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Teik Hin Tan,Zanariah Hussein,Fathinul Fikri Ahmad Saad,Ibrahim Lutfi Shuaib 대한핵의학회 2015 핵의학 분자영상 Vol.49 No.2
Purpose To evaluate the diagnostic performance of 68Ga- DOTATATE 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT), 18F-FDGPET/CTand 131I-MIBG scintigraphy in themapping of metastatic pheochromocytoma and paraganglioma. Materials and Methods Seventeen patients (male=8, female=9; age range, 13–68 years) with clinically proven or suspicious metastatic pheochromocytoma or paraganglioma were included in this prospective study. Twelve patients underwent all three modalities, whereas five patients underwent 68Ga-DOTATATE and 131I-MIBG without 18FFDG. A composite reference standard derived from anatomical and functional imaging findings, along with histopathological information, was used to validate the findings. Results were analysed on a per-patient and on per-lesion basis. Sensitivity and accuracy were assessed using McNemar’s test. Results On a per-patient basis, 14/17 patients were detected in 68Ga-DOTATATE, 7/17 patients in 131I-MIBG, and 10/12 patients in 18F-FDG. The sensitivity and accuracy of 68Ga- DOTATATE, 131I-MIBG and 18F-FDG were (93.3 %, 94.1 %), (46.7 %, 52.9 %) and (90.9 %, 91.7 %) respectively. On a per-lesion basis, an overall of 472 positive lesions were detected; of which 432/472 were identified by 68Ga-DOTATA TE, 74/472 by 131I-MIBG, and 154/300 (patient, n=12) by 18F-FDG. The sensitivity and accuracy of 68Ga-DOTATATE, 131I-MIBG and 18F-FDG were (91.5 %, 92.6 % p<0.0001), (15.7 %, 26.0 % p<0.0001) and (51.3 %, 57.8 % p<0.0001) respectively. Discordant lesions were demonstrated on 68Ga- DOTATATE, 131I-MIBG and 18F-FDG. Conclusions Ga-DOTATATE PET/CT shows high diagnostic accuracy than 131I-MIBG scintigraphy and 18F-FDG PET/ CT in mapping metastatic pheochromocytoma and paraganglioma.
Habeeb Ibrahim Abdul Razack,Sam Mathew,Fathinul Fikri Ahmad Saad,Saleh A. Alqahtani 한국과학학술지편집인협의회 2021 Science Editing Vol.8 No.2
The flood of research output and increasing demands for peer reviewers have necessitated the intervention of artificial intelligence (AI) in scholarly publishing. Although human input is seen as essential for writing publications, the contribution of AI slowly and steadily moves ahead. AI may redefine the role of science communication experts in the future and transform the scholarly publishing industry into a technology-driven one. It can prospectively improve the quality of publishable content and identify errors in published content. In this article, we review various AI and other associated tools currently in use or development for a range of publishing obligations and functions that have brought about or can soon leverage much-demanded advances in scholarly communications. Several AI-assisted tools, with diverse scope and scale, have emerged in the scholarly market. AI algorithms develop summaries of scientific publications and convert them into plain-language texts, press statements, and news stories. Retrieval of accurate and sufficient information is prominent in evidence-based science publications. Semantic tools may empower transparent and proficient data extraction tactics. From detecting simple plagiarism errors to predicting the projected citation impact of an unpublished article, AI’s role in scholarly publishing is expected to be multidimensional. AI, natural language processing, and machine learning in scholarly publishing have arrived for writers, editors, authors, and publishers. They should leverage these technologies to enable the fast and accurate dissemination of scientific information to contribute to the betterment of humankind.