Diagnostic Performance of Diffusion Tensor Imaging with Readout-segmented Echo-planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers

Diffusion tensor imaging: survival analysis prediction in breast cancer patients

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

High-resolution diffusion-weighted imaging compared with conventional diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging with regard to image quality and assessment of breast cancer morphology

PURPOSE

To evaluate the image quality and tumor morphology depiction ability of high resolution (HR) diffusion- weighted imaging (f-DWI) in comparison to conventional DWI (c-DWI) and dynamic contrast- enhanced magnetic resonance imaging (DCE-MRI) in the primary breast cancer setting.

METHODS

The f-DWI, c-DWI, and DCE-MRIs of 160 malignant breast masses were evaluated retrospectively by two independent radiologists. Data on image quality [sharpness, distortion, and perceived signalto- noise ratio (SNR)], apparent diffusion coefficient (ADC) value, lesion size, and tumor morphology (shape, margin, and internal pattern) obtained on f-DWI, c-DWI, and DCE-MRI were compared. Consistency between the readers and imaging methods for morphological parameters was analyzed.

RESULTS

The ADC values measured on f-DWI were significantly lower than those measured on c-DWI for both readers (P < 0.001 for each), whereas mean lesion size was significantly larger in c-DWI than in f-DWI and DCE-MRI for both readers (P < 0.001 for each). Higher consistency values were obtained for f-DWI compared with c-DWI when correlated with DCE-MRI for each morphological parameter. The least distorted images were obtained using DCE-MRI compared with c-DWI and f-DWI for both readers, whereas the highest distortion scores were obtained using c-DWI. Sharpness and perceived SNR scores were rated as significantly higher for f-DWI and DCE-MRI images compared with c-DWI by both readers (P < 0.001 for all). The concordance between c-DWI and DCE-MRI was fair to slight (κ = 0.15 to 0.41), whereas concordance between f-DWI and DCE-MRI was significantly better (κ = 0.68 to 0.87) for each reader and for all morphological parameters (P < 0.001). The highest concordance between the readers was achieved in margin assessment (κ = 0.87 to 0.89) regardless of the MRI method, followed by shape and internal pattern parameters (κ = 0.63 to 0.79).

CONCLUSION

The results demonstrated that f-DWI produces higher-quality images than c-DWI, enabling the morphological features to be identified in similar detail to that offered by HR DCE-MRI. Accordingly, f-DWI, as a method that highly correlates with DCE in determining the morphological characteristics of breast cancers, seems to have potential in the evaluation of breast tumors in patients for whom the use of contrast media is contraindicated.

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

The road to breast cancer screening with diffusion MRI

Breast cancer is the leading cause of cancer in women with a huge medical, social and economic impact. Mammography (MMG) has been the gold standard method until now because it is relatively inexpensive and widely available. However, MMG suffers from certain limitations, such as exposure to X-rays and difficulty of interpretation in dense breasts. Among other imaging methods, MRI has clearly the highest sensitivity and specificity, and breast MRI is the gold standard for the investigation and management of suspicious lesions revealed by MMG. Despite this performance, MRI, which does not rely on X-rays, is not used for screening except for a well-defined category of women at risk, because of its high cost and limited availability. In addition, the standard approach to breast MRI relies on Dynamic Contrast Enhanced (DCE) MRI with the injection of Gadolinium based contrast agents (GBCA), which have their own contraindications and can lead to deposit of gadolinium in tissues, including the brain, when examinations are repeated. On the other hand, diffusion MRI of breast, which provides information on tissue microstructure and tumor perfusion without the use of contrast agents, has been shown to offer higher specificity than DCE MRI with similar sensitivity, superior to MMG. Diffusion MRI thus appears to be a promising alternative approach to breast cancer screening, with the primary goal of eliminating with a very high probability the existence of a life-threatening lesion. To achieve this goal, it is first necessary to standardize the protocols for acquisition and analysis of diffusion MRI data, which have been found to vary largely in the literature. Second, the accessibility and cost-effectiveness of MRI examinations must be significantly improved, which may become possible with the development of dedicated low-field MRI units for breast cancer screening. In this article, we will first review the principles and current status of diffusion MRI, comparing its clinical performance with MMG and DCE MRI. We will then look at how breast diffusion MRI could be implemented and standardized to optimize accuracy of results. Finally, we will discuss how a dedicated, low-cost prototype of breast MRI system could be implemented and introduced to the healthcare market.

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.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Diffusion-weighted MR is useful to assess peripheral nerve invasion of soft tissue tumor

To investigate the feasibility of readout-segmented diffusion-weighted (rsDW) magnetic resonance (MR) in assessing nerve invasion of soft tissue tumor. Forty-four patients with soft tissue mass in upper leg suspected of nerve invasion underwent rsDW MR. Nerve invasion by tumor was rated by 2 radiologists, respectively. Sensitivity and specificity of rsDW MR in identifying nerve invasion were calculated, with operation findings as reference of standard. Apparent diffusion coefficient and fraction of anisotropy of nerve were obtained using DW MR, and then were compared between invaded nerves and noninvasion cases. Inter-reader agreement in using rsDW MR to rate nerve invasion was excellent (kappa = 0.891 ± 0.043, P < 0.001). Sensitivity and specificity of rsDW MR in identifying nerve invasion were 89% and 88%, respectively. Apparent diffusion coefficient was significantly higher in invaded nerves versus normal nerves (1.45 ± 0.67 × 10-3 mm2/s vs 1.39 ± 0.46 × 10-3 mm2/s, P < 0.05). Fraction of anisotropy was significantly lower in invaded nerves versus normal nerves (0.22 ± 0.11 vs 0.37 ± 0.13, P < .05). Readout-segmented DW MR was feasible in assessing sciatic nerve invasion by soft tissue tumor in selected patients.

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.

Diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation between normal and abnormally thickened endometrium: prospective study

Background

Diffusion tensor imaging (DTI) can be beneficial to differentiate between endometrium and other uterine layers. It is believed that it can be used to differentiate between normal and abnormally thickened endometrium. The purpose of this study was to find out the diagnostic value of DTI as an extension of DWI in characterization of abnormally thickened endometrium and differentiate it from normal.

Results

This study included 68 females, results of 3 of them were excluded (unable to complete the study), so the final number was 65 females subdivided into 2 groups; (A) control: 24 (13 premenopausal and 11 asymptomatic postmenopausal), (B) pathological thickened endometrium: 41 (11 premenopausal and 30 postmenopausal): benign (21 patients) and malignant (20 patients). The collected data was correlated to the histopathological results (as the gold standard) in cases of endometrial pathologies. The mean DW-ADC values for normal, benign, and malignant patients were 1.43 ± 0.13, 1.56 ± 0.17, and 0.86 ± 0.16 respectively and with significant statistical difference between normal and benign endometrial lesions (P value = 0.006), and between normal and malignant endometrial lesions, and between benign and malignant endometrial lesions (P value ˂ 0.001).

The DTI-FA mean values for normal, benign, and malignant patients were 0.349 ± 0.08, 0.29 ± 0.09, and 0.299 ± 0.08 respectively and with significant statistical difference between normal and benign endometrial lesions (P value = 0.02), but there is no significant statistical difference regarding DTI-FA values between normal and malignant endometrial lesions or between benign and malignant endometrial lesions (P value ˃ 0.05). Also, there is a significant statistical difference regarding DTI-MD mean values between normal (1.59 ± 0.06) and benign (1.37 ± 0.09), normal and malignant (0.71 ± 0.25), and between benign and malignant endometrial lesions (P value ˂ 0.001). The DT-MD had a higher sensitivity, specificity, and accuracy than both DW-ADC and DT-FA in differentiating normal, benign, and malignant endometrial pathologies.

Conclusion

DTI (added to DWI) is a valuable non-invasive tool that can increase the accuracy in differentiating normal, benign, and malignant endometrial conditions, helping early management, and decrease the possibility of misdiagnosis.

Use of diffusion-weighted imaging and diffusion tensor imaging in assessment of myometrial invasion in patients of endometrial carcinoma and its correlation with histopathological grading (Prospective study)

Background

Endometrial cancer (EMC) is considered one of the most common gynecological cancers worldwide. In particular, the depth of myometrial invasion and histological grade of endometrial cancers (EMCs) are strong prognostic factors. Diffusion tensor measurements as mean diffusivity (MD) and fractional anisotropy (FA) values could be useful for assessing the depth of tumor invasion and its histological grade. The study aimed to evaluate the role of diffusion-weighted imaging (DWI) and diffusion tensor imaging in the detection of myometrial invasion in cases of endometrial carcinoma and prediction of its grade in vivo.

Results

This study included 50 female patients with pathologically proved endometrial carcinoma, and their ages ranged from 38 to 67 years; the mean age was 56.15 years (± 8.229 standard deviation “SD”). There was a significant statistical difference regarding the mean values of diffusion tensor fractional anisotropy (DT-FA), diffusion tensor mean diffusivity (DT-MD) and diffusion-weighted apparent diffusion coefficient(DW-ADC) values in differentiating between intact and infiltrated myometrium with (P value ≤ 0.001). The accuracy of DT-MD, DT-FA and DWI-ADC was 98%, 90% and 86%, respectively, in the detection of myometrial invasion. There was a statistically significant difference in the mean values of DT-FA, DT-MD and DW-ADC for differentiating endometrioid adenocarcinoma grades with the overall P values (˂0.001). The accuracy of DT-FA, DT- MD and DWI-ADC for differentiating grade 3 from grade 1 or 2 endometrioid adenocarcinoma was 94.9%, 84.6% and 74.4%, respectively. For differentiating grade 1 from grade 2 or 3 endometrioid adenocarcinoma, the accuracy of DT-FA, DT-MD and DWI-ADC was 90%, 89.7% and 84.6%, respectively. Mean DT-FA, DT-MD and DW-ADC values were inversely proportional to the degree of pathological grading with r =  − 0.867, − 0.762 and − 0.706, respectively.

Conclusion

Diffusion tensor imaging and DWI are helpful in the assessment of myometrial invasion and have a high negative correlation with histopathological grading in patients with endometrial cancer.

Diagnostic Value of Combined Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging with Diffusion Tensor Imaging in Predicting Parametrial Infiltration in Cervical Cancer

Objective

This study sought to determine the diagnostic value of combined intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) in predicting parametrial infiltration (PMI) in patients with cervical cancer.

Materials and Methods

We enrolled 65 patients with cervical cancer confirmed by radical hysterectomy (25 PMI-negative and 40 PMI-positive) who underwent IVIM and DTI pretreatment. The parameters of IVIM (ADC, D, D  ^∗ , and f) and DTI (average diffusion coefficient (DCavg) and fractional anisotropy (FA)) were recorded by two observers. All parameter differences were tested, and the receiver operating characteristic (ROC) curves were generated to estimate the diagnostic performance of significant metrics and their combinations.

Results

Compared to the PMI-negative group, the PMI-positive group had significantly lower D (0.632 ± 0.017 vs. 0.773 ± 0.024, p < 0.001) and lower FA (0.073 ± 0.002 vs. 0.085 ± 0.003, p=0.003). The area under the ROC curve (AUC) of D and FA was 0.801 and 0.726, respectively, and the combination of D and FA improved the AUC to 0.931, with a sensitivity and specificity of 80.0% and 97.5%, respectively.

Conclusion

D and FA values could be used to help diagnose PMI in patients with cervical cancer. The combination of IVIM and DTI was more valuable than either option alone.

Improving the image quality of DWI in breast cancer: comparison of multi-shot DWI using multiplexed sensitivity encoding to conventional single-shot echo-planar imaging DWI

Objective:

To compare diffusion-weighted images (DWI) acquired using single-shot echo-planar imaging (ss-EPI) and multiplexed sensitivity encoding (MUSE) in breast cancer.

Methods

20 females with pathologically confirmed breast cancer (age 51 ± 12 years) were imaged with ss-EPI-DWI and MUSE-DWI. ADC, normalised ADC (nADC), blur and distortion metrics and qualitative image quality scores were compared. The Crété-Roffet and Mattes mutual information metrics were used to evaluate blurring and distortion, respectively. In a breast phantom, six permutations of MUSE-DWI with varying parallel acceleration factor and number of shots were compared. Differences in ADC and nADC were compared using the coefficient of variation in the phantom and a paired t-test in patients. Differences in blur, distortion and qualitative metrics were analysed using a Wilcoxon signed-rank test.

Results:

There was a low coefficient of variation (<2%) in ADC between ss-EPI-DWI and all MUSE-DWI permutations acquired using the phantom. 22 malignant and three benign lesions were identified in 20 patients. ADC values measured using MUSE were significantly lower compared to ss-EPI for malignant but not benign lesions (p < 0.001, p = 0.21). nADC values were not significantly different (p = 0.62, p = 0.28). Blurring and distortion improved with number of shots and acceleration factor, and significantly improved with MUSE in patients (p < 0.001, p = 0.002). Qualitatively, image quality improved using MUSE.

Conclusion:

MUSE improves the image quality of breast DWI compared to ss-EPI.

Advances in knowledge:

MUSE-DWI has superior image quality and reduced blurring and distortion compared to ss-EPI-DWI in breast cancer.

Relationship between MRI findings and invasive breast cancer with podoplanin-positive cancer-associated fibroblasts

OBJECTIVE

Purpose of our study is to assess the relationship between MRI findings and invasive breast cancer (IBC) with cancer-associated fibroblasts (CAFs) that are positive for podoplanin.

METHODS

We retrospectively analyzed the consecutive 109 IBCs. The IBCs were dichotomized as with (+) or without (-) podoplanin-positive CAFs. In MRI analyses, the dichotomized IBCs were compared the lesion to muscle ratio (L/M ratio) in STIR images, the ADC value, the distribution of kinetic parameters, and morphological findings.

RESULTS

Of the 109 IBCs, 28 (26%) IBCs had podoplanin(+) CAFs. Compared to the podoplanin(-) group, the podoplanin(+) group tended to have a more malignant pathological status. In the STIR images, the podoplanin(+) group had significantly higher L/M ratio (7.59 vs. 6.55, p = 0.040). In a dynamic study, the podoplanin(+) group had a significantly higher percentage of the washout pattern (42.21% vs. 29.43%, p = 0.045). There were 23 mass lesions and 5 non-mass enhancement (NME) lesions in the podoplanin(+) group, and 69 mass lesions and 12 NME lesions in the podoplanin(-) group. The mass lesions of the podoplanin(-) group had a significantly higher likelihood of showing an irregular shape (n = 47 vs. 8, p = 0.035). The podoplanin(+) group's lesions had a significantly higher likelihood of showing a circumscribed margin (n = 14 vs. 6, p < 0.001) and a rim enhancement (n = 10 vs. 13, p = 0.047). In multivariate analyses, only high nuclear grade was significant predictive value of podoplanin(+) CAFs.

CONCLUSION

Although not significant in multivariate analyses, MRI findings may be used to determine the podoplanin-positive CAF status of invasive breast cancer.

Diffusion tensor imaging of oral carcinoma: Clinical evaluation and comparison with histopathological findings

PURPOSE

This study aims to assess the usefulness of diffusion tensor imaging (DTI) as a noninvasive method for the evaluation of histological grade and lymph node metastasis in patients with oral carcinoma (OC).

MATERIALS AND METHODS

Thirty-six consecutive patients with histologically confirmed OC underwent examination by 3-T MRI. DTI was performed using a single-shot echo-planar imaging sequence with b values of 0 and 1000 s/mm² and motion-probing gradients in 12 noncollinear directions. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) maps were compared with histopathological findings. The DTI parameters were correlated with the histological grade of the OCs based on the World Health Organization grading criteria and the presence or absence of lymph node metastasis.

RESULTS

The FA values (0.275 ± 0.058) of OC were significantly lower than those of normal tongue, muscle, and parotid glands (P < 0.001 for all), and the MD, AD, and RD values (1.220 ± 0.149, 1.434 ± 0.172, and 1.019 ± 0.165 × 10^-3 mm²/s, respectively) were significantly higher than their respective normal values (P < 0.001 for all). Significant inverse correlations with histological grades were shown for FA, MD, AD, and RD values in OC patients (r = -0.862, r = -0.797, r = -0.747, and r = -0.844, respectively; P < 0.001 for all). In addition, there was a significant difference in the FA values of metastatic and nonmetastatic lymph nodes (0.186 vs. 0.276), MD (0.923 vs. 1.242 × 10^-3 mm²/s), AD (1.246 vs. 1.621 × 10^-3 mm²/s), and RD (0.792 vs. 1.100 × 10^-3 mm²/s; P < 0.001 for all).

CONCLUSIONS

DTI may be clinically useful for the noninvasive evaluation of histological grade and lymph node metastasis in OC patients.

Diffusion tensor imaging of rectal carcinoma: Clinical evaluation and its correlation with histopathological findings

OBJECTIVE

This study sought to assess the feasibility of diffusion tensor imaging (DTI) to noninvasively evaluate histological grade and lymph node metastasis in patients with rectal carcinoma (RC).

METHODS

Thirty-seven consecutive patients with histologically confirmed RC were examined by 1.5-T MRI. DTI was performed using a single-shot echo-planar imaging sequence with b values of 0 and 1000 s/mm² and motion-probing gradients in nine noncollinear directions. Fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) maps were compared with histopathological findings.

RESULTS

The FA values (0.357 ± 0.047) of the RCs were significantly lower than those of the normal rectal wall, muscle, prostate, and uterus (P < 0.001 for all), while the AD, MD, and RD values (1.221 ± 0.131, 0.804 ± 0.075, and 0.667 ± 0.057 × 10^-3 mm²/s, respectively) were also significantly lower than their respective normal values (P < 0.001 for all). The FA, AD, MD, and RD values for RC additionally showed significant inverse correlations with histological grades (r = -0.781, r = -0.750, r = -0.718, and r = -0.682, respectively; P < 0.001 for all). Further, the FA (0.430 vs. 0.611), AD (1.246 vs. 1.608 × 10^-3 mm²/s), MD (0.776 vs. 1.036 × 10^-3 mm²/s), and RD (0.651 vs. 0.824 × 10^-3 mm²/s) (P < 0.001 for all) of the metastatic and nonmetastatic lymph nodes were significantly different.

CONCLUSIONS

DTI may be clinically useful for the noninvasive evaluation of histological grade and lymph node metastasis in patients with RC.

Evaluation of Malignant Breast Lesions Using High-resolution Readout-segmented Diffusion-weighted Echo-planar Imaging: Comparison with Pathology

PURPOSE

We aimed to investigate the performance of high resolution-diffusion-weighted imaging (HR-DWI) using readout-segmented echo-planar imaging in visualizing malignant breast lesions and evaluating their extent, using pathology as a reference.

METHODS

This retrospective study included patients who underwent HR-DWI with surgically confirmed malignant breast lesions. Two radiologists blinded to the final diagnosis evaluated HR-DWI independently and identified the lesions, measuring their maximum diameters. Another radiologist confirmed if those lesions were identical to the pathology. The maximum diameters of the lesions between HR-DWI and pathology were compared, and their correlations were calculated using Spearman's correlation coefficient. Apparent diffusion coefficient (ADC) values of the lesions were measured.

RESULTS

Ninety-five mass/64 non-mass lesions were pathologically confirmed in 104 females. Both radiologists detected the same 93 mass lesions (97.9%). Spearman's correlation coefficient for mass lesions were 0.89 and 0.90 (P < 0.0001 and 0001) for the two radiologists, respectively. The size differences within 10 mm were 90.3% (84/93) and 94.6% (88/93) respectively. One radiologist detected 35 non-mass lesions (54.7%) and another radiologist detected 32 non-mass lesions (50.0%), of which 28 lesions were confirmed as identical. Spearman's correlation coefficient for non-mass lesions were 0.59 and 0.22 (P = 0.0002 and 0.22), respectively. The mean ADC value of mass lesions and non-mass lesions were 0.80 and 0.89 × 10^-3 mm²/s, respectively.

CONCLUSION

Using HR-DWI, malignant mass lesions were depicted with excellent agreement with the pathological evaluation. Approximately half of the non-mass lesions could not be identified, suggesting a current limitation of HR-DWI.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Diffusion-Tensor Imaging of Uterine Cervical Carcinoma: Correlation With Histopathologic Findings

The authors investigated the usefulness of diffusion-tensor imaging (DTI) for evaluating tumor invasion depth, histologic grade, and lymph node metastasis in patients with cervical carcinoma (CC). Fifteen consecutive patients with histologically confirmed CC underwent 1.5-T magnetic resonance imaging and DTI. The CCs were clearly depicted as hypointense areas on all DTI maps. Fractional anisotropy, mean diffusivity, and axial diffusivity showed significantly inverse correlations with CC histologic grades and were significantly different between metastatic and nonmetastatic lymph nodes.

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising

To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Rationale and Objectives: Controversy still exists on the diagnosability of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions.

Materials and Methods: The studies that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD), and three diffusion eigenvalues (λ1, λ2, and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0.

Results: Sixteen eligible studies incorporating 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of ductal carcinoma in situ (DCIS) (P = 0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92%, and 0.97, followed by MD (AUC = 0.92, sensitivity = 87%, specificity = 83%) and FA (AUC = 0.76, sensitivity = 70%, specificity = 70%) in the differential diagnosis of breast lesions.

Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attention in clinical practice.

Application of in vivo MR methods in the study of breast cancer metabolism

In the last two decades, various in vivo MR methodologies have been evaluated for their potential in the study of cancer metabolism. During malignant transformation, metabolic alterations occur, leading to morphological and functional changes. Among various MR methods, in vivo MRS has been extensively used in breast cancer to study the metabolism of cells, tissues or whole organs. It provides biochemical information at the metabolite level. Altered choline, phospholipid and energy metabolism has been documented using proton (¹ H), phosphorus (³¹ P) and carbon (¹³ C) isotopes. Increased levels of choline-containing compounds, phosphomonoesters and phosphodiesters in breast cancer, which are indicative of altered choline and phospholipid metabolism, have been reported using in vivo, in vitro and ex vivo NMR studies. These changes are reversed on successful therapy, which depends on the treatment regimen given. Monitoring the various tumor intermediary metabolic pathways using nuclear spin hyperpolarization of ¹³ C-labeled substrates by dynamic nuclear polarization has also been recently reported. Furthermore, the utility of various methods such as diffusion, dynamic contrast and perfusion MRI have also been evaluated to study breast tumor metabolism. Parameters such as tumor volume, apparent diffusion coefficient, volume transfer coefficient and extracellular volume ratio are estimated. These parameters provide information on the changes in tumor microstructure, microenvironment, abnormal vasculature, permeability and grade of the tumor. Such changes seen during cancer progression are due to alterations in the tumor metabolism, leading to changes in cell architecture. Due to architectural changes, the tissue mechanical properties are altered; this can be studied using magnetic resonance elastography, which measures the elastic properties of tissues. Moreover, these structural MRI methods can be used to investigate the effect of therapy-induced changes in tumor characteristics. This review discusses the potential of various in vivo MR methodologies in the study of breast cancer metabolism.

Multiscale Time-Sharing Elastography Algorithms and Transfer Learning of Clinicopathological Features of Uterine Cervical Cancer for Medical Intelligent Computing System

Intelligent medical diagnosis and computing system faces many challenges in complex object recognition, large-scale data imaging and real-time diagnosis, such as poor real-time computing, low efficiency of data storage and low recognition rate of lesions. In order to solve the above problems, this paper proposes a medical intelligent computing system and a series of algorithms for the clinical pathology of cervical cancer based on the multi-scale imaging and transfer learning framework. Firstly, based on data dimensions, imaging errors and other factors, this paper designs a multi-scale time-sharing elastic imaging algorithm based on image reconstruction time and data sample characteristics. Then, taking the burst imaging cohort and the calculation data set of new cervical cancer cases as the objects, based on the difficulties of cervical cancer feature modeling, this paper proposes the transfer learning algorithm of clinical and pathological features of cervical cancer. Finally, a medical intelligent computing system for cervical cancer pathology analysis and calculation with high efficiency and reliability is established. A series of proposed algorithms are compared with single-scale Retinex (SSR), which is based on single-scale Retinex migration learning (SSR-TL). The experimental results show that the proposed algorithm in cervical cancer pathological imaging and scoring, as well as the feature extraction and recognition of lesions, especially the efficiency of system execution, is obviously due to the comparison algorithm.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Diffusion tensor imaging parameters in differentiation recurrent breast cancer from post-operative changes in patients with breast-conserving surgery

AIM OF THE WORK

To investigate mean diffusivity (MD) and fractional anisotropy (FA) measured by diffusion tensor imaging (DTI) as complementary tools to differentiate recurrent breast cancer from post-operative changes in patients with breast-conserving surgery (BCS).

PATIENTS AND METHODS

Prospective study was conducted upon 30 patients with BCS that underwent DTI and dynamic contrast MR imaging. DTI was performed using an axial two-dimensional spin-echo echo-planar imaging sequence. The MD and FA of the lesions were calculated by 2 observers. A single pixel seed isotropic region of interest was placed in the solid part of the tumor on the axial color FA map guided by an enhanced part of the tumor. The final diagnosis was done by biopsy for all patients.

RESULTS

/s) used for differentiation between entities revealed sensitivity (76.9%, 92.3%), specificity (82.4%, 64.7%) and accuracy (80%, 76.7%) of both observers respectively. At ROC curve analysis of FA, the AUC was 0.82 and 0.75 by both observers. The threshold FA (0.82, 0.75) was used for differentiation between entities revealed sensitivity (92.3%, 76.9%), specificity (70.6%, 70.6%) and accuracy of (80.0%, 73.3%) of both observers respectively. There was a strong positive correlation of MD (r = 0.86) and FA (r = 0.73) of both observers. Combined analysis of FA and MD used for differentiation between entities had AUC (0.90, 0.88) revealed sensitivity (92.3%, 92.3%), specificity (82.4%, 70.6%) and accuracy of (86.7%, 80.0%) for both observers respectively.

CONCLUSIONS

Combined analysis of MD and FA of DTI may play an important role as a non-invasive method for differentiation recurrent breast cancer from post-operative changes in patients with BCS.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

Application of Quantitative Microstructural MR Imaging with Atlas-based Analysis for the Spinal Cord in Cervical Spondylotic Myelopathy

Mapping of MR fiber g-ratio, which is the ratio of the diameter of the axon to the diameter of the neuronal fiber, is introduced in this article. We investigated the MR fiber g-ratio, the axon volume fraction (AVF) and the myelin volume fraction (MVF) to evaluate microstructural changes in the spinal cord in patients with cervical spondylotic myelopathy (CSM) in vivo, using atlas-based analysis. We used diffusion MRI data acquired with a new simultaneous multi-slice accelerated readout-segmented echo planar imaging sequence for diffusion analysis for AVF calculation and magnetization transfer saturation imaging for MVF calculation. The AVFs of fasciculus gracilis in the affected side spinal cord, fasciculus cuneatus and lateral corticospinal tracts (LSCT) in the affected and unaffected side spinal cord were significantly lower (P = 0.019, 0.001, 0019, 0.000, and 0.002, respectively) than those of normal controls. No difference was found in the MVFs. The fiber g-ratio of LSCT was significantly lower (P = 0.040) in the affected side spinal cords than in the normal controls. The pathological microstructural changes in the spinal cord in patients with CSM, presumably partial axonal degenerations with preserved myelin. This technique has the potential to be a clinical biomarker in patients with CSM in vivo.