Yun Suhk Suh
Seoul National University · Medicine
About the Lab
Professor Yun Suhk Suh's research lab specializes in translational oncology and gastrointestinal cancer research, with a focus on improving clinical outcomes in gastric cancer through personalized medicine and preclinical modeling. The lab investigates prognostic biomarkers, tumor evolution in patient-derived xenografts (PDXs), and the development of predictive nomograms for survival after D2 gastrectomy. Key research directions include identifying molecular drivers of lymph node metastasis, evaluating cost-effectiveness of screening programs, and optimizing surgical strategies for early gastric cancer.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We developed a nomogram predicting 5- and 10-year overall survival after D2 gastrectomy for gastric cancer. Validation using the SNUH and CIAH data sets revealed good discrimination and calibration, suggesting good clinical utility. The nomogram improved individualized predictions of survival.
The screening group had significantly lower medical care expenses and showed a significantly better prognosis than the nonscreening group. On the basis of the GDP per capita, the NCSP for gastric cancer was cost-effective for treatment prognosis.
Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substan
For middle-third EGC, LAPPG can be considered as a better treatment option than LADG in terms of nutritional advantage and lower incidence of gallstone.
To evaluate the postoperative prognosis of AEJ within the stomach, AEJ II and AEJ III should be considered a part of gastric cancer irrespective of EGJ involvement.
DNA microarray analysis and validation by RT-PCR and TMA showed that overexpression of PAI-1 is related to aggressive LN metastasis in AGC.
After carefully considering indications, uDelta can be a feasible and can be a reproducible reconstruction method after SIDG in early gastric cancer.
LPPG can be used as an alternative surgical option for cT1N0M0 gastric cancer in the mid portion of the stomach.
Based on rapid development of laparoscopic techniques and instruments, single-incision laparoscopic surgery (SILS) is expected to be the next step of "more" minimally invasive surgery. A few institutions gradually started to report their experience of single incision gastrectomy (SIG) for gastric cancer, but it is still difficult to accept that SIG can be performed as a popular procedure because of its technical difficulty. For wide adoption of SIG, the simplicity, safety and reproducibility of
The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experi
The laparoscopic harvesting of an omental flap was successfully performed in eight patients without conversion to open surgery. The mean time to the initial detection of ICG-enhanced fluorescence uptake was 3.25 ± 1.16 minutes. On intraoperative Doppler ultrasonography, a pulseless area ≥10% was detected in five patients (62.5%). However, NIR imaging revealed no patients had an ischemic portion ≥10%. There were no ICG-related intraoperative or postoperative complications. All patients showed pat
The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.
Research Areas
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