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

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.

Potential of MRI in Assessing Treatment Response After Neoadjuvant Radiation Therapy Treatment in Breast Cancer Patients: A Scoping Review

The objective of this scoping review is to evaluate the potential of Magnetic Resonance Imaging (MRI) and to determine which of the available MRI techniques reported in the literature are the most promising for assessing treatment response in breast cancer patients following neoadjuvant radiotherapy (NRT). Ovid Medline, Embase, CINAHL, and Cochrane databases were searched to identify relevant studies published from inception until March 13, 2023. After primary selection, 2 reviewers evaluated each study using a standardized data extraction template, guided by set inclusion and exclusion criteria. A total of 5 eligible studies were selected. The positive and negative predictive values for MRI predicting pathological complete response across the studies were 67% to 88% and 76% to 85%, respectively. MRI's potential in assessing postradiotherapy tumor sizes was greater for volume measurements than uni-dimensional longest diameter measurements; however, overestimation in surgical tumor sizes was observed. Apparent diffusion coefficient (ADC) values and Time to Enhance (TTE) was seen to increase post-NRT, with a notable difference between responders and nonresponders at 6 months, indicating a potential role in assessing treatment response. In conclusion, this review highlights tumor volume measurements, ADC, and TTE as promising MRI metrics for assessing treatment response post-NRT in breast cancer. However, further research with larger cohorts is needed to confirm their utility. If MRI can accurately identify responders from nonresponders to NRT, it could enable a more personalized and tailored treatment approach, potentially minimizing radiation therapy related toxicity and enhancing cosmetic outcomes.

Improving signal-to-noise ratio by maximal convolution of longitudinal and transverse magnetization components in MRI: application to the breast cancer detection

PURPOSE

The extraction of information from images provided by medical imaging systems may be employed to obtain the specific objectives in the various fields. The quantity of signal to noise ratio (SNR) plays a crucial role in displaying the image details. The higher the SNR value, the more the information is available.

METHODS

In this study, a new function has been formulated using the appropriate suggestions on convolutional combination of the longitudinal and transverse magnetization components related to the relaxation times of T1 and T2 in MRI, where by introducing the distinct index on the maximum value of this function, the new maps are constructed toward the best SNR. Proposed functions were analytically simulated using Matlab software and evaluated with respect to various relaxation times. This proposed method can be applied to any medical images. For instance, the T1- and T2-weighted images of the breast indicated in the reference [35] were selected for modelling and construction of the full width at x maximum (FWxM) map at the different values of x-parameter from 0.01 to 0.955 at 0.035 and 0.015 intervals. The range of x-parameter is between zero and one. To determine the maximum value of the derived SNR, these intervals have been first chosen arbitrarily. However, the smaller this interval, the more precise the value of the x-parameter at which the signal to noise is maximum.

RESULTS

The results showed that at an index value of x = 0.325, the new map of FWxM (0.325) will be constructed with a maximum derived SNR of 22.7 compared to the SNR values of T1- and T2-maps by 14.53 and 17.47, respectively.

CONCLUSION

By convolving two orthogonal magnetization vectors, the qualified images with higher new SNR were created, which included the image with the best SNR. In other words, to optimize the adoption of MRI technique and enable the possibility of wider use, an optimal and cost-effective examination has been suggested. Our proposal aims to shorten the MRI examination to further reduce interpretation times while maintaining primary sensitivity.

SIGNIFICANCE

Our findings may help to quantitatively identify the primary sources of each type of solid and sequential cancer.

Future of Breast Radiology

The landscape of breast imaging has transformed significantly since mammography's introduction in the 1960s, accelerated by ultrasound and imageguided biopsies in the 1990s. The emergence of magnetic resonance imaging (MRI) in the 2000s added a valuable dimension to advanced imaging. Multimodality and multiparametric imaging have firmly established breast radiology's pivotal role in managing breast disorders. A shift from conventional to digital radiology emerged in the late 20th and early 21st centuries, enabling advanced techniques like digital breast tomosynthesis, contrast-enhanced mammography, and artificial intelligence (AI) integration. AI's impending integration into breast radiology may enhance diagnostics and workflows. It involves computer-aided diagnosis (CAD) algorithms, workflow support algorithms, and data processing algorithms. CAD systems, developed since the 1980s, optimize cancer detection rates by addressing false positives and negatives. Radiologists' roles will evolve into specialized clinicians collaborating with AI for efficient patient care and utilizing advanced techniques with multiparametric imaging and radiomics. Wearable technologies, non-contrast MRI, and innovative modalities like photoacoustic imaging show potential to enhance diagnostics. Imaging-guided therapy, notably cryotherapy, and theranostics, gains traction. Theranostics, integrating therapy and diagnostics, holds potential for precise treatment. Advanced imaging, AI, and novel therapies will revolutionize breast radiology, offering refined diagnostics and personalized treatments. Personalized screening, AI's role, and imaging-guided therapies will shape the future of breast radiology.

Diffusion-weighted MRI for predicting and assessing treatment response of liver metastases from CRC - A systematic review and meta-analysis

The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST criteria. However, therapy may alter tissue composition and not only tumor size, therefore, functional imaging techniques such as diffusion-weighted magnetic resonance imaging (DWI) may offer a more comprehensive assessment of treatment response. The aim of this systematic review and meta-analysis was to evaluate the use of DWI in the prediction and assessment of response to treatment in colorectal liver metastases and to determine if there is a baseline apparent diffusion coefficient (ADC) cut-off value that can predict a favorable response. A literature search was conducted using the MEDLINE/PubMed database, and risk of bias was evaluated using the QUADAS-2 tool. The mean differences between responders and non-responders were pooled. A total of 16 studies met the inclusion criteria, and various diffusion-derived techniques and coefficients were found to have potential for predicting and assessing treatment response. However, discrepancies were noted between studies. The most consistent predictor of response was a lower baseline ADC value calculated using traditional mono-exponential methods. Non-mono-exponential techniques for calculating DWI-derived parameters were also reported. A meta-analysis of a subset of studies failed to establish a cut-off value of ADC due to heterogeneity, but revealed a pooled mean difference of -0.12 × 10^-3 mm²/s between responders and non-responders. The results of this systematic review suggest that diffusion-derived techniques and coefficients may contribute to the evaluation and prediction of treatment response in colorectal liver metastases. Further controlled prospective studies are needed to confirm these findings and to guide clinical and radiological decision-making in the management of patients with CRC liver metastases.

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.

Assessment of diffusion-weighted MRI in predicting response to neoadjuvant chemotherapy in breast cancer patients

To compare region of interest (ROI)-apparent diffusion coefficient (ADC) on diffusion-weighted imaging (DWI) measurements and Ki-67 proliferation index before and after neoadjuvant chemotherapy (NACT) for breast cancer. 55 women were enrolled in this prospective single-center study, with a final population of 47 women (49 cases of invasive breast cancer). ROI-ADC measurements were obtained on MRI before and after NACT and were compared to histological findings, including the Ki-67 index in the whole study population and in subgroups of "pathologic complete response" (pCR) and non-pCR. Nineteen percent of women experienced pCR. There was a significant inverse correlation between Ki-67 index and ROI-ADC before NACT (r = - 0.443, p = 0.001) and after NACT (r = - 0.614, p < 0.001). The mean Ki-67 index decreased from 45.8% before NACT to 18.0% after NACT (p < 0.001), whereas the mean ROI-ADC increased from 0.883 × 10^-3 mm²/s before NACT to 1.533 × 10^-3 mm²/s after NACT (p < 0.001). The model for the prediction of Ki67 index variations included patient age, hormonal receptor status, human epidermal growth factor receptor 2 status, Scarff-Bloom-Richardson grade 2, and ROI-ADC variations (p = 0.006). After NACT, a significant increase in breast cancer ROI-ADC on diffusion-weighted imaging was observed and a significant decrease in the Ki-67 index was predicted. Clinical trial registration number: clinicaltrial.gov NCT02798484, date: 14/06/2016.

Contrast-enhanced ultrasonography for early prediction of response of neoadjuvant chemotherapy in breast cancer

Neoadjuvant chemotherapy (NAC) is widely accepted as a primary treatment for inoperable or locally advanced breast cancer before definitive surgery. However, not all advanced breast cancers are sensitive to NAC. Contrast-enhanced ultrasonography (CEUS) has been considered to assess tumor response to NAC as it can effectively reflect the condition of blood perfusion and lesion size. Therefore, this study aimed to evaluate the diagnostic performance of CEUS to predict early response in different regions of interest in breast tumors under NAC treatment. This prospective study included 82 patients with advanced breast cancer. Parameters of TIC (time-intensive curve) between baseline and after the first cycle of NAC were calculated for the rate of relative change (Δ), including Δpeak, ΔTTP (time to peak), ΔRBV (regional blood volume), ΔRBF (regional blood flow) and ΔMTT (mean transit time). The responders and non-responders were distinguished by the Miller-Payne Grading (MPG) system and parameters from different regions of tumors were compared in these two groups. For ROI 1(the greatest enhancement area in the central region of the tumor), there were significant differences in Δpeak1, ΔRBV1 and ΔRBF1 between responders and non-responders. For ROI 2 (the greatest enhancement area on edge of the tumor), there were significant differences in Δpeak2 and ΔRBF2 between the groups. The Δpeak1 and ΔRBF2 showed good prediction (AUC 0.798-0.820, p ≤ 0.02) after the first cycle of NAC. When the cut-off value was 0.115, the ΔRBF2 had the highest diagnostic accuracy and the maximum NPV. Quantitative TIC parameters could be effectively used to evaluate early response to NAC in advanced breast cancer.

Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.

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.

Nucleic Acid Sensing Pathways in DNA Repair Targeted Cancer Therapy

The repair of DNA damage is a complex process, which helps to maintain genome fidelity, and the ability of cancer cells to repair therapeutically DNA damage induced by clinical treatments will affect the therapeutic efficacy. In the past decade, great success has been achieved by targeting the DNA repair network in tumors. Recent studies suggest that DNA damage impacts cellular innate and adaptive immune responses through nucleic acid-sensing pathways, which play essential roles in the efficacy of DNA repair targeted therapy. In this review, we summarize the current understanding of the molecular mechanism of innate immune response triggered by DNA damage through nucleic acid-sensing pathways, including DNA sensing via the cyclic GMP-AMP synthase (cGAS), Toll-like receptor 9 (TLR9), absent in melanoma 2 (AIM2), DNA-dependent protein kinase (DNA-PK), and Mre11-Rad50-Nbs1 complex (MRN) complex, and RNA sensing via the TLR3/7/8 and retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs). Furthermore, we will focus on the recent developments in the impacts of nucleic acid-sensing pathways on the DNA damage response (DDR). Elucidating the DDR-immune response interplay will be critical to harness immunomodulatory effects to improve the efficacy of antitumor immunity therapeutic strategies and build future therapeutic approaches.

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.

Value of diffusion-weighted MRI in predicting early response to neoadjuvant chemotherapy of breast cancer: comparison between ROI-ADC and whole-lesion-ADC measurements

OBJECTIVE

The aim of the study was to assess DWI with ROI-ADC and WL-ADC measurements in early response after NAC in breast cancer.

METHODS

Between January 2016 and December 2019, 55 women were enrolled in this prospective single-center study. MRI was performed at three time points for each patient: before treatment (MRI 1: DW and DCE MRI), after one cycle of NAC (MRI 2: noncontrast DW MRI), and after completion of NAC before surgery (MRI 3: DW and DCE MRI). ROI-ADC and WL-ADC measurements were obtained on MRI and were compared to histology findings and to the RCB class. Patients were categorized as having pCR or non-pCR.

RESULTS

Among 48 patients, 9 experienced pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, whereas WL-ADC did not predict pCR. An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response.

CONCLUSION

After one cycle of NAC, a significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses.

KEY POINTS

• An increase of WL-ADC between MRI 1 and 2 of more than 25.5% had a sensitivity of 83.3% and a specificity of 75.5% in predicting radiologic complete response. • An increase of ROI-ADC between MRI 1 and 2 of more than 47.5% had a sensitivity of 88.9% and a specificity of 63.4% in predicting pCR, and a sensitivity of 83.3% and a specificity of 64.9% in predicting radiologic complete response. • A significant increase in breast tumor ROI-ADC at DWI predicted complete pathologic and radiologic responses.

Factors affecting the value of diffusion-weighted imaging for identifying breast cancer patients with pathological complete response on neoadjuvant systemic therapy: a systematic review

This review aims to identify factors causing heterogeneity in breast DWI-MRI and their impact on its value for identifying breast cancer patients with pathological complete response (pCR) on neoadjuvant systemic therapy (NST). A search was performed on PubMed until April 2020 for studies analyzing DWI for identifying breast cancer patients with pCR on NST. Technical and clinical study aspects were extracted and assessed for variability. Twenty studies representing 1455 patients/lesions were included. The studies differed with respect to study population, treatment type, DWI acquisition technique, post-processing (e.g., mono-exponential/intravoxel incoherent motion/stretched exponential modeling), and timing of follow-up studies. For the acquisition and generation of ADC-maps, various b-value combinations were used. Approaches for drawing regions of interest on longitudinal MRIs were highly variable. Biological variability due to various molecular subtypes was usually not taken into account. Moreover, definitions of pCR varied. The individual areas under the curve for the studies range from 0.50 to 0.92. However, overlapping ranges of mean/median ADC-values at pre- and/or during and/or post-NST were found for the pCR and non-pCR groups between studies. The technical, clinical, and epidemiological heterogeneity may be causal for the observed variability in the ability of DWI to predict pCR accurately. This makes implementation of DWI for pCR prediction and evaluation based on one absolute ADC threshold for all breast cancer types undesirable. Multidisciplinary consensus and appropriate clinical study design, taking biological and therapeutic variation into account, is required for obtaining standardized, reliable, and reproducible DWI measurements for pCR/non-pCR identification.

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

Retrospective Correction of ADC for Gradient Nonlinearity Errors in Multicenter Breast DWI Trials: ACRIN6698 Multiplatform Feasibility Study

The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm²/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm²/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.

Tissue Sodium Concentration Quantification at 7.0-T MRI as an Early Marker for Chemotherapy Response in Breast Cancer: A Feasibility Study

Background Tissue sodium concentration (TSC) is elevated in breast cancer and can determine chemotherapy response. Purpose To test the feasibility of using a sodium 23 (²³Na) MRI protocol at 7.0 T for TSC quantification to predict early treatment outcomes of neoadjuvant chemotherapy in breast cancer and to determine whether those quantitative values provide additional information about efficacy. Materials and Methods Women with primary breast cancer were included in this prospective study. From July 2017 to June 2018, participants underwent 7.0-T ²³Na MRI. Multichannel data sets were acquired with a density-adapted, three-dimensional radial projection reconstruction pulse sequence. Two-dimensional tumor size and TSC were evaluated before and after the first and second chemotherapy cycle, and statistical tests were performed based on the presence or absence of a pathologic complete response (pCR). Results Fifteen women with breast cancer and six healthy women were enrolled. The mean baseline tumor size in women with a pCR was 7.0 cm² ± 5.0 (standard deviation), and the mean baseline tumor size in women without a pCR was 19.0 cm² ± 12.0. After the first chemotherapy cycle, women with a pCR showed a reduced tumor size of 32.9% (2.3 cm²/7.0 cm²), compared with 15.3% (2.9 cm²/19.0 cm²) in those without a pCR. The areas under the receiver operating characteristic curve for tumor size reduction after the first and second chemotherapy cycle were 0.73 (95% CI: 0.09, 0.50; P = .12) and 0.93 (95% CI: 0.04, 0.60; P < .001), respectively. Women with a pCR had a mean baseline TSC of 69.4 mmol/L ± 6.1, with a reduction of 12.0% (8.3 mmol/L), whereas those without a pCR had a mean baseline TSC of 71.7 mmol/L ± 5.7, with a reduction of 4.7% (3.4 mmol/L) after the first cycle. The areas under the receiver operating characteristic curve for TSC after the first and second cycles were 0.96 (95% CI: 0.86, 1.00; P < .001) and 1.000 (95% CI: 1.00, P < .001), respectively. Conclusion Using 7.0-T MRI for tissue sodium concentration quantification to predict early treatment outcomes of neoadjuvant chemotherapy in breast cancer is feasible, with reduced tissue sodium concentration indicative of cancer response. © RSNA, 2021 Online supplemental material is available for this article.

Contrast-Enhanced Ultrasonography in the Diagnosis and Treatment Modulation of Breast Cancer

The aim of this paper is to highlight the role of contrast-enhanced ultrasound in breast cancer in terms of diagnosis, staging and follow-up of the post-treatment response. Contrast-enhanced ultrasound (CEUS) is successfully used to diagnose multiple pathologies and has also clinical relevance in breast cancer. CEUS has high accuracy in differentiating benign from malignant lesions by analyzing the enhancement characteristics and calculating the time-intensity curve’s quantitative parameters. It also has a significant role in axillary staging, especially when the lymph nodes are not suspicious on clinical examination and have a normal appearance on gray-scale ultrasound. The most significant clinical impact consists of predicting the response to neoadjuvant chemotherapy, which offers the possibility of adjusting the therapy by dynamically evaluating the patient. CEUS is a high-performance, feasible, non-irradiating, accessible, easy-to-implement imaging method and has proven to be a valuable addition to breast ultrasound.

Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.

Background parenchymal enhancement: behavior during neoadjuvant chemotherapy for breast cancer and relationship with a pathological complete response

Objective

To evaluate background parenchymal enhancement (BPE) and its characteristics, as well as its behavior before and after neoadjuvant chemotherapy (NAC), in both breasts of patients with unilateral breast cancer.

Materials and Methods

This was a retrospective, cross-sectional observational study involving a consecutive sample of women with invasive breast cancer who underwent breast magnetic resonance imaging (MRI) between July 2007 and July 2017, as well as undergoing dynamic contrast-enhanced MRI before and after NAC. In both breasts, we evaluated the BPE in accordance with the Breast Imaging Reporting and Data System. We applied logistic regression analysis, and values of p < 0.05 were considered significant.

Results

We evaluated 150 women. The mean age was 45.2 years (range, 20-74 years). The variables correlating independently with a high pre-NAC BPE, in the affected and contralateral breasts, were being under 50 years of age (odds ratio [OR] = 6.55; 95% confidence interval [95% CI]: 2.32-18.46, for both breasts) and a post-NAC BPE reduction (OR = 17.75; 95% CI: 4.94-63.73 and OR = 18.47; 95% CI: 5.19-66.49, respectively).

Conclusion

Patients with invasive unilateral breast cancer who have a high pre-NAC BPE in both breasts tend to be under 50 years of age and to show a post-NAC reduction in BPE.

18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients

PURPOSE

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. ¹⁸F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC.

METHODS

This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage.

CONCLUSION

Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

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.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

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.

Evaluation of the response of breast cancer patients to neoadjuvant chemotherapy by combined contrast-enhanced ultrasonography and ultrasound elastography

The purpose of the present study was to investigate whether contrast-enhanced ultrasonography (CEUS) in combination with ultrasound elastography (UE) is able to accurately predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 65 breast cancer patients who received NAC at the First Affiliated Hospital of Zhejiang University (Hangzhou, China) between February 2016 and August 2017 and were recruited for the present study. Prior to and after NAC, examination by CEUS, UE or their combination was performed. Pathological results were obtained at the end of each chemotherapy cycle, based on which 41 cases were assigned to the response group and 24 to the non-response group. Kappa values were 0.710, 0.434 and 0.836 for CEUS, UE and CEUS+UE, respectively. The area under the receiver operating characteristic curves for CEUS, UE and CEUS+UE for determining the response to NAC was 0.864 [95% confidence interval (CI), 0.765–0.964], 0.715 (95% CI, 0.579–0.850) and 0.910 (95% CI, 0.826–0.993), respectively. It was identified that the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of CEUS+UE were higher than those of CEUS and US individually. The prediction accuracy was 89.2, 90.8 and 100% for CEUS, UE and their combination, respectively. CEUS and UE have their own advantages in evaluating the clinical efficacy of NAC in breast cancer, and a higher accuracy was achieved when the two techniques were applied in combination. Therefore, a combination of CEUS and UE may be a preferred method for the clinical assessment of the efficacy of NAC in breast cancer patients.

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.

Can Multi-Parametric MR Based Approach Improve the Predictive Value of Pathological and Clinical Therapeutic Response in Breast Cancer Patients?

The potential of total choline (tCho), apparent diffusion coefficient (ADC) and tumor volume, both individually and in combination of all these three parameters (multi-parametric approach), was evaluated in predicting both pathological and clinical responses in 42 patients with locally advanced breast cancer (LABC) enrolled for neoadjuvant chemotherapy (NACT). Patients were sequentially examined by conventional MRI; diffusion weighted imaging and in vivo proton MR spectroscopy at 4 time points (pre-therapy, after I, II, and III NACT) at 1.5 T. Miller Payne grading system was used for pathological assessment of response. Of the 42 patients, 24 were pathological responders (pR) while 18 were pathological non-responders (pNR). Clinical response determination classified 26 patients as responders (cR) while 16 as non-responders (cNR). tCho and ADC showed significant changes after I NACT, however, MR measured tumor volume showed reduction only after II NACT both in pR and cR. After III NACT, the sensitivity to detect responders was highest for MR volume (83.3% for pR and 96.2% for cR) while the specificity was highest for ADC (76.5% for pR and 100% for cR). Combination of all three parameters exhibited lower sensitivity (66.7%) than MR volume for pR prediction, however, a moderate improvement was seen in specificity (58.8%). For the prediction of clinical response, multi-parametric approach showed 84.6% sensitivity with 100% specificity compared to MR volume (sensitivity 96.2%; specificity 80%). Kappa statistics demonstrated substantial agreement of clinical response with MR volume (k = 0.78) and with multi-parametric approach (k = 0.80) while moderate agreement was seen for tCho (k = 0.48) and ADC (k = 0.46). The values of k for tCho, MR volume and ADC were 0.31, 0.38, and 0.18 indicating fair, moderate, and slight agreement, respectively with pathological response. Moderate agreement (k = 0.44) was observed between clinical and pathological responses. Our study demonstrated that both tCho and ADC are strong predictors of assessment of early pathological and clinical responses. Multi-parametric approach yielded 100% specificity in predicting clinical response. Following III NACT, MR volume emerged as highly suitable predictor for both clinical and pathological assessments. PCA demonstrated separate clusters of pR vs. pNR and cR vs. cNR at post-therapy while with some overlap at pre-therapy.