Identification of residual breast carcinoma following neoadjuvant chemotherapy: diffusion-weighted imaging--comparison with contrast-enhanced MR imaging and pathologic findings

The Role of Diffusion-Weighted Imaging in Characterizing Benign and Malignant Breast Lesions: A Retrospective Study

Introduction  Diffusion-weighted imaging (DWI) is a promising magnetic resonance imaging (MRI) technique for differentiating between benign and malignant breast lesions. This study set out to assess the diagnostic utility of DWI and apparent diffusion coefficient (ADC) values in the characterization of breast lesions. Materials and methods A retrospective analysis comprised 30 patients with breast lesions who had breast MRI with DWI. The histopathological findings, ADC readings, and conventional MRI features were all analyzed. The receiver operating characteristic (ROC) curve analysis method was utilized to assess the diagnostic accuracy of DWI. Results Out of the 30 lesions, 22 (73.3%) were benign and eight (26.7%) were malignant. Malignant lesions exhibited significantly lower ADC values (p < 0.001) compared to benign lesions. An ADC cutoff value of 1.1 × 10-3 mm2/s was optimal for differentiating benign from malignant lesions, yielding 90.81% sensitivity, 91.51% specificity, and 91.5% accuracy. Conclusion Combining DWI with quantitative ADC analysis is a helpful, non-invasive method for the characterization of breast lesions. It shows excellent diagnostic accuracy in identifying benign and malignant lesions, which may cut down on pointless biopsies and help with patient management.

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-positive/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.

The assessment of pathological response to neoadjuvant chemotherapy in muscle-invasive bladder cancer patients with DCE-MRI and DWI: a systematic review and meta-analysis

OBJECTIVE

The purpose of this meta-analysis was to determine the value of dynamic contrast-enhanced-MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in evaluating the pathological response of muscle invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC), and further indirectly compare the diagnostic performance of DCE-MRI and DWI.

METHODS

Literatures associated to DCE-MRI and DWI in the evaluation of pathological response of MIBC to NAC were searched from PubMed, Cochrane Library, web of science, and EMBASE databases. The quality assessment of diagnostic accuracy studies 2 tool was used to assess the quality of studies. Pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curves (AUC) with their 95% confidence intervals (CIs) were calculated to evaluate the diagnostic performance of DCE-MRI and DWI in predicting the pathological response to NAC in patients with MIBC.

RESULTS

There were 11 studies involved, 6 of which only underwent DCE- MRI examination, 4 of which only underwent DWI examination, and 1 of which underwent both DCE- MRI and DWI examination. The pooled SE, SP, PLR, NLR, DOR of DCE-MRI were 0.88 (95% CI: 0.78-0.93), 0.88 (95% CI: 0.67-0.96), 7.4 (95% CI: 2.3-24.2), 0.14 (95% CI: 0.07-0.27), and 53 (95% CI: 10-288), respectively. The pooled SE, SP, PLR, NLR, DOR of DWI were 0.83 (95% CI: 0.75-0.88), 0.88 (95% CI: 0.81-0.93), 7.1 (95% CI: 4.3-11.7), 0.20 (95% CI: 0.14-0.28), and 36 (95% CI:18-73), respectively. The AUCs of SROC curve for DCE-MRI and DWI were 0.93 (95% CI: 0.91-0.95) and 0.92 (95% CI: 0.89-0.94), respectively. There were no significant differences between DWI and DCE-MRI for SE, SP, and AUC.

CONCLUSION

This meta-analysis demonstrated high diagnostic performance of both DCE-MRI and DWI in predicting the pathological response to NAC in MIBC. DWI might be a potential substitute for DCE-MRI, with no significant difference in diagnostic performance between the two. However, caution should be taken when applying our results, as our results were based on indirect comparison.

ADVANCES IN KNOWLEDGE

No previous studies have comprehensively analysed the value of DCE-MRI and DWI in evaluating the pathological response to NAC in MIBC. According to the current study, both DCE-MRI and DWI yielded high diagnostic performance, with the AUCs of 0.93 and 0.92, respectively. Indirect comparison no significant difference in the diagnostic performanceof DCE-MRI and DWI.

Imaging findings for response evaluation of ductal carcinoma in situ in breast cancer patients treated with neoadjuvant systemic therapy: a systematic review and meta-analysis

OBJECTIVES

In approximately 45% of invasive breast cancer (IBC) patients treated with neoadjuvant systemic therapy (NST), ductal carcinoma in situ (DCIS) is present. Recent studies suggest response of DCIS to NST. The aim of this systematic review and meta-analysis was to summarise and examine the current literature on imaging findings for different imaging modalities evaluating DCIS response to NST. More specifically, imaging findings of DCIS pre- and post-NST, and the effect of different pathological complete response (pCR) definitions, will be evaluated on mammography, breast MRI, and contrast-enhanced mammography (CEM).

METHODS

PubMed and Embase databases were searched for studies investigating NST response of IBC, including information on DCIS. Imaging findings and response evaluation of DCIS were assessed for mammography, breast MRI, and CEM. A meta-analysis was conducted per imaging modality to calculate pooled sensitivity and specificity for detecting residual disease between pCR definition no residual invasive disease (ypT0/is) and no residual invasive or in situ disease (ypT0).

RESULTS

Thirty-one studies were included. Calcifications on mammography are related to DCIS, but can persist despite complete response of DCIS. In 20 breast MRI studies, an average of 57% of residual DCIS showed enhancement. A meta-analysis of 17 breast MRI studies confirmed higher pooled sensitivity (0.86 versus 0.82) and lower pooled specificity (0.61 versus 0.68) for detection of residual disease when DCIS is considered pCR (ypT0/is). Three CEM studies suggest the potential benefit of simultaneous evaluation of calcifications and enhancement.

CONCLUSIONS AND CLINICAL RELEVANCE

Calcifications on mammography can remain despite complete response of DCIS, and residual DCIS does not always show enhancement on breast MRI and CEM. Moreover, pCR definition effects diagnostic performance of breast MRI. Given the lack of evidence on imaging findings of response of the DCIS component to NST, further research is demanded.

KEY POINTS

• Ductal carcinoma in situ has shown to be responsive to neoadjuvant systemic therapy, but imaging studies mainly focus on response of the invasive tumour. • The 31 included studies demonstrate that after neoadjuvant systemic therapy, calcifications on mammography can remain despite complete response of DCIS and residual DCIS does not always show enhancement on MRI and contrast-enhanced mammography. • The definition of pCR has impact on the diagnostic performance of MRI in detecting residual disease, and when DCIS is considered pCR, pooled sensitivity was slightly higher and pooled specificity slightly lower.

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.

Predicting the Early Response to Neoadjuvant Therapy with Breast MR Morphological, Functional and Relaxometry Features-A Pilot Study

Aim: To evaluate the role of MR relaxometry and derived proton density analysis in the prediction of early treatment response after two cycles of neoadjuvant therapy (NAT), in patients with breast cancer. Methods: This was a prospective study that included 59 patients with breast cancer, who underwent breast MRI prior (MRI1) and after two cycles of NAT (MRI2). The MRI1 included a sequential acquisition with five different TE’s (50, 100, 150, 200 and 250 ms) and a TR of 5000 ms. Post-processing was used to obtain the T2 relaxometry map from the MR acquisition. The tumor was delineated and seven relaxometry and proton density parameters were extracted. Additional histopathology data, T2 features and ADC were included. The response to NAT was reported based on the MRI2 as responders: partial response (>30% decreased size) and complete response (no visible tumor stable disease (SD); and non-responders: stable disease or progression (>20% increased size). Statistics was done using Medcalc software. Results: There were 50 (79.3%) patients with response and 13 (20.7%) non-responders to NAT. Age, histologic type, “in situ” component, tumor grade, estrogen and progesterone receptors, ki67% proliferation index and HER2 status were not associated with NAT response (all p > 0.05). The nodal status (N) 0 was associated with early response, while N2 was associated with non-response (p = 0.005). The tumor (T) and metastatic (M) stage were not statistically significant associated with response (p > 0.05). The margins, size and ADC values were not associated with NAT response (p-value > 0.05). The T2 min relaxometry value was associated with response (p = 0.017); a cut-off value of 53.58 obtained 86% sensitivity (95% CI 73.3−94.2), 69.23 specificity (95% CI 38.6−90.9), with an AUC = 0.715 (p = 0.038). The combined model (T2 min and N stage) achieved an AUC of 0.826 [95% CI: 0.66−0.90, p-value < 0.001]. Conclusions: MR relaxometry may be a useful tool in predicting early treatment response to NAT in breast cancer patients.

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.

DCE-MRI Performance in Triple Negative Breast Cancers: Comparison with Non-Triple Negative Breast Cancers

BACKGROUND

Triple negative breast cancers is considered to have the worst prognosis in breast cancer. Dynamic contrast enhanced magnetic resonance imaging has been widely used in the diagnosis of breast cancer because that is more sensitive to breast cancer. However, there are few reports about the MRI characteristics of triple negative breast cancers.

OBJECTIVE

The aim of the study was to evaluate the imaging finding in triple negative breast cancers compared with non-TNBC and attempt to predict it.

METHOD

In total, 223 patients with a preoperative diagnosis of breast cancer were enrolled in the study. Dynamic contrast enhanced magnetic resonance imaging was performed before being diagnosed with breast cancer, and histopathological assessment was confirmed after biopsy or operation. The patients were divided into 2 groups based on immunohistochemical, namely the triple negative breast cancers or non-triple negative breast cancers.

RESULTS

The 2 groups demonstrated significant differences regarding the tumor size, margin, outline, burr sign, enhancement, inverted nipple(P<0.05). A multivariate logistic regression analysis was performed to further validate the association of these features, however, only margin [odds ratio (OR), 0.038; 95% confidence interval (CI), 0.014-0.100; <0.001], outline [odds ratio (OR), 0.039; 95% confidence interval (CI), 0.008-0.200; <0.001], burr sign [odds ratio (OR), 2.786; 95% confidence interval (CI), 1.225-6.333; 0.014] and enhancement [odds ratio (OR), 0.131; 95% confidence interval (CI), 0.037-0.457; P=0.001] were associated with TNBC.

CONCLUSION

The results indicated that the specific dynamic contrast enhanced magnetic resonance imaging features can be possible predictors of pathological results, with a consequent prognostic value.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI

RATIONALE AND OBJECTIVES

The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC.

MATERIALS AND METHODS

Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E₉₀), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model.

RESULTS

This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E₉₀ (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively.

CONCLUSION

The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.

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.

Diffusion-weighted imaging or MR spectroscopy: Which to use for the assessment of the response to chemotherapy in breast cancer patients?

Background

Diffusion-weighted MRI (DWI) and MR spectroscopy (MRS) both are noninvasive MR sequences that could be used as a reliable tool to assess the functional behavior of the breast cancer. The aim of the study was to assess the value of DWI and MRS in predicting the early response to neo-adjuvant chemotherapy (NAC) and absence of residual disease after treatment.

Results

One hundred thirty-three patients diagnosed with breast cancer and scheduled for NAC were enrolled in this study. All lesions were subjected to qualitative and quantitative analysis of DCE-MRI, DWI and MRS, where the lesions size, kinetic parameters, ADC values and MRS choline peak were recorded before the start of NAC and after completion of chemotherapy. The results of each MRI modality were correlated with the findings that were found at the pathology report of the complete surgical specimen. The sensitivity and specificity of the MR modalities to predict pathological complete remission post-NAC were 73.68% and 83.33%, respectively, using the kinetic curve pattern, 78.95% and 83.33%, respectively, using the ADC value and finally 78.95% and 91.67%, respectively, using the MRS choline peak. Similar sensitivity (89.47%) to predict pathological complete remission was presented by the ADC value and the MRS choline peak together when compared to the ADC value and dynamic curve patterns.

Conclusion

DWI and MRS are valuable MRI techniques and their accuracy in detecting residual disease is almost similar to that of DCE MRI. The inclusion of these sequences in the imaging protocol of NAC candidates improve monitoring of the response to treatment and allow early distinction between complete, partial and non-responders' cases in breast cancer patients.

The diagnostic performance of diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in evaluating the pathological response of breast cancer to neoadjuvant chemotherapy: A meta-analysis

PURPOSE

To evaluate and compare the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the pathological response of breast cancer to neoadjuvant chemotherapy (NAC).

METHODS

We searched PubMed, EMBASE, Cochrane Library, and Web of Science systematically to identify relevant studies from inception to December 2020. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess the methodological quality of the included studies. We extracted sufficient data to construct 2 × 2 tables and then used STATA 12.0 to perform data pooling, heterogeneity testing, meta-regression analysis and subgroup analysis.

RESULTS

A total of 41 articles were enrolled in this study, including 27 studies (2107 patients) on DCE-MRI and 23 studies (1321 patients) on DWI. The pooled sensitivity and specificity of DCE-MRI were 0.75 and 0.79, and the pooled sensitivity and specificity of DWI were 0.77 and 0.75. There was no significant difference in sensitivity (P = 0.598) and specificity (P = 0.218) ​​between DCE-MRI and DWI. And meta-regression analysis showed that both magnetic field strength and the time of examination had significant effects on heterogeneity.

CONCLUSIONS

DWI might be a potential substitute for DCE-MRI in predicting the pathological response of breast cancer to NAC as there was no significant difference in the diagnostic performance between the two. However, considering that not all included studies directly compared the diagnostic performance of DWI and DCE-MRI in the same patients and the heterogeneity of the included studies, caution should be exercised in applying our results.

Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models

Background

To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables.

Methods

In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed.

Results

Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC_200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC_200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001).

Conclusion

Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.

Breast MRI for Evaluation of Response to Neoadjuvant Therapy

Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.^©RSNA, 2021.

Predicting breast cancer response to neoadjuvant chemotherapy using ensemble deep transfer learning based on CT images

OBJECTIVE

To develop an ensemble a deep transfer learning model of CT images for predicting pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).

METHODS

The data were obtained from the public dataset 'QIN-Breast' from The Cancer Imaging Archive (TCIA). CT images were gathered before and after the first cycle of NAC. CT images of 121 breast cancer patients were used to train and test the model. Among these patients, 58 achieved a pCR and 63 showed a non-pCR based pathology examination of surgical results after NAC. The dataset was split into training and testing subsets with a ratio of 7:3. In addition, the number of training samples in the dataset was increased from 656 to 1,968 by performing an image augmentation method. Two deep transfer learning models namely, DenseNet201 and ResNet152V2, and the ensemble model with a concatenation of two models, were trained and tested using CT images.

RESULTS

The ensemble model obtained the highest accuracy of 100% on the testing dataset. Furthermore, we received the best performance of 100% in recall, precision and f1-score value for the ensemble model. This supports the fact that the ensemble model results in better-generalized model and leads to efficient framework. Although a 0.004 and 0.003 difference were seen between the AUC of two base models (DenseNet201 and ResNet152V2) and the proposed ensemble, this increase in the model quality is critical in medical research. T-SNE revealed that in the proposed ensemble, no points were clustered into the wrong class. These results expose the strong performance of the proposed ensemble.

CONCLUSION

The study concluded that the ensemble model can increase the ability to predict breast cancer response to first-cycle NAC than two DenseNet201 and ResNet152V2 models.

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26–66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677–0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722–0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Purpose

To assess the accuracy of magnetic resonance imaging (MRI) measurements in locally advanced oestrogen receptor-positive and human epidermal growth factor receptor 2-negative breast tumours before, during and after neoadjuvant endocrine treatment (NET) for evaluation of tumour response in comparison with clinical and pathological assessments.

Methods

This prospective study enrolled postmenopausal patients treated neoadjuvant with letrozole and exemestane given sequentially in an intra-patient cross-over regimen. Fifty-four patients were initially recruited, but only 35 fulfilled the inclusion criteria and confirmed to participate with a median age of 77. Tumours were scanned with MRI prior to treatment, during the eighth week of treatment and prior to surgery. Additionally, changes in longest diameter on clinical examination (CE) and tumour size at pathology were determined. Pre- and post-operative measurements of tumour size were compared in order to evaluate tumour response.

Results

The correlation between post-treatment MRI size and pathology was moderate and higher with a correlation coefficient (r) 0.64 compared to the correlation between CE and pathology r = 0.25. Post-treatment MRI and clinical results had a negligible bias towards underestimation of lesion size. Tumour size on MRI and CE had 0.82 cm and 0.52 cm lower mean size than tumour size measured by pathology, respectively.

Conclusions

The higher correlation between measurements of residual disease obtained on MRI and those obtained with pathology validates the accuracy of imaging assessment during NET. MRI was found to be more accurate for estimating complete responses than clinical assessments and warrants further investigation in larger cohorts to validate this finding.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05852-7) contains supplementary material, which is available to authorized users.

The diagnostic accuracy of magnetic resonance imaging in predicting pathologic complete response after neoadjuvant chemotherapy in patients with different molecular subtypes of breast cancer

Background

Patients treated with neoadjuvant chemotherapy (NAC) who achieve a pathologic complete response (pCR) can be identified preoperatively and can potentially be spared the morbidity of surgery. The objective of this retrospective study was to estimate the diagnostic accuracy of preoperative magnetic resonance imaging (MRI) in predicting pCR in patients with different molecular subtypes of breast cancer and to provide a basis for the selection of surgical methods.

Methods

We retrospectively reviewed breast MRI data from August 2015 to December 2018 of patients who underwent four or more cycles of NAC. Factors associated with radiological complete response (rCR) and pCR were analyzed in univariable and multivariable settings. The accuracy of MRI and the correlation between rCR and pCR were also analyzed in each tumor subtype.

Results

A total of 177 women with a primary tumor fulfilled the study criteria; 18 of these patients (10.2%) achieved rCR, and 21 (11.9%) achieved a pCR. MRI diagnosis of rCR was significantly correlated with pCR with a Spearman's correlation coefficient of 0.686 in the entire population. The sensitivity, specificity, accuracy, pCR predictive value (PPV), and non-pCR predictive value (NPV) were estimated to be 66.67%, 97.44%, 93.79%, 77.78%, and 95.60%, respectively. Statistically significant correlations between rCR and pCR were found in Luminal B high Ki67% (P<0.001), HER2-positive (P=0.0035), and triple-negative (P<0.001) subtypes, but not in Luminal A and Luminal B low Ki67% subtypes. On univariate analysis, the tumor characteristics significantly associated with both rCR and pCR were small tumor, lymph node metastasis (LNM) negativity, early clinical stage, high grade, high Ki67% index, and different molecular subtype. On multivariate logistic regression analysis, grade 3 tumors (P=0.013), Ki67% ≥40% (P<0.000), and stage I tumor (P=0.006) were independently associated with rCR. However, grade 3 tumors (P=0.001), triple-negative breast cancer (TNBC), and clinical stages I and II tumors (P=0.003; P=0.030) were independently associated with the likelihood of attaining a pCR.

Conclusions

The overall accuracy of MRI in predicting pCR in invasive breast cancer patients who received NAC was 93.8%. The performance of MRI differed among molecular subtypes, and the highest PPV was found in TNBC (100%) and Luminal B high Ki67% (75%) subtypes.

Evaluation of breast carcinoma regression after preoperative chemotherapy by label-free multiphoton imaging and image analysis

Neoadjuvant chemotherapy is increasingly being used in breast carcinoma as it significantly improves the prognosis and consistently leads to an increased rate of breast preservation. How to accurately assess tumor response after treatment is a crucial factor for developing reasonable therapeutic strategy. In this study, we were in an attempt to monitor tumor response by multimodal multiphoton imaging including two-photon excitation fluorescence and second-harmonic generation imaging. We found that multiphoton imaging can identify different degrees of tumor response such as a slight, significant, or complete response and can detect morphological alteration associated with extracellular matrix during the progression of breast carcinoma following preoperative chemotherapy. Two quantitative optical biomarkers including tumor cellularity and collagen content were extracted based on automatic image analysis to help monitor changes in tumor and its microenvironment. Furthermore, tumor regression grade diagnosis was tried to evaluate by multiphoton microscopy. These results may offer a basic framework for using multiphoton microscopic imaging techniques as a helpful diagnostic tool for assessing breast carcinoma response after presurgical treatment.

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.

Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients

PURPOSE

The aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.

MATERIALS AND METHODS

This institutional review board-approved prospective study included 38 women (median age, 46.5 years; range, 25-70 years) with breast cancer who were scheduled for NAC and underwent mpMRI of the breast at 3 T with dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and T2-weighted imaging before and after 2 cycles of NAC. For each lesion, 23 features were extracted: qualitative T2-weighted and DCE-MRI features according to BI-RADS (Breast Imaging Reporting and Data System), quantitative pharmacokinetic DCE features (mean plasma flow, volume distribution, mean transit time), and DWI apparent diffusion coefficient (ADC) values. To apply machine learning to mpMRI, 8 classifiers including linear support vector machine, linear discriminant analysis, logistic regression, random forests, stochastic gradient descent, decision tree, adaptive boosting, and extreme gradient boosting (XGBoost) were used to rank the features. Histopathologic residual cancer burden (RCB) class (with RCB 0 being a pCR), recurrence-free survival (RFS), and disease-specific survival (DSS) were used as the standards of reference. Classification accuracy with area under the receiving operating characteristic curve (AUC) was assessed using all the extracted qualitative and quantitative features for pCR as defined by RCB class, RFS, and DSS using recursive feature elimination. To overcome overfitting, 4-fold cross-validation was used.

RESULTS

Machine learning with mpMRI achieved stable performance as shown by mean classification accuracies for the prediction of RCB class (AUC, 0.86) and DSS (AUC, 0.92) based on XGBoost and the prediction of RFS (AUC, 0.83) with logistic regression. The XGBoost classifier achieved the most stable performance with high accuracies compared with other classifiers. The most relevant features for the prediction of RCB class were as follows: changes in lesion size, complete pattern of shrinkage, and mean transit time on DCE-MRI; minimum ADC on DWI; and peritumoral edema on T2-weighted imaging. The most relevant features for prediction of RFS were as follows: volume distribution, mean plasma flow, and mean transit time; DCE-MRI lesion size; minimum, maximum, and mean ADC with DWI. The most relevant features for prediction of DSS were as follows: lesion size, volume distribution, and mean plasma flow on DCE-MRI, and maximum ADC with DWI.

CONCLUSIONS

Machine learning with mpMRI of the breast enables early prediction of pCR to NAC as well as survival outcomes in breast cancer patients with high accuracy and thus may provide valuable predictive information to guide treatment decisions.

Multiparametric MR imaging to assess response following neoadjuvant systemic treatment in various breast cancer subtypes: Comparison between different definitions of pathologic complete response

OBJECTIVES

To validate the performance of multiparametric magnetic resonance (MR) imaging to assess pathologic response to neoadjuvant systemic therapy (NST) in various breast cancer subtypes considering two definitions of pCR: absence of any residual invasive cancer or DCIS (ypT0) and absence of invasive tumour cells (ypT0/is).

METHODS

Institutional review board-approved retrospective study, with waiver of the need to obtain informed consent. From January 2015 to June 2017, 81 women with 82 breast cancers undergoing NST were included. Eighteen lesions (22%) were immunohistochemically HER2-positive, 12 (15%) triple negative (TN), 42 (51%) luminal B-like and 10 (12%) luminal B-like/HER2-positive. Breast MR imaging was performed before and after NST. A comparative analysis considering pCR as ypT0 and ypT0/is was carried out. Performance of univariate and multivariate models to potentially predict pathologic response were evaluated.

RESULTS

ypT0 was attained in 23% (19/82) of cases and ypT0/is in 33% (27/82) of cases. In both scenarios, HER2-positive subtype achieved the best response, 53% and 48%, respectively. A significant relationship was found between late enhancement and pathologic response (p < 0.001) regardless of pCR definition. In the ypT0 scenario, mean ADC ratio in the pCR subgroup was significantly higher than that in the non-pCR subgroup (p = 0.021) but no significant relationship was noted in ypT0/is. A multivariate model including MR late enhancement, ADC ratio and tumor subtype identified pathologic response with 86% and 84% accuracy when ypT0 and ypT0/is were considered, respectively.

CONCLUSION

MR imaging late enhancement and ADC ratio along with breast cancer IHC subtype identify pathologic response following NST with high accuracy, achieving the highest NPV in TN and HER2-positive tumors and the highest PPV in luminal B-like subtypes, regardless of the definition of pCR as ypT0 or ypT0/is. In light of these findings and given that residual DCIS does not have an impact on survival rates, ypT0/is seems to be the preferable definition of pCR.

Can unenhanced MRI of the breast replace contrast-enhanced MRI in assessing response to neoadjuvant chemotherapy?

BACKGROUND

The goals of neoadjuvant chemotherapy (NAC) are to reduce tumor volume and to offer a prognostic indicator in assessing treatment response. Contrast-enhanced magnetic resonance imaging (CE-MRI) is an established method for evaluating response to NAC in patients with breast cancer.

PURPOSE

To validate the role of unenhanced MRI (ue-MRI) compared to CE-MRI for assessing response to NAC in women with breast cancer.

MATERIAL AND METHODS

Seventy-one patients with ongoing NAC for breast cancer underwent MRI before, during, and at the end of NAC. Ue-MRI was performed with T2-weighted sequences with iterative decomposition of water and fat and diffusion-weighted sequences. CE-MRI was performed using three-dimensional T1-weighted sequences before and after administration of gadobenate dimeglumine. Two blinded observers rated ue-MRI and CE-MRI for the evaluation of tumor response. Statistical analysis was performed to compare lesion size and ADC values changes during therapy, as well as inter-observer agreement.

RESULTS

There were no statistically significant differences between ue-MRI and CE-MRI sequences for evaluation of lesion size at baseline and after every cycle of treatment ( P > 0.05). The mean tumor ADC values at baseline and across the cycles of NAC were significantly different for the responder group.

CONCLUSION

Ue-MRI can achieve similar results to CE-MRI for the assessment of tumor response to NAC. ADC values can differentiate responders from non-responders.

Correlation between apparent diffusion coefficient values in breast magnetic resonance imaging and prognostic factors of breast invasive ductal carcinoma

BACKGROUND

We wanted to examine whether the apparent diffusion coefficient values obtained by diffusion-weighted imaging techniques could indicate an early prognostic assessment for patients with Invasive Ductal Carcinoma and, therefore, influence the treatment decision making.

OBJECTIVE

The main objective was to evaluate the correlation between the apparent diffusion coefficient values obtained by diffusion-weighted imaging and the key prognostic factors in breast invasive ductal carcinoma. Secondary objectives were to analyze the eventual correlations between magnetic resonance imaging findings and prognostic factors in breast cancer; and to perform a comparison between results in 1.5 and 3.0 T scanners.

METHODS

Breast magnetic resonance imaging with diffusion-weighted imaging sequence was performed on 100 patients, who were proven histopathologically to have breast invasive ductal carcinoma. We compared the apparent diffusion coefficient values, obtained previous to biopsy, with the main prognostic factors in breast cancer: tumor size, histologic grade, hormonal receptors, Ki67 index, human epidermal growth factor receptor type 2, and axillary lymph node status. The Mann-Whitney U test and the Kruskal-Wallis analysis were used to establish these correlations.

RESULTS

The mean apparent diffusion coefficient value was inferior in the estrogen receptor-positive group than in the estrogen receptor-negative group (1.04 vs 1.17 × 10-3 mm2/s, P = .004). Higher histologic grade related to larger tumor size (P = .002). We found association between spiculated margins and positive axillary lymph node status [odds ratio = 4.35 (1.49-12.71)]. There were no differences in apparent diffusion coefficient measurements between 1.5 and 3.0 T magnetic resonance imaging scanners (P = .513).

CONCLUSIONS

Low apparent diffusion coefficient values are related with positive expression of estrogen receptor. Larger tumors and spiculated margins are associated to worse prognosis. Rim enhancement is more frequently observed in estrogen receptor-negative tumors. There are no differences in apparent diffusion coefficient measurements between different magnetic resonance imaging scanners.

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.

Diffusion MRI of cancer: From low to high b-values

Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advances in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties-cellularity, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: 1) cancer detection and diagnosis; 2) cancer grading, staging, and classification; and 3) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multicenter clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:23-40.

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.

Impact of Advancing Technology on Diagnosis and Treatment of Breast Cancer

New emerging breast imaging techniques have shown great promise in breast cancer screening, evaluation of extent of disease, and response to neoadjuvant therapy. Tomosynthesis, allows 3-dimensional imaging of the breast, and increases breast cancer detection. Fast abbreviated MRI has reduced time and costs associated with traditional breast MRI while maintaining cancer detection. Diffusion-weighted imaging is a functional MRI technique that does not require contrast and has shown potential in screening, lesion characterization and also evaluation of treatment response. New image-guided preoperative localizations are available that have increased patient satisfaction and decreased operating room delays.

MR Imaging Features of Tubular Carcinoma: Preliminary Experience in Twelve Masses

Objective

We retrospectively analyzed the magnetic resonance (MR) imaging features and diffusion-weighted imaging findings of the 12 masses of 10 patients with tubular carcinoma (TC), including mammography and sonography findings.

Materials and Methods

Mammographic, sonographic and magnetic resonance imaging features in 12 histopathologically confirmed masses diagnosed as TC of the breast within 10 patients were evaluated. Morphologic characteristics, enhancement features, apparent diffusion coefficient (ADC) values were reviewed.

Results

On mammography (n=5), TC appeared as high density masses with indistinct, spiculated or obscured margins. Sonographically, TC appeared as a hypoechoic appearance (n=12) with posterior acoustic shadowing in nine. On MR imaging, the margins of ten of twelve masses were irregular. Internal enhancement patterns were heterogeneous in 10 patients. Dynamic enhancement patterns illustrated plateau kinetics (n=8). On the T2-weighted images 4 masses were hypointense, and 8 were hyperintense; hypointense internal septation was found in seven of these. Tubular carcinoma appeared as hyperintense on diffusion-weighted imaging with ADC values of 0.85±0.16×10-3 mm²/s that was lower than the normal parenchyma of 1.25±0.25×10-3 mm²/s.

Conclusion

According to our study with a limited number of cases, tubular carcinomas can be described as hyperintense breast carcinomas with or without dark internal septation like appearance on T2-weighted images. Low ADC values from DW imaging can be used to differentiate TC from hyperintense benign breast lesions.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

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.

Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

BACKGROUND

We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC).

METHODS

Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis).

RESULTS

There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV).

CONCLUSION

Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.

Diffusion-weighted MR imaging in prediction of response to neoadjuvant chemotherapy in patients with breast cancer

This study aims to evaluate the potential of apparent diffusion coefficient (ADC) derived from diffusion-weighted MR imaging for predicting the treatment response to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Magnetic resonance imaging was performed prior to NACT and after two cycles of NACT. The correlation between mean ADC_pre values, mean ADC_post values, changes in ADC values and changes in tumor diameters after NACT was examined using Spearman rank correlation. A total of 164 breast cancers were enrolled in this study. Mean ADC_pre values of responders ([0.85 ± 0.16] × 10^-3 mm²/s) and non-responders ([0.84 ± 0.21] × 10^-3 mm²/s) had no significant difference (P = 0.759). While mean ADC_post value of responders was significantly higher than that of non-responders ([1.17 ± 0.37] × 10^-3 mm²/s vs. [1.01 ± 0.28] × 10^-3 mm²/s; P = 0.002). Both mean ADC_post values (r = 0.288, P = 0.000) and changes in mean ADC values (r = 0.222, P = 0.004) were positively correlated to changes in tumor diameter after NACT, except for mean ADC_pre values (r = 0.031, P = 0.695). Our results indicated that mean ADC_post values and changes in ADC values after NACT might be a biological marker for assessing the efficacy of chemotherapy.

Comparison and Optimization of 3.0 T Breast Images Quality of Diffusion-Weighted Imaging with Multiple B-Values

RATIONALE AND OBJECTIVES

Breast 3.0 T magnetic resonance diffusion-weighted imaging (MR-DWI) of benign and malignant lesions were obtained to measure and calculate the signal-to-noise ratio (SNR), signal intensity ratio (SIR), and contrast-to-noise ratio (CNR) of lesions at different b-values. The variation patterns of SNR and SIR were analyzed with different b-values and the images of DWI were compared at four different b-values with higher image quality. The effect of SIR on the differential diagnostic efficiency of benign and malignant lesions was compared using receiver operating characteristic curves to provide a reference for selecting the optimal b-value.

MATERIALS AND METHODS

A total of 96 qualified patients with 112 lesions and 14 patients with their contralateral 14 normal breasts were included in this study. The single-shot echo planar imaging sequence was used to perform the DWI and a total of 13 b-values were used: 0, 50, 100, 200, 400, 600, 800, 1000, 1200, 1500, 1800, 2000, and 2500 s/mm². On DWI, the suitable regions of interest were selected. The SNRs of normal breasts (SNR_normal), SNR_lesions, SIR, and CNR of benign and malignant lesions were measured on DWI with different b-values and calculated. The variation patterns of SNR, SIR, and CNR values on DWI for normal breasts, benign lesions, and malignant lesions with different b-values were analyzed by using Pearson correlation analysis. The SNR and SIR of benign and malignant lesions with the same b-values were compared using t-tests. The diagnostic efficiencies of SIR with different b-values for benign and malignant lesions were evaluated using receiver operating characteristic curves.

RESULTS

Breast DWI had higher CNR for b-values ranging from 600 to 1200 s/mm². It had the best CNR at b = 1000 s/mm² for the benign lesions and at b = 1200 s/mm² for the malignant lesions. The signal intensity and SNR values of normal breasts decreased with increasing b-values, with a negative correlation (r = -0.945, P < 0.01). The mean SNR values of benign and malignant lesions were negatively correlated (r = -0.982 and -0.947, respectively, and P < 0.01), gradually decreasing with increasing b-values. The mean SIR value of benign lesions gradually decreased with increasing b-values, a negative correlation (r = -0.991, P < 0.01). The mean SIR values of malignant lesions gradually increased with increasing b-values between 0 and 1200 s/mm², and gradually decreased with increasing b-values ≥ 1500 s/mm². For b-values of 600, 800, 1000, and 1200 s/mm², the sensitivity and specificity of SIR in identifying benign and malignant lesions gradually increased with increasing b-values, peaking at 1200 s/mm².

CONCLUSIONS

Breast DWI had higher image quality for b-values ranging from 600 to 1200 s/mm², and was best for b-values ranging from 1000 to 1200 s/mm². The SIR had the highest diagnostic efficiency in differentiating benign and malignant lesions for a b-value of 1200 s/mm².

Quantitative magnetic resonance imaging biomarkers in oncological clinical trials: Current techniques and standardization challenges

Radiological imaging plays an important role in oncological trials to provide imaging biomarkers for disease staging, stratifying patients, defining dose setting, and evaluating the safety and efficacy of new candidate drugs and innovative treatment. This paper reviews the techniques of most commonly used quantitative magnetic resonance imaging (qMRI) biomarkers (dynamic contrast enhanced, dynamic susceptibility contrast, and diffusion weighted imaging) and their applications in oncological trials. Challenges of incorporating qMRI biomarkers in oncological trials are discussed including understanding biological mechanisms revealed by MRI biomarkers, consideration of rigorous trial design and standardized implementation of qMRI protocols.

Role of Magnetic Resonance Imaging in Detection of Pathologic Complete Remission in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy: A Meta-analysis

Pathologic complete remission after neoadjuvant chemotherapy has a role in guiding the management of breast cancer. The present meta-analysis examined the accuracy of contrast-enhanced magnetic resonance imaging (CE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI) in detecting the response to neoadjuvant chemotherapy and compared CE-MRI with ultrasonography, mammography, and positron emission tomography/computed tomography (PET/CT). Medical subject heading terms and related keywords were searched to generate a compilation of eligible studies. The pooled sensitivity, specificity, diagnostic odds ratio, area under summary receiver operating characteristic curve (AUC), and Youden index (Q* index) were used to estimate the diagnostic efficacy of CE-MRI, DW-MRI, ultrasonography, mammography, and PET/CT. A total of 54 studies of CE-MRI and 8 studies of DW-MRI were included. The overall AUC and the Q* index values for CE-MRI and DW-MRI were 0.88 and 0.94 and 0.80 and 0.85, respectively. According to the summary receiver operating characteristic curves, CE-MRI resulted in a higher AUC value and Q* index compared with ultrasonography and mammography but had values similar to those of DW-MRI and PET/CT. CE-MRI accurately assessed pathologic complete remission in specificity, and PET/CT and DW-MRI accurately assessed pathologic complete remission in sensitivity. The present meta-analysis indicates that CE-MRI has high specificity and DW-MRI has high sensitivity in predicting pathologic complete remission after neoadjuvant chemotherapy. CE-MRI is more accurate than ultrasonography or mammography. Additionally, PET/CT is valuable for predicting pathologic complete remission. CE-MRI, combined with PET/CT or DW-MRI, might allow for a more precise assessment of pathologic complete remission.

Neoadjuvant Systemic Therapy in Breast Cancer: Association of Contrast-enhanced MR Imaging Findings, Diffusion-weighted Imaging Findings, and Tumor Subtype with Tumor Response

Purpose To investigate the performance of tumor subtype and various magnetic resonance (MR) imaging parameters in the assessment of tumor response to neoadjuvant systemic therapy (NST) in patients with breast cancer and to outline a model of pathologic response, considering pathologic complete response (pCR) as the complete absence of any residual invasive cancer or ductal carcinoma in situ (DCIS). Materials and Methods This was an institutional review board-approved retrospective study, with waiver of the need to obtain informed consent. From November 2009 to December 2014, 111 patients with histopathologically confirmed invasive breast cancer who were undergoing NST were included (mean age, 54 years; range, 27-84 years). Breast MR imaging was performed before and after treatment. Presence of late enhancement was assessed. Apparent diffusion coefficients (ADCs) were obtained by using two different methods. ADC ratio (mean posttreatment ADC/mean pretreatment ADC) was calculated. pCR was defined as absence of any residual invasive cancer or DCIS. Multivariate regression analysis and receiver operating characteristic analysis were performed. Results According to their immunohistochemical (IHC) profile, tumors were classified as human epidermal growth factor receptor 2 (HER2) positive (n = 51), estrogen receptor (ER) positive/HER2 negative (n = 40), and triple negative (n = 20). pCR was achieved in 19% (21 of 111) of cases; 86% of them were triple-negative or HER2-positive subtypes. Absence of late enhancement at posttreatment MR imaging was significantly associated with pCR (area under the curve [AUC], 0.85). Mean ADC ratio significantly increased when pCR was achieved (P < .001). A κ value of 0.479 was found for late enhancement (P < .001), and the intraclass correlation coefficient for ADCs was 0.788 (P < .001). Good correlation of ADCs obtained with the single-value method and those obtained with the mean-value methods was observed. The model combining the IHC subtype, ADC ratio, and late enhancement had the highest association with pathologic response, achieving an AUC of 0.92 (95% confidence interval: 0.86, 0.97). Conclusion Triple-negative or HER2-positive tumors showing absence of late enhancement and high ADC ratio after NST are associated with pCR. ^© RSNA, 2016 Online supplemental material is available for this article.

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

Objective

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

Methods

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

Results

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

Conclusions

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

Key Points

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

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

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

Pilot study on the detection of antiandrogen resistance using serial diffusion-weighted imaging of bone metastases in prostate cancer

PURPOSE

To evaluate serial apparent diffusion coefficient (ADC) measurements of bone metastases in prostate cancer to determine whether antiandrogen resistance can be detected and time to progression estimated.

MATERIALS AND METHODS

Diffusion-weighted imaging (DWI) was performed at 1.5T in nine patients with treatment-naïve metastatic prostate cancer (20 lesions) before antiandrogen treatment, after 1, 2, and 3 months of treatment, and thereafter every 4 months over 31 months or until antiandrogen resistance was detected. Tumor volumes were stable over time. Time courses of the ADCs when averaged over entire lesions and on functional diffusion maps (fDMs) were analyzed using marginal linear model (MLM) analysis.

RESULTS

Starting at 1 month, MLM analysis revealed decreasing mean ADCs (P = 0.001) over time. Simultaneously, the percentage of voxels with significantly higher ADCs decreased (P = 0.004), whereas the percentage of voxels with significantly lower ADCs increased (P < 0.001) on fDMs. Both mean ADCs (P = 0.042) and percentages of voxels with significantly higher ADCs on fDMs (P = 0.039) decreased more rapidly over time in patients with a shorter progression-free interval (PFI). Likewise, higher (P = 0.001) and more rapidly increasing (P = 0.002) percentages of voxels with significantly lower ADCs on fDMs were associated with a shorter PFI.

CONCLUSION

The results of our pilot study suggest that the evolution of ADCs over time may permit early identification of antiandrogen resistance in bone metastases. J. Magn. Reson. Imaging 2016;43:1407-1416.

Magnetic resonance imaging evaluation of residual tumors in breast cancer after neoadjuvant chemotherapy: surgical implications

BACKGROUND

Magnetic resonance imaging (MRI) can be used to guide breast cancer surgery with breast conservation for large tumors with a substantially reduced size after neoadjuvant chemotherapy (NAC).

PURPOSE

To evaluate the value of dynamic contrast-enhanced MRI (DCE-MRI) for measuring residual tumor size and enhancement patterns following preoperative NAC.

MATERIAL AND METHODS

Eighty-nine patients with breast cancer underwent breast DCE-MRI; 38 patients (39 lesions) were treated with NAC and examined for residual disease following therapy. Two patients were excluded because surgery had been performed >2 weeks after the final MR examination. Thus, we correlated the DCE-MRI results of 36 patients (37 lesions) with postoperative histopathological findings. Residual disease was confirmed by more enhancement compared to normal glandular tissue at the initial tumor site. Residual tumor size on DCE-MRI was compared with postoperative pathology findings. Tumor enhancement patterns on DCE-MRI were analyzed and correlated with pathological classification.

RESULTS

MRI revealed 34 cases of residual tumors, with two false positives and one false negative. Pathological and MR measurements were correlated (r = 0.793). The correlation of mass enhancement size (r = 0.87, n = 14) with pathology and DCE-MRI was higher than for non-mass-like enhancement (NME) (r = 0.735, n = 23). The distribution of pathologic classification was significantly different between different MRI enhancement patterns (P = 0.006). Mass enhancement had higher cellularity than NME.

CONCLUSION

MRI is useful for evaluating residual carcinoma following NAC. Mass enhancement with higher cellularity after NAC can be evaluated more accurately, which is suitable for evaluating lumpectomy. However, other approaches are required for NME, which has lower cellularity.

Accuracy of magnetic resonance imaging for predicting pathological complete response of breast cancer after neoadjuvant chemotherapy: association with breast cancer subtype

A pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a signature of favorable prognosis in breast cancer. The aim of this study was to assess the accuracy of magnetic resonance imaging (MRI) in predicting the pCR after NAC. 265 women with stage II or III breast cancer who underwent surgery after NAC were retrospectively investigated for MRI findings before and after the NAC. Correlation of pCR with an “imaging complete response” (iCR), defined as no detectable tumor on all serial images with dynamic contrast-enhanced T1-weighted imaging, was evaluated with respect to each tumor subtype. Of 265 cases, 44 (16.6 %) and 24 (9.1 %) were diagnosed as iCR and pCR, respectively. Nineteen of the 44 iCR cases (43.2 %) were assessed as pCR, and 216 (97.7 %) of the 221 non-iCR cases were assessed as non-pCR. The accuracy (ACC), the pCR predictive value (PPV) and the non-pCR predictive value (NPV) were 88.7, 43.2, and 97.7 %, respectively. When assessed according to each tumor subtype, the ACC, PPV and NPV were 93.2, 21.4 and 100 % for luminal subtype, 70.8, 0 and 89.5 % for luminal/HER2 subtype, 75, 57.1 and 88.8 % for HER2-enriched subtype, and 90.9, 72.7 and 97 % for triple-negative subtype, respectively. MRI is a valuable modality for predicting pCR of breast cancer after NAC treatment. However, its accuracy varies greatly in different breast cancer subtypes. Whereas MRI closely predicts pCR in the triple-negative subtype, iCR in the luminal subtype is often an over-estimation. On the other hand, residual lesions identified by MRI are reliable markers of non-pCR for the luminal subtype.

Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) can probe pre-treatment differences or monitor early response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). Thirty-six patients with locally advanced breast cancer were imaged using multiple-b DWI with 12 b values ranging from 0 to 1000  s/mm(2) at the baseline, and 28 patients were repeatedly scanned after the second cycle of NAC. Subjects were divided into pathologic complete response (pCR) and nonpathologic complete response (non-pCR) groups according to the surgical pathologic specimen. Parameters (D, D*, f, maximum diameter [MD] and volume [V]) before and after 2 cycles of NAC and their corresponding change (Δparameter) between pCR and non-pCR groups were compared using the Student t test or nonparametric test. The diagnostic performance of different parameters was judged by the receiver-operating characteristic curve analysis. Before NAC, the f value of pCR group was significantly higher than that of non-pCR (32.40% vs 24.40%, P = 0.048). At the end of the second cycle of NAC, the D value was significantly higher and the f value was significantly lower in pCR than that in non-pCR (P = 0.001; P = 0.015, respectively), whereas the D* value and V of the pCR group was slightly lower than that of the non-pCR group (P = 0.507; P = 0.676, respectively). ΔD was higher in pCR (-0.45 × 10(-3)  mm(2)/s) than that in non-pCR (-0.07 × 10(-3)  mm(2)/s) after 2 cycles of NAC (P < 0.001). Δf value in the pCR group was significantly higher than that in the non-pCR group (17.30% vs 5.30%, P = 0.001). There was no significant difference in ΔD* between the pCR and non-pCR group (P = 0.456). The prediction performance of ΔD value was the highest (AUC [area under the curve] = 0.924, 95% CI [95% confidence interval] = 0.759-0.990). When the optimal cut-off was set at -0.163 × 10(-3)  mm(2)/s, the values for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were up to 100% (95% CI = 66.4-100), 73.7% (95% CI = 48.8-90.9), 64.3% (95% CI = 35.6-86.0), and 100% (95% CI = 73.2-99.3), respectively. IVIM-derived parameters, especially the D and f value, showed potential value in the pre-treatment prediction and early response monitoring to NAC in locally advanced breast cancer. ΔD value had the best prediction performance for pathologic response after NAC.