Relationship of Angulation of Maxillary Impacted Canines with Maxillary Lateral Incisor Root Resorption

Objective: To assess the relationship of ɑ and ß angles of maxillary impacted canines with maxillary lateral incisor root resorption. Material and Methods: For this, 40 patients (between 11 and 45 years) with impacted maxillary canines presenting to a private orthodontic clinic were evaluated.20% of the cases were male and 80% were female. The ɑ and ß angles were measured on panoramic radiographs to determine the angulation of impacted canine. CBCT scans had been taken by ProMax 3D CBCT system were used to examine lateral root reorption. The relationship between the size of ɑ and ß angles of impacted maxillary canines and root resorption in the adjacent maxillary lateral incisor was analyzed using SPSS version 22. Results: The mean size of ɑ and ß angles had no significant correlation with the presence/absence, location, or severity of root resorption in the adjacent lateral incisor or the buccolingual position of impacted canine. The ɑ and ß angles in impacted maxillary canines cannot be used to determine the incidence or severity of root resorption in the adjacent lateral incisor. Conclusion: No significant association between ɑ and ß angles and incidence or severity of root resorption in the adjacent lateral incisor was found

Relationship of Angulation of Maxillary Impacted Canines with Maxillary Lateral Incisor Root Resorption

Objective: To assess the relationship of ɑ and ß angles of maxillary impacted canines with maxillary lateral incisor root resorption. Material and Methods: For this, 40 patients (between 11 and 45 years) with impacted maxillary canines presenting to a private orthodontic clinic were evaluated.20% of the cases were male and 80% were female. The ɑ and ß angles were measured on panoramic radiographs to determine the angulation of impacted canine. CBCT scans had been taken by ProMax 3D CBCT system were used to examine lateral root reorption. The relationship between the size of ɑ and ß angles of impacted maxillary canines and root resorption in the adjacent maxillary lateral incisor was analyzed using SPSS version 22. Results: The mean size of ɑ and ß angles had no significant correlation with the presence/absence, location, or severity of root resorption in the adjacent lateral incisor or the buccolingual position of impacted canine. The ɑ and ß angles in impacted maxillary canines cannot be used to determine the incidence or severity of root resorption in the adjacent lateral incisor. Conclusion: No significant association between ɑ and ß angles and incidence or severity of root resorption in the adjacent lateral incisor was found

Current state of pediatric neuro-oncology imaging, challenges and future directions

Imaging plays a central role in neuro-oncology including primary diagnosis, treatment planning, and surveillance of tumors. The emergence of quantitative imaging and radiomics provided an uprecedented opportunity to compile mineable databases that can be utilized in a variety of applications. In this review, we aim to summarize the current state of conventional and advanced imaging techniques, standardization efforts, fast protocols, contrast and sedation in pediatric neuro-oncologic imaging, radiomics-radiogenomics, multi-omics and molecular imaging approaches. We will also address the existing challenges and discuss future directions

Early Effects of Percutaneous Nephrolithotomy on Glomerular Filtration Rate and Determining the Potential Risk Factors Responsible for Acute Postoperative Renal Function Impairment

Background: In this study, the early effects of percutaneous nephrolithotomy (PCNL) on glomerular filtration rate (GFR) was assessed in different postoperative times and likewise, we determined the correlation of different variables with significant postoperative GFR drop  after PCNL.Materials and Methods: Patient records of 486 cases that had undergone PCNL from January 2010 to October 2011 were reviewed retrospectively. GFR in six hours, one, two and three days after PCNL and in the discharge day were calculated and then compared with preoperative level. Correlation between different variables (Perioperative hemorrhage, co-morbidities, previous stone surgery, renal anomaly, number of access, stone burden and location) and risk of acute postoperative renal function impairment (GFR drop greater than 25%) were assessed.Results: Mean preoperative GFR was 87.85±29.41ml/min/1.73m² which decreased to 86.18±28.77, 78.45±28.74, 78.79±26.94, 84.24±29.71 and 86.18±28.77 in 6, 24, 48 and 72 hours after surgery and discharge day post PCNL, respectively. GFR significantly decreased in one and two days after surgery (p value<0.0001 and p value <0.05) but returned to near preoperative values in 3th post PCNL day. Among different variables, only perioperative bleeding (Cut-off point for serum hemoglobin drop was 2.8 mg/dL) was concomitant with significant postoperative renal function impairment.Conclusion: Our findings revealed that co-morbidities, large or multiple stones, multiple punctures and previous history of stone surgery have no significant impact on surgical outcomes. Postoperative GFR returned to near preoperative values in a few days after operation. Avoidance of significant perioperative bleeding is an important point to prevent post PCNL renal insufficiency

Effect of the COVID-19 Pandemic on Seizure Control Status in Patients With Epilepsy

Background: Previous studies have shown that patients with epilepsy (PWE) perceived significant disruption in the quality and provision of care due to the coronavirus disease 2019 (COVID-19) pandemic. The present study aimed to investigate the effect of this pandemic on seizure control status and changes in seizure frequency in PWE. Methods:A consecutive sample of adult PWE registered in the database of Shiraz Epilepsy Center (Shiraz, Iran) was included in the study. In July 2021, phone interviews were conducted with all selected patients. Information such as age, sex, last seizure, seizure type, and frequency during the 12 months before the study, and history of COVID-19 contraction was extracted. The seizure control status of the patients in 2019 (pre-pandemic) was compared with that during the COVID-19 pandemic. Data were analyzed using SPSS software with the Fisher’s exact test and Pearson’s Chi squared test. P Results: A total of 158 patients were included in the study, out of which 62 (39.2%) patients had a stable seizure control status, 47 (29.7%) had fewer seizures, and 50 (31.6%) had more seizures. Breakthrough seizures were reported by 32 (34.4%) patients. Seizure frequency increased in 18 (27.7%) and decreased in 46 (70.7%) patients. Conclusion: Overall, the COVID-19 pandemic has not been a major precipitating factor nor has it affected the seizure control status of PWE. In treated epilepsy, a fluctuating course with periods of seizure freedom followed by relapses is part of its natural history

Mayo Adhesive Probability Score Does Not Have Prognostic Ability in Locally Advanced Renal Cell Carcinoma

Nephrectomy remains standard treatment for renal cell carcinoma (RCC). The Mayo Adhesive Probability (MAP) score is predictive of adherent perinephric fat and associated surgical complexity, and is determined by assessing perinephric fat and stranding. MAP has additionally predicted progression-free survival (PFS), though primarily reported in stage T1-T2 RCC. Here, we examine MAP’s ability to predict overall survival (OS) and PFS in T3-T4 RCC. From our prospectively maintained RCC database, patients that underwent radical nephrectomy (2009-2016) with available abdominal imaging (<90 days preop) and T3/T4 RCC underwent MAP scoring. Survival analyses were conducted with MAP scores as individual (0-5) and dichotomized (0-3 vs 4-5) using Kaplan-Meier method. Multivariable Cox proportional hazard regression models for PFS and OS were built with backward elimination. 141 patients were included. 134 (95%) and 7 (5%) had pT3 and pT4 disease, respectively. 46.1% of patients had an inferior vena cava thrombus. Mean MAP score was 3.22±1.52, with 75 (53%) patients having a score between 0-3 and 66 (47%) having a score of 4-5. Both male gender (p=0.006) and clear cell histology (p=0.012) were associated with increased MAP scores. On Kaplan-Meier and multivariable analysis, no significant associations were identified between MAP and PFS (HR=1.01, 95% CI 0.85-1.20, p=0.93) or OS (HR=1.01, 95% CI 0.84-1.21, p=0.917). In this cohort of patients with locally advanced RCC, high MAP scores were not predictive of worse PFS or OS

Hydatidosis of the Pelvic Cavity: A Big Masquerade

We report and discuss a case of primary hydatidosis of the pelvic cavity in a woman who presented with severe weight loss and abdominal pain. This unusual presentation was initially considered as a tumor process until surgical exploration and microscopic studies confirmed the diagnosis. The gynecologists should be aware of possibility of primary hydatid cyst of the pelvic cavity and should be considered in the differential diagnosis of cystic pelvic masses, especially in areas where the disease is endemic

Evolution and implementation of radiographic response criteria in neuro-oncology

Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024