Journal of Gastric Cancer

The Korean Gastric Cancer Association (대한위암학회)
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Determination of Survival of Gastric Cancer Patients With Distant Lymph Node Metastasis Using Prealbumin Level and Prothrombin Time: Contour Plots Based on Random Survival Forest Algorithm on High-Dimensionality Clinical and Laboratory Datasets

  • Zhang, Cheng (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Xie, Minmin (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Zhang, Yi (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Zhang, Xiaopeng (Department of Noncommunicable Diseases and Health Education, Hefei Center for Disease Prevention and Control) ;
  • Feng, Chong (Department of Noncommunicable Diseases and Health Education, Hefei Center for Disease Prevention and Control) ;
  • Wu, Zhijun (Department of Oncology, Ma'anshan Municipal People's Hospital) ;
  • Feng, Ying (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Yang, Yahui (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Xu, Hui (Department of Oncology, The First Affiliated Hospital of Anhui Medical University) ;
  • Ma, Tai (Department of Oncology, The First Affiliated Hospital of Anhui Medical University)
  • Received : 2022.01.29
  • Accepted : 2022.03.17
  • Published : 2022.04.30
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Abstract

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

Keywords

Acknowledgement

The authors gratefully acknowledge Mr. Zhenhui He for his help in data electronization.

References

  1. Qiu MZ, Shi SM, Chen ZH, Yu HE, Sheng H, Jin Y, et al. Frequency and clinicopathological features of metastasis to liver, lung, bone, and brain from gastric cancer: a SEER-based study. Cancer Med 2018;7:3662-3672. https://doi.org/10.1002/cam4.1661
  2. In H, Solsky I, Palis B, Langdon-Embry M, Ajani J, Sano T. Validation of the 8th edition of the AJCC TNM staging system for gastric cancer using the national cancer database. Ann Surg Oncol 2017;24:3683-3691. https://doi.org/10.1245/s10434-017-6078-x
  3. Tan HL, Chia CS, Tan GH, Choo SP, Tai DW, Chua CW, et al. Metastatic gastric cancer: Does the site of metastasis make a difference? Asia Pac J Clin Oncol 2019;15:10-17. https://doi.org/10.1111/ajco.13025
  4. Patel PR, Yao JC, Hess K, Schnirer I, Rashid A, Ajani JA. Effect of timing of metastasis/disease recurrence and histologic differentiation on survival of patients with advanced gastric cancer. Cancer 2007;110:2186-2190. https://doi.org/10.1002/cncr.23046
  5. Sawaki K, Kanda M, Ito S, Mochizuki Y, Teramoto H, Ishigure K, et al. Survival times are similar among patients with peritoneal, hematogenous, and nodal recurrences after curative resections for gastric cancer. Cancer Med 2020;9:5392-5399. https://doi.org/10.1002/cam4.3208
  6. Sato S, Kunisaki C, Tanaka Y, Sato K, Miyamoto H, Yukawa N, et al. Curative-intent surgery for stage IV advanced gastric cancer: Who can undergo surgery and what are the prognostic factors for long-term survival? Ann Surg Oncol 2019;26:4452-4463. https://doi.org/10.1245/s10434-019-07790-1
  7. Kim JH, Lee HH, Seo HS, Jung YJ, Park CH. Stage-specific difference in timing and pattern of initial recurrence after curative surgery for gastric cancer. Surg Oncol 2019;30:81-86. https://doi.org/10.1016/j.suronc.2019.05.023
  8. Agnes A, Biondi A, Laurino A, Strippoli A, Ricci R, Pozzo C, et al. A detailed analysis of the recurrence timing and pattern after curative surgery in patients undergoing neoadjuvant therapy or upfront surgery for gastric cancer. J Surg Oncol 2020;122:293-305. https://doi.org/10.1002/jso.25959
  9. Takahashi R, Ohashi M, Kano Y, Ida S, Kumagai K, Nunobe S, et al. Timing and site-specific trends of recurrence in patients with pathological stage II or III gastric cancer after curative gastrectomy followed by adjuvant S-1 monotherapy. Gastric Cancer 2019;22:1256-1262. https://doi.org/10.1007/s10120-019-00953-9
  10. Fang WL, Lan YT, Huang KH, Liu CA, Hung YP, Lin CH, et al. Clinical significance of circulating plasma DNA in gastric cancer. Int J Cancer 2016;138:2974-2983. https://doi.org/10.1002/ijc.30018
  11. Zhang C, Jing LW, Li ZT, Chang ZW, Liu H, Zhang QM, et al. Identification of a prognostic 28-gene expression signature for gastric cancer with lymphatic metastasis. Biosci Rep 2019;39:BSR20182179. https://doi.org/10.1042/BSR20182179
  12. Zeng D, Zhou R, Yu Y, Luo Y, Zhang J, Sun H, et al. Gene expression profiles for a prognostic immunoscore in gastric cancer. Br J Surg 2018;105:1338-1348. https://doi.org/10.1002/bjs.10871
  13. Izuishi K, Mori H. Recent strategies for treating stage IV gastric cancer: roles of palliative gastrectomy, chemotherapy, and radiotherapy. J Gastrointestin Liver Dis 2016;25:87-94. https://doi.org/10.15403/jgld.2014.1121.251.rv2
  14. Rulli E, Ghilotti F, Biagioli E, Porcu L, Marabese M, D'Incalci M, et al. Assessment of proportional hazard assumption in aggregate data: a systematic review on statistical methodology in clinical trials using timeto-event endpoint. Br J Cancer 2018;119:1456-1463. https://doi.org/10.1038/s41416-018-0302-8
  15. Greenwood CJ, Youssef GJ, Letcher P, Macdonald JA, Hagg LJ, Sanson A, et al. A comparison of penalised regression methods for informing the selection of predictive markers. PLoS One 2020;15:e0242730. https://doi.org/10.1371/journal.pone.0242730
  16. Adeli E, Li X, Kwon D, Zhang Y, Pohl KM. logistic regression confined by cardinality-constrained sample and feature selection. IEEE Trans Pattern Anal Mach Intell 2020;42:1713-1728. https://doi.org/10.1109/tpami.2019.2901688
  17. Dong D, Fang MJ, Tang L, Shan XH, Gao JB, Giganti F, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol 2020;31:912-920. https://doi.org/10.1016/j.annonc.2020.04.003
  18. Li J, Dong D, Fang M, Wang R, Tian J, Li H, et al. Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol 2020;30:2324-2333. https://doi.org/10.1007/s00330-019-06621-x
  19. Cai WY, Dong ZN, Fu XT, Lin LY, Wang L, Ye GD, et al. Identification of a tumor microenvironment-relevant gene set-based prognostic signature and related therapy targets in gastric cancer. Theranostics 2020;10:8633-8647. https://doi.org/10.7150/thno.47938
  20. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat 2008;2:841-860. https://doi.org/10.1214/08-AOAS169
  21. Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, et al. mlr3: a modern object-oriented machine learning framework in R. J Open Source Softw 2019;44:1903.
  22. Wright MN, Ziegler A. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 2017;77:1-17.
  23. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001;29:1189-1232. https://doi.org/10.1214/aos/1013203451
  24. Mokadem I, Dijksterhuis WP, van Putten M, Heuthorst L, de Vos-Geelen JM, Haj Mohammad N, et al. Recurrence after preoperative chemotherapy and surgery for gastric adenocarcinoma: a multicenter study. Gastric Cancer 2019;22:1263-1273. https://doi.org/10.1007/s10120-019-00956-6
  25. Saka M, Katai H, Fukagawa T, Nijjar R, Sano T. Recurrence in early gastric cancer with lymph node metastasis. Gastric Cancer 2008;11:214-218. https://doi.org/10.1007/s10120-008-0485-4
  26. Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 2006;7:91. https://doi.org/10.1186/1471-2105-7-91
  27. Parvandeh S, Yeh HW, Paulus MP, McKinney BA. Consensus features nested cross-validation. Bioinformatics 2020;36:3093-3098. https://doi.org/10.1093/bioinformatics/btaa046
  28. Wu ZJ, Xu H, Wang R, Bu LJ, Ning J, Hao JQ, et al. Cumulative score based on preoperative fibrinogen and pre-albumin could predict long-term survival for patients with resectable gastric cancer. J Cancer 2019;10:6244-6251. https://doi.org/10.7150/jca.35157
  29. Wang Z, Zhang L, Wang J, Wang Y, Dong Q, Piao H, et al. Prealbumin-to-globulin ratio can predict the chemotherapy outcomes and prognosis of patients with gastric cancer receiving first-line chemotherapy. J Immunol Res 2020;2020:6813176.
  30. Lu J, Xu BB, Zheng ZF, Xie JW, Wang JB, Lin JX, et al. CRP/prealbumin, a novel inflammatory index for predicting recurrence after radical resection in gastric cancer patients: post hoc analysis of a randomized phase III trial. Gastric Cancer 2019;22:536-545. https://doi.org/10.1007/s10120-018-0892-0
  31. Wang N, Xi W, Lu S, Jiang J, Wang C, Zhu Z, et al. A novel inflammatory-nutritional prognostic scoring system for stage III gastric cancer patients with radical gastrectomy followed by adjuvant chemotherapy. Front Oncol 2021;11:650562. https://doi.org/10.3389/fonc.2021.650562
  32. Bullock AF, Greenley SL, McKenzie GA, Paton LW, Johnson MJ. Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis and meta-analysis. Eur J Clin Nutr 2020;74:1519-1535. https://doi.org/10.1038/s41430-020-0629-0
  33. Chen X, Zhao G, Zhu L. Home enteral nutrition for postoperative elderly patients with esophageal cancer. Ann Palliat Med 2021;10:278-284. https://doi.org/10.21037/apm-20-2197
  34. Yin HM, He Q, Chen J, Li Z, Yang W, Hu X. Drug metabolism-related eight-gene signature can predict the prognosis of gastric adenocarcinoma. J Clin Lab Anal 2021;35:e24085.
  35. Unlu B, Versteeg HH. Effects of tumor-expressed coagulation factors on cancer progression and venous thrombosis: is there a key factor? Thromb Res 2014;133 Suppl 2:S76-S84. https://doi.org/10.1016/S0049-3848(14)50013-8
  36. Eom BW, Lee JH, Lee JS, Kim MJ, Ryu KW, Choi IJ, et al. Survival analysis of gastric cancer patients with tumor thrombus in the portal vein. J Surg Oncol 2012;105:310-315. https://doi.org/10.1002/jso.22083
  37. Rhee J, Han SW, Oh DY, Im SA, Kim TY, Bang YJ. Clinicopathologic features and clinical outcomes of gastric cancer that initially presents with disseminated intravascular coagulation: a retrospective study. J Gastroenterol Hepatol 2010;25:1537-1542. https://doi.org/10.1111/j.1440-1746.2010.06289.x
  38. Elshawi R, Al-Mallah MH, Sakr S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med Inform Decis Mak 2019;19:146. https://doi.org/10.1186/s12911-019-0874-0
  39. Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 2008;26:1364-1370. https://doi.org/10.1200/JCO.2007.12.9791
  40. Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open 2020;3:e2028086. https://doi.org/10.1001/jamanetworkopen.2020.28086
  41. Zhou R, Zhang J, Zeng D, Sun H, Rong X, Shi M, et al. Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I-III colon cancer. Cancer Immunol Immunother 2019;68:433-442. https://doi.org/10.1007/s00262-018-2289-7