intrinsic susceptibility-weighted (ISW) MR imaging in patients with primary human breast adenocarcinoma and to assess the relationship between baseline transverse relaxation rate (R2*) and T2* relaxivity change (ΔR2*) and the response to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Institutional ethics approval and informed consent were obtained. Between September 2001 and January 2008, 83 women (median age, 46 years; age range, 26-72 years) with breast cancer were recruited to undergo dynamic contrast medium-enhanced (DCE), dynamic susceptibility contrast-enhanced (DSC), and ISW MR imaging before and after two cycles of NAC. After excluding necrotic, infiltrating, and invasive lobular carcinomas, 31 patients were available for baseline assessment and 27 were available for response assessment. Transfer constant, leakage space, rate constant, initial area under the gadolinium concentration-time curve at 60 seconds, relative blood volume (rBV), relative blood flow (rBF), and R2* were calculated. Relationships between baseline R2* and histopathologic variables (tumor grade, estrogen receptor status, progesterone receptor status, human epidermal growth factor 2 status), tumor size, and dynamic MR imaging parameters were sought. Baseline adenocarcinoma R2* (n = 31) and ΔR2* (n = 27) were correlated with final pathologic response. RESULTS: Inverse correlations between baseline R2* and rBV (ρ = -0.48, P = .013) and rBF (ρ = -0.44, P = .024) were found, but not after NAC. No relationships were observed between baseline R2* and other kinetic imaging parameters, histopathologic characteristics, or tumor size (P > .05). Baseline R2* values were lower in tumors than in normal breast tissue (31.8 sec(-1) vs 36.2 sec(-1), P = .017) but not after NAC. Increases in R2* were observed after treatment (31.1 sec(-1) vs 34.8 sec(-1), P = .006), with larger increases correlating with pathologic response. ΔR2* was not as effective as DCE or DSC MR imaging parameters in the prediction of response. CONCLUSION: R2* is influenced by blood volume in untreated breast adenocarcinomas. Increases in R2* after two cycles of NAC correlate with pathologic response. Therapy-induced uncoupling of the relationship between R2* and rBV and rBF is consistent with responding tumors becoming hypoxic early during treatment. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100421/-/DC1.

Measuring proliferation in breast cancer: practicalities and applications.

Various methods are available for the measurement of proliferation rates in tumours, including mitotic counts, estimation of the fraction of cells in S-phase of the cell cycle and immunohistochemistry of proliferation-associated antigens. The evidence, advantages and disadvantages for each of these methods along with other novel approaches is reviewed in relation to breast cancer. The potential clinical applications of proliferative indices are discussed, including their use as prognostic indicators and predictors of response to systemic therapy.

Assessment of clinical response after two cycles of primary chemotherapy in breast cancer.

BACKGROUND: Complete clinical (cCR) and pathological (pCR) response to neoadjuvant chemotherapy in breast cancer is associated with improved survival. Various imaging and immunological techniques have been tested as predictors of response early in the course of chemotherapy, but their predictive value has not been compared with that of a simple early clinical assessment. PATIENTS AND METHODS: Two hundred breast cancer patients (T2-4, N0-1) were treated with neoadjuvant chemotherapy. Clinical response after two cycles of treatment was compared with final clinical and pathological response. The likelihood of achieving cCR or pCR was compared by response after two cycles. RESULTS: Overall final clinical response rate was 79% (30.5% cCR and 11.9% pCR). After two cycles of chemotherapy, clinical response rate was 54.5%. For responders after two cycles, final cCR = 51.3% and pCR = 21.5%. For non-responders after two cycles, cCR = 5.5% and pCR = 1.2%. Response after two cycles predicts for pCR (P = 0.003; sensitivity 95.2%, specificity 52.9%). CONCLUSIONS: Clinical response after two cycles of chemotherapy predicts for pCR and is a valid early endpoint that could be incorporated into the design of future neoadjuvant trials.

Inter- and intraobserver variability in the evaluation of dynamic breast cancer MRI.

PURPOSE: To quantify variations within and between observers ascribable to manual region of interest (ROI) placement in patients with breast cancer undergoing dynamic MRI. MATERIALS AND METHODS: Expert and nonexpert observers independently outlined tumor ROIs on 30 dynamic T(1)-weighted (T(1)W) MRI scans on five occasions over two months. Lesion size (number of pixels) and kinetic parameter estimates, including the transfer constant (K(trans)), were calculated for each ROI placement. Inter- and intraobserver variability was assessed with respect to the interval between drawings, lesion morphology, and observer experience. RESULTS: For the nonexpert, the variability reduced with decreasing time intervals between ROI drawings (the coefficient of variance (wCV) values at two months, two weeks, one day, and same-day time intervals were respectively 11.6%, 10.7%, 4.8%, and 2.6% for lesion size, and 8.9%, 9.7%, 6.7%, and 3.2% for K(trans)). For the expert observer, the variability was smaller overall and more constant, but improved for same-day ROI placements (region size wCV: 7.5%, 6.2%, 7.1%, and 3.7%; K(trans) wCV: 5.4%, 5.3%, 5.6%, and 4.5%). CONCLUSION: Significant observer variability in manual ROI placement occurs in dynamic MRI of breast cancer. For serial patient studies, ROI placements should be outlined at the same sitting to minimize observer error.