The supracerebellar infratentorial approach in pineal region tumors: Technique and outcome in an underprivileged setting

Background: Pineal region tumors represent 1.5–8.5% of the pediatric brain tumors. Management includes endoscopic third ventriculostomy and biopsy in cases presenting with hydrocephalus. In addition, surgical resection provides survival advantage in selected cases. The supracerebellar infratentorial approach is a widely preferred approach for such region.Methods: After approval of the local ethics committee of Alexandria University and acquisition of the appropriate formal consents according to the committee’s standards, we have reviewed the records of fifteen cases presenting with pineal region tumors in Alexandria main university hospital from 2013 to 2016. The mean age at the diagnosis was 14 years (2–54 years). All cases had supracerebellar infratentorial approach for surgical resection. Follow up period was from 12 to 59 months.Results: All 15 cases presented with hydrocephalus and increased intracranial pressure manifestations. Out of the 15 cases, 3 cases were germ-cell tumors, 2 cases were pineoblastomas, one parenchymal tumor with intermediate differentiation (PPID), one pineocytoma, 2 cases were anaplastic ependymomas and 6 cases were astrocytomas. Gross total resection (GTR) was achieved in 4 cases, subtotal resection was achieved in 7 cases and partial resection in 4 cases. Major surgical complications included severe postoperative cerebellar edema in 2 cases that required further decompression and hemorrhage in one case that has been managed conservatively.Conclusion: In Alexandria university, the supracerebellar infratentorial approach is considered a safe approach with minimal morbidity and no surgery related mortality.Keywords: Pineal region tumors, Tectal tumors, Supracerebellar infratentorial approac

Ultrastructure of the anterior adhesive apparatus of the gill parasite Macrogyrodactylus clarii and skin parasite M. congolensis (Monogenea; Gyrodactylidae) from the catfish Clarias gariepinus

Transmission electron microscopy (TEM) was used for the first time to study the anterior adhesive apparatus of the monogeneans Macrogyrodactylus clarii Gussev, 1961 and M. congolensis (Prudhoe, 1957) Yamaguti, 1963 inhabiting gills and skin respectively of the same catfish Clarias gariepinus. Despite the different microhabitats occupied by these parasites, the present study revealed that they have a similar anterior adhesive system. In both parasites, the anterior adhesive apparatus consists of three types of gland cells: G1 cells that produce rod-shaped bodies (S1), G2 cells manufacture irregularly shaped bodies (S2) and G3 cells form mucoid-like secretions (S3). In the cytoplasm of G1 cells, a single layer of microtubules encloses each developing rod-shaped body. A unique feature of S1 secretory bodies is that some fully developed S1 bodies are attached to each other, forming large condensed globules in the cytoplasm of G1 gland cells and terminal portion of the G1 ducts, but none were detected in the adhesive sacs outside the ducts. In the adhesive sacs, G1 ducts open with multiple apertures whereas each of the G2 and G3 ducts have a single opening. The adhesive sacs are lined with two types of tegument (st1 and st2). A third tegument type (st3) connects the st2 tegument with the general body tegument. Only st1 has microvilli. Each adhesive sac is provided with a spike-like sensillum and single uniciliated sense organ. The possible functions of microvilli in increasing the surface area and assistance in spreading and mixing of the adhesive secretion, and the role of sense organs associated with the adhesive sacs are discussed

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics

BACKGROUND Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. METHODS A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. RESULTS IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. CONCLUSION NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others

NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics.

BACKGROUND Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. METHODS A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. RESULTS IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. CONCLUSION NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Dimensions of Agile Maturity in CS Education

Agile software development is becoming a matured, effective approach and has wide acceptance according to the recently published trends. Due to its success, agile practices have moved into other disciplines including Computing Education. Most of the computer science academic programs are currently rigid and use waterfall process model in delivery. Lightweight process framework like Agile is recommended to computer science education in order to improve quality and reacting to changes and industry requirements. This paper discusses and presents a framework to adopting and evaluating agile practices in computer science education