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

Diffusion-weighted imaging in addition to contrast-enhanced MRI in identifying complete response in HER2-positive breast cancer

OBJECTIVES

The aim of this study is to investigate the added value of diffusion-weighted imaging (DWI) to dynamic-contrast enhanced (DCE)-MRI to identify a pathological complete response (pCR) in patients with HER2-positive breast cancer and radiological complete response (rCR).

MATERIALS AND METHODS

This is a single-center observational study of 102 patients with stage I-III HER2-positive breast cancer and real-world documented rCR on DCE-MRI. Patients were treated between 2015 and 2019. Both 1.5 T/3.0 T single-shot diffusion-weighted echo-planar sequence were used. Post neoadjuvant systemic treatment (NST) diffusion-weighted images were reviewed by two readers for visual evaluation and ADCmean. Discordant cases were resolved in a consensus meeting. pCR of the breast (ypT0/is) was used to calculate the negative predictive value (NPV). Breast pCR-percentages were tested with Fisher's exact test. ADCmean and ∆ADCmean(%) for patients with and without pCR were compared using a Mann-Whitney U-test.

RESULTS

The NPV for DWI added to DCE is 86% compared to 87% for DCE alone in hormone receptor (HR)-/HER2-positive and 67% compared to 64% in HR-positive/HER2-positive breast cancer. Twenty-seven of 39 non-rCR DWI cases were false positives. In HR-negative/HER2-positive breast cancer the NPV for DCE MRI differs between MRI field strength (1.5 T: 50% vs. 3 T: 81% [p = 0.02]). ADCmean at baseline, post-NST, and ∆ADCmean were similar between patients with and without pCR.

CONCLUSION

DWI has no clinically relevant effect on the NPV of DCE alone to identify a pCR in early HER2-positive breast cancer. The added value of DWI in HR-positive/HER2-positive breast cancer should be further investigated taken MRI field strength into account.

CLINICAL RELEVANCE STATEMENT

The residual signal on DWI after neoadjuvant systemic therapy in cases with early HER2-positive breast cancer and no residual pathologic enhancement on DCE-MRI breast should not (yet) be considered in assessing a complete radiologic response.

KEY POINTS

Radiologic complete response is associated with a pathologic complete response (pCR) in HER2+ breast cancer but further improvement is warranted. No relevant increase in negative predictive value was observed when DWI was added to DCE. Residual signal on DW-images without pathologic enhancement on DCE-MRI, does not indicate a lower chance of pCR.

Non-contrast Breast MR Imaging

Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Towards a fast PET/MRI protocol for breast cancer imaging: maintaining diagnostic confidence while reducing PET and MRI acquisition times

OBJECTIVES

To investigate the diagnostic feasibility of a shortened breast PET/MRI protocol in breast cancer patients.

METHODS

Altogether 90 women with newly diagnosed T1tumor-staged (T1ts) and T2tumor-staged (T2ts) breast cancer were included in this retrospective study. All underwent a dedicated comprehensive breast [18F]FDG-PET/MRI. List-mode PET data were retrospectively reconstructed with 20, 15, 10, and 5 min for each patient to simulate the effect of reduced PET acquisition times. The SUVmax/mean of all malign breast lesions was measured. Furthermore, breast PET data reconstructions were analyzed regarding image quality, lesion detectability, signal-to-noise ratio (SNR), and image noise (IN). The simultaneously acquired comprehensive MRI protocol was then shortened by retrospectively removing sequences from the protocol. Differences in malignant breast lesion detectability between the original and the fast breast MRI protocol were evaluated lesion-based. The 20-min PET reconstructions and the original MRI protocol served as reference.

RESULTS

In all PET reconstructions, 127 congruent breast lesions could be detected. Group comparison and T1ts vs. T2ts subgroup comparison revealed no significant difference of subjective image quality between 20, 15, 10, and 5 min acquisition times. SNR of qualitative image evaluation revealed no significant difference between different PET acquisition times. A slight but significant increase of IN with decreasing PET acquisition times could be detected. Lesion SUVmax group comparison between all PET acquisition times revealed no significant differences. Lesion-based evaluation revealed no significant difference in breast lesion detectability between original and fast breast MRI protocols.

CONCLUSIONS

Breast [18F]FDG-PET/MRI protocols can be shortened from 20 to below 10 min without losing essential diagnostic information.

KEY POINTS

• A highly accurate breast cancer evaluation is possible by the shortened breast [18F]FDG-PET/MRI examination protocol. • Significant time saving at breast [18F]FDG-PET/MRI protocol could increase patient satisfaction and patient throughput for breast cancer patients at PET/MRI.

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer: 2022 Update

Imaging plays a vital role in managing patients undergoing neoadjuvant chemotherapy, as treatment decisions rely heavily on accurate assessment of response to therapy. This document provides evidence-based guidelines for imaging breast cancer before, during, and after initiation of neoadjuvant chemotherapy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI

Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.

Evaluation of pretreatment ADC values as predictors of treatment response to neoadjuvant chemotherapy in patients with breast cancer - a multicenter study

BACKGROUND

Magnetic resonance imaging (MRI) can be used to diagnose breast cancer. Diffusion weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can reflect tumor microstructure in a non-invasive manner. The correct prediction of response of neoadjuvant chemotherapy (NAC) is crucial for clinical routine. Our aim was to compare ADC values between patients with pathological complete response (pCR) and non-responders based upon a multi-center design to improve the correct patient selection, which patient would more benefit from NAC and which patient would not.

METHODS

For this study, data from 4 centers (from Japan, Brazil, Spain and United Kingdom) were retrospectively acquired. The time period was overall 2003-2019. The patient sample comprises 250 patients (all female; median age, 50.5). In every case, pretreatment breast MRI with DWI was performed. pCR was assessed by experienced pathologists in every center using the surgical specimen in the clinical routine work up. pCR was defined as no residual invasive disease in either breast or axillary lymph nodes after NAC. ADC values between the group with pCR and those with no pCR were compared using the Mann-Whitney U test (two-group comparisons). Univariable and multivariabe logistic regression analysis was performed to predict pCR status.

RESULTS

Overall, 83 patients (33.2%) achieved pCR. The ADC values of the patient group with pCR were lower compared with patients without pCR (0.98 ± 0.23 × 10^- 3 mm²/s versus 1.07 ± 0.24 × 10^- 3 mm²/s, p = 0.02). The ADC value achieved an odds ratio of 4.65 (95% CI 1.40-15.49) in univariable analysis and of 3.0 (95% CI 0.85-10.63) in multivariable analysis (overall sample) to be associated with pCR status. The odds ratios differed in the subgroup analyses in accordance with the molecular subtype.

CONCLUSIONS

The pretreatment ADC-value is associated with pathological complete response after NAC in breast cancer patients. This could aid in clinical routine to reduce treatment toxicity for patients, who would not benefit from NAC. However, this must be tested in further studies, as the overlap of the ADC values in both groups is too high for clinical prediction.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice

OBJECTIVES

To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI.

METHODS

An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test.

RESULTS

Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10-3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values.

CONCLUSION

The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform.

KEY POINTS

• Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

MRI as a biomarker for breast cancer diagnosis and prognosis

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial

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.

Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods

Background

The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods.

Methods

A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype.

Results

Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001).

Conclusions

The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T₂-weighted MRI-based radiomics model for predicting the survival of gliomas.

A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T₂-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T₂-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining.

The 14 radiomic features-based prediction model was constructed from preoperative T₂-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration.

The preoperative T₂-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.

The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?

PURPOSE

To explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer.

METHODS

Eighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR).

RESULTS

Correlations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC_(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and - 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC_{(0, 1000)}, MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively.

CONCLUSION

MK was correlated with HR expression. ADC_{(0, 1000)} and DKI were correlated with Ki67 expression. ADC_{(0, 1000)} and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.

Effectiveness of ADC Difference Value on Pre-neoadjuvant Chemotherapy MRI for Response Evaluation of Breast Cancer

Background: Neoadjuvant chemotherapy (NAC) is known to be a suitable treatment and first-line defense for locally advanced breast cancer. However, the NAC response may include unexpected outcomes, and it is not easy to predict the NAC response precisely. Especially, early detection of those patients who do not benefit from NAC is needed to reduce unnecessary therapy and side effects. Objective: The purpose of this study was to determine whether the pretreatment apparent diffusion coefficient (ADC) value is effective for predicting the response of breast cancer to NAC. Method: Forty-nine breast cancer cases with pre- and post-NAC breast MRI were enrolled. MRI was performed using a 1.5-T scanner with the basic protocol including diffusion-weighted imaging. ADC difference value (ADC-diff) was calculated in all cases. Results: ADC-diff was high in complete response and partial response cases (p < .05). ADC-diff correlated with the DWI rim sign, with a positive DWI rim sign being associated with a higher ADC-diff (p < .05). Conclusion: High-ADC difference value on the pretreatment MRI can provide information for a better response of NAC on breast cancer.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.

Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.

Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).

Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Fusion of high b-value diffusion-weighted and unenhanced T1-weighted images to diagnose invasive breast cancer: factors associated with false-negative results

OBJECTIVES

We sought factors associated with false-negative results in the diagnosis of invasive breast cancer via non-contrast breast magnetic resonance imaging (MRI) using fused high b-value diffusion-weighted imaging (DWI) and unenhanced T1-weighted images (T1WI).

METHODS

Between 2018 and 2019, 316 consecutive women (mean age, 54.6 years) with invasive breast cancer who underwent preoperative breast MRI, including fused high b-value DWI and unenhanced T1WI, were retrospectively evaluated. Malignancy confidence ratings of the most suspicious breast lesions evident on fused DWI were derived by two radiologists using a 6-point Likert-type scale. Both clinicopathological and imaging features were analyzed. Multivariate regression analysis was performed to identify factors associated with false-negative DWI results in the diagnosis of invasive breast cancer.

RESULTS

Of the 316 breast cancers, fused DWI yielded 289 (91.5%) true-positive and 27 (8.5%) false-negative results. Multivariate analysis showed that small tumor size (≤ 1 cm) (odds ratio [OR], 5.95; 95% confidence interval [CI], 2.11, 16.81; p = 0.001), presence of calcifications in the tumor (OR, 3.41; 95% CI, 1.27, 9.15; p = 0.015), and a moderate/marked background diffusion signal (ORs, 4.23 and 19.18; 95% CI, 1.31, 13.67 and 6.51, 56.46; p = 0.016 and p < 0.001, respectively) were significantly associated with false-negative results. In subgroup analysis of 141 screening-detected cancers, a marked background diffusion signal (OR, 7.94; 95% CI, 2.30, 27.35; p = 0.001) remained significantly associated with false-negative results in the multivariate analysis.

CONCLUSIONS

In addition to histopathological features, a higher background diffusion signal was associated with false-negative results in the diagnosis of invasive breast cancer via non-contrast MRI using fused high b-value DWI and unenhanced T1WI.

KEY POINTS

• Subcentimeter tumors and presence of calcifications in the tumor are associated with false-negative diffusion-weighted imaging results in the diagnosis of invasive breast cancer. • A higher degree of background diffusion signal may lead to false-negative interpretation of diffusion-weighted imaging in patients with invasive breast cancer.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Diffusion-Weighted Magnetic Resonance Imaging of the Breast: Standardization of Image Acquisition and Interpretation

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a rapid, unenhanced imaging technique that measures the motion of water molecules within tissues and provides information regarding the cell density and tissue microstructure. DW MRI has demonstrated the potential to improve the specificity of breast MRI, facilitate the evaluation of tumor response to neoadjuvant chemotherapy and can be employed in unenhanced MRI screening. However, standardization of the acquisition and interpretation of DW MRI is challenging. Recently, the European Society of Breast Radiology issued a consensus statement, which described the acquisition parameters and interpretation of DW MRI. The current article describes the basic principles, standardized acquisition protocols and interpretation guidelines, and the clinical applications of DW MRI in breast imaging.

Pretreatment apparent diffusion coefficient does not predict therapy response to neoadjuvant chemotherapy in breast cancer

Background

Some reports indicated that apparent diffusion coefficient can predict pathologic response to treatment in breast cancer (BC). The purpose of the present meta-analysis was to provide evident data regarding use of ADC values for prediction of treatment response in BC.

Methods

MEDLINE library, EMBASE and SCOPUS databases were screened for associations between ADC and treatment response for neoadjuvant chemotherapy in breast cancer (BC) up to March 2020. Overall, 22 studies met the inclusion criteria. For the present analysis, the following data were extracted from the collected studies: authors, year of publication, study design, number of patients/lesions, mean and standard deviation of the pretreatment ADC values. The methodological quality of the included studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for responders and non responders.

Results

The acquired 22 studies comprised 1827 patients with different BC. Of the 1827 patients, 650 (35.6%) were reported as responders and 1177 (64.4%) as non-responders to the neoadjuvant chemotherapy. The pooled calculated pretreatment mean ADC value of BC in responders was 0.98 (95% CI = [0.94; 1.03]). In non-responders, it was 1.05 (95% CI = [1.00; 1.10]). The ADC values of the groups overlapped significantly.

Conclusion

Pretreatment ADC alone cannot predict response to neoadjuvant chemotherapy in BC.

Tumor texture parameters of invasive ductal breast carcinoma in neoadjuvant chemotherapy: early identification of non-responders on breast MRI

PURPOSE

Texture analysis (TA) parameters (variance of SI, mean of gradient, variance of gradient, kurtosis of SI, and entropy) in patients with invasive ductal carcinoma (IDC) contribute to objective assessment of neoadjuvant chemotherapy (NACT) activity. The objective was to assess TA parameters in early identification of non-responders (NR) in NACT, after the 2nd cycle of NACT.

MATERIAL AND METHODS

Fifty patients (N = 50) were included in the retrospective analysis of baseline and MRI following the 2nd cycle of NACT. TA parameters were computed and correlated to the lesion size and DWI-ADC in NR (N1 = 25). Additional matched responders (R, N2 = 25) assessed for the same parameters, served as the control group.

RESULTS

Tumor size and ADC did not change significantly in NR after the 2nd cycle of NACT (2.88 ± 0.38 vs. 2.76 ± 0.36 [cm], p = 0.131; 1.01 ± 0.14 vs. 1.05 ± 0.13 [mm²/s × 10^-3], p = 0.363), but TA parameters changed significantly: variance of gradient (346.5 ± 12.6 vs. 355.6 ± 16.9, p = 0.01), kurtosis of SI (1.47 ± 0.09 vs. 1.54 ± 0.11, p = 0.02), entropy LH (60.39 ± 4.34 vs. 64.42 ± 3.05, p = 0.001) and entropy HL (61.02 ± 5.51 vs. 65.63 ± 3.63, p < 0.00001). TA parameters, particularly entropy (EN LH 64.42 ± 3.05 vs. 61.59 ± 1.76, p < 0.0001; EN HL 65.63 ± 3.63 vs. 62.89 ± 2.05, p < 0.0001), significantly differ between NR and R in early response assessment.

CONCLUSION

Entropy, kurtosis of SI and variance of gradient tend to increase in NR. TA parameters significantly differ between NR and R after the 2nd cycle of NACT. TA parameters, related to morpho-functional parameters may contribute to early NR identification.

Turbo Spin-echo Diffusion-weighted Imaging Compared with Single-shot Echo-planar Diffusion-weighted Imaging: Image Quality and Diagnostic Performance When Differentiating between Ductal Carcinoma in situ and Invasive Ductal Carcinoma

Purpose:

To compare the image quality between turbo spin-echo (TSE)-diffusion weighted imaging (DWI) and single-shot echo-planar imaging (EPI)-DWI, and to verify the diagnostic performance of the apparent diffusion coefficient (ADC) parameters of the two techniques by using histogram analysis in terms of differentiation between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) lesions.

Methods:

Ninety-four women with 94 lesions diagnosed as breast cancer by surgery underwent IRB-approved preoperative magnetic resonance imaging, including TSE and EPI-DWI with b-values of 50 and 850 s/mm². Twenty lesions were identified as DCIS and 74 as IDC. Image quality [signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion] was evaluated quantitatively and compared between the TSE and EPI-DWI. A histogram analysis of the entire tumor voxel-based ADC data was performed, and the 10th, 25th, 50th, 75th, and 90th percentile values of each technique were compared between DCIS and IDC lesions.

Results:

The SNR and CNR of TSE-DWI were significantly higher than those of EPI-DWI (P < 0.0001 and < 0.0001). The geometric distortion of TSE-DWI was significantly lower than that of EPI-DWI (P < 0.0001). In TSE-DWI, the 10th, 25th, 50th, and 75th percentile values were significantly different between the DCIS and IDC lesions (P = 0.0010, 0.0004, 0.0008, and 0.0044, respectively). In EPI-DWI, the 50th and 75th percentile values were significantly different between the two groups (P = 0.0009 and 0.0093). There was no significant difference in the area under the curve of the receiver operating characteristic analysis of the 10th, 25th, 50th, and 75th percentile values of TSE-DWI, and the 50th and 75th percentile values of EPI-DWI (P = 0.29).

Conclusion:

The image quality of TSE-DWI was better than that of EPI-DWI. DCIS lesions were distinguished from IDC lesions with a wider range of percentile values in TSE-DWI than in EPI-DWI, although diagnostic performance was not significantly different between the techniques.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Diffusion-Weighted Magnetic Resonance Imaging of Patients with Breast Cancer Following Neoadjuvant Chemotherapy Provides Early Prediction of Pathological Response - A Prospective Study

The purpose of this study was to evaluate the capacity of diffusion-weighted magnetic resonance imaging (DW-MRI) for early prediction of pathological response in breast cancer patients undergoing neoadjuvant chemotherapy (NCT). This prospective unicentric study evaluated 62 patients who underwent NCT. MRI was performed prior to the start of treatment (MR1), after the first NCT cycle (MR2), and upon completion of NCT (MR3). Pathological response was used as the gold-standard. Patients’ median age was 45.5 years and the median tumor size was 40 mm. Twenty-four (38.7%) tumors presented complete pathological response (pCR). The percent increase in apparent diffusion coefficient (ADC) value between MR1 and MR2 was higher in the pCR group (p < 0.001). When the minimum increase in ADC between MR1 and MR2 was set at 25%, sensitivity was 83%, specificity was 84%, positive predictive value was 77%, negative predictive value was 89%, and accuracy was 84% for an early prediction of pCR to NCT. Meanwhile, there were no significant changes in major tumor dimensions between MR1 and MR2. In conclusion, an increase in ADC after the first cycle of NCT correlates well with pCR after the chemotherapy in our cohort, precedes reduction in tumor size on conventional MRI, and may therefore be used as an early predictor of treatment response.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy

OBJECTIVES

To evaluate whether multiparametric breast-MRI, obtained before the initiation of neoadjuvant systemic therapy (NST) for operable breast cancer, predicts which cancer will achieve a pathological complete response (pCR) after the completion of NST.

METHODS

This was an IRB-approved retrospective study on 31 consecutive patients (median age, 56 years) with operable invasive breast cancer (median size: 22 mm; triple-negative: 11/31 [35%], HER2-positive: 7/31 [23%], triple-positive: 13/31 [42%]) who underwent multiparametric DCE-MRI before the initiation of NST. The MRI protocol consisted of high-resolution dynamic contrast-enhanced MRI (DCE-MRI), T2-TSE, and DWI (b-values 0, 100, 800 s/mm²). The results of surgical pathology after the completion of NST served as a standard of reference. Patient characteristics (age and menopausal status), pathological tumor characteristics (type, stage, nuclear grade, ER/PR and HER2 receptor status, and Ki-67 staining), and MRI characteristics (size, morphology, T2 signal intensity, enhancement kinetics, and ADC values) before NST were evaluated and compared between patients achieving pCR vs. non-pCR.

RESULTS

Among 31 patients, 17 achieved pCR (55%) and 14 non-pCR (45%). No correlation was observed between patient- or tumor pathology-derived characteristics and pCR vs. non-pCR. Among MRI-derived tumor characteristics, tumor growth orientation parallel to Cooper's ligaments (p = 0.002) and wash-out rates (p = 0.019) correlated with pCR. Pre-NST ADC values were lower in patients achieving pCR (P = 0.086).

CONCLUSIONS

A tumor growth pattern parallel with Cooper's ligaments and a fast wash-out rate on pre-treatment multiparametric MRI are predictive of pCR and more closely associated with pCR than ADC values.

Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial

Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.

Diffusion-weighted MR imaging of locally advanced breast carcinoma: the optimal time window of predicting the early response to neoadjuvant chemotherapy

Background

It is very difficult to predict the early response to NAC only on the basis of change in tumor size. ADC value derived from DWI promises to be a valuable parameter for evaluating the early response to treatment. This study aims to establish the optimal time window of predicting the early response to neoadjuvant chemotherapy (NAC) for different subtypes of locally advanced breast carcinoma using diffusion-weighted imaging (DWI).

Methods

We conducted an institutional review board-approved prospective clinical study of 142 patients with locally advanced breast carcinoma. All patients underwent conventional MR and DW examinations prior to treatment and after first, second, third, fourth, sixth and eighth cycle of NAC. The response to NAC was classified into a pathologic complete response (pCR) and a non-pCR group. DWI parameters were compared between two groups, and the optimal time window for predicting tumor response was established for each chemotherapy regimen.

Results

For all the genomic subtypes, there were significant differences in baseline ADC value between pCR and non-pCR group (p < 0.05). The time point prior to treatment could be considered as the ideal time point regardless of genomic subtype. In the group that started with taxanes or anthracyclines, for Luminal A or Luminal B subtype, postT1 could be used as the ideal time point during chemotherapy; for Basal-like or HER2-enriched subtype, postT2 as the ideal time point during chemotherapy. In the group that started with taxanes and anthracyclines, for HER2-enriched, Luminal B or Basal-like subtype, postT1 could be used as the ideal time point during chemotherapy; for Luminal A subtype, postT2 as the ideal time point during chemotherapy.

Conclusions

The time point prior to treatment can be considered as the optimal time point regardless of genomic subtype. For each chemotherapy regimen, the optimal time point during chemotherapy varies across different genomic subtypes.

Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial

BACKGROUND

Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker.

PURPOSE

To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures.

STUDY TYPE

Prospective.

SUBJECTS

In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer.

FIELD STRENGTH/SEQUENCE

DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T.

ASSESSMENT

A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient.

STATISTICAL TESTS

Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients.

RESULTS

In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10^-3 mm² /sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96).

DATA CONCLUSION

Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

Pre-treatment functional MRI of breast cancer: T2* evaluation at 3 T and relationship to dynamic contrast-enhanced and diffusion-weighted imaging

PURPOSE

Baseline T2* relaxation time has been proposed as an imaging biomarker in cancer, in addition to Dynamic Contrast-Enhanced (DCE) MRI and diffusion-weighted imaging (DWI) parameters. The purpose of the current work is to investigate sources of error in T2* measurements and the relationship between T2* and DCE and DWI functional parameters in breast cancer.

METHODS

Five female volunteers and thirty-two women with biopsy proven breast cancer were scanned at 3 T, with Research Ethics Committee approval. T2* values of the normal breast were acquired from high-resolution, low-resolution and fat-suppressed gradient-echo sequences in volunteers, and compared. In breast cancer patients, pre-treatment T2*, DCE MRI and DWI were performed at baseline. Pathologically complete responders at surgery and non-responders were identified and compared. Principal component analysis (PCA) and cluster analysis (CA) were performed.

RESULTS

There were no significant differences between T2* values from high-resolution, low-resolution and fat-suppressed datasets (p > 0.05). There were not significant differences between baseline functional parameters in responders and non-responders (p > 0.05). However, there were differences in the relationship between T2* and contrast-agent uptake in responders and non-responders. Voxels of similar characteristics were grouped in 5 clusters, and large intra-tumoural variations of all parameters were demonstrated.

CONCLUSION

Breast T2* measurements at 3 T are robust, but spatial resolution should be carefully considered. T2* of breast tumours at baseline is unrelated to DCE and DWI parameters and contribute towards describing functional heterogeneity of breast tumours.

Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

BACKGROUND

Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes.

METHODS

We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (K^trans, k_ep, and v_e) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed.

RESULTS

The mean of v_e was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of v_e was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of v_e was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of v_e was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher v_e coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006).

CONCLUSIONS

Various v_e parameters correlated with breast cancer prognostic factors and molecular subtypes.

Diffusion-weighted imaging in monitoring the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis

Background

Diffusion-weighted imaging (DWI) is suggested as an non-invasive and non-radioactive imaging modality in the identification of pathological complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NACT). A growing number of trials have been investigating in this aspect and some studies found a superior performance of DWI compared with conventional imaging techniques. However, the efficiency of DWI is still in dispute. This meta-analysis aims at evaluating the accuracy of DWI in the detection of pCR to NACT in patients with breast cancer.

Methods

Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were drawn to estimate the diagnostic effect of DWI to NACT. Summary receiver operating characteristic curve (SROC), the area under the SROC curve (AUC), and Youden index (*Q) were also calculated. The possible sources of heterogeneity among the included studies were explored using single-factor meta-regression analyses. Publication bias and quality assessment were assessed using Deek’s funnel plot and QUADAS-2 form respectively.

Results

Twenty studies incorporated 1490 participants were enrolled in our analysis. Pooled estimates revealed a sensitivity of 0.89 (95% CI, 0.86–0.91), a specificity of 0.72 (95% CI, 0.68–0.75), and a DOR of 27.00 (95% CI, 15.60–46.73). The AUC of SROC curve and *Q index were 0.9088 and 0.8408, respectively. The results of meta-regression analyses showed that pCR rate, time duration of study population, and study design were not the sources of heterogeneity.

Conclusion

A relatively high sensitivity and specificity of DWI in diagnosing pCP for patients with breast cancer underwent NACT treatment was found in our meta-analysis. This finding indicated that the use of DWI might provide an accurate and precise assessment of pCR to NACT.

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer

Patients with locally advanced invasive breast cancers are often treated with neoadjuvant chemotherapy prior to definitive surgical intervention. The primary aims of this approach are to: 1) reduce tumor burden thereby permitting breast conservation rather than mastectomy; 2) promptly treat possible metastatic disease, whether or not it is detectable on preoperative staging; and 3) potentially tailor future chemotherapeutic decisions by monitoring in-vivo tumor response. Accurate radiological assessment permits optimal management and planning in this population. However, assessment of tumor size and response to treatment can vary depending on the modality used, the measurement technique (such as single longest diameter, 3-D measurements, or calculated tumor volume), and varied response of different tumor subtypes to neoadjuvant chemotherapy (such as concentric shrinkage or tumor fragmentation). As discussed in further detail, digital mammography, digital breast tomosynthesis, US and MRI represent the key modalities with potential to help guide patient management. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Variability of non-Gaussian diffusion MRI and intravoxel incoherent motion (IVIM) measurements in the breast

We prospectively examined the variability of non-Gaussian diffusion magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) measurements with different numbers of b-values and excitations in normal breast tissue and breast lesions. Thirteen volunteers and fourteen patients with breast lesions (seven malignant, eight benign; one patient had bilateral lesions) were recruited in this prospective study (approved by the Internal Review Board). Diffusion-weighted MRI was performed with 16 b-values (0–2500 s/mm² with one number of excitations [NEX]) and five b-values (0–2500 s/mm², 3 NEX), using a 3T breast MRI. Intravoxel incoherent motion (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) parameters were estimated from IVIM and Kurtosis models using 16 b-values, and synthetic apparent diffusion coefficient (sADC) values were obtained from two key b-values. The variabilities between and within subjects and between different diffusion acquisition methods were estimated. There were no statistical differences in ADC₀, K, or sADC values between the different b-values or NEX. A good agreement of diffusion parameters was observed between 16 b-values (one NEX), five b-values (one NEX), and five b-values (three NEX) in normal breast tissue or breast lesions. Insufficient agreement was observed for IVIM parameters. There were no statistical differences in the non-Gaussian diffusion MRI estimated values obtained from a different number of b-values or excitations in normal breast tissue or breast lesions. These data suggest that a limited MRI protocol using a few b-values might be relevant in a clinical setting for the estimation of non-Gaussian diffusion MRI parameters in normal breast tissue and breast lesions.

Short tau inversion recovery in breast diffusion-weighted imaging: signal-to-noise ratio and apparent diffusion coefficients using a breast phantom in comparison with spectral attenuated inversion recovery

OBJECTIVE

This study aimed to compare the signal-to-noise ratios (SNRs) and apparent diffusion coefficients (ADCs) obtained using two fat suppression techniques in breast diffusion-weighted imaging (DWI) of a phantom.

MATERIALS AND METHODS

The breast phantom comprised agar gels with four different concentrations of granulated sugar (samples 1, 2, 3, and 4). DWI with short tau inversion recovery (STIR-DWI) and that with spectral attenuated inversion recovery (SPAIR-DWI) were performed using 3.0-T magnetic resonance imaging, and the obtained SNRs and ADCs were compared. ADCs were also compared between the right and left breast phantoms.

RESULTS

For samples 3 and 4, SNRs obtained using STIR-DWI were lower than those obtained using SPAIR-DWI. For samples 2, 3, and 4, overall ADCs obtained using STIR-DWI were significantly higher than those obtained using SPAIR-DWI (p < 0.001 for all), although no significant difference was observed for sample 1 (p = 0.62). STIR-DWI shows a positive bias and wide limits of agreement in Bland-Altman plot. The coefficients of variance of overall ADCs were good in STIR-DWI and SPAIR-DWI. For all samples, STIR-DWI demonstrated slightly larger percentage differences in ADCs between the right and left phantoms than SPAIR-DWI.

CONCLUSION

SNRs and ADCs obtained using STIR-DWI are influenced by the T ₁ value; a shorter T ₁ value decreases SNRs, overestimates ADCs, and induces the measurement error in ADCs. STIR-DWI showed a larger difference in ADCs between the right and left phantoms than SPAIR-DWI.

Diffusion-weighted imaging in identifying breast cancer pathological response to neoadjuvant chemotherapy: A meta-analysis

Background

Diffusion-weighted imaging (DWI) is increasingly used to identify pathological complete responses (pCRs) to neoadjuvant chemotherapy (NAC) in breast cancer. The aim of the present study was to assess the utility of DWI using a pooled analysis.

Materials and Methods

Literature databases were searched prior to July 2017. Fifteen studies with a total of 1181 patients were included. The data were extracted to perform pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis and subgroup analyses.

Result

The methodological quality was moderate. Remarkable heterogeneity was detected, primarily due to a threshold effect. The pooled weighted values were a sensitivity of 0.88 (95% confidence interval (CI): 0.81, 0.92), a specificity of 0.79 (95% CI: 0.70, 0.86), a positive likelihood ratio of 4.1 (95% CI: 2.9, 5.9), a negative likelihood ratio of 0.16 (95% CI: 0.10, 0.24), and a diagnostic odds ratio of 26 (95% CI: 15, 46). The area under the receiver operator characteristic curve was 0.91 (95% CI: 0.88, 0.93). In the subgroup analysis, the pooled specificity of change in the apparent diffusion coefficient (ADC) subgroup was higher than that in the pre-treatment ADC subgroup (0.80 [95% CI: 0.71, 087] vs. 0.63 [95% CI: 0.52, 0.73], P = 0.027).

Conclusions

DWI may be an accurate and nonradioactive imaging technique for identifying pCRs to NAC in breast cancer. Nonetheless, there are a variety of issues when assessing DWI techniques for estimating breast cancer responses to NAC, and large scale and well-designed clinical trials are needed to assess the technique's diagnostic value.

Relative enhanced diffusivity: noise sensitivity, protocol optimization, and the relation to intravoxel incoherent motion

OBJECTIVE

To explore the relationship between relative enhanced diffusivity (RED) and intravoxel incoherent motion (IVIM), as well as the impact of noise and the choice of intermediate diffusion weighting (b value) on the RED parameter.

MATERIALS AND METHODS

A mathematical derivation was performed to cast RED in terms of the IVIM parameters. Noise analysis and b value optimization was conducted by using Monte Carlo calculations to generate diffusion-weighted imaging data appropriate to breast and liver tissue at three different signal-to-noise ratios.

RESULTS

RED was shown to be approximately linearly proportional to the IVIM parameter f, inversely proportional to D and to follow an inverse exponential decay with respect to D*. The choice of intermediate b value was shown to be important in minimizing the impact of noise on RED and in maximizing its discriminatory power. RED was shown to be essentially a reparameterization of the IVIM estimates for f and D obtained with three b values.

CONCLUSION

RED imaging in the breast and liver should be performed with intermediate b values of 100 and 50 s/mm², respectively. Future clinical studies involving RED should also estimate the IVIM parameters f and D using three b values for comparison.

DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

This work evaluates quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE-MRI returns estimates of the volume transfer constant (K^trans ) and the extravascular extracellular volume fraction (v_e ). DW-MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA-MB-231. Tumors were grown to approximately 275 mm³ and sorted into the following groups: saline controls, low-dose Abraxane (15 mg/kg) and high-dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in K^trans was significantly higher than controls (p < 0.05) on day one for the high-dose group and on days one and three for the low-dose group. The percentage change in tumor volume was significantly different between the high-dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high-dose group (p = 0.03) and low-dose group (p = 0.052) compared with the control group. Co-immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α-smooth muscle actin (α-SMA)] indicated significantly higher vascular maturation in the low-dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high-dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and K^trans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.

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.

Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting

OBJECTIVE

To discuss standard-of-care and emerging imaging techniques employed for screening and detection, diagnosis and staging, monitoring response to therapy, and guiding cancer treatments.

DATA SOURCES

Published journal articles indexed in the National Library of Medicine database and relevant websites.

CONCLUSION

Imaging plays a fundamental role in the care of cancer patients and specifically, breast cancer patients in the neoadjuvant setting, providing an excellent opportunity for interprofessional collaboration between oncologists, researchers, radiologists, and oncology nurses. Quantitative imaging strategies to assess cellular, molecular, and vascular characteristics within the tumor is needed to better evaluate initial diagnosis and treatment response.

IMPLICATIONS FOR NURSING PRACTICE

Nurses caring for patients in all settings must continue to seek education on emerging imaging techniques. Oncology nurses provide education about the test, ensure the patient has appropriate pre-testing instructions, and manage patient expectations about timing of results availability.

Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients

OBJECTIVE

To examine the prognostic capabilities of intravoxel incoherent motion (IVIM) metrics and their ability to predict response to neoadjuvant treatment (NAT). Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI.

METHODS

This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions). Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12-14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC) from monoexponential decay, IVIM mean values (tissue diffusivity D_t, perfusion fraction f_p, and pseudodiffusivity D_p) and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT), excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests.

RESULTS

Average, kurtosis, and skewness of pseudodiffusion D_p significantly differentiated RECIST responders from nonresponders. ADC and D_t values generally increased (∼70%) and VTT% values generally decreased (∼20%) post-treatment.

CONCLUSION

D_p metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/D_t parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT.