MRI measurements of breast tumor volume predict response to neoadjuvant chemotherapy and recurrence-free survival

Ductal Carcinoma in Situ: Quantitative Preoperative Breast MR Imaging Features Associated with Recurrence after Treatment

Purpose To investigate whether specific imaging features on breast magnetic resonance (MR) images are associated with ductal carcinoma in situ (DCIS) recurrence risk after definitive treatment. Materials and Methods Patients with DCIS who underwent preoperative dynamic contrast material-enhanced (DCE) MR imaging between 2004 and 2014 with ipsilateral recurrence more than 6 months after definitive surgical treatment were retrospectively identified. For each patient, a control subject with DCIS that did not recur was identified and matched on the basis of clinical, histopathologic, and treatment features known to affect recurrence risk. On DCE MR images, lesion characteristics (longest diameter, functional tumor volume [FTV], peak percentage enhancement [PE], peak signal enhancement ratio [SER], and washout fraction) and normal tissue features (background parenchymal enhancement [BPE] volume, mean BPE) were quantitatively measured. MR imaging features were compared between patients and control subjects by using the Wilcoxon signed-rank test, with adjustment for multiple comparisons. Results Of 415 subjects with DCIS who underwent preoperative MR imaging, 14 experienced recurrence and 11 had an identifiable matching control subject (final cohort, 11 patients and 11 control subjects). Median time to recurrence was 14 months, and median follow-up for control subjects was 102 months. When compared with matched control subjects, patients with DCIS recurrence exhibited significantly greater FTV (median, 9.3 cm³ vs 1.3 cm³, P = .01), lesion peak SER (median, 1.7 vs 1.2; P = .03), and mean BPE (median, 58.3% vs 41.1%; P = .02). Conclusion Quantitative lesion and normal breast tissue characteristics at preoperative MR imaging in women with newly diagnosed DCIS show promise for association with breast cancer recurrence after treatment. ^© RSNA, 2017.

Evaluation of the Tumor Response After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation Between Dynamic Contrast-enhanced Magnetic Resonance Imaging and Pathologic Tumor Cellularity

BACKGROUND

We evaluated the tumor response after neoadjuvant chemotherapy (NAC) in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging parameters assessed using a commercially available computer-aided system. We also analyzed their correlation with pathologic tumor cellularity.

MATERIALS AND METHODS

We retrospectively reviewed the data from 130 patients with breast cancer who had undergone NAC followed by surgery from January to October 2013. Maximum diameter, volume, peak enhancement, and persistent, plateau, and washout-enhancing components were measured using a computer-aided system on DCE MR images and correlated with the Miller-Payne grading system. Patients with a Miller-Payne grade of 5 were classified into the pathologic complete response (pCR) group. Patients with grades 1, 2, 3, and 4 were included in the non-pCR group. Diagnostic performance was evaluated using receiver operating characteristic curve analysis.

RESULTS

Twenty patients were included in the pCR group and 110 patients in the non-pCR group. Of the 6 parameters, the rate of tumor volume reduction (r = 0.729, P < .001) showed the strongest correlation with the Miller-Payne grading system, followed by the maximum diameter (r = 0.706, P < .001) and washout component (r = 0.606, P < .001). The area under the receiver operating characteristic curve (Az value) was the largest for the rate of volume reduction (Az = 0.895), followed by the maximum diameter (Az = 0.891).

CONCLUSION

The tumor volume changes in breast cancers before and after NAC, measured automatically using a commercially available computer-aided system and a clinical DCE MR imaging protocol might be the most accurate tool for evaluation of the pathologic response after NAC.

Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer

Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R² = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. ^© RSNA, 2017 Online supplemental material is available for this article.

Pre-operative assessment of residual disease in locally advanced breast cancer patients: A sequential study by quantitative diffusion weighted MRI as a function of therapy

PURPOSE

The potential of diffusion weighted imaging (DWI) in assessing pathologic response and surgical margins in locally advanced breast cancer patients (n=38) undergoing neoadjuvant chemotherapy was investigated.

METHODS

DWI was performed at pre-therapy (Tp0), after I (Tp1) and III (Tp3) NACT at 1.5T. Apparent diffusion coefficient (ADC) of whole tumor (ADC_WT), solid tumor (ADC_ST), intra-tumoral necrosis (ADC_Nec) was determined. Further, ADC of 6 consecutive shells (5mm thickness each) including tumor margin to outside tumor margins (OM1 to OM5) was calculated and the data analyzed to define surgical margins.

RESULTS

Of 38 patients, 6 were pathological complete responders (pCR), 19 partial responders (pPR) and 13 were non-responders (pNR). Significant increase was observed in ADC_ST and ADC_WT in pCR and pPR following therapy. Pre-therapy ADC was significantly lower in pCR compared to pPR and pNR indicating the heterogeneous nature of tumor which may affect drug perfusion and consequently the response. ADC of outside margins (OM1, OM2, and OM3) was significantly different among pCR, pPR and pNR at Tp3 which may serve as response predictive parameter. Further, at Tp3, ADC of outside margins (OM1, OM2, and OM3) was significantly lower compared to that seen at Tp0 in pCR, indicating the presence of residual disease in these shells.

CONCLUSION

Pre-surgery information may serve as a guide to define cancer free margins and the extent of residual disease which may be useful in planning breast conservation surgery.

Role of Imaging in the Era of Precision Medicine

Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics

PURPOSE

To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI.

METHODS

Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers' annotations. The segmented tumors were then compared between readers using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson's correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement.

RESULTS

The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96.

CONCLUSIONS

There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.

Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model

Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies.

Effect of MR Imaging Contrast Thresholds on Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes: A Subgroup Analysis of the ACRIN 6657/I-SPY 1 TRIAL

Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (ΔFTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery. Early percent enhancement threshold (PEt) and signal enhancement ratio threshold (SER_t) were varied. The predictive performance of resulting FTV predictors was evaluated using the area under the receiver operating characteristic curve. A total number of 116 patients were studied both as a full cohort and in the following groups defined by hormone receptor (HR) and HER2 receptor subtype: 45 HR+/HER2−, 39 HER2+, and 30 triple negatives. High AUCs were found at different ranges of PE_t and SER_t levels in different subtypes. Findings from this study suggest that the predictive performance to treatment response by MRI varies by contrast thresholds, and that pathologic complete response prediction may be improved through subtype-specific contrast enhancement thresholds. A validation study is underway with a larger patient population.

Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation.

Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer

PURPOSE

Diffusion-weighted imaging (DWI) and ¹⁸ F-fluorodeoxyglucose-positron emission tomography (¹⁸ F-FDG-PET) independently correlate with malignancy in breast cancer, but the relationship between their structural and metabolic metrics is not completely understood. This study spatially correlates diffusion, perfusion, and glucose avidity in breast cancer with simultaneous PET/MR imaging and compares correlations with clinical prognostics.

METHODS

In this Health Insurance Portability and Accountability Act-compliant prospective study, with written informed consent and approval of the institutional review board and using simultaneously acquired FDG-PET and DWI, tissue diffusion (D_t ), and perfusion fraction (f_p ) from intravoxel incoherent motion (IVIM) analysis were registered to FDG-PET within 14 locally advanced breast cancers. Lesions were analyzed using 2D histograms and correlation coefficients between D_t , f_p , and standardized uptake value (SUV). Correlations were compared with prognostics from biopsy, metastatic burden from whole-body PET, and treatment history.

RESULTS

SUV||D_t correlation coefficient significantly distinguished treated (0.11 ± 0.24) from nontreated (-0.33 ± 0.26) patients (P = 0.005). SUV||f_p correlations were on average negative for the whole cohort (-0.17 ± 0.13).

CONCLUSION

Simultaneously acquired and registered FDG-PET/DWI allowed quantifiable descriptions of breast cancer microenvironments that may provide a framework for monitoring and predicting response to treatment. Magn Reson Med 78:1147-1156, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer

Objective

To explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer.

Methods

Institutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis.

Results

There was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p < 0.02) before the mid-therapy point. The best predictor was lesion diameter change observed before mid-therapy (AUC = 0.93). Segmented volume was not able to differentiate between pCR and non-pCR at any time-point. The ADC values from 3D-ROI were not significantly different from 2D data (p = 0.06). The best AUC (0.79) for pCR prediction using DWI was median ADC measured before mid-therapy of NACT.

Conclusions

The results of this study should be considered in NACT monitoring planning, especially in MRI protocol designing and time point selection.

Key Points

• Mid-therapy diameter changes are the best predictors of pCR in neoadjuvant chemotherapy.

• Volumetric measures are not strictly superior in therapy monitoring to lesion diameter.

• Size measures perform as a better predictor than ADC values.

Neoadjuvant chemotherapy in breast cancers

With advances in science and technology, there are more innovations in the approach to management of patients with breast cancer. Neoadjuvant chemotherapy that is designed to be used prior to surgical removal of a tumor has received significant attention. Currently, neoadjuvant chemotherapy is offered to patients with locally advanced breast cancer and also those breast cancer patients who may benefit from size reduction before conservation therapy. There is now sufficient evidence that if neoadjuvant chemotherapy leads to complete pathologic response, the patient will enjoy a better outcome. Therefore, assessment of the degree of response to neoadjuvant chemotherapy has a major impact on patient selection and the follow-up management of each patient and defines patient outcome.

Three-Dimensional Assessment of Automated Breast Volume Scanner Compared with Handheld Ultrasound in Pre-Operative Breast Invasive Ductal Carcinomas: A Pilot Study of 51 Cases

The aim of the work described here was to compare the accuracy of conventional handheld ultrasound (HHUS) with that of an automated breast volume scanner (ABVS) in 3-D assessment of pre-operative invasive ductal carcinomas. HHUS and ABVS were used in 51 patients to obtain the largest tumor diameter, tumor volume and tumor surface area. The volumetric measurement was also obtained from ABVS data with medical software. With tumor size and volume on pathology as the gold standard, Bland-Altman analysis was used to compare variability. Correlation coefficients and receiver operating characteristic curves were established for all measurements for T2 classification. The correlation coefficients of all ABVS measurements were stronger than those of HHUS measurements, with the ABVS volumetric measurement significantly different with a higher accuracy of 88.24% (45/51) and predicting T-classification with higher area under the receiver operating characteristic curves (0.936). Therefore, 3-D measurements provide stronger correlations with pathology in tumor size measurement. However, more clinical trials are needed to confirm our findings.

Survival is associated with complete response on MRI after neoadjuvant chemotherapy in ER-positive HER2-negative breast cancer

Background

Pathological complete remission (pCR) of estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer is rarely achieved after neoadjuvant chemotherapy (NAC). In addition, the prognostic value of pCR for this breast cancer subtype is limited. We explored whether response evaluation by magnetic resonance imaging (MRI) is associated with recurrence-free survival after NAC in ER-positive/HER2-negative breast cancer.

Methods

MRI examinations were performed in 272 women with ER-positive/HER2-negative breast cancer before, during and after NAC. MRI interpretation included lesion morphology at baseline, changes in morphology and size, and contrast uptake kinetics. These MRI features, clinical characteristics and final pathology were correlated with recurrence-free survival.

Results

The median follow up time was 41 months. There were 35 women with events, including 19 breast-cancer-related deaths. On multivariable analysis, age younger than 50 years (hazard ratio (HR) = 2.55, 95 % confidence interval (CI) 1.3, 5.02, p = 0.007), radiological complete response after NAC (HR = 14.11, CI 1.81, 1818; p = 0.006) and smaller diameters of washout/plateau enhancement at MRI after NAC (HR = 1.02, CI 1.00, 1.04, p = 0.036) were independently associated with best recurrence-free survival. Pathological response was not significant; HR = 2.12, CI 0.86, 4.64, p = 0.096.

Conclusions

MRI after NAC in ER-positive/HER2-negative tumors may be predictive of recurrence-free survival. A radiological complete response at MRI after NAC is associated with an excellent prognosis.

Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment

Breast cancer is the second leading cause of cancer death among US women, and the chance of a woman developing breast cancer sometime during her lifetime is one in eight. Early detection and diagnosis to allow appropriate locoregional and systemic treatment are key to improve the odds of surviving its diagnosis. Emerging data also suggest that different breast cancer subtypes (phenotypes) may respond differently to available adjuvant therapies. There is a growing understanding that not all patients benefit equally from systemic therapies, and therapeutic approaches are being increasingly personalized based on predictive biomarkers of clinical benefit. Optimal use of established and novel radiological imaging methods, such as magnetic resonance imaging and positron emission tomography, which have different biophysical mechanisms can simultaneously identify key functional parameters. These methods provide unique multiparametric radiological signatures of breast cancer, that will improve the accuracy of early diagnosis, help select appropriate therapies for early stage disease, and allow early assessment of therapeutic benefit.

Background Parenchymal Enhancement on Preoperative Magnetic Resonance Imaging: Association With Recurrence-Free Survival in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy

To retrospectively investigate whether background parenchymal enhancement (BPE) of the contralateral breast on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is associated with therapeutic outcomes following neoadjuvant chemotherapy (NAC) in unilateral invasive breast cancer. The institutional review board approved this study, and informed consent was waived. Between 2009 and 2011, 93 women with unilateral invasive breast cancer (43 premenopausal women who performed pre-NAC MRI between days 7 and 20 of the menstrual cycle and 50 postmenopausal women) underwent NAC with pre- and post-NAC DCE-MRI before surgery. MRI features (BPE [minimal, mild, moderate, marked] of the contralateral breast, lesion size and number, lesion kinetics, and changes in lesion size) and clinicopathologic features were analyzed. Patients were grouped according to BPE category (high [moderate or marked] or low [minimal or mild]). Cox regression modeling was used to determine associations between MRI features and recurrence-free survival (RFS) after controlling for clinicopathologic variables. The mean follow-up period was 48.2 months. Twenty-three recurrences occurred (2 ipsilateral breasts, 6 regional, and 15 distant). On multivariate analysis, high BPE on pre-NAC MRI (hazard ratio [HR] = 3.851, P = 0.006) and triple-negative cancer (HR = 3.192, P = 0.002) were independent factors associated with worse RFS. A greater reduction of lesion size on post-NAC MRI (HR = 0.984, P = 0.021) was associated with better RFS. High BPE on pre-NAC MRI is significantly associated with worse RFS in an NAC setting. This study suggests that BPE on pre-NAC DCE-MRI may have potential as a predictor of long-term outcomes in breast cancer patients who undergo NAC.

Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes

The purpose of this study is to evaluate the predictive performance of magnetic resonance imaging (MRI) markers in breast cancer patients by subtype. Sixty-four patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy were enrolled in this study. Each patient received a dynamic contrast-enhanced (DCE-MRI) at baseline, after 1 cycle of chemotherapy and before surgery. Functional tumor volume (FTV), the imaging marker measured by DCE-MRI, was computed at various thresholds of percent enhancement (PE_t) and signal-enhancement ratio (SER_t). Final FTV before surgery and percent changes of FTVs at the early and final treatment time points were used to predict patients’ recurrence-free survival. The full cohort and each subtype defined by the status of hormone receptor and human epidermal growth factor receptor 2 (HR+/HER2-, HER2+, triple negative) were analyzed. Predictions were evaluated using the Cox proportional hazard model when PE_t changed from 30% to 200% in steps of 10% and SER_t changed from 0 to 2 in steps of 0.2. Predictions with high hazard ratios and low p-values were considered as strong. Different profiles of FTV as predictors for recurrence-free survival were observed in each breast cancer subtype and strong associations with survival were observed at different PE_t/SER_t combinations that resulted in different FTVs. Findings from this retrospective study suggest that the predictive performance of imaging markers based on FTV may be improved with enhancement thresholds being optimized separately for clinically-relevant subtypes defined by HR and HER2 receptor expression.

Quantification of Ultrasonic Scattering Properties of In Vivo Tumor Cell Death in Mouse Models of Breast Cancer

INTRODUCTION: Quantitative ultrasound parameters based on form factor models were investigated as potential biomarkers of cell death in breast tumor (MDA-231) xenografts treated with chemotherapy. METHODS: Ultrasound backscatter radiofrequency data were acquired from MDA-231 breast cancer tumor–bearing mice (n = 20) before and after the administration of chemotherapy drugs at two ultrasound frequencies: 7 MHz and 20 MHz. Radiofrequency spectral analysis involved estimating the backscatter coefficient from regions of interest in the center of the tumor, to which form factor models were fitted, resulting in estimates of average scatterer diameter and average acoustic concentration (AAC). RESULTS: The ∆AAC parameter extracted from the spherical Gaussian model was found to be the most effective cell death biomarker (at the lower frequency range, r² = 0.40). At both frequencies, AAC in the treated tumors increased significantly (P = .026 and .035 at low and high frequencies, respectively) 24 hours after treatment compared with control tumors. Furthermore, stepwise multiple linear regression analysis of the low-frequency data revealed that a multiparameter quantitative ultrasound model was strongly correlated to cell death determined histologically posttreatment (r² = 0.74). CONCLUSION: The Gaussian form factor model–based scattering parameters can potentially be used to track the extent of cell death at clinically relevant frequencies (7 MHz). The 20-MHz results agreed with previous findings in which parameters related to the backscatter intensity (i.e., AAC) increased with cell death. The findings suggested that, in addition to the backscatter coefficient parameter ∆AAC, biological features including tumor heterogeneity and initial tumor volume were important factors in the prediction of cell death response.

Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL

PURPOSE

To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response (PCR).

MATERIALS AND METHODS

This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrast-enhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (ΔFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics.

RESULTS

Female patients (n = 162) with FTV and RFS were included. At univariate analysis, FTV2, FTV4, and ΔFTV4 had significant association with RFS, as did HR/HER2 status and RCB class. PCR approached significance at univariate analysis and was not significant at multivariate analysis. At univariate analysis, FTV2 and RCB class had the strongest predictive performance (C statistic = 0.67; 95% confidence interval [CI]: 0.58, 0.76), greater than for FTV4 (0.64; 95% CI: 0.53, 0.74) and PCR (0.57; 95% CI: 0.39, 0.74). At multivariate analysis, a model with FTV2, ΔFTV2, RCB class, HR/HER2 status, age, and race had the highest C statistic (0.72; 95% CI: 0.60, 0.84).

CONCLUSION

Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.

Magnetic resonance examination to predict pathological complete response following neoadjuvant chemotherapy: when is it appropriate for HER2-positive and triple-negative breast cancers?

BACKGROUND

To clarify appropriate timing for magnetic resonance examination to predict pathological complete response to neoadjuvant chemotherapy for patients with human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancers in terms of tumor volume change.

METHODS

Between September 2009 and December 2014, 113 women with HER2-positive (n = 51) and triple-negative (n = 62) invasive breast cancers undergoing neoadjuvant chemotherapy were enrolled. Patients with HER2-positive tumors underwent neoadjuvant chemotherapy with an anthracycline-based regimen followed by docetaxel with trastuzumab. Patients with triple-negative tumors underwent neoadjuvant chemotherapy with anthracycline-based (first in most cases) and taxane-based regimens. Magnetic resonance imaging was performed before neoadjuvant chemotherapy, between the regimens (midpoint examination), and after neoadjuvant chemotherapy (final examination). Response ratio of tumor volume was calculated and receiver-operating characteristic analyses for them for both subtypes were performed at the midpoint and final examinations.

RESULTS

Twenty-eight women with HER2-positive tumors (54.9 %) and 29 women with triple-negative tumors (46.8 %) had pathological complete response. The response ratios were better in cases with pathological complete response than in those without (p = 0.0341, p < 0.0001). The area under the curve at the final examination was higher than that at the midpoint examination for HER2-positive tumors (p = 0.039); whereas for the triple-negative tumors, no significant difference between the two examinations was shown (p = 0.5218).

CONCLUSIONS

Magnetic resonance examination to predict pathological complete response would be feasible after completion of a regimen including trastuzumab for HER2-positive tumors and at the midpoint of neoadjuvant chemotherapy for triple-negative tumors.

Effective factors to raise diagnostic performance of breast MRI for diagnosing pathologic complete response in breast cancer patients after neoadjuvant chemotherapy

BACKGROUND

Although MRI is a highly effective tool in evaluating residual disease after neoadjuvant chemotherapy (NAC), there are many reports of discordance between the response of MRI and pathology. To increase MR accuracy, additional methods, which reflect post-NAC changes, should be considered in diagnosis.

PURPOSE

To evaluate effective methods that raise the diagnostic performance of MRI for predicting pathologic complete response (pCR) in breast cancer after neoadjuvant chemotherapy (NAC).

MATERIAL AND METHODS

For 98 invasive breast carcinoma patients, chemotherapeutic response to MRI was evaluated for the following parameters: tumor size, tumor distribution pattern, kinetic curve analysis, and background parenchymal enhancement pattern (BPE). BPE was categorized as "minimal", "mild", "moderate", or "marked", according to the ACR BI-RADS criteria.

RESULTS

After NAC, the mean size of tumors decreased by 40% in non-pCR and by 59% in pCR groups, respectively. The sensitivity, specificity, false positive rate and false negative rate of MRI were 96% (78/81), 53% (9/17), 47% (8/17), and 4% (3/81), respectively. At pre-NAC MRI, the most common kinetic curve was delayed washout pattern (68%, 67/98); however, at post-NAC MRI the persistent pattern (55%, 47/86). Grouped lesion was the most common tumor distribution pattern on pre-NAC MRI (28%, 27/98), while on post-NAC solitary mass (40%, 34/86). The most common BPE at pre- and post-NAC MRI was mild and minimal enhancement, respectively.

CONCLUSION

To improve the diagnostic accuracy of MRI, we should consider additional factors including: tumor distribution pattern, BPE, kinetic curve analysis, and tumor size.

Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study

Background

We aimed to investigate the efficacy of computer-aided detection (CAD) for MRI in the assessment of tumor extent, lymph node status, and multifocality in invasive breast cancers in comparison with other breast imaging modalities.

Methods

Two radiologists measured the maximum tumor size, as well as, analyzed lymph node status and multifocality in 86 patients with invasive breast cancers using mammography, ultrasound, CT, MRI with and without CAD, and 18-fludeoxyglucose positron emission tomography (FDG-PET). The assessed data were compared with pathology.

Results

For tumor extent, there were no significant differences between pathological size and measured size using mammography, ultrasound, CT, or MRI with and without CAD (P > 0.05). For evaluation of lymph node status, ultrasound had the best kappa coefficients (0.522) for agreement between imaging and pathology, and diagnostic performance with 92.1% specificity and 90.0% positive predictive value. For multifocality, MRI with CAD had the highest area under the receiver operating characteristic curve (AUC = 0.888).

Conclusions

CAD for MRI is feasible to assess tumor extent and multifocality in invasive breast cancer patients. However, CAD is not effective in evaluation of nodal status.

The evolution of breast imaging: past to present

The practice of breast imaging has transitioned through a wide variety of technologic advances from the early days of direct-exposure film mammography to xeromammography to screen-film mammography to the current era of full-field digital mammography and digital breast tomosynthesis. Along with these technologic advances, organized screening, federal regulations based on the Mammography Quality Standards Act, and the development of the American College of Radiology Breast Imaging Reporting and Data System have helped to shape the specialty of breast imaging. With the development of breast ultrasonography and breast magnetic resonance imaging, both complementary to mammography, additional algorithms for diagnostic workup and screening high-risk subgroups of women have emerged. A substantial part of breast imaging practice these days also involves breast interventional procedures-both percutaneous biopsy to obtain tissue diagnosis and localization procedures to guide surgical excision. This article reviews the evolution of breast imaging starting from a historical perspective and progressing to the present day.

Dedicated Breast CT: Feasibility for Monitoring Neoadjuvant Chemotherapy Treatment

Objectives:

In this prospective pilot study, the feasibility of non-contrast dedicated breast computed tomography (bCT) to determine primary tumor volume and monitor its changes during neoadjuvant chemotherapy (NAC) treatment was investigated.

Materials and Methods:

Eleven women who underwent NAC were imaged with a clinical prototype dedicated bCT system at three time points – pre-, mid-, and post-treatment. The study radiologist marked the boundary of the primary tumor from which the tumor volume was quantified. An automated algorithm was developed to quantify the primary tumor volume for comparison with radiologist's segmentation. The correlation between pre-treatment tumor volumes from bCT and MRI, and the correlation and concordance in tumor size between post-treatment bCT and pathology were determined.

Results:

Tumor volumes from automated and radiologist's segmentations were correlated (Pearson's r = 0.935, P < 0.001) and were not different over all time points [P = 0.808, repeated measures analysis of variance (ANOVA)]. Pre-treatment tumor volumes from MRI and bCT were correlated (r = 0.905, P < 0.001). Tumor size from post-treatment bCT was correlated with pathology (r = 0.987, P = 0.002) for invasive ductal carcinoma larger than 5 mm and the maximum difference in tumor size was 0.57 cm. The presence of biopsy clip (3 mm) limited the ability to accurately measure tumors smaller than 5 mm. All study participants were pathologically assessed to be responders, with three subjects experiencing complete pathologic response for invasive cancer and the reminder experiencing partial response. Compared to pre-treatment tumor volume, there was a statistically significant (P = 0.0003, paired t-test) reduction in tumor volume at mid-treatment observed with bCT, with an average tumor volume reduction of 47%.

Conclusions:

This pilot study suggests that dedicated non-contrast bCT has the potential to serve as an expedient imaging tool for monitoring tumor volume changes during NAC. Larger studies are needed in future.

Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast

OBJECT

Contrast-enhanced T1-weighted imaging is usually included in MRI procedures for automatic tumor segmentation. Use of an MR contrast agent may not be appropriate for some applications, however. We assessed the feasability of automatic tumor segmentation by multiparametric cluster analysis that uses intrinsic MRI contrast only.

MATERIALS AND METHODS

Multiparametric MRI consisting of quantitative T1, T2, and apparent diffusion coefficient (ADC) mapping was performed in mice bearing subcutaneous tumors (n = 21). k-means and fuzzy c-means clustering with all possible combinations of MRI parameters, i.e. feature vectors, and 2-7 clusters were performed on the multiparametric data. Clusters associated with tumor tissue were selected on the basis of the relative signal intensity of tumor tissue in T2-weighted images. The optimum segmentation method was determined by quantitative comparison of automatic segmentation with manual segmentation performed by three observers. In addition, the automatically segmented tumor volumes from seven separate tumor data sets were quantitatively compared with histology-derived tumor volumes.

RESULTS

The highest similarity index between manual and automatic segmentation (SI manual,automatic = 0.82 ± 0.06) was observed for k-means clustering with feature vector {T2, ADC} and four clusters. A strong linear correlation between automatically and manually segmented tumor volumes (R (2) = 0.99) was observed for this segmentation method. Automatically segmented tumor volumes also correlated strongly with histology-derived tumor volumes (R (2) = 0.96).

CONCLUSION

Automatic segmentation of mouse subcutaneous tumors can be achieved on the basis of endogenous MR contrast only.

Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators

OBJECTIVES

To determine associations between dynamic contrast-enhanced MR imaging (DCE-MRI) parameters and survival intervals in patients with locally advanced breast cancer treated with neoadjuvant chemotherapy (NAC), surgery, and adjuvant therapies. Further, to compare the prognostic value of DCE-MRI parameters against traditional survival indicators.

METHODS

DCE-MRI and MR tumour volume measures were obtained prior to treatment and post 2nd NAC cycle. To demonstrate which parameters were associated with survival, Cox's proportional hazards models (CPHM) were employed. To avoid over-parameterisation, only those MR parameters with at least a borderline significant result were entered into the final CPHM.

RESULTS

When considering disease-free survival positive axillary nodal status (hazard ratio [HR] 6.79), younger age (HR 3.37), negative oestrogen receptor status (HR 3.24), pre-treatment Maximum Enhancement Index (MaxEI) (HR 6.51), and percentage change in MaxEI (HR 1.02) represented the retained CPHM covariates. Similarly, positive axillary nodal status (HR 11.47), negative progesterone receptor status (HR 4.37) and percentage change in AUC90 (HR 1.01) represented the retained predictive variables for overall survival.

CONCLUSIONS

Multivariate survival analysis has demonstrated that DCE-MRI parameters obtained prior to NAC and/or post 2nd cycle can provide independent prognostic information that can complement traditional prognostic indicators available prior to treatment.

KEY POINTS

• MR-derived DCE-MRI parameters obtained prior to treatment have prognostic value. • Early treatment-induced reductions in DCE-MRI parameters represents a positive prognostic indicator. • DCE-MRI parameters provide independent prognostic information that can complement traditional prognostic indicators.

Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters

Tumor response to neoadjuvant chemotherapy in patients (n=30) with locally advanced breast cancer (LABC) was examined using quantitative ultrasound. Three ultrasound backscatter parameters, the integrated backscatter coefficient (IBC), average scatterer diameter (ASD), and average acoustic concentration (AAC), were estimated from tumors prior to treatment and at four times during neoadjuvant chemotherapy treatment (weeks 0, 1, 4, 8, and prior to surgery) and compared to ultimate clinical and pathological tumor responses. Results demonstrated that among all parameters, AAC was the best indicator of tumor response early after starting treatment. The AAC parameter increased substantially in treatment-responding patients as early as one week after treatment initiation, further increased at week 4, and attained a maximum at week 8. In contrast, the backscatter parameters from non-responders did not show any changes after treatment initiation. The two patient populations exhibited a statistically significant difference in changes of AAC (p<0.001) and ASD (p=0.023) over all treatment times examined. The best prediction of treatment response was achieved with the combination of AAC and ASD at week 4 (82% sensitivity, 100% specificity, and 86% accuracy) of 12-18 weeks of treatment. The survival of patients with responsive ultrasound parameters was higher than patients with non-responsive ultrasound parameters (35 ± 11 versus 27 ± 11 months, respectively, p=0.043). This study demonstrates that ultrasound parameters derived from the ultrasound backscattered power spectrum can potentially serve as non-invasive early measures of clinical tumor response to chemotherapy treatments.

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.

Prediction of pathological complete response of breast cancer patients undergoing neoadjuvant chemotherapy: usefulness of breast MRI computer-aided detection

OBJECTIVE

To evaluate the usefulness of MR computer-aided detection (CAD) in patients undergoing neoadjuvant chemotherapy for prediction of the pathological complete response of tumours.

METHODS

148 patients with breast cancer (mean age, 47.3 years; range, 29-72 years) who underwent neoadjuvant chemotherapy were included in our study. They underwent MRI before and after neoadjuvant chemotherapy, and we reviewed the pathological result as the gold standard. The computer-generated kinetic features for each lesion were recorded, and the features analysed included "threshold enhancement" at 50% and 100% minimum thresholds; degree of initial peak enhancement; and enhancement profiles comprising lesion percentages of washout, plateau and persistent enhancement. The final pathological size and character of tumours were correlated with post-chemotherapy mammography, ultrasonography and MR CAD findings. Kruskal-Wallis test and intraclass correlation coefficient were used to analyse the findings.

RESULTS

We divided the 148 patients into complete pathological response and non-complete pathological response groups. A complete pathological response was defined as no histopathological evidence of any residual invasive cancer cells in the breast or axillary lymph nodes. 39 patients showed complete pathological response, and 109 patients showed non-complete pathological response. Between enhancement profiles of MR CAD, plateau proportion of tumours was significantly correlated with the pathological response of tumours (mean proportion of plateau on complete pathological response group was 27%, p = 0.007).

CONCLUSION

When plateau proportion of tumours is high, we can predict non-complete pathological response of neoadjuvant chemotherapy.

ADVANCES IN KNOWLEDGE

MR CAD can be a useful tool for the assessment of response to neoadjuvant chemotherapy and prediction of pathological results.

Surgical patterns of care in patients with invasive breast cancer treated with neoadjuvant systemic therapy and breast magnetic resonance imaging: results of a secondary analysis of TBCRC 017

BACKGROUND

Neoadjuvant chemotherapy (NCT) downstages advanced primary tumors, with magnetic resonance imaging (MRI) being the most sensitive imaging predictor of response. However, the impact of MRI evaluation on surgical treatment decisions in the neoadjuvant setting has not been well described. We report surgical patterns of care across 8 National Cancer Institute comprehensive cancer centers in women receiving both NCT and MRI to evaluate the impact of MRI findings on surgical planning.

METHODS

Seven hundred seventy women from 8 institutions received NCT with MRI obtained both before and after systemic treatment. Univariate and multivariate analyses of imaging, patient-, and tumor-related covariates associated with choice of breast surgery were conducted.

RESULTS

MRI and surgical data were available on 759 of 770 patients. A total of 345 of 759 (45 %) patients received breast-conserving surgery and 414 of 759 (55 %) received mastectomy. Mastectomy occurred more commonly in patients with incomplete MRI response versus complete (58 vs. 43 %) (p = 0.0003). On multivariate analysis, positive estrogen receptor status (p = 0.02), incomplete MRI response (p = 0.0003), higher baseline T classification (p < 0.0001), younger age (p < 0.0006), and institution (p = 0.003) were independent predictors of mastectomy. A statistically significant trend toward increasing use of mastectomy with increasing T stage at presentation (p < 0.0001) was observed in patients with incomplete response by MRI only. Among women with complete response on MRI, 43 % underwent mastectomy.

CONCLUSIONS

Within a multi-institutional cohort of women undergoing neoadjuvant treatment for breast cancer, MRI findings were not clearly associated with extent of surgery. This study shows that receptor status, T stage at diagnosis, young age, and treating institution are more significant determinants of surgical treatment choice than MRI response data.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

PURPOSE

To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy.

METHODS

Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation.

RESULTS

DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups.

CONCLUSIONS

The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.

Utility of MRI and PET/CT after neoadjuvant chemotherapy in breast cancer patients: correlation with pathological response grading system based on tumor cellularity

BACKGROUND

MRI and PET/CT are useful for assessing breast cancer patients after neoadjuvant chemotherapy (NAC).

PURPOSE

To investigate the utility of MRI and PET/CT in the prediction of pathologic response to neoadjuvant chemotherapy using Miller-Payne grading system in patients with breast cancer.

MATERIAL AND METHODS

From January 2008 to December 2010, 59 consecutive patients with pathologically proven breast cancer, who underwent neoadjuvant chemotherapy followed by surgery were retrospectively enrolled. The maximal diameter decrease rate and volume reduction rate by three-dimensional (3D) MRI and standardized uptake value (SUV) reduction rate by PET/CT were calculated and correlated with the Miller-Payne grading system using the Spearman rank correlation test. Patients with Miller-Payne grades 1 or 2 were classified into the non-responder group and patients with grades 3, 4, and 5 were in the responder group. To differentiate between responders and non-responders, receiver-operating characteristic (ROC) analysis was performed.

RESULTS

The volume reduction rate was 64.87 ± 46.95, diameter decrease rate was 48.09 ± 35.02, and SUV decrease rate was 62.10 ± 32.17. Among three parameters, the volume reduction rate was most correlated with histopathologic grades of regression (ρ = 0.755, P < .0001) followed by diameter decrease rate (ρ = 0.660, P < 0.0001), and SUV decrease rate of primary breast mass (ρ = 0.561, P = 0.0002). The area under the ROC curve (Az) value was largest in the volume reduction rate (Az = 0.9), followed by SUV decrease rate (Az = 0.875), and diameter decrease rate (Az = 0.849). The best cut-offs for differentiating responders from non-responders in the ROC curve analysis were a 50% decrease in diameter, 68.9% decrease in volume, and 60.1% decrease in SUV after NAC.

CONCLUSION

Volumetric measurement using 3D MRI combined with conventional diameter measurement may be more accurate to evaluate pathologic response after NAC.

Monitoring of tumor response to Cisplatin using optical spectroscopy

INTRODUCTION

Anatomic imaging alone is often inadequate for tuning systemic treatment for individual tumor response. Optically based techniques could potentially contribute to fast and objective response monitoring in personalized cancer therapy. In the present study, we evaluated the feasibility of dual-modality diffuse reflectance spectroscopy-autofluorescence spectroscopy (DRS-AFS) to monitor the effects of systemic treatment in a mouse model for hereditary breast cancer.

METHODS

Brca1(-/-); p53(-/-) mammary tumors were grown in 36 mice, half of which were treated with a single dose of cisplatin. Changes in the tumor physiology and morphology were measured for a period of 1 week using dual-modality DRS-AFS. Liver and muscle tissues were also measured to distinguish tumor-specific alterations from systemic changes. Model-based analyses were used to derive different optical parameters like the scattering and absorption coefficients, as well as sources of intrinsic fluorescence. Histopathologic analysis was performed for cross-validation with trends in optically based parameters.

RESULTS

Treated tumors showed a significant decrease in Mie-scattering slope and Mie-to-total scattering fraction and an increase in both fat volume fraction and tissue oxygenation after 2 days of follow-up. Additionally, significant tumor-specific changes in the fluorescence spectra were seen. These longitudinal trends were consistent with changes observed in the histopathologic analysis, such as vital tumor content and formation of fibrosis.

CONCLUSIONS

This study demonstrates that dual-modality DRS-AFS provides quantitative functional information that corresponds well with the degree of pathologic response. DRS-AFS, in conjunction with other imaging modalities, could be used to optimize systemic cancer treatment on the basis of early individual tumor response.

Preoperative MRI improves prediction of extensive occult axillary lymph node metastases in breast cancer patients with a positive sentinel lymph node biopsy

RATIONALE AND OBJECTIVES

To test the ability of quantitative measures from preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have four or more positive axillary lymph nodes on completion axillary dissection.

MATERIALS AND METHODS

A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (June 2005-January 2010). Clinical/pathologic factors, primary lesion size, and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (P < .05) to predict four or more positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model.

RESULTS

Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of four or more metastatic nodes. Receiver operating characteristic curves demonstrated an area under the curve of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (P = .003).

CONCLUSIONS

Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict the presence of metastases in four or more nodes. DCE-MRI may help identify sentinel node-positive patients requiring further local-regional therapy.

Optimized breast MRI functional tumor volume as a biomarker of recurrence-free survival following neoadjuvant chemotherapy

PURPOSE

To evaluate optimal contrast kinetics thresholds for measuring functional tumor volume (FTV) by breast magnetic resonance imaging (MRI) for assessment of recurrence-free survival (RFS).

MATERIALS AND METHODS

In this Institutional Review Board (IRB)-approved retrospective study of 64 patients (ages 29-72, median age of 48.6) undergoing neoadjuvant chemotherapy (NACT) for breast cancer, all patients underwent pre-MRI1 and postchemotherapy MRI4 of the breast. Tumor was defined as voxels meeting thresholds for early percent enhancement (PEthresh) and early-to-late signal enhancement ratio (SERthresh); and FTV (PEthresh, SERthresh) by summing all voxels meeting threshold criteria and minimum connectivity requirements. Ranges of PEthresh from 50% to 220% and SERthresh from 0.0 to 2.0 were evaluated. A Cox proportional hazard model determined associations between change in FTV over treatment and RFS at different PE and SER thresholds.

RESULTS

The plot of hazard ratios for change in FTV from MRI1 to MRI4 showed a broad peak with the maximum hazard ratio and highest significance occurring at PE threshold of 70% and SER threshold of 1.0 (hazard ratio = 8.71, 95% confidence interval 2.86-25.5, P < 0.00015), indicating optimal model fit.

CONCLUSION

Enhancement thresholds affect the ability of MRI tumor volume to predict RFS. The value is robust over a wide range of thresholds, supporting the use of FTV as a biomarker.

Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images

PURPOSE

Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Computerized analysis is increasingly used to quantify breast MRI features in applications such as computer-aided lesion detection and fibroglandular tissue estimation for breast cancer risk assessment. Automated segmentation of the whole-breast as an organ from the other parts imaged is an important step in aiding lesion localization and fibroglandular tissue quantification. For this task, identifying the chest wall line (CWL) is most challenging due to image contrast variations, intensity discontinuity, and bias field.

METHODS

In this work, the authors develop and validate a fully automated image processing algorithm for accurate delineation of the CWL in sagittal breast MRI. The CWL detection is based on an integrated scheme of edge extraction and CWL candidate evaluation. The edge extraction consists of applying edge-enhancing filters and an edge linking algorithm. Increased accuracy is achieved by the synergistic use of multiple image inputs for edge extraction, where multiple CWL candidates are evaluated by the dynamic time warping algorithm coupled with the construction of a CWL reference. Their method is quantitatively validated by a dataset of 60 3D bilateral sagittal breast MRI scans (in total 3360 2D MR slices) that span the full American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density range. Agreement with manual segmentation obtained by an experienced breast imaging radiologist is assessed by both volumetric and boundary-based metrics, including four quantitative measures.

RESULTS

In terms of breast volume agreement with manual segmentation, the overlay percentage expressed by the Dice's similarity coefficient is 95.0% and the difference percentage is 10.1%. More specifically, for the segmentation accuracy of the CWL boundary, the CWL overlay percentage is 92.7% and averaged deviation distance is 2.3 mm. Their method requires ≈ 4.5 min for segmenting each 3D breast MRI scan (56 slices) in comparison to ≈ 35 min required for manual segmentation. Further analysis indicates that the segmentation performance of their method is relatively stable across the different BI-RADS density categories and breast volume, and also robust with respect to a varying range of the major parameters of the algorithm.

CONCLUSIONS

Their fully automated method achieves high segmentation accuracy in a time-efficient manner. It could support large scale quantitative breast MRI analysis and holds the potential to become integrated into the clinical workflow for breast cancer clinical applications in the future.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results

PURPOSE

To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologist's subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR).

RESULTS

All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR.

CONCLUSION

Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.

Analysis of the correlation between endorectal MRI response to neoadjuvant chemotherapy and biochemical recurrence in patients with high-risk localized prostate cancer

BACKGROUND

Intermediate end points are desirable to expedite the integration of neoadjuvant systemic therapy into the treatment strategy for high-risk localized prostate cancer. Endorectal magnetic resonance imaging at 1.5 Tesla (1.5T erMRI) response has been utilized as an end point in neoadjuvant trials but has not been correlated with clinical outcomes.

METHODS

Data were pooled from two trials exploring neoadjuvant chemotherapy in high-risk localized prostate cancer. Trial 1 explored docetaxel for 6 months and Trial 2 explored docetaxel plus bevacizumab for 4.5 months, both before radical prostatectomy. erMRI was done at baseline and end of chemotherapy. 1.5T erMRI response, based upon T2W sequences, was recorded. Multivariable Cox regression was undertaken to evaluate the association between clinical parameters and biochemical recurrence.

RESULTS

There were 53 evaluable patients in the combined analysis: 20 (33%) achieved a PSA response, 16 (27%) achieved an erMRI partial response and 24 (40%) achieved an erMRI minor response. Median follow-up was 4.2 years, and 33 of 53 evaluable (62%) patients developed biochemical recurrence. On multivariable analysis, PSA response did not correlate with biochemical recurrence (hazard ratio=0.58, 95% confidence interval (CI) 0.25-1.33) and paradoxically erMRI response was associated with a significantly shorter time to biochemical recurrence (hazard ratio=2.47, 95% CI 1.00-6.13).

CONCLUSIONS

Response by 1.5T erMRI does not correlate with a decreased likelihood of biochemical recurrence in patients with high-risk localized prostate cancer treated with neoadjuvant docetaxel and may be associated with inferior outcomes. These data do not support the use of 1.5T erMRI response as a primary end point in neoadjuvant chemotherapy trials.

Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer

The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.

Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer

PURPOSE

To evaluate the accuracy of the apparent diffusion coefficient (ADC) provided by diffusion-weighted imaging (DWI) in predicting the response to neoadjuvant chemotherapy (NACT) at baseline in patients according to their breast tumour phenotypes.

MATERIALS & METHODS

This retrospective study was approved by our institutional review board. One hundred eighteen consecutive women with locally advanced breast cancer who had undergone NACT followed by breast surgery were included. DWI was performed at 1.5 T less than 2 weeks before NACT. We studied the correlation between pretreatment ADC and response in pathology after surgery according to immunohistochemical features and intrinsic subtypes (luminal A, luminal B, HER2-enriched, and triple-negative tumours).

RESULTS

After surgery, the pathologist recognized 24 complete responders (CRps) and 94 non-complete responders (NCRps). No difference was identified between the pretreatment ADCs of the CRp and NCRp patients. There were differences in pretreatment ADCs among the luminal A (1.001 ± 0.143 × 10(-3) mm(2)/s), luminal B (0.983 ± 0.150 × 10(-3) mm(2)/s), HER2-enriched (1.132 ± 0.216 × 10(-3) mm(2)/s), and triple-negative (1.168 ± 0.245 × 10(-3) mm(2)/s; P = 0.0003) tumour subtypes. In triple-negative tumours, the pretreatment ADC was higher in NCRp (1.060 ± 0.143 × 10(-3) mm(2)/s) than in CRp patients (1.227 ± 0.271 × 10(-3) mm(2)/s; P = 0.047).

CONCLUSION

Pretreatment ADC can predict the response of breast cancer to NACT if tumour subtypes are considered. Key Points • Apparent diffusion coefficient helps clinicians to assess patients with breast cancer. • Pretreatment ADC is related to tumour grade and hormone receptor status. • Pretreatment ADC is lower in luminal A and B than in triple-negative tumours. • Pretreatment ADC is higher in complete than in non-complete responders to neoadjuvant chemotherapy.

MRI enhancement in stromal tissue surrounding breast tumors: association with recurrence free survival following neoadjuvant chemotherapy

RATIONALE AND OBJECTIVES

Normal-appearing stromal tissues surrounding breast tumors can harbor abnormalities that lead to increased risk of local recurrence. The objective of this study was to develop a new imaging methodology to characterize the signal patterns of stromal tissue and to investigate their association with recurrence-free survival following neoadjuvant chemotherapy.

MATERIALS AND METHODS

Fifty patients with locally-advanced breast cancer were imaged with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before (V1) and after one cycle (V2) of adriamycin-cytoxan therapy. Contrast enhancement in normal-appearing stroma around the tumor was characterized by the mean percent enhancement (PE) and mean signal enhancement ratio (SER) in distance bands of 5 mm from the tumor edge. Global PE and SER were calculated by averaging all stromal bands 5 to 40 mm from tumor. Proximity-dependent PE and SER were analyzed using a linear mixed effects model and Cox proportional hazards model for recurrence-free survival.

RESULTS

The mixed effects model displayed a decreasing radial trend in PE at both V1 and V2. An increasing trend was less pronounced in SER. Survival analysis showed that the hazard ratio estimates for each unit decrease in global SER was statistically significant at V1 [estimated hazard ratio = 0.058, 95% Wald CI (0.003, 1.01), likelihood ratio p = 0.03]; but was not so for V2.

CONCLUSIONS

These findings show that stromal tissue outside the tumor can be quantitatively characterized by DCE-MRI, and suggest that stromal enhancement measurements may be further developed for use as a potential predictor of recurrence/disease-free survival following therapy.

High-resolution diffusion-weighted imaging for monitoring breast cancer treatment response

RATIONALE AND OBJECTIVES

The aim of this work was to compare a high-resolution diffusion-weighted imaging (HR-DWI) acquisition (voxel size = 4.8 mm(3)) to a standard diffusion-weighted imaging (STD-DWI) acquisition (voxel size = 29.3 mm(3)) for monitoring neoadjuvant therapy-induced changes in breast tumors.

MATERIALS AND METHODS

Nine women with locally advanced breast cancer were imaged with both HR-DWI and STD-DWI before and after 3 weeks (early treatment) of neoadjuvant taxane-based treatment. Tumor apparent diffusion coefficient (ADC) metrics (mean and histogram percentiles) from both DWI methods were calculated, and their relationship to tumor volume change after 12 weeks of treatment (posttreatment) measured by dynamic contrast enhanced magnetic resonance imaging was evaluated with a Spearman's rank correlation.

RESULTS

The HR-DWI pretreatment 15th percentile tumor ADC (P = .03) and early treatment 15th, 25th, and 50th percentile tumor ADCs (P = .008, .010, .04, respectively) were significantly lower than the corresponding STD-DWI percentile ADCs. The mean tumor HR-ADC was significantly lower than STD-ADC at the early treatment time point (P = .02), but not at the pretreatment time point (P = .07). A significant early treatment increase in tumor ADC was found with both methods (P < .05). Correlations between HR-DWI tumor ADC and posttreatment tumor volume change were higher than the STD-DWI correlations at both time points and the lower percentile ADCs had the strongest correlations.

CONCLUSION

These initial results suggest that the HR-DWI technique has potential for improving characterization of low tumor ADC values over STD-DWI and that HR-DWI may be of value in evaluating tumor change with treatment.

Microwave imaging for neoadjuvant chemotherapy monitoring: initial clinical experience

INTRODUCTION

Microwave tomography recovers images of tissue dielectric properties, which appear to be specific for breast cancer, with low-cost technology that does not present an exposure risk, suggesting the modality may be a good candidate for monitoring neoadjuvant chemotherapy.

METHODS

Eight patients undergoing neoadjuvant chemotherapy for locally advanced breast cancer were imaged longitudinally five to eight times during the course of treatment. At the start of therapy, regions of interest (ROIs) were identified from contrast-enhanced magnetic resonance imaging studies. During subsequent microwave examinations, subjects were positioned with their breasts pendant in a coupling fluid and surrounded by an immersed antenna array. Microwave property values were extracted from the ROIs through an automated procedure and statistical analyses were performed to assess short term (30 days) and longer term (four to six months) dielectric property changes.

RESULTS

Two patient cases (one complete and one partial response) are presented in detail and demonstrate changes in microwave properties commensurate with the degree of treatment response observed pathologically. Normalized mean conductivity in ROIs from patients with complete pathological responses was significantly different from that of partial responders (P value = 0.004). In addition, the normalized conductivity measure also correlated well with complete pathological response at 30 days (P value = 0.002).

CONCLUSIONS

These preliminary findings suggest that both early and late conductivity property changes correlate well with overall treatment response to neoadjuvant therapy in locally advanced breast cancer. This result is consistent with earlier clinical outcomes that lesion conductivity is specific to differentiating breast cancer from benign lesions and normal tissue.