Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer

The value of Synthetic MRI in discriminating metastatic and non-metastatic lymph nodes in head and neck squamous cell carcinoma, compared with DWI and subjective experience

OBJECTIVES

To explore the role of Synthetic MRI (SyMRI) histogram parameters in differentiating metastatic from non-metastatic cervical lymph nodes (LNs) in head and neck squamous cell carcinoma (HNSCC) patients, and construct a practical model.

METHODS

A total of 149 pathologically confirmed LNs (metastatic LNs: 58, non-metastatic LNs: 91) were included in the study. LNs were stratified and randomly divided into a training set and an independent validation set in a ratio of 7:3. Histogram parameters derived from SyMRI, ADC values, and the short and long diameters of each LN were obtained. Significantly different parameters between metastatic and non-metastatic LNs were selected in the training set, and logistic regression analysis was adopted to construct different models. ROC analysis and AUC were performed to assess the diagnostic performance of different models and subjective analysis.

RESULTS

The AUCs of the three models were 0.882 (SyMRI_model), 0.755 (DWI), and 0.952 (Combined_model) in the validation set. The Combined_model, constructed based on SyMRI, ADC values, and size, had the highest diagnostic potency in both training and validation sets, with an accuracy of 0.905 and 0.864 in the two sets, respectively. The diagnostic performance of the Combined_model was superior to multi-radiologists' subjective experience, not only in LNs from validation set (AUC: 0.952 vs. 0.705 ∼ 0.845) but also in the cohort of sub-centimeter LNs (AUC: 0.878 vs. 0.429 ∼ 0.628) (all P < 0.001).

CONCLUSION

Histogram parameters derived from SyMRI are feasible in discriminating metastatic from non-metastatic cervical LNs in HNSCC, and the diagnostic efficacy is optimal when combined with DWI and size.

Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features

OBJECTIVES

To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.

METHODS

We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.

RESULTS

NAC responders had higher DCE-MRI-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-MRI-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status (OR, 0.187; 95% CI: 0.038, 0.914; P = 0.038) and tumor margin (OR, 20.643; 95% CI: 2.892, 147.365; P = 0.003) were identified as independent factors influencing the lack of significant efficacy of neoadjuvant chemotherapy (NAC) in breast cancer.

CONCLUSIONS

Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.

CLINICAL TRIAL NUMBER

Not applicable.

Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method

PURPOSE

To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.

METHODS

In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (n = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (n = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models.

RESULTS

In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859-0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632-0. 814, 0.641-0.788, 0.621-0.709, 0.620-0.701and 0.611-0.785.

CONCLUSION

The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.

Enhanced preoperative prediction of breast lesion pathology, prognostic biomarkers, and molecular subtypes using multiple models diffusion-weighted MR imaging

This study aims to comprehensively evaluate the clinical utility of five diffusion models, including conventional mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential (SEM), and continuous-time random-walk (CTRW), for preoperatively predicting of breast lesion pathology, prognostic biomarkers, and molecular subtypes. We retrospectively analyzed 132 patients with pathologically verified breast lesions (41 benign and 91 malignant) who underwent a full protocol preoperative breast MRI protocol, including a diffusion-weighted imaging (DWI) sequence with nine b values (0 to 2000 s/mm2) on a 3.0T MR scanner. The diffusion parameters from each model-Mono (ADC), IVIM (D, D*, f), DKI (MD, MK), SEM (DDC, α) and CTRW (Dm, α, β)-were quantitatively calculated and compared between benign and malignant breast lesions, as well as across different prognostic biomarker statuses in breast cancer, using Mann-Whitney U-tests. For molecular subtypes comparisons, we employed the Kruskal-Wallis test followed by Bonferroni. All parameters, except IVIM-D*, significantly differentiated benign from malignant lesions. Notably, IVIM-D and DKI-MK values were significantly different between estrogen receptor (ER)-positive and ER-negative tumors. Progesterone receptor (PR)-positive cancers exhibited lower Mono-ADC, IVIM-D, DKI-MD, SEM-DDC, CTRW-Dm, and CTRW-α values, alongside higher DKI-MK value compared to PR-negative cancers (p < 0.05). Significant differences in IVIM-D, IVIM-D*, and DKI-MK values were observed between human epidermal growth factor receptor 2 (HER2)-negative and HER2-positive tumors. Furthermore, higher SEM-α and CTRW-β values, along with lower DKI-MD and SEM-DDC values, were noted in the high Ki-67 expression group compared to the low Ki-67 group (p < 0.05). All five diffusion models proved valuable for breast cancer diagnosis, with the CTRW model exhibiting the highest diagnostic performance, although the difference was not statistically significant. The diffusion parameters derived from these models can effectively assist in distinguishing prognostic factors and molecular subtypes of breast cancer.

An Exploratory Study: Can Native T1 Mapping Differentiate Sarcoma from Benign Soft Tissue Tumors at 1.5 T and 3 T?

Background/Objectives: T1 relaxation time has been shown to be valuable in detecting and characterizing tumors in various organs. This study aims to determine whether native T1 relaxation time can serve as a useful tool in distinguishing sarcomas from benign tumors. Methods: In this retrospective study, patients with histologically confirmed soft tissue sarcomas and benign tumors were included. Only patients who had not undergone prior treatment or surgery and whose magnetic resonance imaging (MRI) included native T1 mapping were considered. Images were acquired using both 1.5 T and 3 T MRI scanners. T1 histogram parameters were measured in regions of interest encompassing the entire tumor volume, as well as in healthy muscle tissue. Results: Out of 316 cases, 16 sarcoma cases and 9 benign tumor cases were eligible. The T1 values observed in sarcoma did not significantly differ from those in benign lesions in both 1.5 T and 3 T MRIs (p1.5T = 0.260 and p3T = 0.119). However, T1 values were found to be lower in healthy tissues compared to sarcoma at 3 T (p = 0.020), although this difference did not reach statistical significance at 1.5 T (p = 0.063). At both 1.5 T and 3 T, no significant difference between healthy muscle measured in sarcoma cases or benign tumor cases was observed (p1.5T = 0.472 and p3T = 0.226). Conclusions: T1 mapping has the potential to serve as a promising tool for differentiating sarcomas from benign tumors in baseline assessments. However, the standardization of imaging protocols and further improvements in T1 mapping techniques are necessary to fully realize its potential.

The value of multiple diffusion metrics based on whole-lesion histogram analysis in evaluating the subtypes and proliferation status of non-small cell lung cancer

Objective

Limited studies have explored the utility of whole-lesion histogram analysis in discerning the subtypes and proliferation status of non-small cell lung cancer (NSCLC), despite its potential to provide comprehensive tissue assessment through the computation of additional quantitative metrics. This study sought to assess the significance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram parameters in discriminating between squamous cell carcinoma (SCC) and adenocarcinoma (AC), and to examine the correlation of each parameter with the proliferative marker Ki-67.

Materials and methods

Patients with space-occupying lesions detected by chest CT examination and with further routine MRI, DKI and IVIM functional sequence scans were enrolled. Based on the pathological results, seventy patients with NSCLC were selected and divided into AC and SCC groups. Histogram parameters of IVIM (D, D*, f) and DKI (Dapp, Kapp) were calculated, and the Mann-Whitney U test or independent samples t test was used to analyze the differences in each histogram parameter of the SCC and AC groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the histogram parameters. The correlation coefficient between histogram parameters and Ki-67 was calculated using Spearman's or Pearson's methods.

Results

The D 10th percentile, D 90th percentile, D mean, D median, Dapp 10th percentile, Dapp 90th percentile, Dapp mean, Dapp median, Dapp skewness, Dapp SD of the AC groups were significantly higher than those of the SCC groups, while the Kapp entropy and Kapp SD of the SCC groups were significantly higher than those of the AC groups. All the above differences were statistically significant (all P < 0.05). ROC curve analysis revealed that Dapp mean showed the best performance for differentiating AC from SCC lesions, with an area under the ROC curve of 0.832 (95% confidence interval [CI]: 0.707-0.919). But there was no statistically significant difference in diagnostic efficacy compared to other histogram parameters (all P>0.05). Dapp 90thpercentile, Dapp mean, Kapp skewnes showed a slight negative correlation with Ki-67 expression (r value -0.340, -0.287, -0.344, respectively; P< 0.05), while the other histogram parameters showed no significant correlation with Ki-67 (all P > 0.05).

Conclusions

Our study demonstrates the utility of IVIM and DKI histogram analyses in differentiating NSCLC subtypes, particularly AC and SCC. Correlations with the Ki-67 index suggest that Dapp mean, Dapp 90th percentile, and Kapp skewness may serve as markers of tumor aggressiveness, supporting their use in NSCLC diagnosis and treatment planning.

Synthetic MRI and amide proton transfer-weighted MRI for differentiating between benign and malignant sinonasal lesions

OBJECTIVES

To explore the value of the synthetic MRI (SyMRI), combined with amide proton transfer-weighted (APTw) MRI for quantitative and morphologic assessment of sinonasal lesions, which could provide relative scale for the quantitative assessment of tissue properties.

METHODS

A total of 80 patients (31 malignant and 49 benign) with sinonasal lesions, who underwent the SyMRI and APTw examination, were retrospectively analyzed. Quantitative parameters (T1, T2, proton density (PD)) and APT % were obtained through outlining the region of interest (ROI) and comparing the two groups utilizing independent Student t test or a Wilcoxon test. Receiver operating characteristic curve (ROC), Delong test, and logistic regression analysis were performed to assess the diagnostic efficiency of one-parameter and multiparametric models.

RESULTS

SyMRI-derived mean T1, T2, and PD were significantly higher and APT % was relatively lower in benign compared to malignant sinonasal lesions (p < 0.05). The ROC analysis showed that the AUCs of the SyMRI-derived quantitative (T1, T2, PD) values and APT % ranged from 0.677 to 0.781 for differential diagnosis between benign and malignant sinonasal lesions. The T2 values showed the best diagnostic performance among all single parameters for differentiating these two masses. The AUCs of combined SyMRI-derived multiple parameters with APT % (AUC = 0.866) were the highest than that of any single parameter, which was significantly improved (p < 0.05).

CONCLUSION

The combination of SyMRI and APTw imaging has the potential to reflect intrinsic tissue characteristics useful for differentiating benign from malignant sinonasal lesions.

CLINICAL RELEVANCE STATEMENT

Combining synthetic MRI with amide proton transfer-weighted imaging could function as a quantitative and contrast-free approach, significantly enhancing the differentiation of benign and malignant sinonasal lesions and overcoming the limitations associated with the superficial nature of endoscopic nasal sampling.

KEY POINTS

• Synthetic MRI and amide proton transfer-weighted MRI could differentiate benign from malignant sinonasal lesions based on quantitative parameters. • The diagnostic efficiency could be significantly improved through synthetic MRI + amide proton transfer-weighted imaging. • The combination of synthetic MRI and amide proton transfer-weighted MRI is a noninvasive method to evaluate sinonasal lesions.

Noninvasive identification of HER2-zero, -low, or -overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer

PURPOSE

To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients.

METHOD

In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve（AUC）of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903).

CONCLUSIONS

SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC.

Multiparametric MR characterization for human epithelial growth factor receptor 2 expression in bladder cancer: an exploratory study

PURPOSE

To investigate the application value of multiparametric MRI in evaluating the expression status of human epithelial growth factor receptor 2 (HER2) in bladder cancer (BCa).

METHODS

From April 2021 to July 2023, preoperative imaging manifestations of 90 patients with pathologically confirmed BCa were retrospectively collected and analyzed. All patients underwent multiparametric MRI including synthetic MRI, DWI, from which the T1, T2, proton density (PD) and apparent diffusion coefficient (ADC) values were obtained. The clinical and imaging characteristics as well as quantitative parameters (T1, T2, PD and ADC values) between HER2-positive and -negative BCa were compared using student t test and chi-square test. The diagnostic efficacy of parameters in predicting HER2 expression status was evaluated by calculating the area under ROC curve (AUC).

RESULTS

In total, 76 patients (mean age, 63.59 years ± 12.84 [SD]; 55 men) were included: 51 with HER2-negative and 25 with HER2-positive BCa. HER2-positive group demonstrated significantly higher ADC, T1, and T2 values than HER2-negative group (all P < 0.05). The combination of ADC values and tumor grade yielded the best diagnostic performance in evaluating HER2 expression level with an AUC of 0.864.

CONCLUSION

The multiparametric MR characterization can accurately evaluate the HER2 expression status in BCa, which may further guide the determination of individualized anti-HER2 targeted therapy strategies.

Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer?

PURPOSE

To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa).

METHOD

A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage.

RESULTS

Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = - 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = - 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015-0.025).

CONCLUSION

Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PURPOSE

To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC).

METHODS

From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated.

RESULTS

The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722.

CONCLUSIONS

Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Correlation of MRI quantitative perfusion parameters with EGFR, VEGF and EGFR gene mutations in non-small cell cancer

To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r =  - 0.527, - 0.428, - 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r =  - 0.312 to - 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P < 0.05). Quantitative perfusion histogram parameters of DCE-MRI have a certain value in the differential diagnosis of NSCLC, which have the potential to non-invasively evaluate the expression of cell signaling pathway-related protein.

Pretreatment synthetic MRI features for triple-negative breast cancer

AIM

To evaluate the quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) for predicting triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

This prospective study enrolled participants with invasive ductal breast carcinoma (IDBC) and separated them into a TNBC group and a Non-TNBC group. Preoperative breast MRI included both the SyMRI and conventional MRI sequences. The quantitative parameters derived from the SyMRI included T1 and T2 relaxation times, proton density (PD), and their standard deviations (SD). Clinicopathological characteristics, conventional MRI findings, and quantitative synthetic parameters were assessed for all participants. Multivariable logistic regression analysis was performed to determine the potential independent imaging predictors for TNBC preoperatively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of these parameters.

RESULTS

A total of 231 participants with histopathological proven IDBC were included in this study (n=46 in the TNBC group and n=185 in the Non-TNBC group). The TNBC group had significantly larger tumour size (p=0.011) and more frequent intratumoural cystic or necrotic lesions (p<0.001) as compared to the Non-TNBC group. The univariate analysis showed that the TNBC tumours had significantly higher T1 (p=0.006) and T2 (p<0.001) values than Non-TNBC tumours. Subsequent multivariable analysis indicated that T2 values and the presence of cystic or necrotic lesions were the independent predictors for TNBC.

CONCLUSION

The T2 from synthetic imaging and the presence of cystic degeneration or necrosis within the breast cancer may serve as potential imaging biomarkers for preoperative differentiation of TNBC from Non-TNBC.

Feasibility of Quantitative MRI Using 3D-QALAS for Discriminating Immunohistochemical Status in Invasive Ductal Carcinoma of the Breast

BACKGROUND

Two-dimensional synthetic MRI of the breast has limited spatial coverage. Three-dimensional (3D) synthetic MRI could provide volumetric quantitative parameters that may reflect the immunohistochemical (IHC) status in invasive ductal carcinoma (IDC) of the breast.

PURPOSE

To evaluate the feasibility of 3D synthetic MRI using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) for discriminating the IHC status, including hormone receptor (HR), human epidermal growth factor receptor 2 (HER 2), and Ki-67 expression in IDC.

STUDY TYPE

Prospective observational study.

POPULATION

A total of 33 females with IDC of the breast (mean, 52.3 years).

FIELD STRENGTH/SEQUENCE

A 3-T, 3D-QALAS gradient-echo and fat-suppressed T1-weighted 3D fast spoiled gradient-echo sequences.

ASSESSMENT

Two radiologists semiautomatically delineated 3D regions of interest (ROIs) of the whole tumors on the dynamic MRI that was registered to the synthetic T1-weighted images acquired from 3D-QALAS. The mean T1 and T2 were measured for each IDC.

STATISTICAL TESTS

Intraclass correlation coefficient for assessing interobserver agreement. Mann-Whitney U test to determine the relationship between the mean T1 or T2 and the IHC status. Multivariate logistic regression analysis followed by receiver operating characteristics (ROC) analysis for discriminating IHC status. A P value <0.05 was considered statistically significant.

RESULTS

The interobserver agreement was good to excellent. There was a significant difference in the mean T1 between HR-positive and HR-negative lesions, while the mean T2 value differed between HR-positive and HR-negative lesions, between the triple-negative and HR-positive or HER2-positive lesions, and between the Ki-67 level > 14% and ≤ 14%. Multivariate analysis showed that the mean T2 was higher in HR-negative IDC than in HR-positive IDC. ROC analysis revealed that the mean T2 was predictive for discriminating HR status, triple-negative status, and Ki-67 level.

DATA CONCLUSION

3D synthetic MRI using QALAS may be useful for discriminating IHC status in IDC of the breast.

EVIDENCE LEVEL

1.

TECHNICAL EFFICACY

Stage 2.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation

BACKGROUND

Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer.

PURPOSE

To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer.

STUDY TYPE

Retrospective, longitudinal.

POPULATION

A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence.

ASSESSMENT

Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed.

STATISTICAL TESTS

Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis.

RESULTS

Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067).

DATA CONCLUSION

Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC).

METHODS

Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann-Whitney U test was used and ROC curve analysis was further processed.

RESULTS

Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r =  - 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r =  - 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r =  - 0.259 ~ - 0.301; N stage: r =  - 0.348 ~  - 0.456; clinical stage: r =  - 0.419).

CONCLUSIONS

Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage.

KEY POINTS

• SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage.

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis

BACKGROUND

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.

PURPOSE

To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer.

FIELD STRENGTH/SEQUENCE

Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T.

ASSESSMENT

The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed.

STATISTICAL TESTS

Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.

RESULTS

The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and βmean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes.

DATA CONCLUSION

Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Pretreatment synthetic magnetic resonance imaging predicts disease progression in nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy

BACKGROUND

To investigate the potential of synthetic MRI (SyMRI) in the prognostic assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC), and the predictive value when combined with diffusion-weighted imaging (DWI) as well as clinical factors.

METHODS

Fifty-three NPC patients who underwent SyMRI were prospectively included. 10th Percentile, Mean, Kurtosis, and Skewness of T1, T2, and PD maps and ADC value were obtained from the primary tumor. Cox regression analysis was used for analyzing the association between SyMRI and DWI parameters and progression-free survival (PFS), and then age, sex, staging, and treatment as confounding factors were also included. C-index was obtained by bootstrap. Moreover, significant parameters were used to construct models in predicting 3-year disease progression. ROC curves and leave-one-out cross-validation were used to evaluate the performance and stability.

RESULTS

Disease progression occurred in 16 (30.2%) patients at a follow-up of 39.6 (3.5, 48.2) months. T1_Kurtosis, T1_Skewness, T2_10th, PD_Mean, and ADC were correlated with PFS, and T1_Kurtosis (HR: 1.093) and ADC (HR: 1.009) were independent predictors of PFS. The C-index of SyMRI and SyMRI + DWI + Clinic models was 0.687 and 0.779. Moreover, the SyMRI + DWI + Clinic model predicted 3-year disease progression better than DWI or Clinic model (p ≤ 0.008). Interestingly, there was no significant difference between the SyMRI model (AUC: 0.748) and SyMRI + DWI + Clinic model (AUC: 0.846, p = 0.092).

CONCLUSION

SyMRI combined with histogram analysis could predict disease progression in NPC patients, and SyMRI + DWI + Clinic model further improved the predictive performance.

Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma

PURPOSE

To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC).

METHOD

Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting.

RESULTS

Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status.

CONCLUSIONS

Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.

The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study

BACKGROUND

Magnetic resonance imaging (MRI) is commonly used for the diagnosis of nasopharyngeal carcinoma (NPC) and occipital clivus (OC) invasion, but a proportion of lesions may be missed using non-enhanced MRI. The purpose of this study is to investigate the diagnostic performance of synthetic magnetic resonance imaging (SyMRI) in differentiating NPC from nasopharyngeal hyperplasia (NPH), as well as evaluating OC invasion.

METHODS

Fifty-nine patients with NPC and 48 volunteers who underwent SyMRI examination were prospectively enrolled. Eighteen first-order features were extracted from VOIs (primary tumours, benign mucosa, and OC). Statistical comparisons were conducted between groups using the independent-samples t-test and the Mann-Whitney U test to select significant parameters. Multiple diagnostic models were then constructed using multivariate logistic analysis. The diagnostic performance of the models was calculated by receiver operating characteristics (ROC) curve analysis and compared using the DeLong test. Bootstrap and 5-folds cross-validation were applied to avoid overfitting.

RESULTS

The T1, T2 and PD map-derived models had excellent diagnostic performance in the discrimination between NPC and NPH in volunteers, with area under the curves (AUCs) of 0.975, 0.972 and 0.986, respectively. Besides, SyMRI models also showed excellent performance in distinguishing OC invasion from non-invasion (AUC: 0.913-0.997). Notably, the T1 map-derived model showed the highest diagnostic performance with an AUC, sensitivity, specificity, and accuracy of 0.997, 96.9%, 97.9% and 97.5%, respectively. By using 5-folds cross-validation, the bias-corrected AUCs were 0.965-0.984 in discriminating NPC from NPH and 0.889-0.975 in discriminating OC invasion from OC non-invasion.

CONCLUSIONS

SyMRI combined with first-order parameters showed excellent performance in differentiating NPC from NPH, as well as discriminating OC invasion from non-invasion.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

Objective

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.

Materials and Methods

A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.

Results

The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.

Conclusions

Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

BACKGROUND

To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.

METHODS

A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T₁ -weighted imaging (T₁ C), Apparent diffusion coefficient (ADC), and T₂ -weighted imaging (T₂ W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.

RESULTS

For the separation of each subtype from other subtypes on the testing set, the T₁ C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T₂ W-based models achieved AUCs from 0.639 to 0.697.

CONCLUSION

T₁ C-based models performed better than ADC-based models and T₂ W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.

Quantitative Assessment of Restriction Spectrum MR Imaging for the Diagnosis of Breast Cancer and Association With Prognostic Factors

BACKGROUND

Restriction spectrum imaging (RSI) is an advanced quantitative diffusion-weighted magnetic resonance imaging (DWI) technique to assess breast cancer.

PURPOSE

To investigate the ability of RSI to differentiate the benign and malignant breast lesions and the association with prognostic factors of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

Seventy women (mean age, 49.6 ± 12.3 years) with 56 malignant and 19 benign breast lesions.

FIELD STRENGTH/SEQUENCE

3-T; RSI-based DWI sequence with echo-planar imaging technique.

ASSESSMENT

The apparent diffusion coefficient (ADC) and RSI parameters (restricted diffusion f₁ , hindered diffusion f₂ , free diffusion f₃ , and signal fractions f₁ f₂ ) were calculated by two readers for the whole lesion volume and compared between the benign and malignant groups and the subgroups with different statuses of prognostic factors in breast cancer.

STATISTICAL TESTS

Mann-Whitney U test or Student's t-test was applied to compare the quantitative parameters between the different groups. Intraclass correlation coefficient (ICC) was used to assess readers' reproducibility. Binary logistic regression was used to combine parameters. Area under the curve (AUC) of receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of parameters to distinguish benign from malignant breast lesions. A P-value <0.05 was considered statistically significant.

RESULTS

Malignant breast lesions showed significantly lower ADC and f₃ values, and significantly higher f₁ and f₁ f₂ values than the benign lesions, with AUC of 0.951, 0.877, 0.868, and 0.860, respectively. When RSI-derived parameters and ADC were combined, the diagnostic performance was superior to either single parameter (AUC = 0.973). The f₃ value was significantly differed between estrogen receptor (ER)-positive and ER-negative tumors. The ADC, f₁ , f₃ , and f₁ f₂ values were significantly different progesterone receptor (PR)-positive and PR-negative status.

DATA CONCLUSION

The RSI-derived parameters (f₁ , f₃ , and f₁ f₂ ) may facilitate the differential diagnosis between benign and malignant breast lesions.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes

The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson’s r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69−0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61−0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.

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.