Accelerated Breast Diffusion-weighted Imaging Using Multiband Sensitivity Encoding with the CAIPIRINHA Method: Clinical Experience at 3 T.

Purpose To examine the clinical value of multiband (MB) sensitivity encoding (SENSE)-accelerated diffusion-weighted imaging (DWI) for breast imaging by performing quantitative and qualitative comparisons with conventional diffusion-weighted echo-planar imaging, or conventional DWI (cDWI). Materials and Methods In this prospective study (ClinicalTrials.gov identifier NCT03607552), women with breast cancer were recruited from July 2018 to July 2019 to undergo additional MB SENSE DWI during clinical 3-T breast MRI examinations. The cDWI and MB SENSE DWI acquisitions were assessed both quantitatively and qualitatively. Regions of interest were defined for tumorous and normal tissue, and the tumor apparent diffusion coefficient (ADC), contrast-to-noise ratio (CNR), and signal index (SI) were calculated for both DWI methods. Three readers independently reviewed the two acquisitions side by side and provided relative image quality scores. Tumor ADC, CNR, and SI measures were compared between cDWI and MB SENSE DWI acquisitions by using a paired t test, and reader preferences were evaluated by using the sign test. Results The study included 38 women (median age, 48 years; range, 28-83 years). Overall agreement was good between cDWI and MB SENSE DWI tumor ADC measures (intraclass correlation coefficient, 0.87 [95% CI: 0.75, 0.94]), and no differences were evident in the ADC (median, 0.93 × 10-3 mm2/sec vs 0.87 ×10-3 mm2/sec; P = .50), CNR (2.2 vs 2.3; P = .17), or SI (9.2 vs 9.2; P = .23) measurements. The image quality of cDWI and MB SENSE DWI acquisitions were considered equal for 51% of images (58 of 114), whereas MB SENSE DWI was preferred more often than cDWI (37% [42 of 114] vs 12% [14 of 114]; P < .001). The preference for MB SENSE DWI was most often attributed to better fat suppression. Conclusion MB SENSE can be used to accelerate breast DWI acquisition times without compromising the image quality or the fidelity of quantitative ADC measurements. Keywords: MR-Diffusion-weighted Imaging, Breast, Comparative Studies, Technology Assessment Clinical trial registration no. NCT03607552 © RSNA, 2022.

Imaging for Response Assessment in Cancer Clinical Trials.

The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.

Assessment of Quantitative Magnetic Resonance Imaging Background Parenchymal Enhancement Parameters to Improve Determination of Individual Breast Cancer Risk.

OBJECTIVES: The aims of this study were to identify optimal quantitative breast magnetic resonance imaging background parenchymal enhancement (BPE) parameters associated with breast cancer risk and compare performance to qualitative assessments. METHODS: Using a matched case-control cohort of 46 high-risk women who underwent screening magnetic resonance imaging (23 who developed breast cancer matched to 23 who did not), fibroglandular tissue area, BPE area, and intensity metrics (mean, SD, quartiles, skewness, and kurtosis) were quantitatively measured at varying enhancement thresholds. Optimal thresholds for discriminating between cancer and control cohorts were identified for each metric and performance summarized using area under the receiver operating characteristic curve. RESULTS: Women who developed breast cancer exhibited greater BPE area (adjusted P = 0.004) and higher intensity statistics (adjusted P < 0.004, except skewness and kurtosis with P > 0.99) than did control subjects, with areas under the receiver operating characteristic curve ranging from 0.75 to 0.78 at optimized thresholds. CONCLUSIONS: Elevated quantitative BPE parameters, related to both area and intensity of enhancement, are associated with breast cancer development.

MRI, Clinical Examination, and Mammography for Preoperative Assessment of Residual Disease and Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer: ACRIN 6657 Trial.

OBJECTIVE: The objective of our study was to determine the accuracy of preoperative measurements for detecting pathologic complete response (CR) and assessing residual disease after neoadjuvant chemotherapy (NACT) in patients with locally advanced breast cancer. SUBJECTS AND METHODS: The American College of Radiology Imaging Network 6657 Trial prospectively enrolled women with ≥ 3 cm invasive breast cancer receiving NACT. Preoperative measurements of residual disease included longest diameter by mammography, MRI, and clinical examination and functional volume on MRI. The accuracy of preoperative measurements for detecting pathologic CR and the association with final pathology size were assessed for all lesions, separately for single masses and nonmass enhancements (NMEs), multiple masses, and lesions without ductal carcinoma in situ (DCIS). RESULTS: In the 138 women with all four preoperative measures, longest diameter by MRI showed the highest accuracy for detecting pathologic CR for all lesions and NME (AUC = 0.76 and 0.84, respectively). There was little difference across preoperative measurements in the accuracy of detecting pathologic CR for single masses (AUC = 0.69-0.72). Longest diameter by MRI and longest diameter by clinical examination showed moderate ability for detecting pathologic CR for multiple masses (AUC = 0.78 and 0.74), and longest diameter by MRI and longest diameter by mammography showed moderate ability for detecting pathologic CR for tumors without DCIS (AUC = 0.74 and 0.71). In subjects with residual disease, longest diameter by MRI exhibited the strongest association with pathology size for all lesions and single masses (r = 0.33 and 0.47). Associations between preoperative measures and pathology results were not significantly influenced by tumor subtype or mammographic density. CONCLUSION: Our results indicate that measurement of longest diameter by MRI is more accurate than by mammography and clinical examination for preoperative assessment of tumor residua after NACT and may improve surgical planning.

DWI in the Assessment of Breast Lesions.

Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.

Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis.

BACKGROUND: Magnetic resonance imaging (MRI) may guide breast cancer surgery by measuring residual tumor size post-neoadjuvant chemotherapy (NAC). Accurate measurement may avoid overly radical surgery or reduce the need for repeat surgery. This individual patient data (IPD) meta-analysis examines MRI's agreement with pathology in measuring the longest tumor diameter and compares MRI with alternative tests. METHODS: A systematic review of MEDLINE, EMBASE, PREMEDLINE, Database of Abstracts of Reviews of Effects, Heath Technology Assessment, and Cochrane databases identified eligible studies. Primary study authors supplied IPD in a template format constructed a priori. Mean differences (MDs) between tests and pathology (i.e. systematic bias) were calculated and pooled by the inverse variance method; limits of agreement (LOA) were estimated. Test measurements of 0.0 cm in the presence of pathologic residual tumor, and measurements >0.0 cm despite pathologic complete response (pCR) were described for MRI and alternative tests. RESULTS: Eight studies contributed IPD (N = 300). The pooled MD for MRI was 0.0 cm (LOA: +/-3.8 cm). Ultrasound underestimated pathologic size (MD: -0.3 cm) relative to MRI (MD: 0.1 cm), with comparable LOA. MDs were similar for MRI (0.1 cm) and mammography (0.0 cm), with wider LOA for mammography. Clinical examination underestimated size (MD: -0.8 cm) relative to MRI (MD: 0.0 cm), with wider LOA. Tumors "missed" by MRI typically measured 2.0 cm or less at pathology; tumors >2.0 cm were more commonly "missed" by clinical examination (9.3 %). MRI measurements >5.0 cm occurred in 5.3 % of patients with pCR, but were more frequent for mammography (46.2 %). CONCLUSIONS: There was no systematic bias in MRI tumor measurement, but LOA are large enough to be clinically important. MRI's performance was generally superior to ultrasound, mammography, and clinical examination, and it may be considered the most appropriate test in this setting. Test combinations should be explored in future studies.

Accuracy of 3 T versus 1.5 T breast MRI for pre-operative assessment of extent of disease in newly diagnosed DCIS.

OBJECTIVES: While 3T breast magnetic resonance imaging has increased in use over the past decade, there is little data comparing its use for assessing ductal carcinoma in situ (DCIS) versus 1.5 T. We sought to compare the accuracies of DCIS extent of disease measures on pre-operative 3T versus 1.5 T MRI. METHODS: This institutional review board-approved prospective study included 20 patients with ductal carcinoma in situ diagnosed by core needle biopsy (CNB) who underwent pre-operative breast MRI at both 3T (resolution=0.5 mm×0.5 mm×1.3 mm) and 1.5 T (0.85 mm×0.85 mm×1.6 mm). All patients provided informed consent, and the study was HIPPA compliant. Lesion sizes and imaging characteristics (morphologic and kinetic enhancement) were recorded for the 3 T and 1.5 T examinations. Lesion size measures at both field strengths were correlated to final pathology, and imaging characteristics also were compared. RESULTS: Of the initial cohort of 20 patients with CNB-diagnosed DCIS, 19 underwent definitive surgery. Median DCIS sizes of these 19 patients were 6mm (range: 0-67 mm) on 3T, 13 mm (0-60 mm) on 1.5 T, and 6mm (0-55 mm) on surgical pathology. Size correlation between MRI and pathology was higher for 3T (Spearman's ρ=0.66, p=0.002) than 1.5 T (ρ=0.36, p=0.13). In 10 women in which a residual area of suspicious enhancement was identified on both field strengths, there was agreement of morphologic description (NME vs. mass) in nine, and no significant difference in dynamic contrast enhanced kinetics at 3T compared to 1.5 T. CONCLUSIONS: Pre-operative breast MRI at 3T provided higher correlation with final pathology size of DCIS lesions compared to 1.5 T, and may be more accurate for assessment of disease extent prior to definitive surgery.

In vivo assessment of ductal carcinoma in situ grade: a model incorporating dynamic contrast-enhanced and diffusion-weighted breast MR imaging parameters.

PURPOSE: To develop a model incorporating dynamic contrast material-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance (MR) imaging features to differentiate high-nuclear-grade (HNG) from non-HNG ductal carcinoma in situ (DCIS) in vivo. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board and requirement for informed consent was waived. A total of 55 pure DCIS lesions (19 HNG, 36 non-HNG) in 52 women who underwent breast MR imaging at 1.5 T with both DCE and DW imaging (b = 0 and 600 sec/mm(2)) were retrospectively reviewed. The following lesion characteristics were recorded or measured: DCE morphology, DCE maximum lesion size, peak initial enhancement at 90 seconds, worst-curve delayed enhancement kinetics, apparent diffusion coefficient (ADC), contrast-to-noise ratio (CNR) at DW imaging with b values of 0 and 600 sec/mm(2), and T2 signal effects (measured with CNR at b = 0 sec/mm(2)). Univariate and stepwise multivariate logistic regression modeling was performed to identify MR imaging features that optimally discriminated HNG from non-HNG DCIS. Discriminative abilities of models were compared by using the area under the receiver operating characteristic curve (AUC). RESULTS: HNG lesions exhibited larger mean maximum lesion size (P = .02) and lower mean CNR for images with b value of 600 sec/mm(2) (P = .004), allowing discrimination of HNG from non-HNG DCIS (AUC = 0.71 for maximum lesion size, AUC = 0.70 for CNR at b = 600 sec/mm(2)). Differences in CNR for images with b value of 0 sec/mm(2) (P = .025) without corresponding differences in ADC values were observed between HNG and non-HNG lesions. Peak initial enhancement was the only kinetic variable to approach significance (P = .05). No differences in lesion morphology (P = .11) or worst-curve delayed enhancement kinetics (P = .97) were observed. A multivariate model combining CNR for images with b value of 600 sec/mm(2) and maximum lesion size most significantly discriminated HNG from non-HNG (AUC = 0.81). CONCLUSION: The preliminary findings suggest that DCE and DW MR imaging features may aid in identifying patients with high-risk DCIS. Further study may yield a model combining MR characteristics with histopathologic data to facilitate lesion-specific targeted therapies. © RSNA, 2012.