Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry.

BACKGROUND: Tumour heterogeneity is considered an important mechanism of treatment failure. Imaging-based assessment of tumour heterogeneity is showing promise but the relationship between these mathematically derived measures and accepted 'gold standards' of tumour biology such as immunohistochemical measures is not established. METHODS: A total of 20 women with primary breast cancer underwent a research dynamic contrast-enhanced computed tomography prior to treatment with data being available for 15 of these. Texture analysis was performed of the primary tumours to extract 13 locoregional and global parameters. Immunohistochemical analysis associations were assessed by the Spearman rank correlation. RESULTS: Hypoxia-inducible factor-1α was correlated with first-order kurtosis (r = -0.533, P = .041) and higher order neighbourhood grey-tone difference matrix coarseness (r = 0.54, P = .038). Vascular maturity-related smooth muscle actin was correlated with higher order grey-level run-length long-run emphasis (r = -0.52, P = .047), fractal dimension (r = 0.613, P = .015), and lacunarity (r = -0.634, P = .011). Micro-vessel density, reflecting angiogenesis, was also associated with lacunarity (r = 0.547, P = .035). CONCLUSIONS: The associations suggest a biological basis for these image-based heterogeneity features and support the use of imaging, already part of standard care, for assessing intratumoural heterogeneity.

Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

OBJECTIVE: To evaluate the performance of six models of population arterial input function (AIF) in the setting of primary breast cancer and neoadjuvant chemotherapy (NAC). The ability to fit patient dynamic contrast-enhanced MRI (DCE-MRI) data, provide physiological plausible data and detect pathological response was assessed. METHODS: Quantitative DCE-MRI parameters were calculated for 27 patients at baseline and after 2 cycles of NAC for 6 AIFs. Pathological complete response detection was compared with change in these parameters from a reproduction cohort of 12 patients using the Bland-Altman approach and receiver-operating characteristic analysis. RESULTS: There were fewer fit failures pre-NAC for all models, with the modified Fritz-Hansen having the fewest pre-NAC (3.6%) and post-NAC (18.8%), contrasting with the femoral artery AIF (19.4% and 43.3%, respectively). Median transfer constant values were greatest for the Weinmann function and also showed greatest reductions with treatment (-68%). Reproducibility (r) was the lowest for the Weinmann function (r = -49.7%), with other AIFs ranging from r = -27.8 to -39.2%. CONCLUSION: Using the best performing AIF is essential to maximize the utility of quantitative DCE-MRI parameters in predicting response to NAC treatment. Applying our criteria, the modified Fritz-Hansen and cosine bolus approximated Parker AIF models performed best. The Fritz-Hansen and biexponential approximated Parker AIFs performed less well, and the Weinmann and femoral artery AIFs are not recommended. ADVANCES IN KNOWLEDGE: We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.

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.

Predicting response to neoadjuvant chemotherapy in primary breast cancer using volumetric helical perfusion computed tomography: a preliminary study.

OBJECTIVES: To investigate whether CT-derived vascular parameters in primary breast cancer predict complete pathological response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: Twenty prospective patients with primary breast cancer due for NAC underwent volumetric helical perfusion CT to derive whole tumour regional blood flow (BF), blood volume (BV) and flow extraction product (FE) by deconvolution analysis. A pCR was achieved if no residual invasive cancer was detectable on pathological examination. Relationships between baseline BF, BV, FE, tumour size and volume, and pCR were examined using the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was performed to assess the parameter best able to predict response. Intra- and inter-observer variability was assessed using Bland-Altman statistics. RESULTS: Seventeen out of 20 patients completed NAC with four achieving a pCR. Baseline BF and FE were higher in patients who achieved a pCR compared with those who did not (P = 0.032); tumour size and volume were not significantly different (P > 0.05). ROC analysis revealed that BF and FE were able to identify responders effectively (AUC = 0.87; P = 0.03). There was good intra- and inter-observer agreement. CONCLUSIONS: Primary breast cancers which exhibited higher levels of perfusion before treatment were more likely to achieve a pCR to NAC.

Tumor response assessments with diffusion and perfusion MRI.

There is an increasing awareness that the evaluation of tumor response to oncologic treatments based solely on anatomic imaging assessments face many limitations, particularly in this era of novel biologic targeted therapies. Functional imaging techniques such as diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have the ability to depict important tumor biologic features and are able to predict therapy response based on assessments of cellularity and tumor vascularity, which often precede morphologic alterations. In this article we focus on DW-MRI and DCE-MRI as response parameters addressing the technologies involved, quantification methods, and validation for each technique and their current role in imaging response to conventional and novel therapies. We also discuss the challenges that lie ahead in the deployment of these imaging methods into the clinical environment.

Assessing early therapeutic response to bevacizumab in primary breast cancer using magnetic resonance imaging and gene expression profiles.

Antiangiogenic therapy is a promising approach for the treatment of breast cancer. In practice, however, only a subset of patients who receive antiangiogenic drugs demonstrate a significant response. A key challenge, therefore, is to discover biomarkers that are predictive of response to antiangiogenic therapy. To address this issue, we have designed a window-of-opportunity study in which bevacizumab is administered as a short-term first-line treatment to primary breast cancer patients. Central to our approach is the use of a detailed pharmacodynamic assessment, consisting of pre- and post-bevacizumab multi-parametric magnetic resonance imaging scans and core biopsies for exon array gene expression analysis. Here, we illustrate three intrinsic patterns of response to bevacizumab and discuss the molecular mechanisms that may underpin each. Our results illustrate how the combination of dynamic imaging data and gene expression profiles can guide the development of biomarkers for predicting response to antiangiogenic therapy.

Dynamic contrast-enhanced magnetic resonance imaging and blood oxygenation level-dependent magnetic resonance imaging for the assessment of changes in tumor biology with treatment.

Functional magnetic resonance imaging (MRI) techniques are rapidly gaining importance as methods of exploring the pathophysiological properties of breast carcinomas. In the neoadjuvant setting where the primary tumor remains in situ, functional MRI is able to noninvasively evaluate microenvironmental features such as blood flow and oxygenation. Dynamic contrast-enhanced MRI provides information on tumor vascularity with evidence suggesting a role in predicting response to neoadjuvant chemotherapy. The spatial heterogeneity of response to anti-angiogenic and vascular disrupting agents can also be depicted. There is preliminary data supporting blood oxygenation level-dependent MRI as a potential marker of tumor oxygenation, with the ability to characterize tissue oxygenation changes with neoadjuvant chemotherapy. Additionally, advanced MR sequences such as diffusion-weighted MRI and MR spectroscopy have the potential to provide information relating to cellularity and metabolism, respectively.

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.