Purpose: This study was designed to determine (1) rates of clinically meaningful tumor reduction in breast tumor size following neoadjuvant chemotherapy (NAC), (2) which receptor subtypes and MRI phenotypes are associated with clinically meaningful tumor r

Non-invasive diffuse optical tomography (DOT) and diffuse correlation spectroscopy (DCS) can detect and characterize breast cancer and predict tumor responses to neoadjuvant chemotherapy, even in patients with radiographically dense breasts. However, the r

We introduce and demonstrate use of a novel, diffuse optical tomography (DOT) based breast cancer signature for monitoring progression of neoadjuvant chemotherapy. This signature, called probability of malignancy, is obtained by statistical image analysis

INTRODUCTION: The accurate diagnosis of elevated intracranial pressure (eICP) in the emergent setting is a critical determination that presents significant challenges. Several studies show correlation of sonographic optic nerve sheath diameter (ONSD) to eICP, while others show high inter-observer variability or marginal performance with less experienced sonographers. The objective of our study is to assess the ability of bedside ultrasound measurement of ONSD to identify the presence of eICP when performed by a single experienced sonographer. We hypothesize that ONSD measurement is sensitive and specific for detecting eICP and can be correlated with values obtained by external ventricular device (EVD). METHODS: This was a prospective blinded observational study conducted in a neurocritical care unit of a level 1 trauma center. ONSD measurement was performed on a convenience sample of 27 adult patients who required placement of an invasive intracranial monitor as part of their clinical care. One certified sonographer/physician performed all ultrasounds within 24 hours of placement of EVD. The sonographer was blinded to the ICP recorded by invasive monitor at the time of the scan. A mean ONSD value of ≥5.2 mm was taken as positive. RESULTS: The sonographer performed 27 ocular ultrasounds on individual patients. Six (22%) of these patients had eICP (EVD measurement of >20 mmHg). Spearman rank correlation coefficient of ONSD and ICP was 0.408 (p=0.03), demonstrating a moderate positive correlation. A ROC curve was created to determine the optimal cut off value to distinguish an eICP greater than 20 mmHg. The area under the receiver operator characteristic curve was 0.8712 (95% confidence interval [CI]=0.67 to 0.96). ONSD ≥5.2 mm was a good predictor of eICP (>20 mmHg) with a sensitivity of 83.3% (95% CI=35.9% to 99.6%) and specificity of 100% (95% CI=84.6% to 100%). CONCLUSION: While the study suggests ONSD measurements performed by a single skilled operator may be both sensitive and specific for detecting eICP, confirmation in a much larger sample is needed. Ocular ultrasound may provide additional non-invasive means of assessing eICP.

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.

Purpose

To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images.

Materials and methods

This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers.

Results

For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma.

Conclusion

Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women.

Microvascular blood flow contrast is an important hemodynamic and metabolic parameter with potential to enhance in vivo breast cancer detection and therapy monitoring. Here we report on non-invasive line-scan measurements of malignant breast tumors with a

Purpose

This study was designed to determine (1) rates of clinically meaningful tumor reduction in breast tumor size following neoadjuvant chemotherapy (NAC), (2) which receptor subtypes and MRI phenotypes are associated with clinically meaningful tumor reduction, and (3) whether MRI phenotype impacts concordance between pathologic and MRI size.

Methods

We analyzed data from the I-SPY TRIAL, a multicenter, prospective NAC trial. Reduction in tumor size from >4 to ≤4 cm was considered clinically meaningful, as crossing this threshold was considered a reasonable cutoff for potential breast conservation therapy (BCT). MRI phenotypes were scored between one (well-defined) and five (diffuse) on pre-NAC MRIs.

Results

Of 174 patients with tumors >4 cm, 141 (81%) had clinically meaningful tumor reduction. Response to therapy varied by MRI phenotype (p = 0.003), with well-defined phenotypes more likely than diffuse phenotypes to have clinically meaningful tumor shrinkage (91 vs. 72%, p = 0.037). Her2+ and triple-negative (Tneg) tumors had the highest rate of clinically meaningful tumor reduction (p = 0.005). The concordance between tumor diameter on MRI and surgical pathology was highest for Her2+ and Tneg tumors, especially among tumors with solid imaging phenotypes (p = 0.004).

Discussion

NAC allows most patients with large breast tumors to have clinically meaningful tumor reduction, meaning response that would impact ability to undergo BCT. However, response varies by imaging and tumor subtypes. Concordance between tumor size on MRI and surgical pathology was higher in well-defined tumors, especially those with a Tneg subtype, and lower in HR+ diffuse tumors.

Purpose

To compare magnetic resonance (MR) imaging findings and clinical assessment for prediction of pathologic response to neoadjuvant chemotherapy (NACT) in patients with stage II or III breast cancer.

Materials and methods

The HIPAA-compliant protocol and the informed consent process were approved by the American College of Radiology Institutional Review Board and local-site institutional review boards. Women with invasive breast cancer of 3 cm or greater undergoing NACT with an anthracycline-based regimen, with or without a taxane, were enrolled between May 2002 and March 2006. MR imaging was performed before NACT (first examination), after one cycle of anthracyline-based treatment (second examination), between the anthracycline-based regimen and taxane (third examination), and after all chemotherapy and prior to surgery (fourth examination). MR imaging assessment included measurements of tumor longest diameter and volume and peak signal enhancement ratio. Clinical size was also recorded at each time point. Change in clinical and MR imaging predictor variables were compared for the ability to predict pathologic complete response (pCR) and residual cancer burden (RCB). Univariate and multivariate random-effects logistic regression models were used to characterize the ability of tumor response measurements to predict pathologic outcome, with area under the receiver operating characteristic curve (AUC) used as a summary statistic.

Results

Data in 216 women (age range, 26-68 years) with two or more imaging time points were analyzed. For prediction of both pCR and RCB, MR imaging size measurements were superior to clinical examination at all time points, with tumor volume change showing the greatest relative benefit at the second MR imaging examination. AUC differences between MR imaging volume and clinical size predictors at the early, mid-, and posttreatment time points, respectively, were 0.14, 0.09, and 0.02 for prediction of pCR and 0.09, 0.07, and 0.05 for prediction of RCB. In multivariate analysis, the AUC for predicting pCR at the second imaging examination increased from 0.70 for volume alone to 0.73 when all four predictor variables were used. Additional predictive value was gained with adjustments for age and race.

Conclusion

MR imaging findings are a stronger predictor of pathologic response to NACT than clinical assessment, with the greatest advantage observed with the use of volumetric measurement of tumor response early in treatment.