Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods: Hae Dong Lee, Kyung Han Nam, Cheol Min Shin, Hye Seung Lee, Young Hoon Chang, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee, Sang-Hoon Ahn, Hyung-Ho Kim; Cancer Res Treat. 2023;55(4):1240-1249. Published online March 21, 2023; DOI: https://doi.org/10.4143/crt.2022.1330

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Purpose
To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.
Materials and Methods
The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.
Results
LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.
Conclusion
The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

Citations

Citations to this article as recorded by

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis
Kenichi Ishizu, Satoshi Takahashi, Nobuji Kouno, Ken Takasawa, Katsuji Takeda, Kota Matsui, Masashi Nishino, Tsutomu Hayashi, Yukinori Yamagata, Shigeyuki Matsui, Takaki Yoshikawa, Ryuji Hamamoto
npj Digital Medicine.2025;[Epub] CrossRef
The artificial intelligence revolution in gastric cancer management: clinical applications
Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang
Cancer Cell International.2025;[Epub] CrossRef
Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population
Qian Li, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Yuan Tian, Mengchao Sheng, Wei Peng, Yongyou Wu
BMJ Open.2025; 15(3): e098476. CrossRef
Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach
Y. Zou, Q. Mao, Z. Zhao, X. Zhou, Y. Pan, Z. Zuo, W. Zhang
Clinical Radiology.2024; 79(2): e211. CrossRef
eCura and W-eCura: different scores, different populations, same goal
Rui Morais, Diogo Libanio, João Santos-Antunes
Gut.2024; 73(11): e29. CrossRef
A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
Minoru Kato, Yoshito Hayashi, Ryotaro Uema, Takashi Kanesaka, Shinjiro Yamaguchi, Akira Maekawa, Takuya Yamada, Masashi Yamamoto, Shinji Kitamura, Takuya Inoue, Shunsuke Yamamoto, Takashi Kizu, Risato Takeda, Hideharu Ogiyama, Katsumi Yamamoto, Kenji Aoi,
Gastric Cancer.2024; 27(5): 1069. CrossRef
The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide
Amin Zadeh Shirazi, Morteza Tofighi, Alireza Gharavi, Guillermo A. Gomez
Technology in Cancer Research & Treatment.2024;[Epub] CrossRef
Computed Tomography-Based Radiomics Analysis of Different Machine Learning Approaches for Differentiating Pulmonary Sarcomatoid Carcinoma and Pulmonary Inflammatory Pseudotumor
An-Lin Zhang, Yan-Mei Fu, Zhi-Yang He
Iranian Journal of Radiology.2024;[Epub] CrossRef
Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations
Ahao Wu, Tengcheng Hu, Chao Lai, Qingwen Zeng, Lianghua Luo, Xufeng Shu, Pan Huang, Zhonghao Wang, Zongfeng Feng, Yanyan Zhu, Yi Cao, Zhengrong Li
Cancer Medicine.2024;[Epub] CrossRef

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p27 Loss Is Associated with Poor Prognosis in Gastroenteropancreatic Neuroendocrine Tumors: Hee Sung Kim, Hye Seung Lee, Kyung Han Nam, Jiwoon Choi, Woo Ho Kim; Cancer Res Treat. 2014;46(4):383-392. Published online July 17, 2014; DOI: https://doi.org/10.4143/crt.2013.102

Abstract PDF PubReader ePub

Purpose
Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) represent a heterogeneous disease group originating from the neuroendocrine cells. Identification of prognostic markers, related to neuroendocrine tissue-selective tumorigenesis, is necessary to find therapeutic targets.
Materials and Methods
A total of 327 patients with GEP-NETs were included in this study; there were 49 gastric, 29 duodenal, 49 pancreatic, 12 hepatobiliary, 33 appendiceal, 5 proximal colon, and 150 distal colon cases. We performed immunostaining with the tissue microarray method for menin, p27, and p18.
Results
We observed negative staining for menin, p27, and p18 in 34%, 21%, and 56% of GEP-NETs, respectively. The loss of p27, but not menin, was positively correlated with the grade of Ki-67. Menin–/p27–, menin–/p27+, menin+/p27–, and menin+/p27+ phenotype groups included 13%, 22%, 8%, and 57% of patients, respectively. A dichotomized comparison showed that menin– or p27– tumors were significantly associated with foregut and midgut localizations, high World Health Organization (WHO) grade, lymph node metastasis, and more advanced stage as compared to menin+/p27+ patients. Kaplan-Meier analysis for the overall survival showed that p27 loss was significantly associated with decreased survival. Multivariate analysis showed that p27 loss is an independent factor for poor overall survival.
Conclusion
Our results revealed that the loss of p27 is associated with poor prognosis and the menin-p27 pathway is important in the tumorigenesis of GEP-NETs.

Citations

Citations to this article as recorded by

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Status of Surveillance and Nonsurgical Therapy for Small Nonfunctioning Pancreatic Neuroendocrine Tumors
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The link between menin and pleiotrophin in the tumor biology of pancreatic neuroendocrine neoplasms
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Cancer Science.2022; 113(5): 1575. CrossRef
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Meng Dai, Christina S Mullins, Lili Lu, Guido Alsfasser, Michael Linnebacher
World Journal of Gastrointestinal Surgery.2022; 14(5): 383. CrossRef
A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
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Reduced expression of the RNA‐binding protein HuD in pancreatic neuroendocrine tumors correlates with low p27Kip1 levels and poor prognosis
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Yanhong Luo, Hua Zhu, Tao Tan, Jianfeng He
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Cell Cycle Protein Expression in Neuroendocrine Tumors
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