Pretreatment Dynamic Contrast-enhanced MRI Research Articles

Use of Pretreatment Perfusion MRI-based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy.

Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or Ktrans), volume fraction of extravascular and extracellular space (Ve), and maximum contrast agent uptake rate (Slopemax) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (P < .001), washout enhancement (P = .01), androgen receptor expression (P = .009), and programmed death ligand 1 expression (P = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Rauch in this issue.

The Value of Dynamic Contrast-Enhanced MRI in Predicting the Response of Idiopathic Granulomatous Mastitis to Steroid Therapy and Comparison of Clinical and Radiological Outcomes

Background: The aim of this study was to investigate the value of pre-treatment dynamic contrast-enhanced MRI (DCE-MRI) findings in predicting clinical and radiological response to treatment in patients with idiopathic granulomatous mastitis (IGM) receiving steroid therapy and to compare clinical and DCE-MRI results after treatment. Methods: Pre- and post-treatment MRI examinations of 86 patients (with a mean age of 33.8 years; range, 20–57 years) diagnosed with IGM between January 2014 and September 2022 were retrospectively evaluated. Lesion characteristics and longest diameter, number of involved quadrants, retroareolar involvement, and presence of fistula or lymphadenopathy were noted. Patients were categorized into clinical complete response (CCR), clinical non-response (CNR) groups based on clinical response and into radiological complete response (RCR) and radiological non-response (RNR) groups based on radiological response. Results: The analyses revealed no relationship between the lesion type (abscess, NME, abscess, and NME), presence of lymphadenopathy or fistula, number of involved quadrants, retroareolar involvement, and treatment response on pre-treatment MRI (p&gt;0.05). The longest lesion diameter was smaller in the CCR group than in the CNR group (p = 0.02). After treatment, 40.7% (35/86) of the patients achieved RCR + CCR, while 16.3% (14/86) achieved RNR + CNR. On the other hand, 43% (37/86) of the patients achieved CCR + RNR. Conclusion: CCR is more common in patients with smaller lesions. Other pre-treatment MRI findings were not correlated with radiological or clinical response. A significant proportion of patients with CCR had residual lesions radiologically.

Advanced MRI to differentiate schwannomas and metastases in the cerebellopontine angle/internal auditory canal.

Differentiating schwannomas and metastases in the cerebellopontine angles (CPA)/internal auditory canals (IAC) can be challenging. This study aimed to assess the role of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) to differentiate schwannomas and metastases in the CPA/IAC. We retrospectively reviewed 368 patients who were diagnosed with schwannomas or metastases in the CPA/IAC between April 2017 and February 2022 in a single academic center. Forty-three patients had pretreatment DWI and DCE-MRI along with conventional MRI. Normalized mean apparent diffusion coefficient ratio (nADCmean) and DCE-MRI parameters of fractional plasma volume (Vp), flux rate constant (Kep), and forward volume transfer constant were compared along with patients' demographics and conventional imaging features between schwannomas and metastases as appropriate. The diagnostic performances and multivariate logistic regression analysis were performed using the significantly different values. Between 23 schwannomas (15 males; median 48 years) and 20 metastases (9males; median 61 years), nADCmean (median: 1.69 vs. 1.43; p = .002), Vp (median: 0.05 vs. 0.20; p<.001), and Kep (median: 0.41 vs. 0.81 minute-1 ; p<.001) were significantly different. The diagnostic performances of nADCmean, Vp, and Kep were 0.77, 0.90, and 0.83 area under the curves, with cutoff values of 1.68, 0.12, and 0.53, respectively. Vp was identified as the most significant parameter for the tumor differentiation in the multivariate logistic regression analysis (p<.001). DWI and DCE-MRI can help differentiate CPA/IAC schwannomas and metastases, and Vp is the most significant parameter.

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

PurposeTo investigate if the pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics machine learning predicts the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients. MethodsSeventy-eight breast cancer patients who underwent DCE-MRI before NAC and confirmed as pCR or non-pCR were enrolled. Early enhancement mapping images of pretreatment DCE-MRI were created using subtraction formula as follows: Early enhancement mapping = (Signal 1 min – Signal pre)/Signal pre. Images of the whole tumors were manually segmented and radiomics features extracted. Five prediction models were built using five scenarios that included clinical information, subjective radiological findings, first order texture features, second order texture features, and their combinations. In texture analysis workflow, the corresponding variables were identified by mutual information for feature selection and random forest was used for model prediction. In five models, the area under the receiver operating characteristic curves (AUC) to predict the pCR and several metrics for model evaluation were analyzed. ResultsThe best diagnostic performance based on F-score was achieved when both first and second order texture features with clinical information and subjective radiological findings were used (AUC = 0.77). The second best diagnostic performance was achieved with an AUC of 0.76 for first order texture features followed by an AUC of 0.76 for first and second order texture features. ConclusionsPretreatment DCE-MRI can improve the prediction of pCR in breast cancer patients when all texture features with clinical information and subjective radiological findings are input to build the prediction model.

Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PurposeTo compare the performances of deep learning (DL) to radiomics analysis (RA) in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) based on pretreatment dynamic contrast-enhanced MRI (DCE-MRI) in breast cancer.Materials and MethodsThis retrospective study included 356 breast cancer patients who underwent DCE-MRI before NAC and underwent surgery after NAC. Image features and kinetic parameters of tumors were derived from DCE-MRI. Molecular information was assessed based on immunohistochemistry results. The image-based RA and DL models were constructed by adding kinetic parameters or molecular information to image-only linear discriminant analysis (LDA) and convolutional neural network (CNN) models. The predictive performances of developed models were assessed by receiver operating characteristic (ROC) curve analysis and compared with the DeLong method.ResultsThe overall pCR rate was 23.3% (83/356). The area under the ROC (AUROC) of the image-kinetic-molecular RA model was 0.781 [95% confidence interval (CI): 0.735, 0.828], which was higher than that of the image-kinetic RA model (0.629, 95% CI: 0.595, 0.663; P < 0.001) and comparable to that of the image-molecular RA model (0.755, 95% CI: 0.708, 0.802; P = 0.133). The AUROC of the image-kinetic-molecular DL model was 0.83 (95% CI: 0.816, 0.847), which was higher than that of the image-kinetic and image-molecular DL models (0.707, 95% CI: 0.654, 0.761; 0.79, 95% CI: 0.768, 0.812; P < 0.001) and higher than that of the image-kinetic-molecular RA model (0.778, 95% CI: 0.735, 0.828; P < 0.001).ConclusionsThe pretreatment DCE-MRI-based DL model is superior to the RA model in predicting pCR to NAC in breast cancer patients. The image-kinetic-molecular DL model has the best prediction performance.

Abstract P4-06-02: Clinically feasible pharmacokinetic model of dynamic contrast-enhanced MRI is prognostic of recurrence free survival in breast cancer patients treated with neoadjuvant therapy

Abstract Background Dynamic contrast-enhanced (DCE) MRI is commonly used for diagnosis and treatment planning of cancers. Its main use is the identification of tumors in soft tissues, where the tumor progression-induced angiogenic changes result in leaky vasculature which is readily highlighted by contrast enhancement. While allowing tumor morphology assessment, DCE MRI also contains rich information on perfusion and permeability which can be modeled as a pharmacokinetic (PK) process, a subject of extensive investigation in the clinical research setting. Several breast DCE MRI studies have suggested that PK parameters might be indicative of response to therapy and recurrence. However, these studies rely on high temporal resolution DCE MRI for accurate PK modeling, which presents a hurdle to adoption in clinical settings where DCE MRI is acquired with low temporal resolution. Methods We present a novel two-parameter modeling framework which circumvents this temporal limitation, allowing kinetic parameters to be extracted from clinical DCE MRI containing 3-6 timepoints spaced 60-90 seconds apart. We sought to examine whether these kinetic parameters (hereafter KP1 and KP2) binned into "high" and "low" values had prognostic value in determining disease recurrence. KP1 reflects the rate of leakiness from vessels into extravascular space, while KP2 reflects the rate of the reverse process. A study was conducted with a cohort of 458 breast cancer patients treated with neoadjuvant therapy (NAT) in which 117 recurrences occurred. Model parameters were extracted from pre-treatment clinical DCE MRI with low temporal resolution. Univariate and multivariate (Cox Proportional Hazard; CPH) analyses were performed to test the prognostic value of covariates in predicting recurrence free survival (RFS). Results A CPH model was constructed that included age, receptor status, cancer stage, kinetic parameters, and pathologic complete response (pCR) as covariates (Table 1). KP2 and pCR were statistically significantly prognostic markers of RFS (p&amp;lt;0.05). KP1 was not a significant prognostic marker of RFS (p=0.055). Interestingly, KP2 had a hazard ratio (HR) similar to pCR (HR: -1.04 [-1.75, -0.33]; p&amp;lt;0.0039 vs. HR: -0.97 [-1.5, -0.44], p&amp;lt;0.00034). Using univariate analysis, we investigated whether KP2 was prognostic of recurrence. Patients were stratified by therapy type, whether they achieved pCR or had residual disease (RD), and by disease subtype. KP2 was found to be predictive of RFS for patients with RD after treatment with NAT chemotherapy (p&amp;lt;0.0004, n=241, log-rank test), but not for patients treated with anti-HER2 NAT (p=0.84, n=124) or patients that achieved pCR (p=0.68, n=139). KP2 was also found to be prognostic in Hormone Receptor+/HER2- patients with RD (p&amp;lt;0.012, n=151) and Hormone Receptor-/HER2+ patients treated with NAT chemotherapy (p&amp;lt;0.001, n=64). The parameter was not found to be prognostic in patients with triple-negative disease. Conclusions We developed a new prognostic imaging marker for disease recurrence in breast cancer patients treated with NAT that relies on only pre-treatment standard-of-care, low temporal resolution DCE MRI. Since the marker was found to have similar prognostic value in terms of hazard ratio as pCR, but in patients with RD, it could prove to be a powerful tool in patient management after surgery. Table 1.Results of Cox Proportional Hazard model.FactorHazard Ratio95% CIp-valueAge-0.01[-0.02, 0.01]0.51ER+-0.35[-0.85, 0.15]0.17HER2+0.02[-0.38, 0.41]0.93PR+0.05[-0.46, 0.56]0.85Stage II0.54[-0.62, 1.7]0.36Stage III1.08[-0.1, 2.27]0.074KP1-0.68[-1.38, 0.01]0.055KP2-1.04[-1.75, -0.33]0.0039pCR-0.97[-1.5, -0.44]0.00034 Citation Format: Joseph R Peterson, Tyler M. Earnest, Wei Huang, Hiroyuki Abe, John A Cole, Jr. Clinically feasible pharmacokinetic model of dynamic contrast-enhanced MRI is prognostic of recurrence free survival in breast cancer patients treated with neoadjuvant therapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-06-02.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study.

There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS. This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis. Ultimately, 35 participants (21 women, 52 ± 12years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7months. Ktrans (0.036min-1 vs. 0.057min-1, p = .008) and initial area under the time-concentration curve within 90s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. Ktrans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. Ktrans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction. DCE-MRI (particularly Ktrans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS. •Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.

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

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC). A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model. Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively. Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment. • Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Combing MRI Perfusion and 18F-FDG PET/CT Metabolic Biomarkers Helps Predict Survival in Advanced Nasopharyngeal Carcinoma: A Prospective Multimodal Imaging Study

Simple SummaryAmong various types of head and neck cancers, nasopharyngeal carcinoma (NPC) has a distinct epidemiology and prognosis. Tumor recurrence in NPC poses significant clinical challenges and identifying novel prognostic biomarkers is crucial. Imaging biomarkers derived from positron emission tomography (PET) and magnetic resonance imaging (MRI) reflect different biological facets of tumors. However, whether pretreatment PET and MRI imaging biomarkers can complement each other in the survival prediction of NPC patients has not been determined. This prospective study aimed to analyze the prognostic value of imaging biomarkers derived from pretreatment MRI and PET in NPC. The model integrating the MRI perfusion markers with PET metabolic parameter demonstrated higher prognostic performance than the traditional staging system and individual model with either MRI or PET alone. A staging work-up for NPC including both functional MRI and PET parameters may provide clinicians with supplementary prognostic information in addition to morphologic assessment of tumor extent.We prospectively investigated the prognostic value of imaging parameters for nasopharyngeal carcinoma (NPC) using dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT). Patients with stage III–IVb NPC underwent F-FDG PET/CT, DCE-MRI, and DWI before treatment. Kaplan-Meier and Cox-regression analyses were used to assess associations of PET and MRI imaging biomarkers with overall survival (OS) and recurrence-free survival (RFS). We used independent prognosticators to establish prognostic models; model performance was examined using Harrell’s concordance index (c-index). Sixty-one patients were available for analysis, as 13 patients died and 20 experienced recurrence. Total lesion glycolysis (TLG) (p = 0.002) from PET/CT and the initial area under the curve (iAUC) (p = 0.036) from DCE-MRI were identified as independent prognosticators of OS; Epstein-Barr virus (EBV) DNA (p = 0.027), the extracellular volume fraction (Ve) (p = 0.027) from DCE-MRI, and TLG/iAUC (p = 0.025) were significant predictors of RFS. The c-indices of the prognostic models incorporating TLG + iAUC in predicting OS and incorporating EBV DNA + Ve + TLG/iAUC in predicting RFS were 0.79 and 0.76, respectively. These were significantly higher than the corresponding c-indices of the TNM staging system (p = 0.047 and 0.025, respectively); they were also higher than those of models with only MRI or PET biomarkers. In conclusion, the combination of pretreatment DCE-MRI and 18F-FDG PET/CT imaging biomarkers helps survival prediction in advanced NPC. Integrating MRI perfusion with PET metabolism and plasma EBV information may aid clinicians in planning the optimal personalized management strategy.

Abstract PS3-01: Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients

Abstract Background and Purpose:Image-based tumor phenotypes by using computer extraction techniques have been studied for evaluation of breast cancer invasiveness, stage, lymph node involvement, molecular subtypes and genomics. In this project we aimed to investigate ability of computer-extracted breast MR imaging radiomic phenotypes to predict nodal and distant metastasis in breast cancer patients. MATERIALS AND METHODS:This retrospective IRB approved study included 416 biopsy proven breast cancer patients who had pretreatment DCE MRI in a single institution between 2014 and 2018. Patient’s demographic, clinical data, pathology at diagnosis and surgery, nodal and distant metastasis (M1) at follow up were documented. Using QuantX imaging software, the tumor volume of interest was automatically-segmented using the multiple dynamic phases of DCE MRI. A total of 33 radiomic features describing tumor phenotype were extracted from each tumor site. A linear discriminant analysis (LDA) as a classifier with nested feature selection 10-fold cross validation was used to build the radiomic signature for prediction of nodal and distant metastasis occurrence. Receiver operating characteristic (ROC) and precision-recall analyses were used to evaluate performance, with 95% confidence intervals from 1000 bootstraps, and Kaplan-Meier was used to calculate the progression-free survival estimates and associated hazard ratio at the median cutpoint of the probability of metastasis calculated by the LDA in the 10-fold cross-validation. RESULTS:The quantitative DCE MRI radiomic model was able to differentiate between breast cancer patients with and without distant metastatic disease at follow up with area under the ROC of 0.75 (95% CI 0.65; 0.82) and precision-recall curves 0.46 (0.33;0.69), hazard ratio at median cut point is 3.76 (2.27; 6.24), p&amp;lt;0.001. Volume, surface area, sphericity, margin, maximum uptake, and washout rate variation features played the most important role in differentiating between breast cancer patients with and without distant metastasis. The DCE radiomic model was able predict presence of ipsilateral nodal disease (≥1 positive lymph nodes) at surgery with AUC 0.66 (95% CI: 0.60; 0.71), ≥4 positive lymph nodes at surgery with AUC 0.67 (95% CI: 0.60; 0.74), and N2/N3 disease with AUC 0.64 (95% CI: 0.56; 0.72). Effective radius was most important feature for nodal disease prediction. CONCLUSIONS:Our results show that DCE MRI based radiomic phenotypes were able to predict nodal involvement and distant metastasis in breast cancer patients. Quantitative breast DCE MRI radiomics shows promise for noninvasive image based phenotyping for prediction of nodal and distant metastatic disease in breast cancer patients. Citation Format: Gaiane Margishvili Rauch, Karen Drukker, Nabil Elshafeey, Rania M.m. Mohamed, Medina Boge, Beatriz E. Adrada, Rosalind P Candelaria, Mo Salama, Irene Shkatova, Maryellen Giger, Wei T Yang. Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-01.

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

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. To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer. Retrospective. In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions). 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. One reviewer obtained perfusion parameters (Ktrans , kep , ve , and vp ) 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. Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used. Triple-negative breast cancers exhibited higher Ktrans and kep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher Ktrans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher ve than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher kep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher Ktrans and kep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher Ktrans , kep , vp (P < 0.05) than grade I tumors. Tumors with LVSI presented higher Ktrans and kep than tumors without LVSI (P < 0.05). Breast cancer presenting higher Ktrans and kep 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. 3 TECHNICAL EFFICACY STAGE: 2.

Value of diffusion-weighted imaging in the evaluation of therapeutic effect of cyber knife on hepatocellular carcinoma

Objective To investigate the value of DWI in the evaluation of the therapeutic effect of cyber knife in the treatment of hepatocellular carcinoma. Methods Ninety patients who were clinically diagnosed with hepatocellular carcinoma (95 lesions) in the 5th Medical Center of General Hospital of PLA from February 2011 to December 2013 were retrospectively analyzed. All 90 patients underwent pre-treatment dynamic contrast-enhanced MRI scans of liver. Fourty six of them underwent liver MRI scan 3 months after treatment with cyber knife, and 49 patients underwent liver MRI scans 6 months after treatment. According to the evaluation criteria of solid tumor effect, complete necrosis tumor lesions and postoperative residual active lesions were determined. The ADC values of residual active tissue lesion, necrosis lesion in tumor and normal liver were measured. Paired sample t test was used to compare the difference of ADC values of tumor necrosis lesions and normal liver tissues between 3 and 6 months after treatment and before treatment, and ROC was used to evaluate the efficacy of ADC values in predicting complete tissue necrosis after treatment. Results In the 95 lesions, 91 lesions were completely necrotic, and active tissues were found in 4 lesions after treatment. There were statistically significant differences in the ADC values of the lesions at 3 and 6 months after treatment and before treatment (P 0.05). The area under the ROC of using ADC value to predict the complete necrosis after treatment with cyber knife was 0.767, and the ADC value was 1.23×10-3 mm2/s as the diagnostic cutoff value. The sensitivity and specificity of the diagnosis were 76.5% and 70.6%, respectively. Conclusion ADC values can distinguish the necrotic component from the active component of hepatocellular carcinoma after treatment, and can be used to evaluate the therapeutic effect of cyber knife in hepatocellular carcinoma. Key words: Liver neoplasms; Magnetic resonance imaging; Cyber knife

Enhancement of breast cancer on pre-treatment dynamic contrast-enhanced MRI using computer-aided detection is associated with response to neo-adjuvant chemotherapy.

To evaluate whether enhancement of breast cancer on pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as evaluated semi-quantitatively using computer-aided detection (CAD) is associated with response to neo-adjuvant chemotherapy. A total of 84 women, (mean age, 51±10 [SD] years; range: 30-73 years) with 84 breast cancers who underwent MRI before neo-adjuvant chemotherapy were included in this retrospective study. The proportion of each type of signal intensity-time curve (SITC) (type 1: persistent; type 2: plateau; Type 3: washout) within the tumor volume was quantified semi-automatically using a CAD system (Aegis®, Sentinelle medical, Toronto, Canada) and was compared to histological features of the tumors and to pathological response to neo-adjuvant chemotherapy. Pathological complete response was obtained in 29 patients (35%). Proportion of SITC type 1 was greater in non-responders (P=0.019) while proportion of SITC type 3 was greater in responders (P=0.04). Sensitivity, specificity, and accuracy of proportion of SITC type 1 for the identification of incomplete response on pathology were 42% (95% CI: 29%-56%), 90% (95% CI: 73%-98%), and 59% (95% CI: 48%-70%), respectively. Proportion of SITC type 1 (persistent) in breast cancers on pre-treatment MRI as semi-automatically quantified using a CAD system is associated with absence of pathological complete response to neo-adjuvant chemotherapy with good specificity.

Pretreatment dynamic contrast-enhanced MRI biomarkers correlate with progression-free survival in primary central nervous system lymphoma.

Prediction of clinical outcomes in patients with primary central nervous system lymphoma (PCNSL) is important for optimization of treatment planning. Quantitative imaging biomarkers for PCNSL have not yet been established. This study evaluated the prognostic value of pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging for progression-free survival (PFS) in patients with PCNSL. Pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging were retrospectively analyzed in 18 immunocompetent patients with PCNSL. Volumes of interest encompassing the tumors were assessed for measurements of blood plasma volume (Vp), volume transfer constant (Ktrans), and apparent diffusion coefficient. Patients were divided into short and long PFS groups based on median PFS. Imaging and clinical variables were correlated with PFS. Median PFS was 19.6 months. Lower Vpmean and Ktransmean values increased risk for rapid progression (< 19.6 months). Receiver operating characteristic curve analysis demonstrated an optimal Vpmean cutoff value of 2.29 (area under the curve [AUC] = 0.74, sensitivity and specificity = 0.78, p = 0.023) for separating patients with short and long PFS. The optimal Ktransmean cutoff was 0.08 (AUC = 0.74, sensitivity = 0.67, specificity = 0.78, p = 0.025). Kaplan-Meier survival analysis with log-rank test demonstrated significantly (p = 0.015) increased risk of rapid progression for patients with Vpmean < 2.29. Vpmean was significantly (p = 0.03) associated with PFS on univariate Cox analysis. Apparent diffusion coefficient values and clinical factors did not influence PFS. Pretreatment Vp and Ktrans derived from dynamic contrast-enhanced MRI may be novel prognostic quantitative imaging biomarkers of progression-free survival in patients with PCNSL. These data should be prospectively validated in larger patient cohorts.

Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI reproduces as a biomarker of survival in ER-positive/HER2-negative breast cancer patients

ObjectivesTo assess whether contralateral parenchymal enhancement reproduces as an independent biomarker for patient survival in an independent patient cohort from a different cancer institution.MethodsThis is a HIPAA-compliant IRB approved retrospective study. Patients with ER-positive/HER2-negative operable invasive ductal carcinoma and preoperative dynamic contrast-enhanced MRI were consecutively included between 2005 and 2009. The parenchyma of the breast contralateral to known cancer was segmented automatically on MRI and contralateral parenchymal enhancement (CPE) was calculated. CPE was split into tertiles and tested for association with invasive disease-free survival (IDFS) and overall survival (OS). Propensity score analysis with inverse probability weighting (IPW) was used to adjust CPE for patient and tumour characteristics as well as systemic therapy.ResultsThree hundred and two patients were included. The median age at diagnosis was 48 years (interquartile range, 42-57). Median follow-up was 88 months (interquartile range, 76-102); 15/302 (5%) patients died and 37/302 (13%) had a recurrence or died. In context of multivariable analysis, IPW-adjusted CPE was associated with IDFS [hazard ratio (HR) = 0.27, 95% confidence interval (CI) = 0.05-0.68, p = 0.004] and OS (HR = 0.22, 95% CI = 0.00-0.83, p = 0.032).ConclusionsContralateral parenchymal enhancement on pre-treatment dynamic contrast-enhanced MRI as an independent biomarker of survival in patients with ER-positive/HER2-negative breast cancer has been upheld in this study. These findings are a promising next step towards a practical and inexpensive test for risk stratification of ER-positive/HER2-negative breast cancer.Key points• High parenchymal-enhancement in the disease-free contralateral breast reproduces as biomarker for survival.• This is in patients with ER-positive/HER2-negative breast cancer from an independent cancer centre.• This is independent of patient and pathology parameters and systemic therapy.

Correlation Between Tumor Metabolism and Semiquantitative Perfusion MRI Metrics in Non–small Cell Lung Cancer

To correlate semiquantitative parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) and 18F-fluorodeoxyglucose (FDG)-PET for non-small cell lung cancer (NSCLC). A total of 24 NSCLC patients who underwent pretreatment FDG-PET and DCE-MRI were analyzed. SUVmax was measured from FDG-PET. DCE-MRI was obtained on 3T MRI scanner using a 4D T1-weighted high resolution imaging method with volume excitation (4D THRIVE) sequence during free breathing to achieve high temporal resolution. A total of 130 dynamic sequences were obtained over a period of 4.3 minutes with 30 coronal slices, 2.5 mm slice thickness, TR/TE of 10/2.0 ms, FA of 15°, in-plane resolution of up to 2 mm, and temporal resolution of 2 seconds. A bolus injection of 0.2 mL/kg of Gd-DTPA (Magnevist) followed by saline flush was used. DCE-MRI parameters consisting of mean, median, standard deviation (SD) and median absolute deviation (MAD) of peak enhancement, wash-in-slope (WIS), wash-out-slope (WOS), time-to-peak (TTP), time-to-half-peak (TTHP), initial gradient, wash-out gradient, signal enhancement ratio and initial area under the relative signal enhancement curve before 30 seconds, 60 seconds, 90 seconds, 120 seconds, 150 seconds, 180 seconds, TTP and TTHP (IAUCtthp) were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator (LASSO) in combination with leave-one-out cross-validation (LOOCV). In UVA, mean(WOS) (r = -0.456, P = 0.025), mean(IAUCtthp) (r = -0.439, P = 0.032), median(IAUCtthp) (r = -0.543, P = 0.006), and MAD(IAUCtthp) (r = -0.557, P = 0.005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R2 = 0.298, P = 0.0006). Decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp), and increase in SD(TTP) were associated with significant increase in SUVmax. Association was found between SUVmax of FDG-PET and SD and MAD of DCE-MRI metrics derived during Gd-DTPA uptake phase in NSCLC, reflecting that intratumoral heterogeneity of angiogenesis in wash-in phase is associated with tumor metabolism. MAD(IAUCtthp) was consistently significant in both UVA and MVA, suggesting that lower spatial heterogeneity in intensity and rapidity of contrast wash-in is associated with higher metabolic rate in NSCLC. This study will facilitate better understanding of complex relationship between tumor vascularization and metabolism, and eventually help in guiding targeted therapy.

The prognostic value of dynamic contrast-enhanced MRI contrast agent transfer constant Ktrans in cervical cancer is explained by plasma flow rather than vessel permeability

Background:The microvascular contrast agent transfer constant Ktrans has shown prognostic value in cervical cancer patients treated with chemoradiotherapy. This study aims to determine whether this is explained by the contribution to Ktrans of plasma flow (Fp), vessel permeability surface-area product (PS), or a combination of both.Methods:Pre-treatment dynamic contrast-enhanced MRI (DCE-MRI) data from 36 patients were analysed using the two-compartment exchange model. Estimates of Fp, PS, Ktrans, and fractional plasma and interstitial volumes (vp and ve) were made and used in univariate and multivariate survival analyses adjusting for clinicopathologic variables tumour stage, nodal status, histological subtype, patient age, tumour volume, and treatment type (chemoradiotherapy vs radiotherapy alone).Results:In univariate analyses, Fp (HR=0.25, P=0.0095) and Ktrans (HR=0.20, P=0.032) were significantly associated with disease-free survival while PS, vp and ve were not. In multivariate analyses adjusting for clinicopathologic variables, Fp and Ktrans significantly increased the accuracy of survival predictions (P=0.0089).Conclusions:The prognostic value of Ktrans in cervical cancer patients treated with chemoradiotherapy is explained by microvascular plasma flow (Fp) rather than vessel permeability surface-area product (PS).

Pretreatment Dynamic Contrast-Enhanced MRI Improves Prediction of Early Distant Metastases in Patients With Nasopharyngeal Carcinoma

The identification of early distant metastases (DM) in patients with newly diagnosed, previously untreated nasopharyngeal carcinoma (NPC) plays an important role in selecting the most appropriate treatment approach. Here, we sought to investigate the predictive value of distinct MRI parameters for the detection of early DM.Between November 2010 and June 2011, a total of 51 newly diagnosed NPC patients were included. All of the study participants were followed until December 2014 at a single institution after completion of therapy. DM was defined as early when they were detected on pretreatment FDG-PET scans or within 6 months after initial diagnosis. The following parameters were tested for their ability to predict early DM: pretreatment FDG-PET standardized uptake value (SUV), MRI-derived AJCC tumor staging, tumor volume, and dynamic contrast-enhanced (DCE) values. The DCE-derived ve was defined as the volume fraction of the extravascular, extracellular space.Compared with patients without early DM, patients with early DM had higher SUV, tumor volume, DCE mean (median) ve, ve skewness, ve kurtosis, and the largest mean ve selected among sequential slices (P < 0.05). No differences were identified when early DM were defined only according to the results of pretreatment FDG-PET. Among different quantitative DCE parameters, the mean ve had the highest area under curve (AUC, 0.765). However, the AUCs of SUV, tumor volume, mean ve, ve skewness, ve kurtosis, or the largest mean ve selected among the sequential slices did not differ significantly from one another (P = 0.82).Taken together, our results suggest that DCE-derived ve may be a useful parameter in combination with SUV and tumor volume for predicting early DM. Dynamic contrast-enhanced MRI may be complementary to FDG-PET for selecting the most appropriate treatment approach in NPC patients.

Dynamic Contrast-Enhanced MRI in Low-Grade Versus Anaplastic Oligodendrogliomas.

Low-grade and anaplastic oligodendrogliomas are often difficult to differentiate on the basis of conventional MR imaging characteristics. Dynamic contrast-enhanced (DCE) MRI can assess tumor microvasculature and has demonstrated utility for predicting glioma grade and prognosis in primary brain tumors. The aim of our study was to evaluate the performance of plasma volume (Vp) and volume transfer coefficient (K(trans) ) derived from DCE MRI in differentiating between grade II and grade III oligodendrogliomas. Twenty-four consecutive patients with pathologically confirmed oligodendroglioma (World Health Organization grade II, n = 14 and grade III, n = 10) were retrospectively assessed. Pretreatment DCE MRI was performed and regions of interest were manually drawn around the entire tumor volume to calculate Vp and K(trans) . The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were performed to compare pharmacokinetic parameters between the 2 groups. The Vpmean values for grade III oligodendrogliomas were significantly higher (P = .03) than those for grade II oligodendrogliomas. The K(trans) mean values were higher in grade III lesions, but the difference between the 2 groups was not statistically significant (P > .05). Based on ROC analysis, the Vpmean (area under curve = .757, SD = .1) cut-off value that provided the best combination of high sensitivity and specificity to distinguish between grade II and III oligodendrogliomas was 2.35 (P < .03). The results of our study suggest the DCE MRI parameter Vpmean can noninvasively differentiate between grade II and grade III oligodendrogliomas.

Water-Exchange-Modified Kinetic Parameters from Dynamic Contrast-Enhanced MRI as Prognostic Biomarkers of Survival in Advanced Hepatocellular Carcinoma Treated with Antiangiogenic Monotherapy.

BackgroundTo find prognostic biomarkers in pretreatment dynamic contrast-enhanced MRI (DCE-MRI) water-exchange-modified (WX) kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy.MethodsTwenty patients with advanced HCC underwent DCE-MRI and were subsequently treated with sunitinib. Pretreatment DCE-MRI data on advanced HCC were analyzed using five different WX kinetic models: the Tofts-Kety (WX-TK), extended TK (WX-ETK), two compartment exchange, adiabatic approximation to tissue homogeneity (WX-AATH), and distributed parameter (WX-DP) models. The total hepatic blood flow, arterial flow fraction (γ), arterial blood flow (BF A), portal blood flow, blood volume, mean transit time, permeability-surface area product, fractional interstitial volume (v I), extraction fraction, mean intracellular water molecule lifetime (τ C), and fractional intracellular volume (v C) were calculated. After receiver operating characteristic analysis with leave-one-out cross-validation, individual parameters for each model were assessed in terms of 1-year-survival (1YS) discrimination using Kaplan-Meier analysis, and association with overall survival (OS) using univariate Cox regression analysis with permutation testing.ResultsThe WX-TK-model-derived γ (P = 0.022) and v I (P = 0.010), and WX-ETK-model-derived τ C (P = 0.023) and v C (P = 0.042) were statistically significant prognostic biomarkers for 1YS. Increase in the WX-DP-model-derived BF A (P = 0.025) and decrease in the WX-TK, WX-ETK, WX-AATH, and WX-DP-model-derived v C (P = 0.034, P = 0.038, P = 0.028, P = 0.041, respectively) were significantly associated with an increase in OS.ConclusionsThe WX-ETK-model-derived v C was an effective prognostic biomarker for advanced HCC treated with sunitinib.