Document Type : Original Article(s)

Authors

1 Medical Oncology Department, Faculty of Medicine, Zagazig University, Egypt

2 Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, Zagazig University, Egypt

10.30476/mejc.2024.100258.1980

Abstract

Background: Patients with metastatic gastric cancer (mGC) endure a significant symptom burden following subsequent lines of therapy. Accurate survival estimation is crucial for healthcare professionals and patients to make informed decisions regarding therapy options. This study evaluates Chuang's Prognostic Scale (CPS) for predicting survival in mGC patients after receiving at least two lines of palliative systemic therapy (PST).
Method: This prospective study involved two hundred and two patients with mGC. The CPS includes eight categories: cognitive impairment, performance status, weight loss, tiredness, edema, and ascites, with a scoring range from 0 to 8.5. A higher score indicates a poorer prognosis.
Results: After a median follow-up period of 3.35 months, the median CPS value was 4.2. 99 patients had a CPS < 4.2, with a median overall survival (mOS) of 5.86 months, while 103 patients with a CPS ≥ 4.2 had an mOS of 3.96 months (P < 0.001). According to the receiver-operating curve, the cut-off value for CPS was ≤ 4.7, with a disease prevalence of 76.7% and an area under the curve of 0.949 (P < 0.0001). The sensitivity was 82.6%, specificity was 97.87%, positive predictive value was 99.2%, and negative predictive value was 63%. Cox regression analysis revealed that CPS was statistically significantly associated with mOS (P < 0.001). Furthermore, CPS was statistically significantly correlated with metastases to the liver, lung, lymph nodes, and bone (P values were 0.03, 0.02, <0.001, and <0.001, respectively).
Conclusion: CPS is a valuable and accessible tool that can assist in selecting appropriate therapy for patients with mGC after two lines of PST

Highlights

Amrallah Mohammed (PubMed)

Keywords

Main Subjects

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination, and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.30476/mejc.2024.100258.1980

  1. Sexton RE, Al Hallak MN, Diab M, Azmi AS. Gastric cancer: a comprehensive review of current and future treatment strategies. Cancer Metastasis Rev. 2020;39(4):1179-203. doi: 10.1007/s10555-020-09925-3.
  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. doi: 10.3322/caac.21492. Erratum in: CA Cancer J Clin. 2020;70(4):313.
  3. Ren J, Dai Y, Chao F, Tang D, Gu J, Niu G, et al. A nomogram for predicting the cancer-specific survival of patients with initially diagnosed metastatic gastric cancer. Clin Med Insights Oncol. 2022;16:11795549221142095. doi: 10.1177/11795549221142095.
  4. Kopecky K, Monton O, Rosman L, Johnston F. Palliative interventions for patients with advanced gastric cancer: a systematic review. Chin Clin Oncol. 2022;11(6):47. doi: 10.21037/cco-22-102.
  5. Mohammed AA, Al-Zahrani O, Elsayed FM, Elshentenawy A. Prediction of survival outcome using Chuang's prognostic scale in metastatic breast cancer. Indian J Palliat Care. 2021;27(1):43-6. doi: 10.4103/IJPC.IJPC_97_20.
  6. Chuang RB, Hu WY, Chiu TY, Chen CY. Prediction of survival in terminal cancer patients in Taiwan: constructing a prognostic scale. J Pain Symptom Manage. 2004;28(2):115-22. doi: 10.1016/j.jpainsymman.2003.11.008.
  7. Bashiri A, Ghazisaeedi M, Safdari R, Shahmoradi L, Ehtesham H. Improving the prediction of survival in cancer patients by using machine learning techniques: experience of gene expression data: a narrative review. Iran J Public Health. 2017;46(2):165-72.
  8. Ghosn M, Tabchi S, Kourie HR, Tehfe M. Metastatic gastric cancer treatment: Second line and beyond. World J Gastroenterol. 2016;22(11):3069-77. doi: 10.3748/wjg.v22.i11.3069.
  9. Kottorou A, Dimitrakopoulos FI, Tsezou A. Non-coding RNAs in cancer-associated cachexia: clinical implications and future perspectives. Transl Oncol. 2021;14(7):101101. doi: 10.1016/j.tranon.2021.101101.
  10. Rosania R, Chiapponi C, Malfertheiner P, Venerito M. Nutrition in patients with gastric cancer: an update. Gastrointest Tumors. 2016;2(4):178-87. doi: 10.1159/000445188.
  11. Yarema R, Оhorchak М, Hyrya P, Kovalchuk Y, Safiyan V, Karelin I, et al. Gastric cancer with peritoneal metastases: Efficiency of standard treatment methods. World J Gastrointest Oncol. 2020;12(5):569-81. doi: 10.4251/wjgo.v12.i5.569.
  12. Butow PN, Clayton JM, Epstein RM. Prognostic awareness in adult oncology and palliative care. J Clin Oncol. 2020;38(9):877-84. doi: 10.1200/JCO.18.02112.
  13. Hui D, Ross J, Park M, Dev R, Vidal M, Liu D, et al. Predicting survival in patients with advanced cancer in the last weeks of life: How accurate are prognostic models compared to clinicians' estimates? Palliat Med. 2020;34(1):126-33. doi: 10.1177/0269216319873261.
  14. Al-Zahrani AS, El-Kashif AT, Mohammad AA, Elsamany S, Alsirafy SA. Prediction of in-hospital mortality of patients with advanced cancer using the Chuang Prognostic Score. Am J Hosp Palliat Care. 2013;30(7):707-11. doi: 10.1177/1049909112467362.
  15. Alsirafy SA, Zaki O, Sakr AY, Farag DE, El-Sherief WA, Mohammed AA. The use of the Chuang's prognostic scale to predict the survival of metastatic colorectal cancer patients receiving palliative systemic anticancer therapy. Indian J Palliat Care. 2016;22(3):312-6. doi: 10.4103/0973-1075.185043.