Preoperative Dynamic Contrast-enhanced Magnetic Resonance Research Articles

Fibrosarcomatous Transformation in Dermatofibrosarcoma Protuberans of the Male Breast and its Association with Magnetic Resonance Imaging and Immunohistopathologic Features.

Dermatofibrosarcoma Protuberans (DFSP) is a rare soft tissue sarcoma, accounting for approximately 1% of all tumors; however, DFSP of the breast is extremely rare. Moreover, DFSP generally has a low malignant potential and is characterized by a high rate of local recurrence along with a small but definite risk of metastasis. The risk of metastasis is higher in fibrosarcomatous transformation in DFSP than in ordinary DFSP. We have, herein, reported a case of a 61-year-old male patient with fibrosarcomatous transformation in DFSP. Preoperative Dynamic Contrastenhanced Magnetic Resonance Imaging (DCE-MRI) of the breast revealed an oval-shaped mass with heterogeneous internal enhancement, a large vessel embedded within, and a washout curve pattern on kinetic curve analysis. The mass exhibited a hyperintense signal on Diffusion-weighted Imaging (DWI), with a low apparent diffusion coefficient value. Histologically, the bland spindle tumor cells were arranged in a storiform pattern. Areas with the highest histological grade demonstrated increased cellularity, cytological atypia, and mitotic activity. Immunohistochemically, Ki-67 and p53 were highly expressed. Recognizing the risk and accurately diagnosing fibrosarcomatous transformation in male breast DFSP are critical for improving prognosis and establishing appropriate treatment and follow-up plans. This emphasizes the significance of combining immunohistopathological features with DCE-MRI and DWI to assist clinicians in the early and accurate diagnosis of sarcomas arising from male breast DFSP.

Preoperative Dynamic Contrast-Enhanced and Diffusion-Weighted Breast Magnetic Resonance Imaging Findings for Prediction of Lymphovascular Invasion of the Lesions in Node-Negative Invasive Breast Cancer.

Purpose: Our single-centre retrospective study aimed to investigate the relationship between preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings and apparent diffusion coefficient (ADC) values and lymphovascular invasion (LVI) status of the lesions in patients with clinically-radiologically lymph node-negative invasive breast cancer. Methods: A total of 250 breast lesions diagnosed in preoperative magnetic resonance imaging were identified. All patients were divided into 2 subgroups: LVI-negative and LVI-positive according to the pathological findings of surgical specimens. The 2 groups' DCE-MRI findings, ADC values, and histopathological results of lesions were compared. Results: LVI was detected in 100 of 250 lesions. Younger age than 45 years and larger lesion size than 20 mm were found to be associated with the presence of LVI (P < .001). High histological and nuclear grade (P = .001), HER2-enriched molecular subtype (P = .001), and Ki-67 positivity (P = .016) were significantly associated with LVI. The LVI positivity rate was significantly higher in the lesions with medium-rapid initial phase kinetic curve and washout delayed phase kinetic curve (P = .001). The presence of LVI was significantly associated with the presence of peritumoural edema, sentinel lymph node metastasis, adjacent vessel sign, and increased whole breast vascularity (P < .001). When diffusion-weighted imaging findings were evaluated, it was determined that tumoural ADC values lower than 1068 × 10-6 mm2/second (P = .002) and peritumoural-tumoural ADC ratios higher than 1.5 (P = .001) statistically increased the probability of LVI. Conclusion: The patient's age, various histopathological and DCE-MRI findings, tumoural ADC value, and peritumoural-tumoural ADC ratio may be useful in the preoperative prediction of LVI status in breast cancer lesions.

Solitary hypovascular hepatic nodules: comparison and differentiation between peripheral nodular cholangiocarcinoma and atypical liver hemangioma with MRI.

Peripheral nodular cholangiocarcinoma (PCC) and hepatic hemangioma (HG) significantly differ in treatment strategies and prognosis. However, they can present similar imaging characteristics, making them difficult to distinguish. The distinction between PCC and atypical HG using pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) holds substantial significance. Fifty-four cases of solitary hypovascular hepatic nodules (nodules ≤3 cm in diameter and enhancement not exceeding the hepatic parenchyma) confirmed pathologically were collected, including 25 cases of PCC and 29 cases of HG. The clinical and DCE-MRI features were observed, and the apparent diffusion coefficient (ADC) values were compared. The t-test or Fisher's exact test was used to compare the differences between the two groups. Receiver operating characteristic (ROC) curve analysis was performed on the diagnostic results of the two readings before and after the study by three diagnostic physicians. The signal of PCC was more heterogeneous and could be shown as a ring hyperintensity signal on diffusion weighted imaging (DWI). The T2-weighted imaging (T2WI) signal of HG lesions showed a more homogeneous high-intense signal. The ADC value of PCC patients was lower than that of HG patients. Most of the patients in the two groups presented marginal enhancement in the arterial phase with infrequent abnormal perfusion around. The intratumoral vascular traversal signs in the PCC group showed a diagnostic significance. Among the lesions with marginal enhancement at arterial stage, the enhancement in PCC group was more likely to have annular enhancement with the main manifestation of delay reduction and nebulous or separated/astral center. On the other hand, HG tended to be more likely to have nodular enhancement in the arterial phase with delayed invariance at the margins and with no significant enhancement at the center. The area under the curves (AUC) of the three radiologists before and after the two readings were 0.645 and 0.888, respectively. In the differentiation of PCC and atypical liver hemangioma, the presence of a circular hyper-intense signal in DWI, the ADC values, and the pattern and extent of enhancement of marginal and central lesions were of diagnostic significance.

Preoperative MRI features for characterization of vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma.

This study aimed to analyze imaging features based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the identification of vessels encapsulating tumor clusters (VETC)-microvascular invasion (MVI) in hepatocellular carcinoma (HCC), VM-HCC pattern. Patients who underwent hepatectomy and preoperative DCE-MRI between January 2015 and March 2021 were retrospectively analyzed. Clinical and imaging features related to VM-HCC (VETC + /MVI-, VETC-/MVI +, VETC + /MVI +) and Non-VM-HCC (VETC-/MVI-) were determined by multivariable logistic regression analyses. Early and overall recurrence were determined using the Kaplan-Meier survival curve. Indicators of early and overall recurrence were identified using the Cox proportional hazard regression model. In total, 221 patients (177 men, 44 women; median age, 60years; interquartile range, 52-66years) were evaluated. The multivariable logistic regression analyses revealed fetoprotein > 400ng/mL (odds ratio [OR] = 2.17, 95% confidence interval [CI] 1.07, 4.41, p = 0.033), intratumor vascularity (OR 2.15, 95% CI 1.07, 4.31, p = 0.031), and enhancement pattern (OR 2.71, 95% CI 1.17, 6.03, p = 0.019) as independent predictors of VM-HCC. In Kaplan-Meier survival analysis, intratumor vascularity was associated with early and overall recurrence (p < 0.05). Based on DCE-MRI, intratumor vascularity can be used to characterize VM-HCC and is of prognostic significance for recurrence in patients with HCC.

Predictive Value of Preoperative Dynamic Contrast-Enhanced MRI Imaging Features in Breast Cancer Patients with Postoperative Recurrence Time.

Although the implementation of surgery has reduced the mortality of breast cancer, postoperative recurrence is still an important problem bothering patients. DCE-GMRI can not only clearly display the morphological characteristics of breast lesions but also dynamically observe the blood perfusion of the lesions. On account of this, we explored the predictive value of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging features in breast cancer patients on postoperative recurrence time of breast cancer. The results showed that DCE-MRI images can clearly show the hemodynamic characteristics and morphological characteristics of tumor lesions, and have important value in predicting the recurrence time of breast cancer after surgery. The prognosis of early recurrence of breast cancer is worse. DCE-MRI can predict the time of postoperative recurrence and provide important clinical references.

Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.

BackgroundPreoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.MethodsWe retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression.ResultsOf 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027–91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576–8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824.ConclusionsOur predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12957-022-02645-8.

Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer

BackgroundOne-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance. ObjectiveTo build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. MethodsClinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set. ResultsA total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC. ConclusionThis study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance.

Prediction of breast cancer molecular subtypes using DCE-MRI based on CNNs combined with ensemble learning

To design an ensemble learning based prediction model using different breast DCE-MR post-contrast sequence images to distinguish two kinds of breast cancer subtypes (luminal and non-luminal). We retrospectively studied preoperative dynamic contrast enhanced-magnetic resonance imaging and molecular information of 266 breast cancer cases with either luminal subtype (luminal A and luminal B) or non-luminal subtype (human epidermal growth factor receptor 2 and triple negative). Then, multiple bounding boxes covering tumor lesions were acquired from three series of post-contrast DCE-MR sequence images which were determined by radiologists. Afterwards, three baseline convolutional neural networks (CNNs) with same architecture were concurrently trained, followed by preliminary prediction of probabilities from the testing database. Finally, the classification and evaluation of breast subtypes were realized by means of fusing predicted results from three CNNs employed via ensemble learning based on weighted voting. Taking advantage of 5-fold cross validation CV, the average prediction specificity, accuracy, precision and area under the ROC curve on testing dataset for the luminal versus non-luminal are 0.958, 0.852, 0.961, and 0.867, respectively, which empirically demonstrate that our proposed ensemble model has highly reliability and robustness. The breast DCE-MR post-contrast sequence image analysis utilizing the ensemble CNN model based on deep learning could show a valuable and extendible practical application on breast molecular subtype identification.

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.

Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

The aim of the present study was to use pharmacokinetic quantitative parameters with histogram and texture features on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between the luminal A and luminal B molecular subtypes of breast cancer. We retrospectively reviewed the data of 94 patients with histopathologically proven breast cancer. The pharmacokinetic quantitative parameters (Ktrans, Kep, and Ve) with their corresponding histogram and texture features based on preoperative DCE-MRI were obtained. The parameters were compared using the Mann-Whitney U-test between the luminal A and luminal B groups, the human epidermal growth factor receptor-2 (HER2)-positive luminal B and HER2-negative luminal B groups, and the lymph node metastasis (LNM)-positive and LNM-negative groups. Receiver operating characteristic curves were generated for parameters that presented significant between-group differences. The maximum values of Ktrans, Kep, and Ve, and the mean and 90th percentile values of Ve were significantly higher in the luminal B group than in the luminal A group. Among the texture features, only skewness of Ktrans significantly differed between the luminal A and B groups. All histogram features of Ktrans were higher in the HER2-positive luminal B group than in the HER2-negative luminal B group. However, no parameter differed between the LNM-positive and LNM-negative groups. Pharmacokinetic quantitative parameters with histogram and texture features obtained from DCE-MRI are associated with the molecular subtypes of breast cancer, and may serve as potential imaging biomarkers to differentiate between the luminal A and luminal B molecular subtypes.

Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status.

Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB. To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. Retrospective. In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables. 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.

Prognostic value of preoperative dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian cancer

ObjectivesTo investigate whether semi-quantitative and pharmacokinetic perfusion dynamic contrast-enhanced (DCE) parameters are associated with traditional prognostic factors and can predict clinical outcome in ovarian cancer (OC). MethodsThis prospective study, approved by local ethical committee, enrolled 38 patients with primary OC, 2011–2014. After preoperative DCE-MRI (3.0 T), two observers measured perfusion (Ktrans, Kep, Ve, Vp) and semi-quantitative parameters (area under the curve, peak, time-to-peak) by drawing regions of interest (ROIs) covering the large solid lesion (L-ROI) and the most enhancing small area (S-ROI) (NordicICE platform). Kruskal–Wallis was used to analyze associations between MRI parameters and classified prognostic factors. ResultsMean Ktrans values were higher in high-grade serous OC than in other types (L-ROI, P = 0.041; S-ROI, P = 0.018), and lower mean Ktrans values predicted residual tumor (L-ROI P = 0.030; S-ROI, P = 0.012). Higher minimum Vp values were associated with higher International Federation of Gynecology and Obstetrics (FIGO) stage (S-ROI, P = 0.023).Shorter recurrence-free survival was predicted by higher Ve (minimum L-ROI, P = 0.035; maximum S-ROI, P = 0.046), Vp (maximum S-ROI, P = 0.033), and lower time-to-peak (maximum S-ROI, P = 0.047) in Kaplan–Meier analysis. Multiparametric MRI variables combining DCE and diffusion weighted data were also predictive for survival. ConclusionDCE-MRI parameters may represent imaging biomarkers for predicting tumor aggressiveness and prognosis in OC. Higher Ktrans levels were associated with better results in cytoreductive surgery but with earlier recurrence.

Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI.

To investigate whether diffusion-weighted imaging (DWI) aids pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate additional lesions in breast cancer patients. DCE-MRI and DWI were performed on 131 lesions, with available histopathological results. The apparent diffusion coefficient (ADC) of each lesion was measured, and the cut-off value for differentiation between malignant and benign lesions was calculated. A protocol combining the ADC cut-off value with DCE-MRI was validated in a cohort of 107 lesions in 77 patients. When an ADC cut-off value of 1.11 × 10-3 mm2/s from the development cohort was applied to the additional lesions in the validation cohort, the specificity increased from 18.9% to 67.6% (P < 0.001), and the diagnostic accuracy increased from 61.7% to 82.2% (P = 0.05), without significant loss of sensitivity (98.6% vs. 90.0%, P = 0.07). The negative predictive values of lesions in the same quadrant had decreased, as had those of lesions ≥1 cm in diameter. The ADC cut-off value in the validation cohort was 1.05 × 10-3 mm2/s. Additional implementation of DWI for breast lesions in pre-operative MRI can help to obviate unnecessary biopsies by increasing specificity. However, to avoid missing cancers, clinicians should closely monitor lesions located in the same quadrant or lesions ≥1 cm. • DWI can be used to further differentiate lesions during pre-operative cancer staging. • ADC cut-off values were similar in the development and validation cohorts. • DWI improves both PPV and NPV in cases of multicentric lesions. • DWI improves both PPV and NPV in lesions <1 in diameter. • NPVs are decreased in multifocal lesions and lesions ≥1 cm in diameter.

Enhancing patterns of breast cancer on preoperative dynamic contrast-enhanced magnetic resonance imaging and resection margin in breast conserving therapy.

The association between enhancing patterns of preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and resection margins after BCS has not been studied in detail before. We investigated the association between surgical outcomes and enhancing patterns observed on DCE-MRI. 269 enhancing patterns on DCE-MRI scans were selected, and subdivided into the following groups: (1) a single mass-like enhancement, (2) a single non-mass-like enhancement (NME), (3) mass-like enhancing breast cancer with other mass-like enhancing lesions, and (4) mass-like enhancing breast cancer with additional NMEs. Associations between enhancing patterns on DCE-MRI and re-excision rate, size of specimen, and ratio of tumor/specimen were evaluated retrospectively. The conversion rate from breast conserving therapy (BCT) to mastectomy as a result of MRI findings was 13.4%, re-excision rate during BCT was 8.2% and excision rate of another suspicious lesion was 7.4%. The single NME group had the highest re-excision rate after BCT (22.2%) (p = 0.02). The ratio of tumor/specimen (p = 0.61) and mean specimen size (p = 0.38) were not influenced by enhancement patterns. The false positive rate and positive predictive values of using DCE-MRI for defining the extension of breast cancer was 22.2% and 71.4%, respectively. Enhancement patterns on DCE- MRI, especially NME, could increase re-excision rates.

Background Parenchymal Enhancement on Preoperative Magnetic Resonance Imaging: Association With Recurrence-Free Survival in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy.

To retrospectively investigate whether background parenchymal enhancement (BPE) of the contralateral breast on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is associated with therapeutic outcomes following neoadjuvant chemotherapy (NAC) in unilateral invasive breast cancer.The institutional review board approved this study, and informed consent was waived. Between 2009 and 2011, 93 women with unilateral invasive breast cancer (43 premenopausal women who performed pre-NAC MRI between days 7 and 20 of the menstrual cycle and 50 postmenopausal women) underwent NAC with pre- and post-NAC DCE-MRI before surgery. MRI features (BPE [minimal, mild, moderate, marked] of the contralateral breast, lesion size and number, lesion kinetics, and changes in lesion size) and clinicopathologic features were analyzed. Patients were grouped according to BPE category (high [moderate or marked] or low [minimal or mild]). Cox regression modeling was used to determine associations between MRI features and recurrence-free survival (RFS) after controlling for clinicopathologic variables.The mean follow-up period was 48.2 months. Twenty-three recurrences occurred (2 ipsilateral breasts, 6 regional, and 15 distant). On multivariate analysis, high BPE on pre-NAC MRI (hazard ratio [HR] = 3.851, P = 0.006) and triple-negative cancer (HR = 3.192, P = 0.002) were independent factors associated with worse RFS. A greater reduction of lesion size on post-NAC MRI (HR = 0.984, P = 0.021) was associated with better RFS.High BPE on pre-NAC MRI is significantly associated with worse RFS in an NAC setting. This study suggests that BPE on pre-NAC DCE-MRI may have potential as a predictor of long-term outcomes in breast cancer patients who undergo NAC.

Preoperative MRI Improves Prediction of Extensive Occult Axillary Lymph Node Metastases in Breast Cancer Patients with a Positive Sentinel Lymph Node Biopsy

To test the ability of quantitative measures from preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have four or more positive axillary lymph nodes on completion axillary dissection. A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (June 2005-January 2010). Clinical/pathologic factors, primary lesion size, and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (P<.05) to predict four or more positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model. Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of four or more metastatic nodes. Receiver operating characteristic curves demonstrated an area under the curve of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (P=.003). Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict the presence of metastases in four or more nodes. DCE-MRI may help identify sentinel node-positive patients requiring further local-regional therapy.

Increased microvascular proliferation is negatively correlated to tumour blood flow and is associated with unfavourable outcome in endometrial carcinomas

Background:We aimed to study the angiogenic profile based on histomorphological markers in endometrial carcinomas in relation to imaging parameters obtained from preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) and to explore the potential value of these markers to identify patients with poor outcome.Methods:In fifty-four surgically staged endometrial carcinoma patients, immunohistochemical staining with factor VIII and Ki67 allowed assessment of microvessel density (MVD) and microvascular proliferation reflecting tumour angiogenesis. In the same patients, preoperative pelvic DCE-MRI and DWI allowed the calculation of parameters describing tumour microvasculature and microstructure in vivo.Results:Microvascular proliferation was negatively correlated to tumour blood flow (Fb) (r=−0.36, P=0.008), capillary permeability surface area product (PS) (r=−0.39, P=0.004) and transfer from the blood to extravascular extracellular space (EES) (Ktrans) (r=−0.40, P=0.003), and was positively correlated to tumour volume (r=0.34; P=0.004). High-tumour microvascular proliferation, low Fb and low Ktrans were all significantly associated with reduced progression/recurrence-free survival (P<0.05).Conclusion:Disorganised angiogenesis with coexisting microvascular proliferation and low tumour blood flow is a poor prognostic factor supporting that hypoxia is associated with progression and metastatic spread in endometrial carcinomas.

Predicting local recurrence following breast-conserving treatment: parenchymal signal enhancement ratio (SER) around the tumor on preoperative MRI

The level of background parenchymal enhancement around tumor is known to be associated with breast cancer risk. However, there is no study investigating predictive power of parenchymal signal enhancement ratio (SER) around tumor for ipsilateral breast tumor recurrence (IBTR). To investigate whether the breast parenchymal SER around the tumor on preoperative dynamic contrast-enhanced magnetic resonance imaging (MRI) is associated with subsequent IBTR in breast cancer patients who had undergone breast-conserving treatment. Nineteen consecutive women (mean age, 44 years; range, 34-63 years) with breast cancer who developed IBTR following breast-conserving treatment and 114 control women matched for age, as well as T and N stages were included. We compared the clinicopathologic features of the two groups including nuclear grade, histologic grade, hormonal receptor status, human epidermal growth factor receptor-2 (HER-2) status, lymphovascular invasion, negative margin width, use of adjuvant therapy, and parenchymal SER around the tumor on preoperative DCE-MRI. The SER was measured on a slice showing the largest dimension of the tumor. Multivariate conditional logistic regression analysis was used to identify independent factors associated with IBTR. In univariate analysis, ER negativity (odds ratio [OR] = 4.7; P = 0.040), PR negativity (OR = 4.0; P = 0.013), HER-2 positivity (OR = 3.6; P = 0.026), and a parenchymal SER greater than 0.53 (OR = 23.3; P = 0.011) were associated with IBTR. In multivariate analysis, ER negativity (OR = 3.8; P = 0.015) and a parenchymal SER greater than 0.53 (OR = 13.2; P = 0.040) on preoperative MRI were independent factors associated with IBTR. In addition to ER negativity, a higher parenchymal SER on preoperative MRI was an independent factor associated with subsequent IBTR in patients with breast cancer who had undergone breast-conserving treatment.

Commentary on “Predicting the histology of small renal masses using preoperative dynamic contrast-enhanced magnetic resonance imaging.” Kim JH, BaeJH, Lee KW, Kim ME, Park SJ, Park JY, Department of Urology, Soonchunhyang University Hospital, Seoul, Korea

To study if the magnetic resonance imaging can predict the histologic type of small renal cell carcinoma. Dynamic contrast-enhanced magnetic resonance imaging was performed on 63 patients with computed tomography-suspected or ultrasonography-suspected small (≤ 4 cm) renal cell carcinoma from February 2008 to February 2010. Percentage signal intensity change, tumor-to-cortex enhancement index during precontrast phase, corticomedullary phase, and nephrogenic phase were investigated. Among the 60 patients, 42 were proven to have clear cell renal cell carcinoma and 18 patients were proven to have nonclear cell renal cell carcinoma (10 patients with papillary renal cell carcinoma and 8 patients with chromophoberenal cell carcinoma). The percentage signal intensity change in the clear cell-type was higher only in the corticomedullary phase (P = 0.002). The tumor-to-cortex enhancement index in the clear cell type was higher in the corticomedullary and nephrogenic phases (P = 0.007 and P = 0.041, respectively). The most valuable marker was percentage signal intensity change in the corticomedullary phase (area under the receiver operating characteristic curve 0.85). The cutoff value of percentage signal intensity change in the corticomedullary phase was 173%, and the sensitivity and specificity were 81% and 87.5%, respectively. Dynamic contrast-enhanced magnetic resonance imaging could be useful for discriminating the clear cell type from nonclear cell type in small renal cell carcinoma with high sensitivity and specificity.

Predicting the Histology of Small Renal Masses Using Preoperative Dynamic Contrast-enhanced Magnetic Resonance Imaging

To study whether magnetic resonance imaging can predict the histologic type of small renal cell carcinoma. Dynamic contrast-enhanced magnetic resonance imaging was performed in 63 patients with computed tomography- or ultrasonography-suspected small (≤ 4 cm) renal cell carcinoma from February 2008 to February 2010. Percentage signal intensity change, tumor-to-cortex enhancement index during precontrast phase, corticomedullary phase, and nephrogenic phase were investigated. Among the 60 patients, 42 were proven to have clear cell renal cell carcinoma and 18 patients were proven to have non-clear cell renal cell carcinoma (10 patients with papillary renal cell carcinoma, 8 patients with chromophobe renal cell carcinoma). The percentage signal intensity change in the clear cell type was higher only in the corticomedullary phase (P = .002). The tumor-to-cortex enhancement index in the clear cell type was higher in the corticomedullary and nephrogenic phases (P = .007 and P = .041, respectively). The most valuable marker was percentage signal intensity change in the corticomedullary phase (area under the receiver operating characteristic curve 0.85). The cut-off value of percentage signal intensity change in the corticomedullary phase was 173%, and the sensitivity and specificity were 81% and 87.5%, respectively. Dynamic contrast-enhanced magnetic resonance imaging could be useful for discriminating the clear cell type from non-clear cell type in small renal cell carcinoma with high sensitivity and specificity.