Preoperative Contrast-enhanced Magnetic Resonance Imaging Research Articles

Comparison of MRI findings of hypothalamic-optic chiasmatic gliomas and craniopharyngiomas.

Differential diagnosis of hypothalamic-optic chiasmatic gliomas (HOCGs) and craniopharyngiomas on magnetic resonance imaging (MRI) can be quite challenging. To compare the MRI features of HOCGs and cranipharyngiomas. Patients diagnosed with HOCG or craniopharyngioma in histopathological evaluation between 2012 and 2022 and who underwent preoperative contrast-enhanced brain MRI were included. Various MRI features were retrospectively evaluated for each lesion: T2-weighted imaging and fluid attenuation inversion recovery hyperintensity, calcification, cystic change, T1-weighted (T1W) imaging hyperintensity of the cystic component, hemorrhage, involvement of sellar, suprasellar or other adjacent structures, lobulated appearance, presence of hydrocephalus, and contrast enhancement pattern. Apparent diffusion coefficient (ADC) values were also evaluated and compared. Among 38 patients included, 13 (34%) had HOCG and 25 (66%) had craniopharyngioma. Craniopharyngiomas had a significantly higher rate of cystic changes, calcification, and T1W imaging hyperintensity of the cystic component than HOCGs (P <0.05). Of HOCGs, 92% had chiasm involvement, 23% had optic nerve involvement, and 31% had brain stem involvement. On the other hand, chiasm involvement was observed in 8% of craniopharyngiomas, but none had optic nerve and/or brain stem involvement (P <0.05). While 62% (8/13) of HOCGs had diffuse homogeneous enhancement, 80% (20/25) of craniopharyngiomas had a diffuse heterogeneous enhancement pattern. Mean ADC values were significantly higher in craniopharyngiomas compared to HOCGs (2.1 vs. 1.6 ×10-3mm2/s, P <0.05). Although some neuroimaging findings may overlap, features such as presence of cyst and calcification, brain stem and optic pathway involvement, different enhancement patterns, and ADC values may be helpful in the differential diagnosis of HOCGs and craniopharyngiomas.

Magnetic resonance elastography as a preoperative assessment for predicting intrahepatic recurrence in patients with hepatocellular carcinoma

PurposeMagnetic resonance elastography (MRE) is a noninvasive tool for diagnosing hepatic fibrosis with high accuracy. We investigated the preoperative clinical and imaging predictors of intrahepatic recurrence after curative resection of hepatocellular carcinoma (HCC), and evaluated MRE as a predictor of intrahepatic recurrence. MethodsWe retrospectively evaluated 80 patients who underwent preoperative contrast-enhanced magnetic resonance imaging (MRI) with two-dimensional MRE and curative resection for treatment-naïve HCC between May 2019 and December 2021. Liver stiffness (LS) was measured on the elastograms, and the optimal cutoff of LS for predicting intrahepatic recurrence was obtained using receiver operating characteristic (ROC) analysis. An LS above this cutoff was defined as MRE-recurrence. Preoperative imaging features of the tumor were assessed on MRI, including features in the Liver Imaging Reporting and Data System and microvascular invasion (MVI). Recurrence-free survival (RFS) rates were estimated using the Kaplan-Meier method, and differences were compared using the log-rank test. Using a Cox proportional hazards model, we conducted a multivariable analysis to investigate the factors affecting recurrence-free survival. ResultsDuring a median follow-up period of 32 months (range, 4–52 months), thirteen patients (16.3%) developed intrahepatic recurrence. ROC analysis determined an LS cutoff of ≥4.35 kPa to define MRE-recurrence. The 4-year RFS rate was significantly higher in patients without MRE-recurrence than in those with MRE-recurrence (93.4% vs. 48.9%; p = 0.001). In multivariable analysis, MRE-recurrence (Hazard ratio [HR], 5.9; 95% confidence interval [CI], 1.5–23.1) and MVI (HR, 3.4; 95% CI, 1.0–11.3) were independent predictors of intrahepatic recurrence. ConclusionsPatients without MRE-recurrence had significantly higher RFS rates than those with MRE-recurrence. MRE-recurrence and MVI were independent predictors of intrahepatic recurrence in patients after curative resection for HCC.

Contrast Enhanced CT Versus MRI for Accurate Diagnosis of Wall-thickening Type Gallbladder Cancer

IntroductionDiagnosis of wall-thickening type gallbladder cancer (GBC) is challenging. Computed tomography (CT) and magnetic resonance imaging (MRI) are commonly utilized to evaluate gallbladder wall thickening. However, there is a lack of data comparing the performance of CT and MRI for the detection of wall-thickening type GBC. AimWe aim to compare the diagnostic accuracy of CT and MRI in diagnosis of wall-thickening type GBC. Materials and MethodsThis prospective study comprised consecutive patients suspected of wall-thickening type GBC who underwent preoperative contrast-enhanced CT and MRI. The final diagnosis was based on the histopathology of the resected gallbladder lesion. Two radiologists independently reviewed the characteristics of gallbladder wall thickening at CT and MRI. The association of CT and MRI findings with histological diagnosis and the interobserver agreement of CT and MRI findings were assessed. ResultsThirty-three patients (malignancy, 13 and benign, 20) were included. None of the CT findings were significantly associated with GBC. However, at MRI, heterogeneous enhancement, indistinct interface with the liver, and diffusion restriction were significantly associated with malignancy (p=0.006, <0.001, and 0.005, respectively), and intramural cysts were significantly associated with benign lesions (p=0.012). For all MRI findings, the interobserver agreement was substantial to perfect (kappa=0.697-1.000). At CT, the interobserver agreement was moderate to near perfect (k=0.631-1.000). ConclusionThese findings suggest that MRI may be preferred over CT in patients with suspected wall thickening type GBC. However, larger multicenter studies must confirm our findings.

Prediction of myocardial contractility after coronary bypass surgery according to preoperative contrast-enhanced magnetic resonance imaging and echocardiography

To establish the criteria for reversibility of myocardial contractility in patients with coronary artery disease (CAD) after coronary artery bypass grafting considering data of cardiac magnetic resonance imaging (MRI) and echocardiography. We studied the results of coronary artery bypass grafting in 186 patients with CAD complicated by reduced left ventricular ejection fraction (<30%). All patients underwent cardiac MRI and echocardiography before surgery. Immediate and long-term results were evaluated according to echocardiography and MRI data. We confirmed the previously established predictors of improvement in left ventricular contractility: diastolic IVST ≥10.5 mm and PWT ≥9.5 mm, score of LV myocardium damage according to MRI with delayed contrast enhancement (p<0.05). Multivariate analysis makes it possible to calculate prognostic index and obtain information about further myocardial contractility after revascularization with an error of 6%. Echocardiography and contrast-enhanced cardiac MRI are valuable to assess morphological and functional state of the left ventricle in patients with ischemic cardiomyopathy and preoperatively determine functional reserve of the myocardium.

MRI-based radiomics approach for the prediction of recurrence-free survival in triple-negative breast cancer after breast-conserving surgery or mastectomy.

To explore the value of a radiomics signature and develop a nomogram combined with a radiomics signature and clinical factors for predicting recurrence-free survival in triple-negative breast cancer patients. We enrolled 151 patients from the cancer imaging archive who underwent preoperative contrast-enhanced magnetic resonance imaging. They were assigned to training, validation and external validation cohorts. Image features with coefficients not equal to zero in the 10-fold cross-validation were selected to generate a radiomics signature. Based on the optimal cutoff value of the radiomics signature determined by maximally selected log-rank statistics, patients were stratified into high- and low-risk groups in the training and validation cohorts. Kaplan-Meier survival analysis was performed for both groups. Kaplan-Meier survival distributions in these groups were compared using log-rank tests. Univariate and multivariate Cox regression analyses were used to construct clinical and combined models. Concordance index was used to assess the predictive performance of the 3 models. Calibration of the combined model was assessed using calibration curves. Four image features were selected to generate the radiomics signature. The Kaplan-Meier survival distributions of patients in the 2 groups were significantly different in the training (P < .001) and validation cohorts (P = .001). The C-indices of the radiomics model, clinical model, and combined model in the training and validation cohorts were 0.772, 0.700, 0.878, and 0.744, 0.574, 0.777, respectively. The C-indices of the radiomics model, clinical model, and combined model in the external validation cohort were 0.778, 0.733, 0.822, respectively. The calibration curves of the combined model showed good calibration. The radiomics signature can predict recurrence-free survival of patients with triple-negative breast cancer and improve the predictive performance of the clinical model.

Small (≤5 cm) soft tissue tumors of the extremity and superficial trunk: MRI features and demographic characteristics associated with malignancy.

Although a substantial proportion of small soft tissue tumors are malignant, magnetic resonance imaging (MRI) features and demographic characteristics associated with these tumors have not been well described. To investigate the MRI features and demographic characteristics associated with small (≤5 cm) malignant soft tissue tumors, and to identify independent predictors that allow differentiation of small benign and malignant soft tissue tumors. This retrospective study evaluated patients who underwent surgical excision of small soft tissue tumors of the extremities and superficial trunk, and preoperative contrast-enhanced MRI. Seven MRI findings (tumor depth, tumor-fascia relationship, heterogeneity of signal intensity, necrosis, peritumoral edema, peritumoral enhancement, and margin) and two demographic parameters (age and sex) were included in univariate and multivariate logistic regression analyses to identify independent predictors of small malignant soft tissue tumors. A total of 221 patients (102 men; mean age=45.6 ± 17.6 years) with 72 malignant and 149 benign tumors were included. In the univariate analysis, peritumoral edema (odds ratio [OR] = 3.854; P < 0.001) and peritumoral enhancement (OR = 3.966; P < 0.001) and patient age (≥46 years) (OR = 2.154; P = 0.009) were significantly associated with malignancy. Multivariate analysis showed that peritumoral enhancement on MRI (OR = 3.728; P < 0.001) and patient age (≥46 years) (OR = 1.907; P = 0.036) were independent predictors of malignancy. The combination of these two parameters showed accuracy of 75.1%, sensitivity of 55.6%, and specificity of 84.6% to predict malignancy. Among several MRI and demographic features, the presence of peritumoral enhancement on MRI and patient age (≥46 years) were independent predictors of malignancy in small soft tissue tumors.

Diagnostic and prognostic value of magnetic resonance imaging in the detection of tumor depth of invasion and bone invasion in patients with oral cavity cancer.

To evaluate the accuracy of preoperative contrast-enhanced magnetic resonance imaging (MRI) in the assessment of radiological depth of invasion (rDOI) and bone invasion in patients with oral cavity cancer, and the prognostic value of preoperative rDOI. This retrospective study included patients with surgically resected oral cavity cancer and preoperative MRI acquired within four weeks before surgery. Two readers evaluated the MRI to assess the superficial and deep bone invasion, preoperative T stage, and measured the rDOI. The rDOI was compared to the histopathological DOI (pDOI), used as reference standard. Prognostic value of preoperative features for the disease-specific survival was evaluated using the Kaplan-Meier curve and multivariable Cox proportional hazards analysis. The final population included 80 patients (50 males, mean age 67.7 ± 13.6years). There was a strong statistically significant correlation between the rDOI (median 10mm) and the pDOI (median 9mm) (ρ: 0.978, p < 0.001). The agreement between MRI and histopathological T stage was excellent (k = 0.93, 95% CI 0.86, 0.99). The sensitivity and specificity of preoperative MRI were 93.3% and 98.8% for deep bone invasion, while they were 75.0% and 95.8% for superficial bone invasion, respectively. The rDOI > 10mm was associated with poorer disease-specific survival (log-rank p = 0.016). The rDOI remained the only independent preoperative predictor associated with poorer disease-specific survival at multivariable analysis (hazard ratio 5.5; 95% CI 1.14, 26.58; p = 0.033). Preoperative MRI is accurate for the assessment of DOI and bone invasion. The rDOI is an independent preoperative predictor of disease-specific survival in patients with oral cavity cancer.

MR-guided non-invasive typing of brain gliomas using machine learning

To evaluate diagnostic accuracy of deep learning methods for glioma typing according to the 2007 WHO classification based on preoperative magnetic resonance imaging (MRI) data. The study included MR scans of patients with glial tumors undergoing neurosurgical treatment at the Burdenko National Medical Research Center for Neurosurgery. All patients underwent preoperative contrast-enhanced MRI. 2D and 3D MR scans were used for learning of artificial neural networks with two architectures (Resnest200e and DenseNet, respectively) in classifying tumors into 4 categories (WHO grades I-IV). Learning was provided on 80% of random examinations. Classification quality metrics were evaluated in other 20% of examinations (validation and test samples). Analysis included 707 contrast-enhanced T1 welghted images. 3D classification based on DenseNet model showed the best result in predicting WHO tumor grade (accuracy 83%, AUC 0.95). Other authors reported similar results for other methods. The first results of our study confirmed the fundamental possibility of grading axial contrast-enhanced T1 images according to the 2007 WHO classes using deep learning models.

Imaging of vascular malformations with a high-intensity focused ultrasound probe for treatment planning

ObjectiveWe aimed to investigate whether a current commercially available high-intensity focused ultrasound (HIFU) probe can adequately image targeted vascular malformations (VMs) in anticipation of HIFU treatment planning and delivery. MethodsWe enrolled 10 consecutive patients who were scheduled to undergo treatment of symptomatic peripheral VMs confirmed by routine preoperative contrast-enhanced magnetic resonance imaging and soft tissue duplex ultrasound. The lesions were situated no more than 6 cm from the skin. After induction of general anesthesia and before surgical intervention, we prepared and positioned the Sonablate HIFU probe (SonaCare Medical, LLC, Charlotte, NC) to obtain multiple B-mode images of the targeted VM in the transverse and longitudinal dimensions. We then rated the quality of the images and the feasibility of the imaging process itself using a previously devised questionnaire aimed at evaluating the adequacy of the images for potential HIFU treatment planning and delivery. The patients subsequently underwent surgical intervention and clinical follow-up for their VM per the standard protocol. ResultsThe study included 10 participants aged 21 to 67 years (mean ± standard deviation, 36.5 ± 16.5 years). Six patients (60%) identified as female. The VMs imaged consisted of eight venous (80%), one lymphatic (10%), and one combined lymphovenous (10%) malformation. The lesions were in the extremities only (50%), trunk only (20%), trunk and extremities (20%), or neck and extremities (10%). Pain related to the VM was present in all 10 patients (100%). In all 10 patients, the boundary and location of the VM could be visualized via the HIFU probe despite the diminished B-mode imaging resolution. The absence of Doppler functionality in the HIFU probe did not prevent the identification of the VMs in any patient up to a depth of 6 cm. The results from the postimaging survey showed that difficulty in preparing the study device for imaging was 1.1 ± 0.3 and difficulty in use was 1.1 ± 0.1, with a score of 1 equal to easy and 5 to difficult. The stability of the acoustic coupling to the patient was 1.3 ± 0.2, with a score of 1 representing very stable. ConclusionsWe were able to ultrasonically identify and outline all targeted peripheral VMs using a commercially available HIFU probe in anticipation of treatment planning and delivery. Baseline magnetic resonance imaging and soft tissue duplex ultrasound remain essential tools for guiding probe placement and HIFU imaging.

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH).Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models.Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures.Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

Preoperative contrast-enhanced magnetic resonance imaging in ischemic cardiomyopathy patients undergoing surgical left ventricular reconstruction

Purpose. The aim of this study was to identify the signifi cance of myocardial viability indicators calculated based on cardiac magnetic resonance imaging (MRI) with delayed contrast, as predictors of the adverse course of postoperative period in the surgical treatment of patients with ischemic cardiomyopathy.Material and Methods. A total of 178 patients underwent surgical treatment for ischemic cardiomyopathy from March, 2013 to December, 2017. All patients underwent preoperative cardiac MRI scan with paramagnetic contrast.Results. The logistic regression analysis showed that the left ventricular volumetric indicators and the amount of residual viable myocardium, calculated using delayed contrast-enhanced MRI, were the essential predictors of favorable and complicated course of postoperative period in ischemic cardiomyopathy patients. In patients with a residual viable left ventricular myocardium amount over 64.5%, surgical ventricular reconstruction procedure may be performed with a low risk of early postoperative complications (OR 8.25, 95% CI 1.45-46.8, p = 0.017).Conclusion. Cardiac MRI with delayed paramagnetic contrasting is a valuable method contributing to the preoperative identifi cation of a cohort of ischemic cardiomyopathy patients who can benefi t the most from surgical left ventricular reconstruction.

First application of intraoperative MRI of the liver during ALPPS procedure for colorectal liver metastases

PurposeIntraoperative detection of intrahepatic lesions can be demanding. The use of preoperative contrast-enhanced magnetic resonance imaging (MRI) or computer tomography (CT) combined with intraoperative ultrasound of the liver is state of the art. Near totally regressed colorectal liver metastases (CRLM) after neoadjuvant chemotherapy or nodules in severely altered liver tissue as steatosis or cirrhosis are often hard to detect during the operative procedure. Especially differentiation between benign atypical nodules and malignant tumors can be very difficult. The intraoperative use of contrast-enhanced ultrasound or intraoperative navigation are helpful tools. However, both methods show relevant limitations.The use of intraoperative MRI (ioMRI) can overcome this problem. Relevant structures can be marked within the operative site or immediate control of complete tumor resection can be achieved. This might allow immediate surgical optimization in case of failure.MethodsWe report the intraoperative application of ioMRI in a case of a 61-year-old male patient suffering from rectal cancer with 10 synchronous bilobar CRLM who was treated stepwise by multimodal treatment and staged hepatectomy. Intraoperative contrast-enhanced MRI of the liver was used during completion procedure of an extended right hemihepatectomy performed as “Associating Liver Partition and Portal vein Ligation for Staged hepatectomy (ALPPS)”.ResultsioMRI provided excellent images and showed absence of liver metastases in the liver remnant. Procedure of ioMRI was safe, fast and feasible.ConclusionTo the best of our knowledge, we describe the first case of intraoperative application of a contrast-enhanced MRI during open liver surgery at the University Hospital of Dresden.

Can Magnetic Resonance Imaging Predict Pathologic Findings for Endometrioid Endometrial Cancer?

This pilot study aimed to assess the feasibility of precisely measuring tumor diameter and myometrial invasion in patients with endometrioid endometrial cancer (EEC) using preoperative contrast-enhanced magnetic resonance imaging (MRI). Adult patients with confirmed diagnosis of complex hyperplasia with atypia or EEC were included. Three radiologists separately measured tumor diameter and myometrial invasion. Basic descriptive statistics were used to describe patient characteristics and to compare radiology- and pathology-measured tumor diameter and myometrial invasion. Using the pathology results for tumor diameter as the gold standard for comparison, at least 1 radiologist was able to predict largest tumor diameter within 5 mm for 41.7% of patients. Similarly, based on pathology results for myometrial invasion, at least 1 radiologist was able to predict myometrial invasion within 5% for 50% of patients. All radiologists were able to predict superficial (<50%) or deep (≥50%) myometrial invasion for 75% of patients, with greater sensitivity, specificity, and accuracy for deep myometrial invasion. Given variation among radiologic measurements, it is difficult to recommend preoperative MRI as a basis for measuring tumor diameter and myometrial invasion. Even so, the ability to predict superficial versus deep myometrial invasion may benefit patients with EEC for whom surgery is not a viable option or for those seeking fertility-sparing treatment options.

Texture analysis on preoperative contrast-enhanced magnetic resonance imaging identifies microvascular invasion in hepatocellular carcinoma

BackgroundRadiomic texture analysis quantifies tumor heterogeneity. The aim of this study is to determine if radiomics can predict biologic aggressiveness in HCC and identify tumors with MVI. MethodsSingle-center, retrospective review of HCC patients undergoing resection/ablation with curative intent from 2009 to 2017. DICOM images from preoperative MRIs were analyzed with texture analysis software. Texture analysis parameters extracted on T1, T2, hepatic arterial phase (HAP) and portal venous phase (PVP) images. Multivariate logistic regression analysis evaluated factors associated with MVI. ResultsMVI was present in 52.2% (n = 133) of HCCs. On multivariate analysis only T1 mean (OR = 0.97, 95%CI 0.95–0.99, p = 0.043) and PVP entropy (OR = 4.7, 95%CI 1.37–16.3, p = 0.014) were associated with tumor MVI. Area under ROC curve was 0.83 for this final model. Empirical optimal cutpoint for PVP tumor entropy and T1 tumor mean were 5.73 and 23.41, respectively. At these cutpoint values, sensitivity was 0.68 and 0.5, respectively and specificity was 0.64 and 0.86. When both criteria were met, the probability of MVI in the tumor was 87%. ConclusionTumor entropy and mean are both associated with MVI. Texture analysis on preoperative imaging correlates with microscopic features of HCC and can be used to predict patients with high-risk tumors.

Preoperative assessment of breast cancer: Multireader comparison of contrast-enhanced MRI versus the combination of unenhanced MRI and digital breast tomosynthesis

PurposeTo compare the sensitivity for breast cancer (BC) and BC size estimation of preoperative contrast-enhanced magnetic resonance imaging (CEMRI) versus combined unenhanced magnetic resonance imaging (UMRI) and digital breast tomosynthesis (DBT). Patients and methodsWe retrospectively included 56 women who underwent DBT and preoperative 1.5 T CEMRI between January 2016–February 2017. Three readers with 2–10 years of experience in CEMRI and DBT, blinded to pathology, independently reviewed CEMRI (diffusion-weighted imaging [DWI], T2-weighted imaging, pre- and post-contrast T1-weighted imaging) and a combination of UMRI (DWI and pre-contrast T1-weighted imaging) and DBT. We calculated per-lesion sensitivity of CEMRI and UMRI + DBT, and the agreement between CEMRI, UMRI and DBT versus pathology in assessing cancer size (Bland-Altman analysis). Logistic regression was performed to assess features predictive of cancer missing. ResultsWe included 70 lesions (64% invasive BC, 36% ductal carcinoma in situ or invasive BC with in situ component). UMRI + DBT showed lower sensitivity (86–89%) than CEMRI (94–100%), with a significant difference for the most experienced reader only (p = 0.008). False-positives were fewer with UMRI + DBT (4–5) than with CEMRI (18–25), regardless of the reader (p = 0.001–0.005). For lesion size, UMRI showed closer limits of agreement with pathology than CEMRI or DBT. Cancer size ≤1 cm was the only independent predictor for cancer missing for both imaging strategies (Odds ratio 8.62 for CEMRI and 19.16 for UMRI + DBT). ConclusionsUMRI + DBT showed comparable sensitivity and less false-positives than CEMRI in the preoperative assessment of BC. UMRI was the most accurate tool to assess cancer size.

278P - Correlation between radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer and pathologic complete response and their impact in recurrence-free survival

BackgroundPatients with early breast cancer (EBC) achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) have a favorable prognosis. Breast surgery and more chemotherapy treatment might be avoided in patients in whom the presence of residual tumor can be ruled out with high confidence. We investigated the diagnostic accuracy of contrast enhanced MRI (CE-MRI) in predicting pCR and long-term outcome after NACT. MethodsPatients with EBC, who had undergone CE-MRI before and after NACT, were retrospectively analyzed (n=421), and regarding to the absence (radiologic complete remission; rCR) or presence (no-rCR) of residual contrast enhancement. ResultsOverall rCR and pCR rates were 35% (147/421) and 36% (154/421), respectively. We found a total of 70% (101/144) of rCR corresponded to a pCR (meaning the positive predictive value - PPV). In contrast, in 80% (219/272) of patients, residual tumor observed on MRI was pathologically confirmed (meaning the negative predictive value - NPV). Sensitivity to detect a pCR was 65% (101/154), while specificity to detect residual tumor and accuracy were 83% (219/262); and 76% (320/421), respectively. The PPV was significantly lower in luminal compared to HER2 positive and triple negative tumors (10/34 = 29 % vs. 50/62 = 81% and 41/48 = 85% respectively). The concordance between rCR and pCR was moderate (Cohen’s kappa − 0.5) but with low level in luminal tumors (Coheńs Kappa – 0.3). In multivariate analysis both assessments were significantly associated with disease free survival (rCR : HR:0,344 P=0.001; pCR : HR : 0,154 P=0.000), but not in luminal tumors (rCR : HR:0,813 P=0.692; pCR : HR : 0,326 P=0.275). ConclusionsThe accuracy of preoperative CE-MRI to predict pCR after NACT for EBC is moderate but in luminal tumors did not accurately predict pCR. However, rCR was strongly associated with favorable RFS, especially in HER2 positive and triple negative breast cancer tumors. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study.

The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorithms due to the low availability of radiological images. We tried to investigate the feasibility of predicting WHO grades of PNET on contrast-enhanced magnetic resonance (MR) images by deep learning algorithms. Ninety-six patients with PNET underwent preoperative contrast-enhanced MR imaging. Fivefold cross-validation was used in which five iterations of training and validation were performed. Within every iteration, on the training set augmented by synthetic images generated from generative adversarial network (GAN), a convolutional neural network (CNN) was trained and its performance was evaluated on the paired internal validation set. Finally, the trained CNNs from cross-validation and their averaged counterpart were separately assessed on another ten patients from a different external validation set. Averaging the results across the five iterations in the cross-validation, for the CNN model, the average accuracy was 85.13% ± 0.44% and micro-average AUC was 0.9117 ± 0.0053. Evaluated on the external validation set, the average accuracy of the five trained CNNs ranges between 79.08 and 82.35%, and the range of micro-average AUC was between 0.8825 and 0.8932. The average accuracy and micro-average AUC of the averaged CNN were 81.05% and 0.8847, respectively. Synthetic images generated from GAN could be used to alleviate the difficulty of radiological image collection for uncommon disease like PNET. With the help of GAN, the CNN showed the potential to predict the WHO grades of PNET on contrast-enhanced MR images.

Prediction for early recurrence of intrahepatic mass-forming cholangiocarcinoma: quantitative magnetic resonance imaging combined with prognostic immunohistochemical markers

BackgroundPartial hepatectomy is the first option for intrahepatic mass-forming cholangiocarcinoma (IMCC) treatment, which would prolong survival. The main reason for the poor outcome after curative resection is the high incidence of early recurrence (ER). The aim of this study was to investigate the combined predictive performance of qualitative and quantitative magnetic resonance imaging (MRI) features and prognostic immunohistochemical markers for the ER of IMCC.MethodsForty-seven patients with pathologically proven IMCC were enrolled in this retrospective study. Preoperative contrast-enhanced MRI and post-operative immunohistochemical staining of epidermal growth factor receptors (EGFR), vascular endothelial growth factor receptor (VEGFR), P53 and Ki67 were performed. Univariate analysis identified clinic-radiologic and pathological risk factors of ER. Radiomics analysis was performed based on four MRI sequences including fat suppression T2-weighted imaging (T2WI/FS), arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) contrast enhanced imaging. A clinicoradiologic-pathological (CRP) model, radiomics model, and combined model were developed. And ROC curves were used to explore their predictive performance for ER stratification.ResultsEnhancement patterns and VEGFR showed significant differences between the ER group and non-ER group (P = 0.001 and 0.034, respectively). The radiomics model based on AP, PVP and DP images presented superior AUC (0.889, 95% confidence interval (CI): 0.783–0.996) among seven radiomics models with a sensitivity of 0.938 and specificity of 0.839. The combined model, containing enhancement patterns, VEGFR and radiomics features, showed a preferable ER predictive performance compared to the radiomics model or CRP model alone, with AUC, sensitivity and specificity of 0.949, 0.875 and 0.774, respectively.ConclusionsThe combined model was the superior predictive model of ER. Combining qualitative and quantitative MRI features and VEGFR enables ER prediction, thus facilitating personalized treatment for patients with IMCC.

Benefits of MR Diffusion Tensor Imaging and Fiber Tractography Over Conventional MRI in Preoperative Evaluation of Pediatric Brain Tumors

AbstractBackground: Diffusion tensor imaging (DTI) is a nonin-vasive magnetic resonance imaging (MRI) modality which accurately shows the location and orientation of subcortical white matter (WM) tracts. The surgeon benefits from DTI as it depicts the relationship of the intra-axial tumor to local WM tracts in multiple planes.Aim of Study: The purpose of this study is to evaluate the role of MR diffusion tensor imaging and fiber tractography and its benefits over conventional MRI in accurate preoperative localization of important eloquent areas in pediatric brain tissue and their relation to the brain tumor. The aim of the study is to help the surgeon in planning his surgical corridor which causes the least damage and minimizes the postoperative neurological deficits.Patients and Methods: A total of fifty patients with brain tumors were included in this study using pre-operative contrast-enhanced magnetic resonance imaging and DTI fiber tractog-raphy for preoperative localization of important white matter tracts. Diffusion tensor imaging, by improving the recognition and characterization of white matter tracts, offers a glimpse into the brain microstructure at a scale that is not easily accessible with other modalities.Results: The extent of white matter pathway involvement was clearly identified in all patients by using color-coded DT imaging maps and MR Tractography.The study revealed that the most common tract involve-ment pattern detected was pattern (displacement). It was seen in 36 cases representing 72% of all cases. Pattern I (not affected) was detected in 6 cases representing 12%. Pattern III (edematous) was detected in 11 cases representing 22%. Pattern IV (partially disrupted) was detected in 13 cases representing 26%. Pattern V (destroyed) was detected in 3 cases representing 6%. Combined pattern of affection has been detected in most of the cases.Conclusion: The effect of cerebral neoplasms on white matter pathways is not precisely understood with the aid of standard diagnostic modalities. Diffusion-tensor imaging allowed for the preoperative identification of white matter pathways involved by tumor but the preoperative planning should be optimally complemented by various intraoperative procedures in order to insure a good neurological outcome of the surgery and valid functional prognosis.

Role of Diffusion Tensor Imaging in Brain Tumor Surgery.

Object:The objective is to evaluate the role of diffusion tensor imaging (DTI) in intra-axial brain tumor cases (gliomas and metastasis). To preoperatively assess the integrity and location of white matter (WM) tracts and plan the surgical corridor to cause least damage to the WM tracts with minimum postoperative new neurological deficits.Materials and Methods:A total of 34 patients were included in this study. Pre-operative contrast-enhanced magnetic resonance imaging and DTI scans of the patients were taken into consideration. Pre- and post-operative neurological examinations were performed and the outcome was assessed.Results:Preoperative planning of surgical corridor and extent of resection were planned so that maximum possible resection could be achieved without disturbing the WM tracts. DTI indicated the involvement of fiber tracts. A total of 21 (61.7%) patients had a displacement of tracts only and they were not invaded by tumor. A total of 11 (32.3%) patients had an invasion of tracts by the tumor, whereas in 4 (11.7%) patients the tracts were disrupted. Postoperative neurologic examination revealed deterioration of motor power in 4 (11.7%) patients, deterioration of language function in 3 (8.82%) patients, and memory in one patient. Total resection was achieved in 11/18 (61.1%) patients who had displacement of fibers, whereas it was achieved in 5/16 (31.2%) patients when there was infiltration/disruption of tracts.Conclusion:DTI provides crucial information regarding the infiltration of the tract and their displaced course due to the tumor. This study indicates that it is a very important tool for the preoperative planning of surgery. The involvement of WM tracts is a strong predictor of the surgical outcome.