Retrospective analysis of the utility of multiparametric MRI for differentiating between benign and malignant breast lesions in women in China

Predicting lymphovascular invasion in rectal cancer: evaluating the performance of golden-angle radial sparse parallel MRI for rectal perfusion assessment

This study aims to determine whether the dual-parameter approach combined with either time-resolved angiography with stochastic trajectories (TWIST) or golden-angle radial sparse parallel (GRASP) and diffusion-weighted imaging (DWI) has superior diagnostic performance in predicting pathological lymphovascular invasion (pLVI) rectal cancer when compared with traditional single-parameter evaluations using DWI alone. Patients with pathologically confirmed rectal cancer were enrolled. Perfusion (influx forward volume transfer constant [Ktrans] and rate constant [Kep]) and apparent diffusion coefficient (ADC) were measured by two researchers. For both sequences, areas under receiver operating characteristic (ROCs) to predict pLVI-positive rectal cancer were compared. A total of 179 patients were enrolled in our study. A combined analysis of ADC and perfusion parameters (Ktrans) acquired with GRASP yielded a higher diagnostic performance compared with diffusion parameters alone (area under the curve, 0.91 ± 0.03 vs. 0.71 ± 0.06, P < 0.001); However, ADC with GRASP-acquired Kep and ADC with TWIST-acquired perfusion parameters (Ktrans or Kep) did not offer any additional benefit. The Ktrans of the GRASP technique improved the diagnostic performance of multiparametric MRI to predict rectal cancers with pLVI-positive. In contrast, TWIST did not achieve this effect.

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.

Readout-Segmented Echo-Planar Diffusion-Weighted MR Imaging Improves the Differentiation of Breast Cancer Receptor Statuses Compared With Conventional Diffusion-Weighted Imaging

BACKGROUND

Readout-segmented echo-planar diffusion-weighted imaging (RS-EPI) can improve image quality and signal-to-noise ratio, the resulting apparent diffusion coefficient (ADC) value acts as a more sensitive biomarker to characterize tumors. However, data regarding the differentiation of breast cancer (BC) receptor statuses using RS-EPI are limited.

PURPOSE

To determine whether RS-EPI improves the differentiation of receptor statuses compared with conventional single-shot (SS) EPI in breast MRI.

STUDY TYPE

Retrospective.

POPULATION

A total of 151 BC women with the mean age of 50.6 years.

FIELD STRENGTH/SEQUENCE

A 3 T/ RS-EPI and SS-EPI.

ASSESSMENT

The ADCs of the lesion and normal background tissue from the two sequences were collected by two radiologists with 15 years of experience working of breast MRI (M.H.Z. and X.F.C.), and a normalized ADC was calculated by dividing the mean ADC value of the lesion by the mean ADC value of the normal background tissue.

STATISTICAL TESTS

Agreement between the ADC measurements from the two sequences was assessed using the Pearson correlation coefficient and Bland-Altman plots. One-way analysis of variance, Kruskal-Wallis test, and median difference were used to compare the ADC measurements for all lesions and different receptor statuses. A P value less than 0.05 indicated a significant result.

RESULTS

The ADC measurements of all lesions and normal background tissues were significantly higher on RS-EPI than on SS-EPI (1.82 ± 0.33 vs. 1.55 ± 0.30 and 0.83 ± 0.11 vs. 0.79 ± 0.10). The normalized ADC was lower on RS-EPI than on SS-EPI (0.47 ± 0.11 vs. 0.53 ± 0.12, a median difference of -0.04 [95% CI: -0.256 to 0.111]). For both diffusion methods, only the ADC measurement of RS-EPI was higher for human epidermal growth factor receptor-2 (HER-2)-positive tumors than for HER-2-negative tumors (0.87 ± 0.10 vs. 0.81 ± 0.11), and this measurement was associated with HER-2 positive status (adjusted odds ratio [OR] = 654.4); however, similar results were not observed for the ADC measurement of SS-EPI (0.80 ± 0.10 vs. 0.78 ± 0.11 with P = 0.199 and adjusted OR = 0.21 with P = 0.464, respectively).

DATA CONCLUSION

RS-EPI can improve the distinction between HER-2-positive and HER-2-negative breast cancer, complementing the clinical application of diffusion imaging.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast

BACKGROUND

The value of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) histogram analysis for the diagnosis of breast cancer has not been evaluated in previous studies.

PURPOSE

To investigate the diagnostic value of DCE-MRI combined with ADC in benign and malignant breast lesions.

MATERIAL AND METHODS

The clinicopathological imaging data included 168 patients (177 lesions) with breast lesions who underwent convention breast MRI, DCE-MRI, and diffusion-weighted imaging (DWI); they were divided into the benign lesion group (n = 39) and malignant lesion group (n = 129) based on pathology.

RESULTS

Using the type III outflow curve as a diagnostic criterion for malignant breast lesions, the diagnostic sensitivity was 76.9%, the specificity was 80%, the correct rate was 72.2%, and its area under the curve (AUC) was 0.823. Using an enhancement ratio > 100% as a diagnostic criterion for malignant breast lesions, the sensitivity was 61.5%, specificity was 80%, and AUC was 0.723. Using > 3 ipsilateral vessels as a diagnostic criterion for malignant lesions in the breast resulted in a diagnostic sensitivity of 81.6%, a specificity of 80.8%, and an AUC of 0.805.

CONCLUSION

The type of time intensity curve DCE-MRI, the early enhancement rate in the first phase, the number of ipsilateral vessels, and the ADC full volume histogram of the blood supply score and DWI are valuable in the diagnosis of benign and malignant breast lesions.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer

OBJECTIVES

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.

RESULTS

Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.

CONCLUSIONS

Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.

KEY POINTS

• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

Objectives

To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques.

Methods

A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (K^trans, V_e, K_ep), semiquantitative parameters (W_-in, W_-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds.

Results

Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower K_ep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher K_ep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10^-3 mm²/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811.

Conclusions

K_ep and TTP were independently associated with hormone receptor status. In addition, the K_ep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

High resolution diffusion-weighted imaging with readout segmentation of long variable echo-trains for determining myometrial invasion in endometrial carcinoma

Background

We assessed the image quality of endometrial cancer lesions by readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) compared with that by single-shot echo-planar imaging (SS-EPI) DWI, aimed to explore the value of RESOLVE DWI for determining myometrial invasion and clinical stage in endometrial cancer.

Materials and methods

From April 2017 to March 2018, a total of 30 endometrial cancer patients (mean age 52.8 ± 9.0 years), who had undergone RESOLVE DWI and SS-EPI DWI, were included in the study. The image quality of endometrial carcinoma by two kinds of DWI scanning methods was compared qualitatively and quantitatively. The Spearman rank correlation test was used to assess the correlation of qualitative image quality scores between two readers. The accuracy of two DWI methods in detecting myometrial invasion and staging of endometrial carcinoma was calculated according to postoperative pathological results. The indexes were analyzed including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV).

Results

The qualitative score of RESOLVE DWI group was superior to SS-EPI DWI group in every aspect of five aspects (all P < 0.001). Interobserver agreement of depiction was good or excellent in two DWI sequences. Signal to noise ratio and contrast to noise ratio values in RESOLVE DWI group were both higher than those in SS-EPI DWI group (P<0.001). No statistical difference of apparent diffusion coefficient value was observed between two DWI groups (P = 0.261). The specificity, accuracy, PPV, and NPV of estimating myometrial invasion by RESOLVE DWI in three cases (intramucosal lesion, <50% superficial invasion and ≥ 50% deep invasion) were all higher than those by SS-EPI DWI for endometrial carcinoma. Especially RESOLVE DWI was valuable in judging <50% superficial invasion (95%CI:0.586, 0.970). No significant difference in accuracy staging was between the two DWI groups (P = 0.125).

Conclusion

RESOLVE DWI can provide higher quality images of endometrial carcinoma than SS-EPI DWI. The high-quality images are helpful for precise assessment of myometrial invasion in endometrial cancer.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.