Prognostic scoring system to predict mortality after surgery in resectable colorectal cancer

Alldila Hendy Prihanda Suryaningprang; Agi Satria Putranto; Luthfian Aby Nurachman

doi:10.15562/bmj.v12i2.4491

Prognostic scoring system to predict mortality after surgery in resectable colorectal cancer

Alldila Hendy Prihanda Suryaningprang ,

Agi Satria Putranto ,

Luthfian Aby Nurachman ,

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DOI: https://doi.org/10.15562/bmj.v12i2.4491 |
Published: 2023-05-14

Abstract

Link of Video Abstract: https://youtu.be/mYpKu0PcJrM

Background: Colorectal cancer remains as the major problem worldwide. Numerous studies have investigated factors associated with mortality and recurrence rates in colorectal cancer. To facilitate the everyday use of mortality predictors, they should be incorporated into a prognostic scoring system. This study aimed to identify factors affecting mortality in resectable colorectal cancer patients after surgery and to develop a prognostic scoring system capable of predicting mortality in these patients.

Methods: This retrospective cohort study involved colorectal cancer patients at Cipto Mangunkusumo Hospital, Indonesia, from January 2016 – April 2020 diagnosed with resectable colorectal cancer. Data were collected from medical records, operation reports, histopathology reports, and laboratory test results. Mortality was assessed three years after curative surgery.

Result: A total of 214 resectable colorectal cancer patients were included in the study. Tumor size ≥ 5 cm, T3/T4 staging, absence of adjuvant chemotherapy, unachieved free circumferential margin, and CEA levels > 11.4 ng/mL were significantly associated with increased three-year mortality. The constructed three-year mortality prognostic scoring system was able to predict outcomes with a sensitivity of 91.3% and a specificity of 67.6%.

Conclusion: The prognostic scoring system, consisting of five variables, is significantly capable of predicting three-year mortality rates with high sensitivity

References

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Gan S, Wilson K, Hollington P. Surveillance of patients following surgery with curative intent for colorectal cancer. World J Gastroenterol. 2007;13(28):3816–3823.
Zare-Bandamiri M, Fararouei M, Zohourinia S, Daneshi N, Dianatinasab M. Risk factors predicting colorectal cancer recurrence following initial treatment: a 5-year cohort study. Asian Pac J Cancer Prev. 2017;18(9):2465–2470.
Miyoshi N, Ohue M, Yasui M, Noura S, Shingai T, Sugimura K, et al. Novel prognostic prediction models for patients with stage IV colorectal cancer after concurrent curative resection. ESMO open. 2016;1(3):e000052.
Valentini V, Van Stiphout RGPM, Lammering G, Gambacorta MA, Barba MC, Bebenek M, et al. Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of european randomized clinical trials. J Clin Oncol. 2011;29(23):3163–3172.
Renfro LA, Grothey A, Xue Y, Saltz LB, André T, Twelves C, et al. ACCENT-Based Web Calculators to Predict Recurrence and Overall Survival in Stage III Colon Cancer. JNCI J Natl Cancer Inst. 2014;106(12):dju333.
Tong GJ, Zhang GY, Liu J, Zheng ZZ, Chen Y, Niu PP, et al. Comparison of the eighth version of the American Joint Committee on Cancer manual to the seventh version for colorectal cancer: A retrospective review of our data. World J Clin Oncol. 2018;9(7):148–161.
SEER N. Cancer Stat Facts: Colorectal Cancer [Internet]. Cancer statistics. 2022 [cited 2022 Nov 6]. Available from: https://seer.cancer.gov/statfacts/html/colorect.html
Labeda I, Lusikooy RE, Mappincara, Dani MI, Sampetoding S, Kusuma MI, et al. Colorectal cancer survival rates in Makassar, Eastern Indonesia: A retrospective Cohort Study. Ann Med Surg. 2022;74:103211.
Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–1316.
Trevethan R. Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice. Front Public Health. 2017;5:307.
Alese OB, Zhou W, Jiang R, Zakka K, Huang Z, Okoli C, et al. Predictive and prognostic effects of primary tumor size on colorectal cancer survival. Front Oncol. 2021;11:1–12.
Lim JH, Huh JW, Lee WY, Yun SH, Kim HC, Cho YB, et al. Comparison of long-term survival outcomes of T4a and T4b colorectal cancer. Front Oncol. 2021;11:780684.
Birbeck KF, Macklin CP, Tiffin NJ, Parsons W, Dixon MF, Mapstone NP, et al. Rates of circumferential resection margin involvement vary between surgeons and predict outcomes in rectal cancer surgery. Ann Surg. 2002;235(4):449–457.
Liu Q, Luo D, Cai S, Li Q, Li X. Circumferential resection margin as a prognostic factor after rectal cancer surgery: A large population-based retrospective study. Cancer Med. 2018;7(8):3673–3681.
Lakemeyer L, Sander S, Wittau M, Henne-Bruns D, Kornmann M, Lemke J. Diagnostic and prognostic value of CEA and CA19-9 in colorectal cancer. Diseases. 2021;9(1):21.
Hu X, Li YQ, Li QG, Ma YL, Peng JJ, Cai SJ. Baseline peripheral blood leukocytosis is negatively correlated with T-Cell infiltration predicting worse outcome in colorectal cancers. Front Immunol. 2018;9:2354.
Liang X, Yao S, Lu P, Ma Y, Xu H, Yin Z, et al. The prognostic value of new index (LANR) composed of pre-operative lymphocytes, albumin, and neutrophils in patients with resectable colorectal cancer. Front Oncol. 2021;11:610264.
Sindhughosa DA, Mariadi IK, Wibawa IDN, Suryadarma IGA, Purwadi N, Somayana G, Yuliandari CI. Evaluation of mortality risk in liver cirrhosis with albumin-bilirubin (Albi), platelet-albumin-bilirubin (Palbi), and fibrosis-4 (Fib-4) scores. Biomed Pharmacol J. 2021;14(2):985-991.
Achilonu OJ, Fabian J, Bebington B, Singh E, Eijkemans MJC, Musenge E. Predicting colorectal cancer recurrence and patient survival using supervised machine learning approach: a South African population-based study. Front Public Heal. 2021;9(July).
Stræde M, Brabrand M. External validation of the simple clinical score and the HOTEL score, two scores for predicting short-term mortality after admission to an acute medical unit. PLoS One. 2014;9(8):e105695.
Astika N, Sindhughosa DA, Kuswardhani RT, Manuaba IBAP. A preliminary scoring model to predict in-hospital mortality risk for geriatric patients with delirium. Open Access Maced J Med Sci. 2021;9(B):872-877.

[1] 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.

[2] Society A cancer. Colorectal Cancer Early Detection, Diagnosis, and Staging. cancer A-Z. 2020.

[3] Gan S, Wilson K, Hollington P. Surveillance of patients following surgery with curative intent for colorectal cancer. World J Gastroenterol. 2007;13(28):3816–3823.

[4] Zare-Bandamiri M, Fararouei M, Zohourinia S, Daneshi N, Dianatinasab M. Risk factors predicting colorectal cancer recurrence following initial treatment: a 5-year cohort study. Asian Pac J Cancer Prev. 2017;18(9):2465–2470.

[5] Miyoshi N, Ohue M, Yasui M, Noura S, Shingai T, Sugimura K, et al. Novel prognostic prediction models for patients with stage IV colorectal cancer after concurrent curative resection. ESMO open. 2016;1(3):e000052.

[6] Valentini V, Van Stiphout RGPM, Lammering G, Gambacorta MA, Barba MC, Bebenek M, et al. Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of european randomized clinical trials. J Clin Oncol. 2011;29(23):3163–3172.

[7] Renfro LA, Grothey A, Xue Y, Saltz LB, André T, Twelves C, et al. ACCENT-Based Web Calculators to Predict Recurrence and Overall Survival in Stage III Colon Cancer. JNCI J Natl Cancer Inst. 2014;106(12):dju333.

[8] Tong GJ, Zhang GY, Liu J, Zheng ZZ, Chen Y, Niu PP, et al. Comparison of the eighth version of the American Joint Committee on Cancer manual to the seventh version for colorectal cancer: A retrospective review of our data. World J Clin Oncol. 2018;9(7):148–161.

[9] SEER N. Cancer Stat Facts: Colorectal Cancer [Internet]. Cancer statistics. 2022 [cited 2022 Nov 6]. Available from: https://seer.cancer.gov/statfacts/html/colorect.html

[10] Labeda I, Lusikooy RE, Mappincara, Dani MI, Sampetoding S, Kusuma MI, et al. Colorectal cancer survival rates in Makassar, Eastern Indonesia: A retrospective Cohort Study. Ann Med Surg. 2022;74:103211.

[11] Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–1316.

[12] Trevethan R. Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice. Front Public Health. 2017;5:307.

[13] Alese OB, Zhou W, Jiang R, Zakka K, Huang Z, Okoli C, et al. Predictive and prognostic effects of primary tumor size on colorectal cancer survival. Front Oncol. 2021;11:1–12.

[14] Lim JH, Huh JW, Lee WY, Yun SH, Kim HC, Cho YB, et al. Comparison of long-term survival outcomes of T4a and T4b colorectal cancer. Front Oncol. 2021;11:780684.

[15] Birbeck KF, Macklin CP, Tiffin NJ, Parsons W, Dixon MF, Mapstone NP, et al. Rates of circumferential resection margin involvement vary between surgeons and predict outcomes in rectal cancer surgery. Ann Surg. 2002;235(4):449–457.

[16] Liu Q, Luo D, Cai S, Li Q, Li X. Circumferential resection margin as a prognostic factor after rectal cancer surgery: A large population-based retrospective study. Cancer Med. 2018;7(8):3673–3681.

[17] Lakemeyer L, Sander S, Wittau M, Henne-Bruns D, Kornmann M, Lemke J. Diagnostic and prognostic value of CEA and CA19-9 in colorectal cancer. Diseases. 2021;9(1):21.

[18] Hu X, Li YQ, Li QG, Ma YL, Peng JJ, Cai SJ. Baseline peripheral blood leukocytosis is negatively correlated with T-Cell infiltration predicting worse outcome in colorectal cancers. Front Immunol. 2018;9:2354.

[19] Liang X, Yao S, Lu P, Ma Y, Xu H, Yin Z, et al. The prognostic value of new index (LANR) composed of pre-operative lymphocytes, albumin, and neutrophils in patients with resectable colorectal cancer. Front Oncol. 2021;11:610264.

[20] Sindhughosa DA, Mariadi IK, Wibawa IDN, Suryadarma IGA, Purwadi N, Somayana G, Yuliandari CI. Evaluation of mortality risk in liver cirrhosis with albumin-bilirubin (Albi), platelet-albumin-bilirubin (Palbi), and fibrosis-4 (Fib-4) scores. Biomed Pharmacol J. 2021;14(2):985-991.

[21] Achilonu OJ, Fabian J, Bebington B, Singh E, Eijkemans MJC, Musenge E. Predicting colorectal cancer recurrence and patient survival using supervised machine learning approach: a South African population-based study. Front Public Heal. 2021;9(July).

[22] Stræde M, Brabrand M. External validation of the simple clinical score and the HOTEL score, two scores for predicting short-term mortality after admission to an acute medical unit. PLoS One. 2014;9(8):e105695.

[23] Astika N, Sindhughosa DA, Kuswardhani RT, Manuaba IBAP. A preliminary scoring model to predict in-hospital mortality risk for geriatric patients with delirium. Open Access Maced J Med Sci. 2021;9(B):872-877.

Prognostic scoring system to predict mortality after surgery in resectable colorectal cancer

Abstract

References

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