Search

Your search keyword '"Lim, Joon Seok"' showing total 346 results
Start Over Author "Lim, Joon Seok" Publication Type Academic Journals
346 results on '"Lim, Joon Seok"'

6. Determinants of clinical outcomes of gastric cancer patients treated with neoadjuvant chemotherapy: a sub-analysis of the PRODIGY study

Author

Kim, Hyung-Don, Lee, Jong Seok, Park, Young Soo, Yook, Jeong Hwan, Noh, Sung Hoon, Park, Young-Kyu, Kim, Young-Woo, Oh, Sang Cheul, Kim, Jong Gwang, Ryu, Min-Hee, Cheong, Jae-Ho, Kim, HyunKi, Lim, Joon Seok, Lee, Jae-Hyuk, Heo, Suk Hee, Kim, Jin Young, Heo, Mi Hwa, Park, Young Iee, Kim, In-Ho, and Kang, Yoon-Koo

Published
2022
Full Text
View/download PDF

17. Phase II trial of preoperative sequential chemotherapy followed by chemoradiotherapy for high-risk gastric cancer

Author

Kim, Hyo Song, Koom, Woong Sub, Baek, Song-Ee, Kim, Hyoung-Il, Jung, Minkyu, Beom, Seung-Hoon, Kang, Beodeul, Kim, Hyunki, Chang, Jee Suk, Choi, Yoon Young, Son, Taeil, Cheong, Jae-Ho, Noh, Sung Hoon, Kim, Eun Hye, Park, Jun Chul, Shin, Sung Kwan, Lee, Sang Kil, Lee, Yong Chan, Shin, Su-Jin, Chung, Hyunsoo, Jung, Inkyung, Chung, Hyun Cheol, Lim, Joon Seok, Hyung, Woo Jin, and Rha, Sun Young

Published
2019
Full Text
View/download PDF

20. Upfront radical surgery with total mesorectal excision followed by adjuvant FOLFOX chemotherapy for locally advanced rectal cancer (TME-FOLFOX): an open-label, multicenter, phase II randomized controlled trial

Author

Lee, Jii Bum, Kim, Han Sang, Jung, Inkyung, Shin, Sang Joon, Beom, Seung Hoon, Chang, Jee Suk, Koom, Woong Sub, Kim, Tae Il, Hur, Hyuk, Min, Byung Soh, Kim, Nam Kyu, Park, Sohee, Jeong, Seung-Yong, Baek, Jeong-Heum, Kim, Seon Hahn, Lim, Joon Seok, Lee, Kang Young, and Ahn, Joong Bae

Published
2020
Full Text
View/download PDF

24. Marked Loss of Muscle, Visceral Fat, or Subcutaneous Fat After Gastrectomy Predicts Poor Survival in Advanced Gastric Cancer: Single-Center Study from the CLASSIC Trial

Author

Park, Hyung Soon, Kim, Hyo Song, Beom, Seung Hoon, Rha, Sun Young, Chung, Hyun Cheol, Kim, Jee Hung, Chun, You Jin, Lee, Si Won, Choe, Eun-Ah, Heo, Su Jin, Noh, Sung Hoon, Hyung, Woo Jin, Cheong, Jae-Ho, Kim, Hyoung-Il, Son, Taeil, Lim, Joon Seok, Baek, Song-Ee, and Jung, Minkyu

Published
2018
Full Text
View/download PDF

29. Active involvement of patients, radiation oncologists, and surgeons in a multidisciplinary team approach: Guiding local therapy in recurrent, metastatic rectal cancer.

Author

Choi, Seo Hee, Yang, Gowoon, Koom, Woong Sub, Yang, Seung Yoon, Kim, Seung‐Seob, Lim, Joon Seok, Kim, Han Sang, Shin, Sang Joon, and Chang, Jee Suk

Subjects
RECTAL cancer, PATIENT participation, METASTASIS, ONCOLOGISTS, SURGEONS, TELEPHONE calls
Abstract

Background: Despite the extensive implementation of an organized multidisciplinary team (MDT) approach in cancer treatment, there is little evidence regarding the optimal format of MDT. We aimed to investigate the impact of patient participation in MDT care on the actual application rate of metastasis‐directed local therapy. Methods: We identified all 1211 patients with locally advanced rectal cancer treated with neoadjuvant radiochemotherapy at a single institution from 2006 to 2018. Practice patterns, tumor burden and OMD state were analyzed in recurrent, metastatic cases. Results: With a median follow‐up of 60.7 months, 281 patients developed metastases, and 96 (34.2%), 92 (32.7%), and 93 (33.1%) patients had 1, 2–5, and >5 lesions, respectively. In our study, 27.1% were managed in the MDT clinic that mandated the participation of at least four to five board‐certified multidisciplinary experts and patients in decision‐making processes, while the rest were managed through diverse MDT approaches such as conferences, tumor board meetings, and discussions conducted via phone calls or email. Management in MDT clinic was significantly associated with more use of radiotherapy (p = 0.003) and more sessions of local therapy (p < 0.001). At the time of MDT clinic, the number of lesions was 1, 2–5, and >5 in 9 (13.6%), 35 (53.1%), and 19 (28.8%) patients, respectively. The most common states were repeat OMD (28.8%) and de novo OMD (27.3%), followed by oligoprogression (15%) and induced OMD (10.6%). Conclusion: Our findings suggest that active involvement of patients and radiation oncologists, and surgeons in MDT care has boosted the probability of using local therapies for various types of OMD throughout the course of the disease. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

43. Machine Learning-Based Mortality Prediction Model for Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU).

Author

Ko, Ryoung-Eun, Cho, Jaehyeong, Shin, Min-Kyue, Oh, Sung Woo, Seong, Yeonchan, Jeon, Jeongseok, Jeon, Kyeongman, Paik, Soonmyung, Lim, Joon Seok, Shin, Sang Joon, Ahn, Joong Bae, Park, Jong Hyuck, You, Seng Chan, and Kim, Han Sang

Subjects
INTENSIVE care units, CRITICALLY ill, PATIENTS, MACHINE learning, APACHE (Disease classification system), CANCER patients, DESCRIPTIVE statistics, RESEARCH funding, PREDICTION models, TUMORS, SENSITIVITY & specificity (Statistics)
Abstract

Simple Summary: This study describes a new machine-learning-based 28-day mortality prediction model in adult cancer patients admitted to the intensive care unit (ICU). A total of 6900 patients in three patient cohorts were used for the development, internal validation, and external validation, respectively, leading to the generation of a reliable model with high sensitivity and specificity. The CanICU uses nine variables that can be easily obtained in a practical ICU, with the potential benefit of critical care and avoiding unnecessary suffering. Furthermore, this is the largest patient cohort for developing a cancer patient-specific model. CanICU offers improved performance for predicting short- and long-term mortality in critically ill cancer patients admitted to the ICU. CanICU can help physicians determine how to allocate ICU care for patients with cancer according to objective mortality risk. Background: Although cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. This study describes a new ML-based mortality prediction model for critically ill cancer patients admitted to ICU. Patients and Methods: We developed CanICU, a machine learning-based 28-day mortality prediction model for adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (n = 766), Yonsei Cancer Center (YCC, n = 3571), and Samsung Medical Center in Korea (SMC, n = 2563) from 2 January 2008 to 31 December 2017. The accuracy of CanICU was measured using sensitivity, specificity, and area under the receiver operating curve (AUROC). Results: A total of 6900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and a 1-year mortality of 30.0%/36.6%/58.5% in the YCC, SMC, and MIMIC-III cohort. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had 96% sensitivity/73% specificity with the area under the receiver operating characteristic (AUROC) of 0.94 for 28-day, showing better performance than current prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE) or Sequential Organ Failure Assessment (SOFA) score. Application of CanICU in two external data sets across the countries yielded 79–89% sensitivity, 58–59% specificity, and 0.75–0.78 AUROC for 28-day mortality. The CanICU score was also correlated with one-year mortality with 88–93% specificity. Conclusion: CanICU offers improved performance for predicting mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification to allocate ICU care for cancer patients. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results