Peritumoral Enhancement for the Evaluation of Myometrial Invasion in Low-Risk Endometrial Carcinoma on Dynamic Contrast-Enhanced MRI

ObjectivesTo explore the clinical value of subendometrial enhancement (SEE), irregular thin-layered peritumoral early enhancement (ITLPE) and focal irregular peritumoral early enhancement (FIPE) on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for myometrial invasion in patients with low-risk endometrial carcinoma.MethodsSeventy-seven patients with low-risk endometrial carcinoma who preoperatively underwent DCE-MRI were included. Two radiologists independently evaluated and recorded the occurrences of SEE, ITLPE and FIPE on DCE-MRI in all patients. Interobserver agreement was calculated between the two radiologists, and the relationships between SEE, ITLPE, FIPE, and myometrial invasion were analyzed based on histologic findings. For statistically significant findings, the sensitivity and specificity were calculated, and the differences in myometrial invasion evaluations were analyzed. For those with no statistical significance, images were compared with the histopathologic sections.ResultsInter-observer agreement was good (k = 0.80; 95%CI, 0.577–0.955) for SEE, and very good (k = 0.88; 95%CI, 0.761–0.972) (k = 0.86; 95%CI, 0.739–0.973) for ITLPE and FIPE. After consensus, SEE was identified in 12/77 (15.6%) patients; ITLPE and FIPE were found in 53/77 (68.8%) and 30/77 (39.0%) patients, respectively. SEE and ITLPE were significantly correlated with myometrial infiltration (P = 0.000), but FIPE were not (P = 0.725).The sensitivity and specificity of SEE and ITLPE for myometrial invasion in patients with low-risk endometrial carcinoma were 95.0 and 52.9%, and 85.0 and 88.0%, respectively. The area under the curve (AUC) of SEE and ITLPE for myometrial invasion were 0.740 (95%CI, 0.584–0.896), and 0.866 (95%CI, 0.763–0.970), respectively. The sensitivity and specificity were statistically different between SEE and ITLPE for the detection of myometrial invasion (P = 0.031, 0.016). According to the comparison between FIPE and histopathologic findings, the irregular endomyometrial junction was found in 30/77 (38.9%) cases, 24/30 (80.0%) with myometrial infiltration and 6/30 (20.0%) cases without myometrial infiltration.ConclusionsFIPE was the irregular endomyometrial junction. It can be found in patients with or without myometrial infiltration and may lead to the overestimation of myometrial invasion by SEE on DCE-MRI. ITLPE presented high diagnostic performance and specificity for myometrial invasion in patients with low-risk endometrial carcinoma.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h–6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer

Objectives To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients. Methods This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemotherapy, followed by surgery. Response assessment was graded from 1 to 5 according to the tumor regression grade (TRG). Patients were stratified into responders (TRG1 + 2) and non-responders (TRG3 + 4 + 5). 72 radiomics features and vascular permeability parameters were extracted from DCE-MRI. The discriminating performance was assessed with ROC. Decision curve analysis (DCA) was used for comparing three different models. Results This cohort included 82 patients, and 72 tumor radiomics features and vascular permeability parameters acquired from DCE-MRI. mRMR and LASSO were performed to choose the optimized subset of radiomics features, and 3 features were selected to create the radiomics signature that were significantly associated with response (P < 0.001). AUC of combining radiomics signature and DCE-MRI performance in the training (n = 41) and validation (n = 41) cohort was 0.84 (95% CI 0.57–1) and 0.86 (95% CI 0.74–0.97), respectively. This combined model showed the best discrimination between responders and non-responders, and showed the highest positive and positive predictive value in both training set and test set. Conclusions The radiomics features are useful for nCT response prediction in EC patients.

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer

Objective: To investigate the correlations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion histogram parameters and vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) expressions in advanced gastric cancer (AGC).Methods: This retrospective study included 80 pathologically confirmed patients with AGC who underwent DCE-MRI before surgery from February 2017 to May 2021. The DCE-MRI perfusion histogram parameters were calculated by Omni Kinetics software in four quantitative parameter maps. Immunohistochemical methods were used to detect VEGF and EGFR expressions and calculate the immunohistochemical score.Results: VEGF expression was relatively lower in patients with intestinal-type AGC than those with diffuse-type AGC (p &lt; 0.05). For VEGF, Receiver operating characteristics (ROC) curve analysis revealed that Quantile 90 of Ktrans, Meanvalue of Kep and Quantile 50 of Ve provided the perfect combination of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for distinguishing high and low VEGF expression, For EGFR, Skewness of Ktrans, Energy of Kep and Entropy of Vp provided the perfect combination of sensitivity, specificity, PPV and NPV for distinguishing high and low EGFR expression. Ktrans (Quantile 90, Entropy) showed the strongest correlation with VEGF and EGFR in patients with intestinal-type AGC (r = 0.854 and r = 0.627, respectively); Ktrans (Mean value, Entropy) had the strongest correlation with VEGF and EGFR in patients with diffuse-type AGC (r = 0.635 and 0.656, respectively).Conclusion: DCE-MRI perfusion histogram parameters can serve as imaging biomarkers to reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC.