Accuracy of perfusion MRI with high spatial but low temporal resolution to assess invasive breast cancer response to neoadjuvant chemotherapy: a retrospective study

Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.

Efficient estimation of pharmacokinetic parameters from breast dynamic contrast-enhanced MRI based on a convolutional neural network for predicting molecular subtypes

Objective. Tracer kinetic models allow for estimating pharmacokinetic (PK) parameters, which are related to pathological characteristics, from breast dynamic contrast-enhanced magnetic resonance imaging. However, existing tracer kinetic models subject to inaccuracy are time-consuming for PK parameters estimation. This study aimed to accurately and efficiently estimate PK parameters for predicting molecular subtypes based on convolutional neural network (CNN).Approach. A CNN integrating global and local features (GL-CNN) was trained using synthetic data where known PK parameters map was used as the ground truth, and subsequently used to directly estimate PK parameters (volume transfer constantKtransand flux rate constantKep) map. The accuracy assessed by the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and concordance correlation coefficient (CCC) was compared between the GL-CNN and Tofts-based PK parameters in synthetic data. Radiomic features were calculated from the PK parameters map in 208 breast tumors. A random forest classifier was constructed to predict molecular subtypes using a discovery cohort (n= 144). The diagnostic performance evaluated on a validation cohort (n= 64) using the area under the receiver operating characteristic curve (AUC) was compared between the GL-CNN and Tofts-based PK parameters.Main results. The average PSNR (48.8884), SSIM (0.9995), and CCC (0.9995) between the GL-CNN-basedKtransmap and ground truth were significantly higher than those between the Tofts-basedKtransmap and ground truth. The GL-CNN-basedKtransobtained significantly better diagnostic performance (AUCs = 0.7658 and 0.8528) than the Tofts-basedKtransfor luminal B and HER2 tumors. The GL-CNN method accelerated the computation by speed approximately 79 times compared to the Tofts method for the whole breast of all patients.Significance. Our results indicate that the GL-CNN method can be used to accurately and efficiently estimate PK parameters for predicting molecular subtypes.

Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies

Background Ultrafast dynamic contrast-enhanced (DCE) MRI parameters are associated with breast cancer aggressiveness. However, the role of these parameters as predictive biomarkers for pathologic response after neoadjuvant chemotherapy (NAC) has been poorly investigated. Purpose To assess whether semiquantitative perfusion parameters calculated at initial ultrafast DCE MRI are associated with early prediction for pathologic response after NAC in participants with breast cancer. Materials and Methods This prospective single-center study included consecutive women with nonmetastatic invasive breast cancer treated with NAC followed by surgery who underwent initial ultrafast DCE MRI between December 2020 and August 2021. Six semiquantitative ultrafast DCE MRI parameters were calculated for each participant from the fitted time-signal intensity curve. Multivariable logistic regression was used to identify independent predictors of pathologic complete response (pCR) and residual cancer burden (RCB). Results Fifty women (mean age, 49 years ± 12 [SD]) were included in the study; 20 achieved pCR and 25 achieved low RCB (RCB-0 and I). A wash-in slope (WIS) cutoff value of 1.6% per second had a sensitivity of 94% (17 of 18 participants) and a specificity of 59% (19 of 32 participants) for pCR. A WIS of more than 1.6% per second (odds ratio [OR], 8.4 [95% CI: 1.5, 48.2]; P = .02), human epidermal growth factor receptor 2 (HER2) positivity (OR, 6.3 [95% CI: 1.5, 27.4]; P = .01), and tumor-infiltrating lymphocytes of more than 10% (OR, 6.9 [95% CI: 1.3, 37.7]; P = .03) were independent predictive factors of pCR. The area under the receiver operating characteristic curve of the three-component model, which included WIS, tumor-infiltrating lymphocytes, and HER2 positivity, was 0.92 (95% CI: 0.84, 0.99). A WIS of more than 1.6% per second was associated with higher pCR rates in the HER2-positive (OR, 21.7 [95% CI: 1.8, 260.6]; P = .02) breast cancer subgroup. For luminal HER2-negative and triple-negative breast cancers, a WIS of more than 1.6% per second was associated with low RCB (OR, 11.0 [95% CI: 1.1, 106.4]; P = .04). Conclusion The wash-in slope (WIS) assessment at initial ultrafast dynamic contrast-enhanced MRI may be used to predict pathologic complete response (pCR) in participants with breast cancer. The WIS value was used to identify two subsets of human epidermal growth factor receptor 2-positive cancers with distinct pCR rates. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Dynamic contrast enhanced - MRI efficiency in detecting embolization-induced perfusion defects in a rabbit model of critical-limb-ischemia

Critical limb ischemia (CLI) is a severe disease which affects about 2 million people in the US. Its prevalence is assessed at 800/100,000 population. However, no reliable tools are currently available to assess perfusion defects at the muscle tissue level. DCE-MRI is a technique that holds the potential to be effective in achieving this goal. However, preclinical studies performed with DCE-MRI have indicated low sensitivity assessing perfusion at resting state. To improve these previous results, in this work we propose new methodologies for data acquisition and analysis and we also revisit the biological model used for evaluation. Eleven rabbits underwent embolization of a lower limb. They were imaged at day 7 after embolization using DCE-MRI, performed on a 4.7 T small imaging device. Among them, n = 4 rabbits were used for MRI sequence optimization and n = 6 for data analysis after one exclusion. Normalized Areas under the curve (AUCn), and kinetic parameters such as K_trans and V_d resulting from the Tofts-Kety modeling (KTM) were calculated on the embolized and contralateral limbs. Average and heterogeneity features, consisting on standard-deviation and quantiles, were calculated on muscle groups and whole limbs. The Wilcoxon and Fisher-tests were performed to compare embolized and contralateral regions of interests. The Wilcoxon test was also used to compare features of parametric maps. Quantiles of 5 and 95% in the contralateral side were used to define low and high outliers. A P-value <0.05 was considered statistically significant. Average features were inefficient to identify injured muscles, in agreement with the low sensitivity of the technique previously reported by the literature. However, these findings were dramatically improved by the use of additional heterogeneity features (97% of total accuracy for group muscles, P < 0.01 and 100% of total accuracy for the total limbs). The mapping analysis and automatic outlier detection quantification improvement was explained by the presence of local hyperemia that impair the average calculations. The analysis with KTM did not provide any additional information compared to AUCn. The DCE technique can be effective in detecting embolization-induced disorders of limb muscles in a CLI model when heterogeneity is taken into account in the data processing, even without vascular stimulation. The simultaneous presence of areas of ischemia and hyperemia appeared as a signature of the injured limbs. These areas seem to reflect the simultaneous presence of infarcted areas and viable peripheral areas, characterized by a vascular response that is visible in DCE.

Artificial Intelligence in Breast MRI Radiogenomics: Towards Accurate Prediction of Neoadjuvant Chemotherapy Responses

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogenomics for accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction. This requires the automated extraction of parameters from structural breast radiomics data, and finding non-structural feature vectors with diagnostic value, which then are combined with genomics data acquired from exocrine bodies in blood samples from a cohort of cancer patients to enable accurate NAC prediction. A self-attention-based deep learning approach, along with an effective multi-channel tumour image reconstruction algorithm of high dimensionality, is proposed. The aim was to generate non-structural feature vectors for accurate prediction of the NAC responses by combining imaging datasets with exocrine body related genomics analysis.

Molecular MR Imaging of Prostate Cancer

This review summarizes recent developments regarding molecular imaging markers for magnetic resonance imaging (MRI) of prostate cancer (PCa). Currently, the clinical standard includes MR imaging using unspecific gadolinium-based contrast agents. Specific molecular probes for the diagnosis of PCa could improve the molecular characterization of the tumor in a non-invasive examination. Furthermore, molecular probes could enable targeted therapies to suppress tumor growth or reduce the tumor size.

Evaluating the Relationship Between Dynamic Contrast-Enhanced MRI (DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer

BACKGROUND

Dynamic contrast-enhanced MRI (DCE-MRI) is used to evaluate tumor microvasculature. However, studies demonstrating an association between perfusion parameters derived from DCE-MRI and histopathologic characteristics are limited to a small set of histopathologic factors, and the results are inconsistent.

PURPOSE

To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer.

STUDY TYPE

Retrospective.

POPULATION

In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions).

FIELD STRENGTH/SEQUENCE

3.0T, turbo spin-echo (TSE) T₁ -weighted, fat-suppressed T₂ -weighted, TSE T₂ -weighted, and dynamic unenhanced and contrast-enhanced 3D T₁ high-resolution isotropic volume examination.

ASSESSMENT

One reviewer obtained perfusion parameters (K^trans , k_ep , v_e , and v_p ) of each breast cancer from DCE MRI using the extended Tofts model with a fixed baseline T₁ value and a population-based arterial input function. The relationship between DCE-MRI perfusion parameters and histopathologic tumor characteristics used to predict angiogenesis and determine prognosis was evaluated.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used.

RESULTS

Triple-negative breast cancers exhibited higher K^trans and k_ep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher K^trans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher v_e than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher k_ep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher K^trans and k_ep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher K^trans , k_ep , v_p (P < 0.05) than grade I tumors. Tumors with LVSI presented higher K^trans and k_ep than tumors without LVSI (P < 0.05).

DATA CONCLUSION

Breast cancer presenting higher K^trans and k_ep on DCE-MRI was associated with poor prognostic histopathologic factors. Therefore, pretreatment DCE-MRI perfusion parameters may be useful imaging biomarkers for the evaluation of tumor prognosis and angiogenesis.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

The kinetic analysis of breast cancer: An investigation of the optimal temporal resolution for dynamic contrast-enhanced MR imaging

INTRODUCTION

There is wide agreement that morphologic features and enhancement kinetics should be evaluated for MRI of the breast, although there has been no clear consensus concerning optimal temporal resolutions. The objective of this study was to investigate the optimal temporal resolution for the kinetic analysis of breast cancers.

METHODS

Thirty-four patients with 34 enhancing lesions of breast cancer who underwent dynamic contrast-enhanced MRI (DCE-MRI) on a 3.0-T scanner were included in this retrospective study. DCE-MRI was performed with an original temporal resolution of 10-s, and the values of pharmacokinetic parameters (Ktrans, Ve, Kep, and area under the curve (AUC)) were compared with selected data of 30-s and 60-s time intervals.

RESULTS

Among the 34 lesions, 10 showed a wash out pattern, 16 showed a plateau pattern, and 8 showed a persistent enhancement pattern. The Ktrans value in the wash-out pattern was significantly higher than that of other time-intensity curve patterns (p < 0.01). The Kep and AUC also showed significant differences between the wash-out pattern and other types (p < 0.01). On comparing the perfusion parameters among different temporal resolutions, simulations showed that only the AUC differed significantly between the data acquired at a 10-s temporal resolution and that acquired at a 60-s time interval (p < 0.01). Although the comparison of the AUC between the 30-s and 60-s data also showed significant differences (p = 0.01), there was no significant difference between the 10-s and 30-s data (p = 0.17).

CONCLUSIONS

DCE-MRI with a temporal resolution of 30-s preserves the kinetic information. Further prospective studies will be needed to investigate the trade-off between temporal and spatial resolution in DCE-MRI.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer

The purpose of this meta-analysis was to determine the value of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) in evaluating the response of breast cancer to neoadjuvant chemotherapy (NAC). PubMed, Embase, and Cochrane Library databases (from building to July 31, 2018) were searched to collect articles about the therapeutic evaluation of NAC using the quantitative DCE-MRI in patients with breast cancer. The sensitivities and specificities of quantitative DCE-MRI in the evaluation of NAC for breast cancer were extracted from the articles. Meta-DiSc1.4 was applied to evaluate the efficacy of the sensitivity and specificity; forest figure and summary receiver operating characteristics (SROC) were created. A total of 356 articles were enrolled in this study, including 739 cases in total, in which 218 cases were effective and the other 521 cases were ineffective to NAC, considering the pathological results as the gold standard. The sensitivity and specificity in the included 14 articles of quantitative DCE-MRI ( Ktrans, kep, and ve) in comprehensively evaluating NAC for breast cancer were 84 per cent (95% confidence interval (CI): 78–88%) and 83 per cent (95% CI: 79–86%), respectively. The area under SROC was 0.899 (95% CI: 0.867–0.943). The sensitivity and specificity in the three articles of Ktrans evaluating NAC for breast cancer were 84.1 per cent (95% CI: 71.0–92.1%) and 81.3 per cent (95% CI: 70.5%-88.5%), respectively. The area under SROC was 0.899 (95% CI: 0.834–0.962). Our study confirmed that the quantitative DCE-MRI is able to monitor NAC treatment for breast cancer because of its high sensitivity and specificity. However, there is a high degree of heterogeneity in published studies, highlighting the lack of standardization in the field.

Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

BACKGROUND

Breast cancer heterogeneity influences poor prognoses thorough therapy resistance. This study quantitatively evaluated intratumoral heterogeneity through a histogram analysis of dynamic contrast-enhanced MRI (DCE-MRI) pharmacokinetic parameters, and determined correlations with prognostic factors and molecular subtypes.

METHODS

We retrospectively investigated 101 invasive ductal breast cancers from 99 women who underwent preoperative DCE-MRI between July 2012 and November 2014. Pharmacokinetic parameters (K^trans, k_ep, and v_e) were obtained by the Tofts model. For each parameter, the mean, standard deviation, coefficient of variation, skewness, and kurtosis values of tumor were calculated, and prognostic factors and subtypes associations were assessed.

RESULTS

The mean of v_e was lower in cancers with high Ki-67 than in cancers with low Ki-67 (P = 0.002). The coefficient of variation of v_e was higher in cancers with estrogen receptor negativity than in cancers with estrogen receptor positivity (P < 0.001). The coefficient of variation of v_e was also higher in cancers with high Ki-67 than in cancers with low Ki-67 (P < 0.001). The skewness of v_e was higher in cancers with high nuclear grade than in cancers with low nuclear grade (P = 0.006). Triple-negative cancers showed higher v_e coefficient of variation than did those with luminal A (P < 0.001) and B (P = 0.006).

CONCLUSIONS

Various v_e parameters correlated with breast cancer prognostic factors and molecular subtypes.

DCE-MRI and parametric imaging in monitoring response to neoadjuvant chemotherapy in breast carcinoma: a preliminary report

Purpose

Neoadjuvant chemotherapy is recommended in patients with locally advanced breast cancer. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables evaluation of the tumour neovasculature that occurs prior to any volume change, which helps identify early treatment failures and allows prompt implementation of second-line therapy.

Material and methods

We conducted a prospective study in 14 patients with histopathologically proven breast cancer. DCE-MRI data were acquired using multisection, T1-weighted, 3D vibe sequences with fat suppression before, during, and after IV bolus injection (0.1 mmol/kg body weight, Gadoversetamide, Optimark). Post-processing of dynamic contrast perfusion data was done with the vendor’s Tissue 4D software to generate various dynamic contrast parameters, i.e. Ktrans, Kep, Ve, initial area under the time signal curve (IAUC), apparent diffusion coefficient (ADC), and enhancement curve. Patients underwent MRI examinations at baseline, and then after two cycles, and finally at completion of chemotherapy.

Results

Based on Sataloff criteria for pathological responses, four patients out of 14 were responders, and 10 were non-responders. At the 2nd MRI examination, IAUC was significantly smaller in responders than in non-responders (p = 0.023). When the results of the first and second MRI examinations were compared, Kep decreased from baseline to the second MRI (p = 0.03) in non-responders and in responders (p = 0.04). This change was statistically significant in both groups. The ADC values increased significantly in responders from baseline to the third MRI (p = 0.012).

Conclusions

In our study, IAUC and ADC were the only parameters that reliably differentiated responders from non-responders after two and three cycles of chemotherapy.

Perfusion Parameters on Breast Dynamic Contrast-Enhanced MRI Are Associated With Disease-Specific Survival in Patients With Triple-Negative Breast Cancer

OBJECTIVE

The aim of this study was to investigate the association between perfusion parameters on MRI performed before treatment and survival outcome (disease-free survival [DFS], disease-specific survival [DSS]) in patients with triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

Sixty-one patients (median age, 50 years; age range, 27-77 years) with TNBC (tumor size on MRI: median, 25.5 mm; range, 11.0-142.0 mm) were included. We analyzed clinical and pathologic variables and MRI parameters. Cox proportional hazards models were used to determine associations with survival outcome.

RESULTS

The median follow-up time was 46.1 months (range, 13.9-58.4 months). Eleven of 61 (18.0%) patients had events (i.e., local, regional, or distant recurrence or contralateral breast cancer) and seven (11.5%) died of breast cancer. Among the pretreatment variables, a larger tumor size on MR images (hazard ratio [HR] = 1.024, p = 0.003) was associated with worse DFS at univariate analysis. In multivariate pretreatment models for DSS, a higher fractional volume of extravascular extracellular space per unit volume of tissue (v_e) value (HR = 1.658, p = 0.038), higher peak enhancement (HR = 1.843, p = 0.018), and a larger tumor size on MR images (HR = 1.060, p = 0.001) were associated with worse DSS. In multivariate posttreatment models, a larger pathologic tumor size (HR for DFS, 1.074 [p = 0.005]; HR for DSS, 1.050 [p = 0.042]) and metastasis in surgically resected axillary lymph nodes (HR for DFS, 5.789 [p = 0.017]; HR for DSS, 23.717 [p = 0.005]) were associated with worse survival outcome.

CONCLUSION

A higher v_e value, higher peak enhancement, and larger tumor size of the primary tumor on pretreatment MRI were independent predictors of worse DSS in patients with TNBC.

Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) can probe pre-treatment differences or monitor early response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). Thirty-six patients with locally advanced breast cancer were imaged using multiple-b DWI with 12 b values ranging from 0 to 1000  s/mm(2) at the baseline, and 28 patients were repeatedly scanned after the second cycle of NAC. Subjects were divided into pathologic complete response (pCR) and nonpathologic complete response (non-pCR) groups according to the surgical pathologic specimen. Parameters (D, D*, f, maximum diameter [MD] and volume [V]) before and after 2 cycles of NAC and their corresponding change (Δparameter) between pCR and non-pCR groups were compared using the Student t test or nonparametric test. The diagnostic performance of different parameters was judged by the receiver-operating characteristic curve analysis. Before NAC, the f value of pCR group was significantly higher than that of non-pCR (32.40% vs 24.40%, P = 0.048). At the end of the second cycle of NAC, the D value was significantly higher and the f value was significantly lower in pCR than that in non-pCR (P = 0.001; P = 0.015, respectively), whereas the D* value and V of the pCR group was slightly lower than that of the non-pCR group (P = 0.507; P = 0.676, respectively). ΔD was higher in pCR (-0.45 × 10(-3)  mm(2)/s) than that in non-pCR (-0.07 × 10(-3)  mm(2)/s) after 2 cycles of NAC (P < 0.001). Δf value in the pCR group was significantly higher than that in the non-pCR group (17.30% vs 5.30%, P = 0.001). There was no significant difference in ΔD* between the pCR and non-pCR group (P = 0.456). The prediction performance of ΔD value was the highest (AUC [area under the curve] = 0.924, 95% CI [95% confidence interval] = 0.759-0.990). When the optimal cut-off was set at -0.163 × 10(-3)  mm(2)/s, the values for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were up to 100% (95% CI = 66.4-100), 73.7% (95% CI = 48.8-90.9), 64.3% (95% CI = 35.6-86.0), and 100% (95% CI = 73.2-99.3), respectively. IVIM-derived parameters, especially the D and f value, showed potential value in the pre-treatment prediction and early response monitoring to NAC in locally advanced breast cancer. ΔD value had the best prediction performance for pathologic response after NAC.

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

BACKGROUND

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.

METHODS

Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.

RESULTS

A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.

CONCLUSION

Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient

PURPOSE

To evaluate the treatment response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy using magnetic resonance (MR) vascular maps and apparent diffusion coefficient (ADC) at 3T. Materials and Methods Thirty-one patients with LABC who underwent breast MR studies before, after the first course, and after completing neoadjuvant chemotherapy were enrolled. Vascular morphology was retrieved via Hessian matrix and the voxels of the vessels and volume of vessels were measured automatically. Whole tumor mean ADC values were calculated. Clinical responders were defined as >50% tumor reduction in the final MR studies. Pathologically complete responders were also recorded.

RESULTS

There were 21 clinical responders and 10 nonresponders. Compared to the nonresponders after the first course, the responders were characterized by more vascular reduction of the breast lesion and decreased bilateral vascular discrepancy (voxels and volume), and increments in the ADC value and ADC percentage of the lesions (all P < 0.05). There were three pathological complete responders who showed more apparent early vascular reduction of the lesion breast (voxels and volume) and increments in the ADC value than others (P = 0.02, 0.01 and 0.02, respectively).

CONCLUSION

The early changes of MR vascular maps and ADC are associated with the final treatment response of LABC.

Picture the future: emerging imaging modalities

The past century has witnessed accelerated development in imaging modalities. Better anatomical visualisation and improved data analysis have improved survival rates. Through emerging functional, molecular and structural imaging modalities, better anatomical visualisation has been extended to cellular and molecular detail, improving diagnosis and management of diseases. This article reviews the advances made in emerging imaging modalities as well as their potential applications in targeted therapy.

Transferrin-targeted magnetic/fluorescence micelles as a specific bi-functional nanoprobe for imaging liver tumor

In order to delineate the location of the tumor both before and during operation, we developed targeted bi-functional polymeric micelles for magnetic resonance (MR) and fluorescence imaging in liver tumors. Hydrophobic superparamagnetic iron oxide nanoparticles (SPIONs) were loaded into the polymeric micelles through self-assembly of an amphiphilic block copolymer poly(ethylene glycol)-poly(ϵ-caprolactone). After, transferrin (Tf) and near-infrared fluorescence molecule Cy5.5 were conjugated onto the surface of the polymeric micelles to obtain the nanosized probe SPIO@PEG-b-PCL-Tf/Cy5.5 (SPPTC). Imaging capabilities of this nanoprobe were evaluated both in vitro and in vivo. The accumulation of SPPTC in HepG2 cells increased over SPIO@PEG-b-PCL-Cy5.5 (SPPC) by confocal microscopy. The targeted nanoprobe SPPTC possessed favorable properties on the MR and fluorescence imaging both in vitro and in vivo. The MTT results showed that the nanoprobes were well tolerated. SPPTC had the potential for pre-operation evaluation and intra-operation navigation of tumors in clinic.

Clinical application of magnetic resonance imaging in management of breast cancer patients receiving neoadjuvant chemotherapy

Neoadjuvant chemotherapy (NAC), also termed primary, induction, or preoperative chemotherapy, is traditionally used to downstage inoperable breast cancer. In recent years it has been increasingly used for patients who have operable cancers in order to facilitate breast-conserving surgery, achieve better cosmetic outcome, and improve prognosis by reaching pathologic complete response (pCR). Many studies have demonstrated that magnetic resonance imaging (MRI) can assess residual tumor size after NAC, and that provides critical information for planning of the optimal surgery. NAC also allows for timely adjustment of administered drugs based on response, so ineffective regimens could be terminated early to spare patients from unnecessary toxicity while allowing other effective regimens to work sooner. This review article summarizes the clinical application of MRI during NAC. The use of different MR imaging methods, including dynamic contrast-enhanced MRI, proton MR spectroscopy, and diffusion-weighted MRI, to monitor and evaluate the NAC response, as well as how changes of parameters measured at an early time after initiation of a drug regimen can predict final treatment outcome, are reviewed. MRI has been proven a valuable tool and will continue to provide important information facilitating individualized image-guided treatment and personalized management for breast cancer patients undergoing NAC.

Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer

PURPOSE

To evaluate the accuracy of the apparent diffusion coefficient (ADC) provided by diffusion-weighted imaging (DWI) in predicting the response to neoadjuvant chemotherapy (NACT) at baseline in patients according to their breast tumour phenotypes.

MATERIALS & METHODS

This retrospective study was approved by our institutional review board. One hundred eighteen consecutive women with locally advanced breast cancer who had undergone NACT followed by breast surgery were included. DWI was performed at 1.5 T less than 2 weeks before NACT. We studied the correlation between pretreatment ADC and response in pathology after surgery according to immunohistochemical features and intrinsic subtypes (luminal A, luminal B, HER2-enriched, and triple-negative tumours).

RESULTS

After surgery, the pathologist recognized 24 complete responders (CRps) and 94 non-complete responders (NCRps). No difference was identified between the pretreatment ADCs of the CRp and NCRp patients. There were differences in pretreatment ADCs among the luminal A (1.001 ± 0.143 × 10(-3) mm(2)/s), luminal B (0.983 ± 0.150 × 10(-3) mm(2)/s), HER2-enriched (1.132 ± 0.216 × 10(-3) mm(2)/s), and triple-negative (1.168 ± 0.245 × 10(-3) mm(2)/s; P = 0.0003) tumour subtypes. In triple-negative tumours, the pretreatment ADC was higher in NCRp (1.060 ± 0.143 × 10(-3) mm(2)/s) than in CRp patients (1.227 ± 0.271 × 10(-3) mm(2)/s; P = 0.047).

CONCLUSION

Pretreatment ADC can predict the response of breast cancer to NACT if tumour subtypes are considered. Key Points • Apparent diffusion coefficient helps clinicians to assess patients with breast cancer. • Pretreatment ADC is related to tumour grade and hormone receptor status. • Pretreatment ADC is lower in luminal A and B than in triple-negative tumours. • Pretreatment ADC is higher in complete than in non-complete responders to neoadjuvant chemotherapy.