Notes

Conus medullaris participation throughout demyelinating ailments of the CNS: A relative study.
Pancreatic ductal adenocarcinoma (PDAC) is becoming increasingly more common. Treatment for PDAC is dependent not only on stage at diagnosis, but complex anatomical relationships. Recently, the therapeutic approach to this disease has shifted from upfront surgery for technically resectable lesions to a neoadjuvant therapy first approach. Selecting an appropriate regimen and determining treatment response is crucial for optimal oncologic outcome, especially since radiographic imaging has proven unreliable in this setting. Tumor biomarkers have the potential to play a key role in treatment planning, treatment monitoring, and surveillance as an adjunct laboratory test. In this review, we will discuss common chemotherapeutic options, mechanisms of resistance, and potential biomarkers for PDAC. The aim of this paper is to present currently available biomarkers for PDAC and to discuss how these markers may be affected by neoadjuvant chemotherapy treatment. Understanding current chemotherapy regiments and mechanism of resistance can help us understand which markers may be most affected and why; therefore, determining to what ability we can use them as a marker for treatment progression, prognosis, or potential relapse.Objectives This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1 clinical data, Model 2 overall stage, Model 3 radiomics, Model 4 radiomics + overall stage, Model 5 radiomics + overall stage + Epstein-Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan-Meier method was applied for the survival analysis. Results Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.Purpose This study aimed to develop a volumetric independent dose calculation (vIDC) system for verification of the treatment plan in image-guided adaptive brachytherapy (IGABT) and to evaluate the feasibility of the vIDC in clinical practice with simulated cases. Methods The vIDC is based on the formalism of TG-43. Four simulated cases of cervical cancer were selected to retrospectively evaluate the dose distributions in IGABT. Some reference point doses, such as points A and B and rectal points, were calculated by vIDC using absolute coordinate. The 3D dose volume was also calculated to acquire dose-volume histograms (DVHs) with grid resolutions of 1.0 × 1.0 (G1.0), 2.5 × 2.5 (G2.5), and 0.5 × 0.5 mm2 (G0.5). Dosimetric parameters such as D90% and D2cc doses covering 90% of the high-risk critical target volume (HR-CTV) and 2 cc of the organs at risk (OARs) were obtained from DVHs. D90% also converted to equivalent dose in 2-Gy fractions (EQD2) to produce the same radiobiological effect as external beam radint dose verification system for verification of the treatment plan in IGABT. We confirmed that the vIDC is suitable for second-check dose validation of the TPS under various conditions.Background and Purpose Lymph node status is a key factor for the recommendation of organ preservation for patients with locally advanced rectal cancer (LARC) following neoadjuvant therapy but generally confirmed post-operation. This study aimed to preoperatively predict the lymph node status following neoadjuvant therapy using multiparametric magnetic resonance imaging (MRI)-based radiomic signature. Materials and Methods A total of 391 patients with LARC who underwent neoadjuvant therapy and TME were included, of which 261 and 130 patients were allocated to the primary cohort and the validation cohort, respectively. The tumor area, as determined by preoperative MRI, underwent radiomics analysis to build a radiomic signature related to lymph node status. Two radiologists reassessed the lymph node status on MRI. The radiomic signature and restaging results were included in a multivariate analysis to build a combined model for predicting the lymph node status. Stratified analyses were performed to test the predictive ability of the combined model in patients with post-therapeutic MRI T1-2 or T3-4 tumors, respectively. Results The combined model was built in the primary cohort, and predicted lymph node metastasis (LNM+) with an area under the curve of 0.818 and a negative predictive value (NPV) of 93.7% were considered in the validation cohort. Stratified analyses indicated that the combined model could predict LNM+ with a NPV of 100 and 87.8% in the post-therapeutic MRI T1-2 and T3-4 subgroups, respectively. Conclusion This study reveals the potential of radiomics as a predictor of lymph node status for patients with LARC following neoadjuvant therapy, especially for those with post-therapeutic MRI T1-2 tumors.Introduction O6 -methylguanine-methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase (IDH) mutation status are important prognostic factors for patients with glioblastoma. There are conflicting reports about a differential topographical distribution of glioblastoma with vs. without MGMT promoter methylation, possibly caused by molecular heterogeneity in glioblastoma populations. find more We initiated this study to re-evaluate the topographical distribution of glioblastoma with vs. without MGMT promoter methylation in light of the updated WHO 2016 classification. Methods Preoperative T2-weighted/FLAIR and postcontrast T1-weighted MRI scans of patients aged 18 year or older with IDH wildtype glioblastoma were collected. Tumors were semi-automatically segmented, and the topographical distribution between glioblastoma with vs. without MGMT promoter methylation was visualized using frequency heatmaps. Then, voxel-wise differences were analyzed using permutation testing with Threshold Free Cluster Enhancement.
My Website: https://www.selleckchem.com/products/ldk378.html