Quantitative apparent diffusion coefficient measurement obtained by 3.0Tesla MRI as a potential noninvasive marker of tumor aggressiveness in breast cancer

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 contrast-free diffusion MRI multiple index for the early prediction of pathological response to neoadjuvant chemotherapy in breast cancer

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

Prediction of Lymphovascular invasion status in breast cancer based on magnetic resonance imaging radiomics features

OBJECTIVE

This study intended to investigate the feasibility and effectiveness of using clinical magnetic resonance imaging (MRI) radiomics features to predict lymphovascular invasion (LVI) status in breast cancer (BC) patients.

METHODS

A total of 182 BC patients were retrospectively collected and randomly divided into a training set (n = 127) and a validation set (n = 55) in a 7:3 ratio. Based on pathological examination results, the training set was further divided into LVI group (n = 60) and non-LVI group (n = 67), and the validation set was divided into LVI group (n = 24) and non-LVI group (n = 31). General data and MRI examination indicators were compared. Multivariate logistic regression was utilized to analyze MRI radiomics features and clinically relevant indicators that were significant in the baseline information of patients in training set, independent risk factors were identified, and a logistic regression model was built. The accuracy of logistic model was validated using ROC curves in training and validation sets.

RESULTS

Age, pathohistological classification, tumor length, tumor width, presence or absence of Magnetic Resonance Spectroscopy (MRS) cho peak, presence or absence of spicule sign, peritumoral enhancement, and peritumoral edema were statistically significant (P < 0.05) between the two groups. Multivariate logistic regression analysis presented that spicule and peritumoral edema were independent risk factors for LVI in BC patients (P < 0.05). The ROC curve illustrated that AUC of the logistic regression model in the training set was 0.807 (95%CI: 0.730-0.885) and that in the validation set was 0.837 (95%CI: 0.731-0.944).

CONCLUSION

Radiomics features of spicule sign and peritumoral edema were independent risk factors for LVI in BC patients. A logistic regression model based on these factors, along with age, could accurately predict LVI occurrence in BC patients, providing data support for diagnosis and modeling of LVI in BC patients.

Diffusion tensor imaging: survival analysis prediction in breast cancer patients

PURPOSE

We aimed to explore the performance of diffusion-tensor imaging (DTI) and apparent diffusion coefficient (ADC) parameters in evaluating disease-free survival (DFS) and overall survival (OS) in patients with invasive breast cancer.

MATERIAL AND METHODS

A total of 49 women with invasive breast cancer who were diagnosed between 2017 and 2022 were included. All patients underwent breast magnetic resonance imaging (MRI) with DTI and diffusion-weighted imaging (DWI) features, with examiners blinded to the clinical data. Volume anisotropy (VA), fractional anisotropy (FA), and ADC values were measured to assess intratumoral measured heterogeneity. Correlations and differences in diffusion metrics according to OS and DFS status of the cases were analyzed. The discriminative ability of the quantitative findings was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

RESULTS

We evaluated patients with metastases (n = 13, 36.5%) and those without metastases (n = 36, 73.5%). Differences in the ADC, FA, and VA values were observed. The results of Cox regression survival analysis for all the patients included in the survival analysis revealed that DTI metrics contributed to the prediction of overall survival (OS) in the emerging models (p < 0.05). Both FA and VA were associated with OS (p = 0.037 and p = 0.038, respectively). However, ADC was not associated with OS (p = 0.177) or DFS (p = 0.252).

CONCLUSION

To the best of our knowledge, this is the first study to assess the prognostic value of DTI-MRI in breast cancer with statistical survival analysis techniques. We believe that DTI measurements can be used as a biomarker for OS analysis in breast cancer given the available data.

Longitudinal diffusion and volumetric kinetics of head and neck cancer magnetic resonance on a 1.5T MR-Linear accelerator hybrid system: A prospective R-IDEAL Stage 2a imaging biomarker characterization/ pre-qualification study

Objectives

We aim to characterize the serial quantitative apparent diffusion coefficient (ADC) changes of the target disease volume using diffusion-weighted imaging (DWI) acquired weekly during radiation therapy (RT) on a 1.5T MR-Linac and correlate these changes with tumor response and oncologic outcomes for head and neck squamous cell carcinoma (HNSCC) patients as part of a programmatic R-IDEAL biomarker characterization effort.

Methods

Thirty patients with pathologically confirmed HNSCC who received curative-intent RT at the University of Texas MD Anderson Cancer Center, were included in this prospective study. Baseline and weekly Magnetic resonance imaging (MRI) (weeks 1-6) were obtained, and various ADC parameters (mean, 5 th , 10 th , 20 th , 30 th , 40 th , 50 th , 60 th , 70 th , 80 th , 90 th and 95 th percentile) were extracted from the target regions of interest (ROIs). Baseline and weekly ADC parameters were correlated with response during RT, loco-regional control, and the development of recurrence using the Mann-Whitney U test. The Wilcoxon signed-rank test was used to compare the weekly ADC versus baseline values. Weekly volumetric changes (Δvolume) for each ROI were correlated with ΔADC using Spearman's Rho test. Recursive partitioning analysis (RPA) was performed to identify the optimal ΔADC threshold associated with different oncologic outcomes.

Results

There was an overall significant rise in all ADC parameters during different time points of RT compared to baseline values for both gross primary disease volume (GTV-P) and gross nodal disease volumes (GTV-N). The increased ADC values for GTV-P were statistically significant only for primary tumors achieving complete remission (CR) during RT. RPA identified GTV-P ΔADC 5 th percentile >13% at the 3 rd week of RT as the most significant parameter associated with CR for primary tumor during RT (p <0.001). Baseline ADC parameters for GTV-P and GTV-N didn't significantly correlate with response to RT or other oncologic outcomes. There was a significant decrease in residual volume of both GTV-P & GTV-N throughout the course of RT. Additionally, a significant negative correlation between mean ΔADC and Δvolume for GTV-P at the 3 rd and 4 th week of RT was detected (r = -0.39, p = 0.044 & r = -0.45, p = 0.019, respectively).

Conclusion

Assessment of ADC kinetics at regular intervals throughout RT seems to be correlated with RT response. Further studies with larger cohorts and multi-institutional data are needed for validation of ΔADC as a model for prediction of response to RT.

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.

Histogram analysis of breast diffusion kurtosis imaging: a comparison between readout-segmented and single-shot echo-planar imaging sequence

Background

Histogram analysis of the diffusion-weighted imaging (DWI) parameters is widely used to differentiate the breast lesions. However, histogram analysis of the diffusion-kurtosis imaging (DKI) parameters for the single-shot echo-planar imaging (ss-EPI) and readout-segmented echo planar imaging (rs-EPI) sequences has not been compared in breast cancer. Thus, this study is to investigate the diagnostic accuracy and reliability of the histogram parameters derived from the rs-EPI and ss-EPI sequences of DKI parameters in distinguishing between the benign and malignant breast lesions.

Methods

This single-center, retrospective cohort study enrolled 205 consecutive patients with breast lesions (65 benign and 140 malignant). The patients underwent breast magnetic resonance imaging (MRI) with a 3T scanner using the rs-EPI and ss-EPI sequences with 4 b values (0, 50, 1,000, and 2,000 s/mm²). The regions of interest (ROIs) were manually delineated for all the lesion images from both the sequences, and the histogram parameters were extracted from the apparent diffusion coefficient (ADC) and apparent diffusional kurtosis (K_app) maps. Statistical analysis was performed using the Kolmogorov-Smirnov test, the student's t-test, and the receiver operating characteristic (ROC) curves.

Results

The mean, 25th, 50th, 75th, and 100th percentiles, skewness, and kurtosis values derived from apparent diffusion for non-Gaussian distribution (D_app) and K_app maps showed good or excellent intra-observer agreement (ICC: 0.695 to 0.863).The mean and the 25th, 50th, 75th, and 100th percentile values for D_app were significantly lower and the mean and the 25th, 50th, 75th, and 100th percentile values for K_app were significantly higher in the malignant breast lesions compared with those in the benign breast lesions for both the rs-EPI and ss-EPI sequences (all P<0.05). The majority of the histogram K_app and D_app parameters (except skewness and kurtosis) for the benign and malignant lesions showed significant differences between the ss-EPI and the rs-EPI sequences (P<0.05). ROC curve analysis showed that the AUC values for the 75th percentile of K_app (0.854 for rs-EPI, 0.844 for ss-EPI) and the 25th percentile of D_app (0.866 for rs-EPI, 0.858 for ss-EPI) were highest for both DKI sequences. The diagnostic performance of the rs-EPI sequence was better than the ss-EPI sequence for all the histogram parameters except the skewness value of D_app.

Conclusions

Histogram parameters from the rs-EPI sequence were more reliable and accurate in differentiating malignant and benign breast lesions than those from the ss-EPI sequence.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND

Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients.

METHODS

A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K^trans, V_e, K_ep) and semiquantitative DCE-MRI parameters (W_- in, W_- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

LVI-positive patients had a higher K_ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K_ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K_ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence.

CONCLUSION

The quantitative parameter of K_ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

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

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

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.

Objectives

This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients.

Method

The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.

Results

The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10-3 mm2/s (95% CI 0.94-1.04), luminal B: 0.97 × 10-3 mm2/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10-3 mm2/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10-3 mm2/s (95% CI 0.91-1.07).

Conclusions

ADC values cannot be used to discriminate between molecular subtypes of BC.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

OBJECTIVE

Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.

METHODS

We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.

RESULTS

All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.

CONCLUSION

The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

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.

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

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

Correlation of apparent diffusion coefficient values and peritumoral edema with pathologic biomarkers in patients with breast cancer

PURPOSE

To investigate the relationship between breast cancer imaging features on magnetic resonance imaging (MRI) and histopathological characteristics.

METHODS AND MATERIALS

We prospectively enrolled 46 patients who underwent 1.5-T MRI with 68 breast malignant lesions from 2017 until 2019. Peritumoral edema was determined based on visual assessment on T2 weighted imaging. Lesions were categorized into two groups: A: with edema (48 lesions) and B: without edema (20 lesions).

RESULTS

The tumor size was not different among two groups but multifocal-multicentric lesions were more common in the group A (70% vs. 35%). The axillary lymph nodes are most involved in group A. ER and PR positive lesions were more common in group B (90% vs. 56.3%) but in the group A, HER2 positive lesions were found to be more common (31.3% vs. 15%). The mean ADC value in tumors and peritumoral regions were lower (0.97 × 10^-3 mm²/s, P = 0.023) and higher (1.85 × 10^-3 mm²/s, P < 0.0001) in group A, respectively. Peritumoral ADC value was significantly higher in HER2-positive group.

CONCLUSION

Breast carcinomas with peritumoral edema were found to be more multifocal-multicentric, with higher prevalence of axillary lymph node involvement, more HER 2-positive, with lower prevalence of ER/PR-positive, lower tumoral ADC and higher peritumoral ADC values.

Diffusion weighted magnetic resonance imaging (DW-MRI) as a non-invasive, tissue cellularity marker to monitor cancer treatment response

Background

Diffusion weighted magnetic resonance imaging (DW-MRI) holds great potential for monitoring treatment response in cancer patients shortly after initiation of radiotherapy. It is hypothesized that a decrease in cellular density of irradiated cancerous tissue will lead to an increase in quantitative apparent diffusion coefficient (ADC) values. DW-MRI can therefore serve as a non-invasive marker of cell death and apoptosis in response to treatment. In the present study, we aimed to investigate the applicability of DW-MRI in preclinical models to monitor radiation-induced treatment response. In addition, we compared DW-MRI with ex vivo measures of cell density, cell death and apoptosis.

Methods

DW-MRI was tested in two different syngeneic mouse models, a colorectal cancer (CT26) and a breast cancer (4 T1). ADC values were compared with quantitative determinations of apoptosis and cell death by flow cytometry. Furthermore, ADC-values were also compared to histological measurement of cell density on tumor sections.

Results

We found a significant correlation between ADC-values and apoptotic state in the CT26 model (P = 0.0031). A strong correlation between the two measurements of ADC-value and apoptotic state was found in both models, which were also present when comparing ADC-values to cell densities.

Conclusions

Our findings demonstrate that DW-MRI can be used for non-invasive monitoring of radiation-induced changes in cell state during cancer therapy. ADC values reflect ex vivo cell density and correlates well with apoptotic state, and can hereby be described as a marker for the cell state after therapy and used as a non-invasive response marker.

Associations Between Apparent Diffusion Coefficient Values and the Prognostic Factors of Breast Cancer

OBJECTIVES

Breast imaging can offer great information about breast cancer heterogeneity. The purpose of this study was to analyze the relationship between apparent diffusion coefficient (ADC) values and various prognostic factors and investigate whether ADC values are useful for breast cancer diagnosis, evaluation of treatment response, and determination of prognosis.

METHODS

A total of 111 cases of breast cancer were included in this study. Magnetic resonance findings were recorded according to the Breast Imaging Reporting and Data System magnetic resonance imaging lexicon. Diffusion-weighted imaging rim sign and minimum, maximum, and difference ADC values (ADCdiff) were also evaluated.

RESULTS

ADCdiff was related to all prognostic factors such as histological grade, Ki-67, tumor size, molecular subtype, axillary node metastasis, lymphvascular invasion, internal enhancement pattern, intratumoral high T2 signal, peritumoral edema, and diffusion-weighted imaging rim sign, whereas minimum and maximum ADC values showed variable associations.

CONCLUSIONS

Apparent diffusion coefficient values were shown to be correlated with many proven or possible prognostic factors of breast cancer. In particular, ADCdiff can reflect tumor heterogeneity and showed higher correlation.

Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers

Objective

To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers.

Materials and Methods

In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype.

Results

HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined.

Conclusion

Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.

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.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

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.

Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI

Background

Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features.

Materials and methods

From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis.

Result

Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC₉₀ were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC₉₀>1.47×10⁻³ mm²/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%).

Conclusion

Circumscribed margin and rim enhancement on s-MRI and ADC₉₀ are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.

Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions

BACKGROUND

Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved.

PURPOSE

To investigate the performance of whole-volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions.

STUDY TYPE

Retrospective.

POPULATION

In all, 120 patients with breast lesions (62 malignant, 58 benign).

SEQUENCE

DKI sequence with seven b-values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm² ) and DWI sequence with two b-values (0 and 1000 s/mm² ) on 3.0T MRI.

ASSESSMENT

Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume.

STATISTICAL TESTS

Student's t-test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

K_max , D_min , and ADC_min had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and K_max was found to outperform D_min and ADC_min . ADC histogram parameters (from ADC_min to ADC_sd ) were significantly lower than D histogram parameters in all groups.

DATA CONCLUSION

K_max , D_min , and ADC_min were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627-634.

Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role

Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer

BACKGROUND

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization.

PURPOSE

To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T₁ -weighted imaging, T₂ -weighted turbo-spin echo (TSE) with fatsat.

ASSESSMENT

Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes.

STATISTICAL TESTS

Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis.

RESULTS

Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers.

DATA CONCLUSION

Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

Sequential [18F]FDG-[18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience

The aim of this study was to assess whether sequential multiparametric ¹⁸[F]fluoro-desoxy-glucose (¹⁸[F]FDG)/[¹⁸F]fluoromisonidazole ([¹⁸F]FMISO) PET-MRI in breast cancer patients is possible, facilitates information on tumor heterogeneity, and correlates with prognostic indicators. In this pilot study, IRB-approved, prospective study, nine patients with ten suspicious breast lesions (BIRADS 5) and subsequent breast cancer diagnosis underwent sequential combined [¹⁸F]FDG/[18F]FMISO PET-MRI. [¹⁸F]FDG was used to assess increased glycolysis, while [¹⁸F]FMISO was used to detect tumor hypoxia. MRI protocol included dynamic breast contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). Qualitative and quantitative multiparametric imaging findings were compared with pathological features (grading, proliferation, and receptor status) and clinical endpoints (recurrence/metastases and disease-specific death) using multiple correlation analysis. Histopathology was the standard of reference. There were several intermediate to strong correlations identified between quantitative bioimaging markers, histopathologic tumor characteristics, and clinical endpoints. Based on correlation analysis, multiparametric criteria provided independent information. The prognostic indicators proliferation rate, death, and presence/development of recurrence/metastasis correlated positively, whereas the prognostic indicator estrogen receptor status correlated negatively with PET parameters. The strongest correlations were found between disease-specific death and [¹⁸F]FDG_mean (R=0.83, p < 0.01) and between the presence/development of metastasis and [¹⁸F]FDG_max (R=0.79, p < 0.01), respectively. This pilot study indicates that multiparametric [¹⁸F]FDG/[¹⁸F]FMISO PET-MRI might provide complementary quantitative prognostic information on breast tumors including clinical endpoints and thus might be used to tailor treatment for precision medicine in breast cancer.

Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis

BACKGROUND

As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI.

PURPOSE

To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD).

STUDY TYPE

Retrospective.

POPULATION

Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm² ).

FIELD STRENGTH/SEQUENCE

3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI.

ASSESSMENT

Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD.

STATISTICAL TESTS

The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test.

RESULTS

There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029).

DATA CONCLUSION

This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.

Clinical role of breast MRI now and going forward

Magnetic resonance imaging (MRI) is a well-established method in breast imaging, with manifold clinical applications, including the non-invasive differentiation between benign and malignant breast lesions, preoperative staging, detection of scar versus recurrence, implant assessment, and the evaluation of high-risk patients. At present, dynamic contrast-enhanced MRI is the most sensitive imaging technique for breast cancer diagnosis, and provides excellent morphological and to some extent also functional information. To compensate for the limited functional information, and to increase the specificity of MRI while preserving its sensitivity, additional functional parameters such as diffusion-weighted imaging and apparent diffusion coefficient mapping, and MR spectroscopic imaging have been investigated and implemented into the clinical routine. Several additional MRI parameters to capture breast cancer biology are still under investigation. MRI at high and ultra-high field strength and advances in hard- and software may also further improve this imaging technique. This article will review the current clinical role of breast MRI, including multiparametric MRI and abbreviated protocols, and provide an outlook on the future of this technique. In addition, the predictive and prognostic value of MRI as well as the evolving field of radiogenomics will be discussed.

Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma

The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADC_min) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADC_min value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADC_min values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADC_min values for IDC of grades I, II and III were (1.14±0.11)×10^-3, (0.99±0.12)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively. Statistically significant differences were detected in the mean ADC_min value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADC_min values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADC_min value was correlated with the IDC histological grade, and lower ADC_min values were associated with a higher histological grade and more aggressiveness. Thus, the ADC_min value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.

The role of MRI in predicting Ki-67 in breast cancer: preliminary results from a prospective study

Purpose:

In the last decade contrast-enhanced magnetic resonance imaging (MRI) has gained a growing role as a complementary tool for breast cancer diagnosis. Currently the relationship between the kinetic features of a breast lesion and pathologic prognostic factors has become a popular field of research. Our aim is to verify whether breast MRI could be considered a useful tool to predict Ki-67 score, thus resulting as a breast cancer prognosis indicator.

Methods:

From June to December 2014, we enrolled patients with breast cancer who underwent preoperative dynamic contrast-enhanced MRI at the local health agency. We analyzed the time-signal intensity curves calculating the mean values of the following parameters: the basal enhancement (Ebase), the enhancement ratio (ENHratio), the maximum enhancement (Emax), and the steepest slope of the contrast enhancement curve (Smax). Scatterplots and Pearson correlation test were used to investigate the eventual associations among these parameters.

Results:

A total of 27 patients underwent breast MRI during the study period. The mean ± SD Ki-67 percentage was 27.03 ± 16.8; the mean Emax, Smax, Ebase, and ENHratio were 433.9 ± 120.2, 267.3 ± 96.8, 165.5 ± 77.1, and 187.1 ± 94.8, respectively. Scatterplots suggest a positive correlation between Ki-67 and both Emax and Smax. The correlation tests between Ki-67 and Emax, Ki-67 and Smax showed statistical significance.

Conclusions:

Our preliminary data suggest that enhancement pattern is closely linked to breast cancer proliferation, thus proving the relationship between more proliferating tumors and more rapidly enhanced lesions. This is hypothesis-generating for further studies aimed at promoting breast MRI in the early estimation of cancer prognosis and tumor in vivo response to chemotherapy.

ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) as an imaging biomarker for invasive ductal carcinoma (IDC) in the breast.

METHODS

Seventy-one patients undergoing 3.0 Tesla DWI were retrospectively enrolled. Correlations between the ADC values and prognostic factors were evaluated.

RESULTS

Multivariate regression analyses showed that Ki67 expression and molecular subtype were independently associated with the ADC. Discriminant analysis excluded the ADC as a good biomarker for subtype, but the mean ADC significantly distinguished Ki67-positive (low ADC) from Ki67-negative (high ADC) lesions, as observed in the in ROC curves, with a diagnostic sensitivity of 1.00 and a cut-off value of 0.97 × 10^-3 mm²/s.

CONCLUSION

The ADC may be helpful for predicting Ki67 expression in IDC preoperatively.

Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?

OBJECTIVE

MRI is being used increasingly as a modality that can provide important information about breast cancer. Diffusion-weighted imaging (DWI) is an imaging technique from which apparent diffusion coefficient (ADC) values can be calculated in addition to obtaining important structural information which cannot be obtained from other imaging studies. We did not find any significant relationships between ADC values and prognostic factors, but did provide some explanations for conflicting results in the literature.

METHODS

The ADC results of 61 females with invasive ductal carcinomas were evaluated. DWI was performed and ADC values were calculated from the area in which restriction of diffusion was the highest in ADC mapping. B value was 500 and region of interest (ROI) was designated between 49 and 100 mm². Calculations were performed automatically by the device. Tissue samples were obtained for prognostic factor evaluation. The relationships between ADC and prognostic factors were investigated. Comparisons between groups were made with one-way ANOVA and Kruskal Wallis test. Pairwise comparisons were made with Dunn's test. Analyses of categorical variables were made with Chi-square test.

RESULTS

We found a weak negative correlation between ADC and Ki-67 values (r = -0.279; p = 0.029). When we compared ADC values in regard to tumour type, we found no significant differences for tumour grade, Ki-67 positivity, estrogen receptor positivity, progesterone receptor positivity, C-erb B2, lymphovascular invasion and ductal carcinoma in situ or lobular carcinoma in situ component. On a side note, we found that mean ADC values decreased as tumour grade increased; however, this was not statistically significant.

CONCLUSION

The literature contains studies that report conflicting results which may be caused by differences in B values, ROI area and magnetic field strength. Multicentre studies and systematic reviews of these findings may produce crucial data for the use of DWI in breast cancer. Advances in knowledge: To determine if any significant relationship exists between DWI findings and prognostic factors of breast cancer.

Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps

AIM

To investigate the performance of combined semi-quantitative analysis on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) and histogram analysis of diffusion-weighted imaging (DWI) for distinguishing malignant from benign breast masses.

MATERIALS AND METHODS

This study included 178 patients with breast masses (benign:malignant=88:9) who underwent both DCE-MRI and DWI. The semi-quantitative parameters, derived from DCE-MRI, included maximum slope of increase (MSI), signal intensity slope (SI_slope), initial percentage of enhancement (E_initial), percentage of peak enhancement (E_peak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). Histogram parameters derived from apparent diffusion coefficient (ADC) maps included ADC_min, ADC_max, ADC_mean, ADC₁₀, ADC₂₅, ADC₅₀, ADC₇₅, ADC₉₀, skewness, and kurtosis. All parameters were compared between malignant and benign groups, and their differences were tested using independent-samples t-test or Mann-Whitney test. Receiver operating characteristic (ROC) curves were used to determine the diagnostic value of each significant parameter.

RESULTS

Among semi-quantitative parameters, SI_slope exhibited the best diagnostic performance in predicting malignancy (cut-off value, 0.096; ROC, 0.756; sensitivity, 86.7%; specificity, 61.4%). Among histogram parameters, ADC₁₀ exhibited the best diagnostic performance in predicting malignancy (cut-off value, 1.051; ROC, 0.885; sensitivity, 86.7%; specificity, 84.1%). The optimal diagnostic performance of combined ADC₁₀ and SI_slope (area under curve [AUC], 0.888; sensitivity, 82.2%; specificity, 95.5%) was significantly better than SI_slope alone (p<0.001). Moreover, the combination showed higher AUC (0.888 versus 0.885) than ADC₁₀ alone, but the difference was not statistically significant (p=0.914).

CONCLUSION

SI_slope and ADC₁₀ are significant predictors for breast malignancy. The combination of DCE-MRI and DWI improves differentiating performance.

Predictive Models in Differentiating Vertebral Lesions Using Multiparametric MRI

BACKGROUND AND PURPOSE

Conventional MR imaging has high sensitivity but limited specificity in differentiating various vertebral lesions. We aimed to assess the ability of multiparametric MR imaging in differentiating spinal vertebral lesions and to develop statistical models for predicting the probability of malignant vertebral lesions.

MATERIALS AND METHODS

One hundred twenty-six consecutive patients underwent multiparametric MRI (conventional MR imaging, diffusion-weighted MR imaging, and in-phase/opposed-phase imaging) for vertebral lesions. Vertebral lesions were divided into 3 subgroups: infectious, noninfectious benign, and malignant. The cutoffs for apparent diffusion coefficient (expressed as 10^-3 mm²/s) and signal intensity ratio values were calculated, and 3 predictive models were established for differentiating these subgroups.

RESULTS

Of the lesions of the 126 patients, 62 were infectious, 22 were noninfectious benign, and 42 were malignant. The mean ADC was 1.23 ± 0.16 for infectious, 1.41 ± 0.31 for noninfectious benign, and 1.01 ± 0.22 mm²/s for malignant lesions. The mean signal intensity ratio was 0.80 ± 0.13 for infectious, 0.75 ± 0.19 for noninfectious benign, and 0.98 ± 0.11 for the malignant group. The combination of ADC and signal intensity ratio showed strong discriminatory ability to differentiate lesion type. We found an area under the curve of 0.92 for the predictive model in differentiating infectious from malignant lesions and an area under the curve of 0.91 for the predictive model in differentiating noninfectious benign from malignant lesions. On the basis of the mean ADC and signal intensity ratio, we established automated statistical models that would be helpful in differentiating vertebral lesions.

CONCLUSIONS

Our study shows that multiparametric MRI differentiates various vertebral lesions, and we established prediction models for the same.

Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: A potential predictor for an oncotype Dx test recurrence score

PURPOSE

To measure the apparent diffusion coefficient (ADC) values in estrogen receptor-positive (ER+) and axillary lymph node-negative (LN-) invasive breast cancer and investigate the correlation of ADC with Oncotype Dx test recurrence scores (ODxRS).

MATERIALS AND METHODS

This was a Health Insurance Portability and Accountability Act (HIPAA)-compliant single-site retrospective study. Patients underwent preoperative 3.0T MRI scans with additional diffusion-weighted imaging sequential scans (b = 0, 600 and b = 0, 1000 s/mm² ) from January 2011 to 2013. The study population included 31 ER+/LN- invasive breast cancers, which underwent ODxRS genomic testing. ADC₆₀₀ and ADC₁₀₀₀ parametric maps were generated, and ADC values were calculated from a user-drawn region of interest. ODxRS predicts 10-year recurrence risk in individual patients: low (RS <18), intermediate (RS: 18-30), or high (RS >30). All breast lesions, including subgroups of invasive ductal carcinoma (IDC) lesions and mass-only lesions were dichotomized by RS scores, low-risk versus intermediate/high-risk, and statistical analysis was performed using Mann-Whitney's test (statistical significance at P < 0.05) and receiver operating characteristic (ROC) curves. Multivariate analysis was also performed.

RESULTS

Invasive breast cancers, when scored as low-risk by ODxRS, had significantly higher ADC values compared with intermediate/high-risk lesions for both ADC₆₀₀ (P = 0.007) and ADC₁₀₀₀ (P = 0.008) mean values. This was true both when analyzing only mass-lesions (P = 0.03 and 0.01) or only IDCs (P = 0.001 and 0.009).

CONCLUSION

Preliminary findings suggest that lesion ADC values correlate with recurrence risk likelihood stratified using ODxRS. Hence, ADC is a potential surrogate biomarker for tumor aggressiveness.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;47:401-409.

Is the necrosis/wall ADC ratio useful for the differentiation of benign and malignant breast lesions?

OBJECTIVE

To determine whether the necrosis/wall apparent diffusion coefficient (ADC) ratio is useful for the malignant-benign differentiation of necrotic breast lesions.

METHODS

Breast MRI was performed using a 3-T system. In this retrospective study, calculation of the necrosis/wall ADC ratio was based on ADC values measured from the necrosis and from the wall of malignant and benign breast lesions by diffusion-weighted imaging (DWI). By synchronizing post-contrast T₁ weighted images, the separate parts of wall and necrosis were maintained. All the diagnoses were pathologically confirmed. Statistical analyses were conducted using an independent sample t-test and receiver operating characteristic analysis. The intraclass and interclass correlations were evaluated.

RESULTS

A total of 66 female patients were enrolled, 38 of whom had necrotic breast carcinomas and 28 of whom had breast abscesses. The ADC values were obtained from both the wall and necrosis. The mean necrosis/wall ADC ratio (± standard deviation) was 1.61 ± 0.51 in carcinomas, and it was 0.65 ± 0.33 in abscesses. The area under the curve values for necrosis ADC, wall ADC and the necrosis/wall ADC ratio were 0.680, 0.068 and 0.942, respectively. A wall/necrosis ADC ratio cut-off value of 1.18 demonstrated a sensitivity of 97%, specificity of 93%, a positive-predictive value of 95%, a negative-predictive value of 96% and an accuracy of 95% in determining the malignant nature of necrotic breast lesions. There was a good intra- and interclass reliability for the ADC values of both necrosis and wall.

CONCLUSION

The necrosis/wall ADC ratio appears to be a reliable and promising tool for discriminating breast carcinomas from abscesses using DWI. Advances in knowledge: ADC values of the necrosis obtained by DWI are valuable for malignant-benign differentiation in necrotic breast lesions. The necrosis/wall ADC ratio appears to be a reliable and promising tool in the breast imaging field.