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Seung-Mo Hong

Researcher at University of Ulsan

Publications -  383
Citations -  21628

Seung-Mo Hong is an academic researcher from University of Ulsan. The author has contributed to research in topics: Pancreatic cancer & Cancer. The author has an hindex of 53, co-authored 361 publications receiving 17907 citations. Previous affiliations of Seung-Mo Hong include University of Virginia Health System & Johns Hopkins University School of Medicine.

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Lesion localization in patients with hyperparathyroidism using double-phase Tc-99m MIBI parathyroid scintigraphy

TL;DR: Evaluating the diagnostic usefulness of double-phase Tc-99m MIBI parathyroid scintigraphy with single photon emission computed tomography (SPECT) in patients with hyper-parathyroidism found both adenomas and hyperplasias showed significantly increased oxyphil cell contents compared with normalParathyroid glands.
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Identification of Outlying Observations with Quantile Regression for Censored Data

TL;DR: Three outlier detection algorithms based on censored quantile regression are proposed, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches.
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Postresection prognosis of combined hepatocellular carcinoma-cholangiocarcinoma according to the 2010 World Health Organization classification: single-center experience of 168 patients.

TL;DR: In this article, the effects of combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) histology, according to the 2010 World Health Organization (WHO) classification, on patient prognosis were investigated.
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Profiling of conditionally reprogrammed cell lines for in vitro chemotherapy response prediction of pancreatic cancer

TL;DR: In this article, conditionally reprogrammed cells (CRCs) were used to establish patient-derived models for pancreatic ductal adenocarcinoma (PDAC) and perform genetic analysis with responses to anticancer drug.
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A Clinically Applicable 24-Protein Model for Classifying Risk Subgroups in Pancreatic Ductal Adenocarcinomas using Multiple Reaction Monitoring-Mass Spectrometry

TL;DR: In this paper, the authors identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: stable, exocrine-like; activated, and extracellular matrix (ECM) remodeling.