Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models

OBJECTIVES

To determine the value of conventional DWI, continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in discriminating human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC).

METHODS

This prospective study included 158 women who underwent DWI, CTRW, FROC, and SEM and were pathologically categorized into the HER2-zero-expressing group (n = 10), HER2-low-expressing group (n = 86), and HER2-overexpressing group (n = 62). Nine diffusion parameters, namely ADC, αCTRW, βCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM of the primary tumor, were derived from four diffusion models. These diffusion metrics and clinicopathologic features were compared between groups. Logistic regression was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the HER2-expressing statuses. Receiver operating characteristic (ROC) curves were used to evaluate their discriminative ability.

RESULTS

The estrogen receptor (ER) status, progesterone receptor (PR) status, and tumor size differed between HER2-low-expressing and HER2-overexpressing groups (p < 0.001 to p = 0.009). The αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM were significantly lower in HER2-low-expressing BCs than those in HER2-overexpressing BCs (p < 0.001 to p = 0.01). Further multivariable logistic regression analysis showed that the αCTRW was the single best discriminative metric, with an area under the curve (AUC) being higher than that of ADC (0.802 vs. 0.610, p < 0.05); the addition of ER status, PR status, and tumor size to the αCTRW improved the AUC to 0.877.

CONCLUSIONS

The αCTRW could help discriminate the HER2-low-expressing and HER2-overexpressing BCs.

CLINICAL RELEVANCE STATEMENT

Human epidermal growth factor receptor 2 (HER2)-low-expressing breast cancer (BC) might also benefit from the HER2-targeted therapy. Prediction of HER2-low-expressing BC or HER2-overexpressing BC is crucial for appropriate management. Advanced continuous-time random walk diffusion MRI offers a solution to this clinical issue.

KEY POINTS

• Human epidermal receptor 2 (HER2)-low-expressing BC had lower αCTRW, DCTRW, βFROC, DFROC, μFROC, αSEM, and DDCSEM values compared with HER2-overexpressing breast cancer. • The αCTRW was the single best diffusion metric (AUC = 0.802) for discrimination between the HER2-low-expressing and HER2-overexpressing breast cancers. • The addition of αCTRW to the clinicopathologic features (estrogen receptor status, progesterone receptor status, and tumor size) further improved the discriminative ability.

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.

Robustness of Quantitative Diffusion Metrics from Four Models: A Prospective Study on the Influence of Scan-Rescans, Voxel Size, Coils, and Observers

BACKGROUND

Quantitative diffusion metrics provide additional microstructural information of diseases. The robustness of quantitative diffusion metrics should be established before clinical application.

PURPOSE

To evaluate the variability and reproducibility of quantitative diffusion MRI metrics.

STUDY TYPE

Prospective.

POPULATION

14 volunteers (7 men; median age, range, 28, 26-59 years).

FIELD STRENGTH/SEQUENCE

3.0-T/Diffusion spectrum imaging.

ASSESSMENT

Brain MRI studies were performed four times per subject: involving different combinations of coil types and voxel sizes. Regions of interest of 13 brain anatomical sites were drawn by one observer twice and another observer once to allow interobserver and intraobserver reproducibility assessment. Twenty-five quantitative metrics were calculated using four diffusion models.

STATISTICAL TESTS

The variability was evaluated with coefficients of variation (CV), and quartile coefficient of dispersion (QCD). The reproducibility was assessed with intraclass correlation coefficient (ICC), and concordance correlation coefficient (CCC). Wilcoxon signed rank test was used to compare the influence of factors on robustness of quantitative diffusion metrics. A two-tailed P < 0.05 was considered statistically significant.

RESULTS

The variability of quantitative diffusion metrics showed CV of 2.4%-68.2%, and QCD of 0.6%-48.2%, respectively. The reproducibility of scans using 20-channel coils with voxels of 2 × 2 × 2 mm3 and 3 × 3 × 3 mm3 , respectively (ICC 0.03-0.84, CCC 0.03-0.84) was significantly worse than that of repeated scans using a 20-channel coil with a voxel size of 2 × 2 × 2 mm3 (ICC of 0.74-0.97, CCC 0.74-0.97) and that of scans using 20- and 64-channel coils, respectively, with a voxel size of 2 × 2 × 2 mm3 (ICC 0.59-0.95, CCC 0.59-0.95). The intraobserver reproducibility (ICC 0.49-0.94, CCC 0.49-0.94) was significantly better than the interobserver reproducibility (ICC 0.28-0.91, CCC 0.28-0.91).

DATA CONCLUSION

Our study indicated that the voxel size has a greater influence on the reproducibility of quantitative diffusion metrics than scan-rescans and coils. The reproducibility within one observer was higher than that between two observers.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Surgical Interest of an Accurate Real-World Prediction of Primary Systemic Therapy Response in HER2 Breast Cancers

Human epidermal growth factor receptor 2 (HER2)-enriched breast cancers (BC) present the highest rates of pathological response to primary systemic therapy (PST), but they are also the ones that tend to be larger at diagnosis, with microcalcifications and, often, with axillary involvement. If we do not have a reliable method to predict the degree of response, we may not be able to transfer the benefits of PST to surgery. The post-PST surgery planning is guided by the findings in the magnetic resonance imaging (MRI), whose predictive capacity, although high, is far from optimal. Thus, it seems interesting to find other variables to improve it. A retrospective observational study including women with HER2 BC treated with PST and further surgery was conducted. Information regarding clinical, radiological, and histopathological variables was gathered from a total of 132 patients included. Radiological complete response (rCR) was achieved in 65.9% of the sample, and pathological complete response (pCR), according to Miller and Payne criteria, in 58.3% of cases. A higher Ki67 value, the absence of Hormonal Receptors expression, and an rCR was significantly related to a pCR finding. This information impacts directly in surgery planning, as it permits adjustment of the breast resection volume.

Grading meningiomas with diffusion metrics: a comparison between diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging

OBJECTIVES

To compare the histogram features of multiple diffusion metrics in predicting the grade and cellular proliferation of meningiomas.

METHODS

Diffusion spectrum imaging was performed in 122 meningiomas (30 males, 13-84 years), which were divided into 31 high-grade meningiomas (HGMs, grades 2 and 3) and 91 low-grade meningiomas (LGMs, grade 1). The histogram features of multiple diffusion metrics obtained from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the solid tumours were analysed. All values between the two groups were compared with the Man-Whitney U test. Logistic regression analysis was applied to predict meningioma grade. The correlation between diffusion metrics and Ki-67 index was analysed.

RESULTS

The DKI_AK (axial kurtosis) maximum, DKI_AK range, MAP_RTPP (return-to-plane probability) maximum, MAP_RTPP range, NODDI_ICVF (intracellular volume fraction) range, and NODDI_ICVF maximum values were lower (p < 0.0001), whilst the DTI_MD (mean diffusivity) minimum values were higher in LGMs than those in HGMs (p < 0.001). Amongst the DTI, DKI, MAP, NODDI, and combined diffusion models, no significant differences were found in areas under the receiver operating characteristic curves (AUCs) for grading meningiomas (AUCs, 0.75, 0.75, 0.80, 0.79, and 0.86, respectively; all corrected p > 0.05, Bonferroni correction). Significant but weak positive correlations were found between the Ki-67 index and DKI, MAP, and NODDI metrics (r = 0.26-0.34, all p < 0.05).

CONCLUSIONS

Whole tumour histogram analyses of the multiple diffusion metrics from four diffusion models are promising methods in grading meningiomas. The DTI model has similar diagnostic performance compared with advanced diffusion models.

KEY POINTS

• Whole tumour histogram analyses of multiple diffusion models are feasible for grading meningiomas. • The DKI, MAP, and NODDI metrics are weakly associated with the Ki-67 proliferation status. • DTI has similar diagnostic performance compared with DKI, MAP, and NODDI in grading meningiomas.

Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.

Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers

Objective

To determine the preoperative magnetic resonance imaging (MRI) findings of breast cancer on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DWI) in different molecular subtypes.

Materials and methods

A retrospective study was conducted on 116 breast cancer subjects who underwent preoperative MRI and surgery or biopsy. Three radiologists retrospectively assessed the morphological and kinetic characteristics on DCE-MRI and tumor detectability on DWI, by using apparent diffusion coefficient (ADC) values of lesions. The clinicopathologic and MRI features of four subtypes were compared. The correlation between clinical and MRI findings with molecular subtypes was evaluated using the chi-square and ANOVA tests, while the Mann–Whitney test was used to analyze the relationship between ADC and prognostic factors.

Results

One hundred and sixteen women diagnosed with breast cancer confirmed by surgery or biopsy had the following subtypes of breast cancer: luminal A (27, 23.3%), luminal B (56, 48.2%), HER2 positive (14, 12.1%), and triple-negative breast cancer (TNBC) (19, 16.4%), respectively. Among the subtypes, significant differences were found in axillary node metastasis, histological grade, tumor shape, rim enhancement, margin, lesion type, intratumoral T2 signal intensity, Ki-67 index, and paratumoral enhancement (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, and p = 0.02, respectively). On DWI, the mean ADC value of TNBC (0.910 × 10⁻³ mm²/s) was the lowest compared to luminal A (1.477×10⁻³ mm²/s), luminal B (0.955 × 10⁻³ mm²/s), and HER2 positive (0.996 × 10⁻³ mm²/s) (p < 0.001). Analysis of the correlation between different prognostic factors and ADC value showed that only axillary lymph node status and ADC value had a statistically significant difference (p = 0.009).

Conclusion

The morphologic features of MRI can be used as imaging biomarkers to identify the molecular subtypes of breast cancer. In addition, quantitative assessments of ADC values on DWI may also provide biological clues about molecular subtypes.