26 research outputs found

    Prevalence and type distribution of high-risk Human Papillomavirus (HPV) in breast cancer : a Qatar based study

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    Human papillomavirus (HPV) has been implicated in the etiology of a variety of human cancers. Studies investigating the presence of high-risk (HR) HPV in breast tissue have generated considerable controversy over its role as a potential risk factor for breast cancer (BC). This is the first investigation reporting the prevalence and type distribution of high-risk HPV infection in breast tissue in the population of Qatar. A prospective comparison blind research study herein reconnoitered the presence of twelve HR-HPV types’ DNA using multiplex PCR by screening a total of 150 fresh breast tissue specimens. Data obtained shows that HR-HPV types were found in 10% of subjects with breast cancer; of which the presence of HPV was confirmed in 4/33 (12.12%) of invasive carcinomas. These findings, the first reported from the population of Qatar, suggest that the selective presence of HPV in breast tissue is likely to be a related factor in the progression of certain cases of breast cancer

    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

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    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.

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    Federated learning enables big data for rare cancer boundary detection.

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    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

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

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    10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

    Federated Learning Enables Big Data for Rare Cancer Boundary Detection

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    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

    Case of the Week: A 51-year-old Man with Headaches and Visual Disturbances

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    Case of the Week for the American Journal of Neuroradiology, with the following description: Sagittal T1WI pre- (A) and postcontrast (B) show a large, lobulated, heterogeneously-enhancing pituitary mass that expands the sella and demonstrates a suprasellar extension. Axial (C) and sagittal (D) T2WI demonstrate diffuse thickening of the calvarium with heterogeneous marrow signal. Axial (E) and coronal (F) noncontrast CT through the level of the parathyroid gland demonstrate hypodense nodules posterior and inferior to the thyroid lobes (arrows)
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