Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PURPOSE

To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer.

METHODS

This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses.

RESULTS

kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944).

CONCLUSION

The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer

To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.

IMAGGS: a radiogenomic framework for identifying multi-way associations in breast cancer subtypes

Investigating correlations between radiomic and genomic profiling in breast cancer (BC) molecular subtypes is crucial for understanding disease mechanisms and providing personalized treatment. We present a well-designed radiogenomic framework image-gene-gene set (IMAGGS), which detects multi-way associations in BC subtypes by integrating radiomic and genomic features. Our dataset consists of 721 patients, each of whom has 12 ultrasound (US) images captured from different angles and gene mutation data. To better characterize tumor traits, 12 multi-angle US images are fused using two distinct strategies. Then, we analyze complex many-to-many associations between phenotypic and genotypic features using a machine learning algorithm, deviating from the prevalent one-to-one relationship pattern observed in previous studies. Key radiomic and genomic features are screened using these associations. In addition, gene set enrichment analysis is performed to investigate the joint effects of gene sets and delve deeper into the biological functions of BC subtypes. We further validate the feasibility of IMAGGS in a glioblastoma multiforme dataset to demonstrate the scalability of IMAGGS across different modalities and diseases. Taken together, IMAGGS provides a comprehensive characterization for diseases by associating imaging, genes, and gene sets, paving the way for biological interpretation of radiomics and development of targeted therapy.

MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer.

MATERIALS AND METHODS

A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis.

RESULTS

31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results.

CONCLUSION

This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.

Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI

BACKGROUND

Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC.

METHODS

This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (K^trans, K_ep and V_e) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived.

RESULTS

The binary logistic regression analysis revealed that K_{ep_Range} (p = 0.032) and V_{e_SumVariance} (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of K_{ep_Compactness2} (p = 0.049), K_{ep_SphericalDisproportion} (p = 0.049), and V_{e_GlcmEntropy} (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group.

CONCLUSION

Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.

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.

Whole-tumor apparent diffusion coefficient (ADC) analyses of breast lesions based on simultaneous multi-slice readout-segmented echo-planar diffusion-weighted imaging

OBJECTIVE

We aimed to evaluate the effectiveness of simultaneous multi-slice readout-segmented echo-planar diffusion-weighted imaging (SMS rs-EPI DWI), compared with readout-segmented echo-planar diffusion-weighted imaging (NOSMS rs-EPI DWI), in discriminating between benign and malignant breast lesions.

MATERIALS AND METHODS

This retrospective study evaluated breast lesions from 185 consecutive patients who had undergone preoperative breast MRI. The NOSMS rs-EPI DWI and the prototype SMS rs-EPI DWI sequences were performed on a 1.5-T MR scanner. Two independent radiologists evaluated the image quality of the two sequences using a 5-point scale (1 = poor, 5 = excellent) and then assessed the scores through visual grading characteristics analysis (VGC). The values of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and whole-tumor-based histogram parameter (ADC_median) were compared between the two sequences using the Wilcoxon test. Then ROC curves were used for statistical analysis.

RESULTS

The visual assessment showed that the SMS rs-EPI yielded superior overall image quality and lesion delineation than the NOSMS rs-EPI (AUC_VGC = 0.980 and 0.984, respectively; all P_s < 0.001). There were no significant differences in relevant artifacts between the two sequences (AUC_VGC = 0.531, P = 0.462). The SNR values for SMS rs-EPI DWI were significantly lower than for NOSMS rs-EPI DWI (P = 0.019) while there was no significant difference in CNR values between the two sequences (P = 0.955). In addition, evaluation of the diagnostic performance demonstrated that the difference in ADC_median values for both DWI sequences between the malignant and benign lesions was statistically significant (P < 0.001). In contrast, the AUC for ADC_median was higher with SMS rs-EPI than NOSMS rs-EPI (0.879 for SMS vs. 0.839 for NOSMS, P = 0.025).

CONCLUSION

The SMS technique could further improve the image quality and the diagnostic performance of rs-EPI DWI in a comparable time.

Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging

Objectives

To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions.

Study design

From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student’s t-tests, one-way ANOVAs, Mann–Whitney U tests, and receiver operating characteristic curves were used for statistical analysis.

Results

The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 vs. 3.92 ± 0.42 and 4.93 ± 0.29 vs. 3.87 ± 0.47, p < 0.0001). The ICCs for the image quality and lesion conspicuity scores had good agreements among the three readers (all ICCs >0.75). To differentiate benign and malignant breast lesions, the entropy of ADC_A-Zoomit had the highest area (0.78) under the ROC curve.

Conclusions

A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.

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.

Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors

Purpose

To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients.

Methods

This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement.

Results

The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05).

Conclusion

FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.

Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging

Background

To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference.

Methods

Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves.

Results 

The mean, median, 5^th, and 95^th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5^th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC_5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2W_Kurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC_5th & T2W_Kurtosis parameters was also similar to that of the ADC_5th & ADC_{Diff−Variance}.

Conclusions

Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm)

OBJECTIVES

To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm).

METHODS

This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis.

RESULTS

For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001).

CONCLUSIONS

Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI.

KEY POINTS

• For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Risk Stratification of Ductal Carcinoma In Situ and Texture Analysis of Contrast-Enhanced Breast Magnetic Resonance Imaging

OBJECTIVE

The aim of this study was to investigate whether texture analysis of contrast-enhanced T1 weighted images could predict risk of ductal carcinoma in situ (DCIS).

METHODS

The study included 185 DCIS lesions that were classified as either low risk or non-low risk using surgical pathology records. All magnetic resonance imaging texture analyses were performed using postprocessing software, and texture-derived parameters were extracted.

RESULTS

The sphericity, compactness, and spherical disproportion were significantly different in the low-risk and non-low risk groups using the Van Nuys Prognostic Index (mean ± SD, 0.479 ± 0.189 vs 0.414 ± 0.176, 0.161 ± 0.159 vs 0.112 ± 0.134, and 2.569 ± 1.434 vs 2.934 ± 1.374, respectively; P < 0.05). In the univariate analyses, sphericity (odds ratio, 7.091; 95% confidence interval, 1.236-40.666; P = 0.028) and compactness (odds ratio, 9.267; 95% confidence interval, 1.125-76.360; P = 0.039) were significantly associated with a high probability of being low risk according to the Van Nuys Prognostic Index.

CONCLUSIONS

Whole-lesion texture analysis may be helpful in identifying patients classified as having low-risk DCIS before surgery.

A free time point model for dynamic contrast enhanced exploration

Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.

Evaluation of microvascular permeability of skeletal muscle and texture analysis based on DCE-MRI in alloxan-induced diabetic rabbits

OBJECTIVES

To estimate the microvascular permeability and perfusion of skeletal muscle by using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and explore the feasibility of using texture analysis (TA) to evaluate subtle structural changes of diabetic muscles.

METHODS

Twenty-four rabbits were randomly divided into diabetic (n = 14) and control (n = 10) groups, and underwent axial DCE-MRI of the multifidus muscle (0, 4, 8, 12, and 16 weeks after alloxan injection). The pharmacokinetic model was used to calculate the permeability parameters; texture parameters were extracted from volume transfer constant (K^trans) map. The two-sample t test/Mann-Whitney U test, repeated measures analysis of variance/Friedman test, and Pearson correlations were used for data analysis.

RESULTS

In the diabetic group, K^trans and rate constant (K_ep) increased significantly at week 8 and then showed a decreasing trend. Extravascular extracellular space volume fraction (V_e) increased and plasma volume fraction (V_p) decreased significantly from the 8th week. Skewness began to decrease at the 4th week. Median K^trans and entropy increased significantly, while inverse difference moment decreased from the 8th week. Energy decreased while contrast increased only at week 8. Muscle fibre cross-sectional area was negatively correlated with V_e. The capillary-to-fibre ratio was positively correlated with V_p (p < 0.05, all).

CONCLUSIONS

Quantitative DCE-MRI can be used to evaluate microvascular permeability and perfusion in diabetic skeletal muscle at an early stage; TA based on K^trans map can identify microarchitectural modifications in diabetic muscles.

KEY POINTS

• Four quantitative parameters of DCE-MRI can be used to evaluate microvascular permeability and perfusion of skeletal muscle in diabetic models at early stages. • Texture analysis based on K^trans map can identify subtle structural changes in diabetic muscles.

Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?

OBJECTIVE

To explore whether multiple 3D computed tomography texture analysis (3D-CTTA) parameters can predict the therapeutic effects of holmium: YAG laser lithotripsy (LL) on ureteral calculi.

METHODS

The files from 94 patients (102 stones) with proximal ureteral calculi treated only by LL at a single institution were retrospectively retrieved from January 2016 to March 2019. According to intra-operative observations and postoperative reexamination, samples were divided into a completely crushed and a non-crushed group. Preoperative non-contrast-enhanced computed tomography (NCCT) images obtained by multiple CT scanners were imported to MaZda software for 3D texture analysis (TA). The CT-derived value of each target stone was measured, and 15 TA parameters were extracted by delineating volumes of interest (VOIs). Receiver operating characteristic (ROC) curves were drawn to determine the optimal critical value of each parameter based on the Youden index, and univariable and multivariable logistic regression analyses determined the significant factors for LL success.

RESULTS

In univariable analysis, significant differences (p < 0.05) were observed among 7 parameters. In multivariable analysis, Perc.01 3D > 2062 (p = 0.03) and Z-fraction of image in runs (Z-Fraction) > 0.45570 (p = 0.009) were significant independent predictors, with odds ratios (ORs) of 24.204 and 60.329, respectively. In subgroup analysis based on the cutoff value of the CT-derived value (HU = 960), Perc.01 3D (OR = 44.154, 95% CI (2.379, 819.618), p = 0.011) and Z-Fraction (OR = 14.519, 95% CI (2.088, 100.953), p = 0.007) remained statistically significant.

CONCLUSIONS

The combination of 3D-CTTA parameters and the CT-derived value can be used as a quantitative reference to predict whether a target stone could be completely crushed by LL.

KEY POINTS

• Computed tomography texture analysis (CTTA) may be helpful in selecting suitable laser lithotripsy (LL) patients. • 3D-CTTA better predicts stone fragility than commonly used methods (such as the CT-derived value). • The combination of CTTA and the CT-derived value can be used as a preoperative quantitative reference.

Multi-parametric MRI lesion heterogeneity biomarkers for breast cancer diagnosis

PURPOSE

To identify intra-lesion imaging heterogeneity biomarkers in multi-parametric Magnetic Resonance Imaging (mpMRI) for breast lesion diagnosis.

METHODS

Dynamic Contrast Enhanced (DCE) and Diffusion Weighted Imaging (DWI) of 73 female patients, with 85 histologically verified breast lesions were acquired. Non-rigid multi-resolution registration was utilized to spatially align sequences. Four (4) DCE (2^nd post-contrast frame, Initial-Enhancement, Post-Initial-Enhancement and Signal-Enhancement-Ratio) and one (1) DWI (Apparent-Diffusion-Coefficient) representations were analyzed, considering a representative lesion slice. 11 1^st-order-statistics and 16 texture features (Gray-Level-Co-occurrence-Matrix (GLCM) and Gray-Level-Run-Length-Matrix (GLRLM) based) were derived from lesion segments, provided by Fuzzy C-Means segmentation, across the 5 representations, resulting in 135 features. Least-Absolute-Shrinkage and Selection-Operator (LASSO) regression was utilized to select optimal feature subsets, subsequently fed into 3 classification schemes: Logistic-Regression (LR), Random-Forest (RF), Support-Vector-Machine-Sequential-Minimal-Optimization (SVM-SMO), assessed with Receiver-Operating-Characteristic (ROC) analysis.

RESULTS

LASSO regression resulted in 7, 6 and 7 features subsets from DCE, DWI and mpMRI, respectively. Best classification performance was obtained by the RF multi-parametric scheme (Area-Under-ROC-Curve, (AUC) ± Standard-Error (SE), AUC ± SE = 0.984 ± 0.025), as compared to DCE (AUC ± SE = 0.961 ± 0.030) and DWI (AUC ± SE = 0.938 ± 0.032) and statistically significantly higher as compared to DWI. The selected mpMRI feature subset highlights the significance of entropy (1^st-order-statistics and 2^nd-order-statistics (GLCM)) and percentile features extracted from 2^nd post-contrast frame, PIE, SER maps and ADC map.

CONCLUSION

Capturing breast intra-lesion heterogeneity, across mpMRI lesion segments with 1^st-order-statistics and texture features (GLCM and GLRLM based), offers a valuable diagnostic tool for breast cancer.

Whole-liver histogram and texture analysis on T1 maps improves the risk stratification of advanced fibrosis in NAFLD

Objectives

To assess whole-liver texture analysis on T1 maps for risk stratification of advanced fibrosis in patients with suspected nonalcoholic fatty liver disease (NAFLD).

Methods

This retrospective study included 53 patients. Histogram and texture parameters (volume, mean, SD, median, 5th percentile, 95th percentile, skewness, kurtosis, diff-entropy, diff-variance, contrast, and entropy) of T1 maps were calculated based on the semi-automatically segmented whole-liver volume. A two-step approach combining the Nonalcoholic Fatty Liver Disease Fibrosis Score (NFS) and Fibrosis-4 Index (FIB-4) with the liver stiffness measurement (LSM) for the risk stratification was used. Univariate analysis was performed to identify significant parameters. Logistic regression models were then run on the significant features. Diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis.

Results

In total, 33 (62%) subjects had a low risk and 20 (38%) subjects had an intermediate-to-high risk of advanced fibrosis. The following significantly different parameters with the best performance were diff-entropy, entropy, and diff-variance, with AUROC 0.837 (95% CI 0.73–0.95), 0.821 (95% CI 0.71–0.94), and 0.807 (95% CI 0.69–0.93). The optimal combination of median, 5th percentile, and diff-entropy as a multivariate model improved the diagnostic performance to diagnose an intermediate-to-high risk of advanced fibrosis with AUROC 0.902(95% CI 0.79–0.97).

Conclusions

Parameters obtained by histogram and texture analysis of T1 maps may be a noninvasive analytical approach for stratifying the risk of advanced fibrosis in NAFLD.

Key Points

• Variable flip angle (VFA) T1 mapping can be used to acquire 3D T1 maps within a clinically acceptable duration.

• Whole-liver histogram and texture parameters on T1 maps in patients with NAFLD can distinguish those with an intermediate-to-high risk of advanced fibrosis.

• The multivariate model of combination of texture parameters improved the diagnostic performance for a high risk of advanced fibrosis and clinical parameters offer no added value to the multivariate model.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07235-4) contains supplementary material, which is available to authorized users.

Image quality of the CAIPIRINHA-Dixon-TWIST-VIBE technique for ultra-fast breast DCE-MRI: Comparison with the conventional GRE technique

PURPOSE

The aim of this study was to evaluate image quality of the CAIPIRINHA-Dixon-TWIST-Volume-Interpolated Breath-hold Examination (CDT-VIBE) technique for ultra-fast breast dynamic contrast enhanced (DCE) MRI with respect to conventional Gradient-Recalled Echo (GRE) technique.

METHODS

A total of 58 patients underwent a DCE-MRI based on CDT-VIBE sequence (temporal resolution: 11.9 s), immediately followed by 1 phase of a conventional T1 weighted GRE sequence (acquisition time: 68 s). The Signal-to-Noise Ratio (SNR) on phantom images, lesion/parenchyma signal ratio (LPSR), image quality, and morphological characterization were compared between the last phase of CDT-VIBE and conventional GRE images. The image quality was assessed by visual grading analysis (VGA). Reader agreement was assessed using Kappa analysis.

RESULTS

There was no significant difference in SNR (phantom) or LPSR (patient) between CDT-VIBE and conventional GRE images (P > 0.05). Significant parallel acquisition technique (PAT) noise and mild blurriness was observed on CDT-VIBE images. Visual grading analysis (VGA) confirmed significantly worse ratings for CDT-VIBE compared to the conventional GRE sequence in terms of PAT noise, lesion's internal feature clarity, and therefore overall image quality (area under contrast curve [AUC] values: 0.578 ‒ 0.764, P < 0.05), but edge sharpness and lesion conspicuity were equivalent (P > 0.05). Kappa analysis revealed good agreement on image quality scores (к = 0.725 ‒ 0.908) and on morphologic terms (к = 0.745-1.000).

CONCLUSION

The CDT-VIBE sequence provides excellent spatial resolution and adequate image quality in ultra-fast breast DCE-MRI. Further improvement in PAT noise and internal structure blurriness may be necessary.

Clinical Feasibility of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging with Computed Diffusion-Weighted Imaging Technique in Breast Cancer Patients

Background: We evaluated the feasibility of the reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with computed DWI technique by comparison and analysis of the inter-method agreement among acquired rFOV DWI (rFOVA), rFOV DWI with computed DWI technique (rFOVS), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in patients with breast cancer. Methods: A total of 130 patients with biopsy-proven breast cancers who underwent breast MRI from April 2017 to December 2017 were included in this study. The rFOVS were reformatted by calculation of the apparent diffusion coefficient curve obtained from rFOVA b = 0 s/mm² and b = 500 s/mm². Visual assessment of the image quality of rFOVA b = 1000 s/mm², rFOVS, and DCE MRI was performed using a four-point grading system. Morphologic analyses of the index cancer was performed on rFOVA, rFOVS, and DCE MRI. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast of tumor-to-parenchyma (TPC) were calculated. Results: Image quality scores with rFOVA, rFOVS, and DCE MRI were not significantly different (p = 0.357). Lesion analysis of shape, margin, and size of the index cancer also did not show significant differences among the three sequences (p = 0.858, p = 0.242, and p = 0.858, respectively). SNR, CNR, and TPC of DCE MRI were significantly higher than those of rFOVA and rFOVS (p < 0.001, p = 0.001, and p = 0.016, respectively). Significant differences were not found between the SNR, CNR, and TPC of rFOVA and those of rFOVS (p > 0.999, p > 0.999, and p > 0.999, respectively). Conclusion: The rFOVA and rFOVS showed nearly equivalent levels of image quality required for morphological analysis of the tumors and for lesion conspicuity compared with DCE MRI.

High-Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network

BACKGROUND

A generative adversarial network could be used for high-resolution (HR) medical image synthesis with reduced scan time.

PURPOSE

To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HR_pre and HR_post images based on their corresponding low-resolution (LR) images (LR_pre and LR_post ).

STUDY TYPE

This was a retrospective analysis of a prospectively acquired cohort.

POPULATION

In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set.

FIELD STRENGTH/SEQUENCE

Dynamic contrast-enhanced (DCE) MRI with a 1.5T scanner.

ASSESSMENT

Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI-RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI-RADS) categories were calculated between the three readers.

STATISTICAL TEST

Wilcoxon signed-rank tests evaluated differences among the multireader ranking scores.

RESULTS

The mean overall image quality scores of the generated HR_pre and HR_post were significantly higher than those of the original LR_pre and LR_post (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HR_pre and HR_post were significantly higher than those of the original LR_pre and LR_post (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI-RADS categories had good agreements among the three readers (all ICCs >0.75).

DATA CONCLUSION

DCGAN was capable of generating HR of the breast from fast pre- and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1852-1858.