Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

Association of Body Mass Index with Survival in Asian Patients with Colorectal Cancer: Sangwon Lee, Dong Hee Lee, Jae-Hoon Lee, Su-Jin Shin, Hye Sun Lee, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2022;54(3):860-872. Published online October 15, 2021; DOI: https://doi.org/10.4143/crt.2021.656

Abstract PDF Supplementary Material PubReader ePub

Purpose
The clinical significance of body mass index (BMI) on long-term outcomes has not been extensively investigated in Asian patients with colorectal cancer (CRC). This study aims to describe the association between BMI and survival, plus providing BMI cut-off value for predicting prognosis in CRC patients.
Materials and Methods
A total of 1,182 patients who had undergone surgery for stage I-III CRC from June 2004 to February 2014 were included. BMI was categorized into four groups based on the recommendation for Asian ethnicity. The optimal BMI cut-off value was determined to maximize overall survival (OS) difference.
Results
In multivariable analysis, underweight BMI was significantly associated with poor OS (hazard ratio [HR], 2.38; 95% confidence interval [CI], 1.55 to 3.71; p < 0.001) and obese BMI was associated with better OS (HR, 0.72; 95% CI, 0.53 to 0.97; p=0.036) compared with the normal BMI. Overweight and obese BMI were associated with better recurrence-free survival (HR, 0.64; 95% CI, 0.42 to 0.99; p=0.046 and HR, 0.58; 95% CI, 0.38 to 0.89; p=0.014, respectively) compared with the normal BMI group. BMI cutoff value was 20.44 kg/m². Adding the BMI cutoff value to cancer staging could increase discriminatory performance in terms of integrated area under the curve and Harrell’s concordance index.
Conclusion
Compared to normal BMI, underweight BMI was associated with poor survival whereas obese BMI was associated with better survival. BMI cut-off value of 20.44 kg/m² is a useful discriminator in Asian patients with CRC.

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Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies
H. Wen, G. Deng, X. Shi, Z. Liu, A. Lin, Q. Cheng, J. Zhang, P. Luo
ESMO Open.2024; 9(3): 102241. CrossRef
Trends in Anticoagulant Utilization and Clinical Outcomes for Cancer-Associated Thrombosis: A Multicenter Cohort Study in Thailand's Upper-Middle–Income Country Setting
Kirati Kengkla, Surakit Nathisuwan, Warunsuda Sripakdee, Pirun Saelue, Kwanruethai Sengnoo, Aumkhae Sookprasert, Suphat Subongkot
JCO Global Oncology.2024;[Epub] CrossRef
Post‐diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta‐analysis
Nerea Becerra‐Tomás, Georgios Markozannes, Margarita Cariolou, Katia Balducci, Rita Vieira, Sonia Kiss, Dagfinn Aune, Darren C. Greenwood, Laure Dossus, Ellen Copson, Andrew G. Renehan, Martijn Bours, Wendy Demark‐Wahnefried, Melissa M. Hudson, Anne M. Ma
International Journal of Cancer.2024; 155(3): 400. CrossRef
Low muscle mass-to-fat ratio is an independent factor that predicts worse overall survival and complications in patients with colon cancer: a retrospective single-center cohort study
Jiabao Tang, Jingwen Xu, Xiaohua Li, Chun Cao
Annals of Surgical Treatment and Research.2024; 107(2): 68. CrossRef
Comment on “Dense Tumor‐Infiltrating Lymphocytes (TILs) in Liver Metastasis From Colorectal Cancer Are Related to Improved Overall Survival”
Fuji Lai, Sheng Li, Zhonglei Shen
Journal of Surgical Oncology.2024;[Epub] CrossRef
Obesity, the Adipose Organ and Cancer in Humans: Association or Causation?
Elisabetta Trevellin, Silvia Bettini, Anna Pilatone, Roberto Vettor, Gabriella Milan
Biomedicines.2023; 11(5): 1319. CrossRef
Higher body mass index was associated with better prognosis in diabetic patients with stage II colorectal cancer
Xiao-Yu Liu, Bing Kang, Yu-Xi Cheng, Chao Yuan, Wei Tao, Bin Zhang, Zheng-Qiang Wei, Dong Peng
BMC Cancer.2022;[Epub] CrossRef
Preoperative carcinoembryonic antigen to body mass index ratio contributes to prognosis prediction in colorectal cancer
Jia Xiang, Mengyao Ding, Jixing Lin, Tianhui Xue, Qianwen Ye, Bing Yan
Oncology Letters.2022;[Epub] CrossRef

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Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer: Mohamed Hosny Osman, Reham Hosny Mohamed, Hossam Mohamed Sarhan, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2022;54(2):517-524. Published online June 15, 2021; DOI: https://doi.org/10.4143/crt.2021.206

Abstract PDF Supplementary Material PubReader ePub

Purpose
Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.
Materials and Methods
A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.
Results
Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).
Conclusion
ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

Citations

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Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms
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SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
Ping Yang, Hang Qiu, Xulin Yang, Liya Wang, Xiaodong Wang
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The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction
Chenghao Lu, Lu Liu, Minyue Yin, Jiaxi Lin, Shiqi Zhu, Jingwen Gao, Shuting Qu, Guoting Xu, Lihe Liu, Jinzhou Zhu, Chunfang Xu
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洪铭崔
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Fan Zhang, Boqi Xu, Yao Peng, Zhongqi Mao
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Chunxia Guo, Jun Pan, Shan Tian, Yuanjun Gao
Journal of International Medical Research.2023;[Epub] CrossRef

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335 Download
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Gastrointestinal Cancer

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer: Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee; Cancer Res Treat. 2021;53(3):773-783. Published online December 29, 2020; DOI: https://doi.org/10.4143/crt.2020.974

Abstract PDF Supplementary Material PubReader ePub

Purpose
The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach has not been widely studied.
Materials and Methods
Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.
Results
LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.
Conclusion
Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.

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Which Patients with Isolated Para-aortic Lymph Node Metastasis Will Truly Benefit from Extended Lymph Node Dissection for Colon Cancer?: Sung Uk Bae, Hyuk Hur, Byung Soh Min, Seung Hyuk Baik, Kang Young Lee, Nam Kyu Kim; Cancer Res Treat. 2018;50(3):712-719. Published online July 14, 2017; DOI: https://doi.org/10.4143/crt.2017.100

Abstract PDF PubReader ePub

Purpose
The prognosis of patientswith colon cancer and para-aortic lymph node metastasis (PALNM) is poor. We analyzed the prognostic factors of extramesenteric lymphadenectomy for colon cancer patients with isolated PALNM.
Materials and Methods
We retrospectively reviewed 49 patients with PALNM who underwent curative resection between October 1988 and December 2009.
Results
In univariate analyses, the 5-year overall survival (OS) and disease-free survival (DFS) rates were higher in patients with ≤ 7 positive para-aortic lymph node (PALN) (36.5% and 27.5%) than in those with > 7 PALN (14.3% and 14.3%; p=0.010 and p=0.027, respectively), and preoperative carcinoembryonic antigen (CEA) level > 5 was also correlated with a lower 5-year OS and DFS rate of 21.5% and 11.7% compared with those with CEA ≤ 5 (46.3% and 41.4%; p=0.122 and 0.039, respectively). Multivariate analysis found that the number of positive PALN (hazard ratio [HR], 3.291; 95% confidence interval [CI], 1.309 to 8.275; p=0.011) was an independent prognostic factor for OS and the number of positive PALN (HR, 2.484; 95% CI, 0.993 to 6.211; p=0.052) and preoperative CEA level (HR, 1.953; 95% CI, 0.940 to 4.057; p=0.073) were marginally independent prognostic factors for DFS. According to our prognostic model, the 5-year OS and DFS rate increased to 59.3% and 53.3%, respectively, in patients with ≤ 7 positive PALN and CEA level ≤ 5.
Conclusion
PALN dissection might be beneficial in carefully selected patients with a low CEA level and less extensive PALNM.

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Novel Methods for Clinical Risk Stratification in Patients with Colorectal Liver Metastases: Ki-Yeol Kim, Nam Kyu Kim, In-Ho Cha, Joong Bae Ahn, Jin Sub Choi, Gi-Hong Choi, Joon Suk Lim, Kang Young Lee, Seung Hyuk Baik, Byung Soh Min, Hyuk Hur, Jae Kyung Roh, Sang Joon Shin; Cancer Res Treat. 2015;47(2):242-250. Published online September 11, 2014; DOI: https://doi.org/10.4143/crt.2014.066

Abstract PDF PubReader ePub

Purpose
Colorectal cancer patients with liver-confined metastases are classified as stage IV, but their prognoses can differ from metastases at other sites. In this study, we suggest a novel method for risk stratification using clinically effective factors. Materials and Methods Data on 566 consecutive patients with colorectal liver metastasis (CLM) between 1989 and 2010 were analyzed. This analysis was based on principal component analysis (PCA). Results The survival rate was affected by carcinoembryonic antigen (CEA) level (p < 0.001; risk ratio, 1.90), distribution of liver metastasis (p=0.014; risk ratio, 1.46), and disease-free interval (DFI; p < 0.001; risk ratio, 1.98). When patients were divided into three groups according to PCA score using significantly affected factors, they showed significantly different survival patterns (p < 0.001). Conclusion The PCA scoring system based on CEA level, distribution of liver metastasis, and DFI may be useful for preoperatively determining prognoses in order to assist in clinical decisionmaking and designing future clinical trials for CLM treatment.

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