Ann Hepatobiliary Pancreat Surg.  2024 Feb;28(1):14-24. 10.14701/ahbps.23-078.

Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature

• Seow-En I
• Koh YX
• Zhao Y
• Ang BH
• Tan IEH
• Chok AY
• Tan EJKW
• Au MKH

• KMID: 2553361
• DOI: http://doi.org/10.14701/ahbps.23-078

Abstract

This study aims to assess the quality and performance of predictive models for colorectal cancer liver metastasis (CRCLM). A systematic review was performed to identify relevant studies from various databases. Studies that described or validated predictive models for CRCLM were included. The methodological quality of the predictive models was assessed. Model performance was evaluated by the reported area under the receiver operating characteristic curve (AUC). Of the 117 articles screened, seven studies comprising 14 predictive models were included. The distribution of included predictive models was as follows: radiomics (n = 3), logistic regression (n = 3), Cox regression (n = 2), nomogram (n = 3), support vector machine (SVM, n = 2), random forest (n = 2), and convolutional neural network (CNN, n = 2). Age, sex, carcinoembryonic antigen, and tumor staging (T and N stage) were the most frequently used clinicopathological predictors for CRCLM. The mean AUCs ranged from 0.697 to 0.870, with 86% of the models demonstrating clear discriminative ability (AUC > 0.70). A hybrid approach combining clinical and radiomic features with SVM provided the best performance, achieving an AUC of 0.870. The overall risk of bias was identified as high in 71% of the included studies. This review highlights the potential of predictive modeling to accurately predict the occurrence of CRCLM. Integrating clinicopathological and radiomic features with machine learning algorithms demonstrates superior predictive capabilities.

Keyword

Colorectal cancer; Liver metastasis; Prediction; Systematic review

Figure

• 

  Fig. 1 Tree diagram of predictive modeling algorithms included in the systematic review. LASSO, least absolute shrinkage and selection operator.

• 

  Fig. 2 PRISMA flow diagram for data collection. The search returned a total of 141 records, of which 7 studies that reported predictive modeling techniques to predict colorectal caner liver metastasis (CRCLM) were included in the systematic review.

• 

  Fig. 3 Methodological evaluation of the included predictive models. Assessment of the RoB based on PROBAST criteria. (A) Summary of RoB assessment. (B) Summary of applicability assessment. PROBAST, prediction model risk of bias assessment tool; RoB, risk of bias.

Reference

1. Edwards BK, Ward E, Kohler BA, Eheman C, Zauber AG, Anderson RN, et al. 2010; Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 116:544–573. DOI: 10.1002/cncr.24760. PMID: 19998273. PMCID: PMC3619726.

2. Morgan E, Arnold M, Gini A, Lorenzoni V, Cabasag C, Laversanne M, et al. 2023; Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 72:338–344. DOI: 10.1136/gutjnl-2022-327736. PMID: 36604116.

3. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 2018; Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. DOI: 10.3322/caac.21492. PMID: 30207593.

4. Yu X, Zhu L, Liu J, Xie M, Chen J, Li J. 2020; Emerging role of immunotherapy for colorectal cancer with liver metastasis. Onco Targets Ther. 13:11645–11658. DOI: 10.2147/OTT.S271955. PMID: 33223838. PMCID: PMC7671511.

5. Tauriello DV, Calon A, Lonardo E, Batlle E. 2017; Determinants of metastatic competency in colorectal cancer. Mol Oncol. 11:97–119. DOI: 10.1002/1878-0261.12018. PMID: 28085225. PMCID: PMC5423222.

6. Engstrand J, Nilsson H, Strömberg C, Jonas E, Freedman J. 2018; Colorectal cancer liver metastases-a population-based study on incidence, management and survival. BMC Cancer. 18:78. DOI: 10.1186/s12885-017-3925-x. PMID: 29334918. PMCID: PMC5769309.

7. Martin J, Petrillo A, Smyth EC, Shaida N, Khwaja S, Cheow H, et al. 2020; Colorectal liver metastases: current management and future perspectives. World J Clin Oncol. 11:761. DOI: 10.5306/wjco.v11.i10.761. PMID: 33200074. PMCID: PMC7643190.

8. Hugen N, van de Velde CJH, de Wilt JHW, Nagtegaal ID. 2014; Metastatic pattern in colorectal cancer is strongly influenced by histological subtype. Ann Oncol. 25:651–657. DOI: 10.1093/annonc/mdt591. PMID: 24504447. PMCID: PMC4433523.

9. Ivey GD, Johnston FM, Azad NS, Christenson ES, Lafaro KJ, Shubert CR. 2022; Current surgical management strategies for colorectal cancer liver metastases. Cancers (Basel). 14:1063. DOI: 10.3390/cancers14041063. PMID: 35205811. PMCID: PMC8870224.

10. Yu KH, Beam AL, Kohane IS. 2018; Artificial intelligence in healthcare. Nat Biomed Eng. 2:719–731. DOI: 10.1038/s41551-018-0305-z. PMID: 31015651.

11. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. 2017; Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2:230–243. DOI: 10.1136/svn-2017-000101. PMID: 29507784. PMCID: PMC5829945.

12. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. 2021; The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 21:125. DOI: 10.1186/s12911-021-01488-9. PMID: 33836752. PMCID: PMC8035061.

13. Vorontsov E, Cerny M, Régnier P, Di Jorio L, Pal CJ, Lapointe R, et al. 2019; Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases. Radiol Artif Intell. 1:180014. DOI: 10.1148/ryai.2019180014. PMID: 33937787. PMCID: PMC8017429.

14. Paredes AZ, Hyer JM, Tsilimigras DI, Moro A, Bagante F, Guglielmi A, et al. 2020; A novel machine-learning approach to predict recurrence after resection of colorecta liver metastases. Ann Surg Oncol. 27:5139–5147. DOI: 10.1245/s10434-020-08991-9. PMID: 32779049.

15. Taghavi M, Trebeschi S, Simões R, Meek DB, Beckers RC, Lambregts DM, et al. 2021; Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY). 46:249–256. DOI: 10.1007/s00261-020-02624-1. PMID: 32583138.

16. Rompianesi G, Pegoraro F, Ceresa CD, Montalti R, Troisi RI. 2022; Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol. 28:108–122. DOI: 10.3748/wjg.v28.i1.108. PMID: 35125822. PMCID: PMC8793013.

17. Visvikis D, Cheze Le Rest C, Jaouen V, Hatt M. 2019; Artificial intelligence, machine (deep) learning and radio (geno) mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 46:2630–2637. DOI: 10.1007/s00259-019-04373-w. PMID: 31280350.

18. Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. 2021; The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg. 88:105906. DOI: 10.1016/j.ijsu.2021.105906. PMID: 33789826.

19. Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. 2014; Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 11:e1001744. DOI: 10.1371/journal.pmed.1001744. PMID: 25314315. PMCID: PMC4196729.

20. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. 2019; PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 170:W1–W33. DOI: 10.7326/M18-1377. PMID: 30596876.

21. Sidey-Gibbons JAM, Sidey-Gibbons CJ. 2019; Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 19:64. DOI: 10.1186/s12874-019-0681-4. PMID: 30890124. PMCID: PMC6425557.

22. Pfob A, Lu SC, Sidey-Gibbons C. 2022; Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Med Res Methodol. 22:282. DOI: 10.1186/s12874-022-01758-8. PMID: 36319956. PMCID: PMC9624048.

23. Bektaş M, Tuynman JB, Costa Pereira J, Burchell GL, Van Der Peet DL. 2022; Machine learning algorithms for predicting surgical outcomes after colorectal surgery: a systematic review. World J Surg. 46:3100–3110. DOI: 10.1007/s00268-022-06728-1. PMID: 36109367. PMCID: PMC9636121.

24. Jalali A, Alvarez-Iglesias A, Roshan D, Newell J. 2019; Visualising statistical models using dynamic nomograms. PloS One. 14:e0225253. DOI: 10.1371/journal.pone.0225253. PMID: 31730633. PMCID: PMC6857916.

25. Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, et al. 2020; Machine and deep learning methods for radiomics. Med Phys. 47:e185–e202. DOI: 10.1002/mp.13678. PMID: 32418336. PMCID: PMC8965689.

26. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. 2012; Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 48:441–446. DOI: 10.1016/j.ejca.2011.11.036. PMID: 22257792. PMCID: PMC4533986.

27. Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, et al. 2019; Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol. 26:1495–1504. DOI: 10.1016/j.acra.2018.12.019. PMID: 30711405.

28. Yan Y, Liu H, Mao K, Zhang M, Zhou Q, Yu W, et al. 2019; Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma. J Transl Med. 17:193. DOI: 10.1186/s12967-019-1940-1. PMID: 31182111. PMCID: PMC6558904.

29. Li Y, Eresen A, Shangguan J, Yang J, Lu Y, Chen D, et al. 2019; Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer. Am J Cancer Res. 9:2482–2492.

30. Lee S, Choe EK, Kim SY, Kim HS, Park KJ, Kim D. 2020; Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan. BMC Bioinformatics. 21:382. DOI: 10.1186/s12859-020-03686-0. PMID: 32938394. PMCID: PMC7495853.

31. Xiao C, Zhou M, Yang X, Wang H, Tang Z, Zhou Z, et al. 2022; Accurate prediction of metachronous liver metastasis in stage I-III colorectal cancer patients using deep learning with digital pathological images. Front Oncol. 12:844067. DOI: 10.3389/fonc.2022.844067. PMID: 35433467. PMCID: PMC9010865.

32. Hao M, Li H, Wang K, Liu Y, Liang X, Ding L. 2022; Predicting metachronous liver metastasis in patients with colorectal cancer: development and assessment of a new nomogram. World J Surg Oncol. 20:80. DOI: 10.1186/s12957-022-02558-6. PMID: 35279173. PMCID: PMC8918281.

33. Chok AY, Zhao Y, Chen HLR, Tan IE, Chew DHW, Zhao Y, et al. 2023; Elderly patients over 80 years undergoing colorectal cancer resection: development and validation of a predictive nomogram for survival. World J Gastrointest Surg. 15:892–905. DOI: 10.4240/wjgs.v15.i5.892. PMID: 37342856. PMCID: PMC10277950.

34. Xiao J, Wang Y, Chen J, Xie L, Huang J. 2021; Impact of resampling methods and classification models on the imbalanced credit scoring problems. Information Sciences. 569:508–526. DOI: 10.1016/j.ins.2021.05.029.

35. International Joint Conferene on Artificial Intelligence (IJCAI). In : The 1995 International Joint Conference on AI; 1995 Aug 20-25; Montreal, Canada. Montreal: International Joint Conferene on Artificial Intelligence (IJCAI);1995. p. 1137–1143.

36. Javaid M, Haleem A, Singh RP, Suman R, Rab S. 2022; Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw. 3:58–73. DOI: 10.1016/j.ijin.2022.05.002.

37. Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux P, et al. 2017; Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA. 318:1377–1384. DOI: 10.1001/jama.2017.12126. PMID: 29049590.

38. Schulz A, Viktil E, Godt JC, Johansen CK, Dormagen JB, Holtedahl JE, et al. 2016; Diagnostic performance of CT, MRI and PET/CT in patients with suspected colorectal liver metastases: the superiority of MRI. Acta Radiol. 57:1040–1048. DOI: 10.1177/0284185115617349. PMID: 26622057.

39. Niekel MC, Bipat S, Stoker J. 2010; Diagnostic imaging of colorectal liver metastases with CT, MR imaging, FDG PET, and/or FDG PET/CT: a meta-analysis of prospective studies including patients who have not previously undergone treatment. Radiology. 257:674–684. DOI: 10.1148/radiol.10100729. PMID: 20829538.

40. Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. 2019; Radiomics and artificial intelligence for biomarker and prediction model development in oncology. Comput Struct Biotechnol J. 17:995–1008. DOI: 10.1016/j.csbj.2019.07.001. PMID: 31388413. PMCID: PMC6667772.

41. Fan L, Fang M, Tu W, Zhang D, Wang Y, Zhou X, et al. 2019; Radiomics signature: a biomarker for the preoperative distant metastatic prediction of stage I nonsmall cell lung cancer. Acad Radiol. 26:1253–1261. DOI: 10.1016/j.acra.2018.11.004. PMID: 30527455.

42. Zhang L, Dong D, Li H, Tian J, Ouyang F, Mo X, et al. 2019; Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: a retrospective cohort study. EBioMedicine. 40:327–335. DOI: 10.1016/j.ebiom.2019.01.013. PMID: 30642750. PMCID: PMC6413336.

43. Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RT, Hermann G, et al. 2015; CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 114:345–350. DOI: 10.1016/j.radonc.2015.02.015. PMID: 25746350. PMCID: PMC4400248.

44. Busse PJ, Mathur SK. 2010; Age-related changes in immune function: effect on airway inflammation. J Allergy Clin Immunol. 126:690–699. DOI: 10.1016/j.jaci.2010.08.011. PMID: 20920759. PMCID: PMC3297963.

45. Parcesepe P, Giordano G, Laudanna C, Febbraro A, Pancione M. 2016; Cancer-associated immune resistance and evasion of immune surveillance in colorectal cancer. Gastroenterol Res Pract. 2016:6261721. DOI: 10.1155/2016/6261721. PMID: 27006653. PMCID: PMC4781955.

46. McConnell BB, Yang VW. 2009; The role of inflammation in the pathogenesis of colorectal cancer. Curr Colorectal Cancer Rep. 5:69–74. DOI: 10.1007/s11888-009-0011-z. PMID: 19756239. PMCID: PMC2743014.

47. Smith HA, Kang Y. 2013; The metastasis-promoting roles of tumor-associated immune cells. J Mol Med (Berl). 91:411–429. DOI: 10.1007/s00109-013-1021-5. PMID: 23515621. PMCID: PMC3697909.

48. Kitamura T, Qian BZ, Pollard JW. 2015; Immune cell promotion of metastasis. Nat Rev Immunol. 15:73–86. DOI: 10.1038/nri3789. PMID: 25614318. PMCID: PMC4470277.

49. Pancione M, Giordano G, Remo A, Febbraro A, Sabatino L, Manfrin E, et al. 2014; Immune escape mechanisms in colorectal cancer pathogenesis and liver metastasis. J Immunol Res. 2014:686879. DOI: 10.1155/2014/686879. PMID: 24741617. PMCID: PMC3987978.

50. Hendifar A, Yang D, Lenz F, Lurje G, Pohl A, Lenz C, et al. 2009; Gender disparities in metastatic colorectal cancer survival. Clin Cancer Res. 15:6391–6397. DOI: 10.1158/1078-0432.CCR-09-0877. PMID: 19789331. PMCID: PMC2779768.

51. Press OA, Zhang W, Gordon MA, Yang D, Lurje G, Iqbal S, et al. 2008; Gender-related survival differences associated with EGFR polymorphisms in metastatic colon cancer. Cancer Res. 68:3037–3042. DOI: 10.1158/0008-5472.CAN-07-2718. PMID: 18413774.

52. Abancens M, Bustos V, Harvey H, Mcbryan J, Harvey BJ. 2020; Sexual dimorphism in colon cancer. Front Oncol. 10:607909. DOI: 10.3389/fonc.2020.607909. PMID: 33363037. PMCID: PMC7759153.

53. Roshan MH, Tambo A, Pace NP. 2016; The role of testosterone in colorectal carcinoma: pathomechanisms and open questions. EPMA J. 7:22. DOI: 10.1186/s13167-016-0071-5. PMID: 27833666. PMCID: PMC5103431.

54. Milette S, Hashimoto M, Perrino S, Qi S, Chen M, Ham B, et al. 2019; Sexual dimorphism and the role of estrogen in the immune microenvironment of liver metastases. Nat Commun. 10:5745. DOI: 10.1038/s41467-019-13571-x. PMID: 31848339. PMCID: PMC6917725.

55. Su BB, Shi H, Wan J. 2012; Role of serum carcinoembryonic antigen in the detection of colorectal cancer before and after surgical resection. World J Gastroenterol. 18:2121–2126. DOI: 10.3748/wjg.v18.i17.2121. PMID: 22563201. PMCID: PMC3342612.

56. Hammarström S. 1999; The carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissues. Sem Cancer Biol. 9:67–81. DOI: 10.1006/scbi.1998.0119. PMID: 10202129.

57. Kamphues C, Andreatos N, Kruppa J, Buettner S, Wang J, Sasaki K, et al. 2021; The optimal cut-off values for tumor size, number of lesions, and CEA levels in patients with surgically treated colorectal cancer liver metastases: an international, multi-institutional study. J Surg Oncol. 123:939–948. DOI: 10.1002/jso.26361. PMID: 33400818.

58. Fletcher RH. 1986; Carcinoembryonic antigen. Ann Intern Med. 104:66–73. DOI: 10.7326/0003-4819-104-1-66. PMID: 3510056.

59. Duffy MJ. 2001; Carcinoembryonic antigen as a marker for colorectal cancer: is it clinically useful? Clin Chem. 47:624–630. DOI: 10.1093/clinchem/47.4.624. PMID: 11274010.

60. Hall C, Clarke L, Pal A, Buchwald P, Eglinton T, Wakeman C, et al. 2019; A review of the role of carcinoembryonic antigen in clinical practice. Ann Coloproctol. 35:294–305. DOI: 10.3393/ac.2019.11.13. PMID: 31937069. PMCID: PMC6968721.

61. Enquist IB, Good Z, Jubb AM, Fuh G, Wang X, Junttila MR, et al. 2014; Lymph node-independent liver metastasis in a model of metastatic colorectal cancer. Nat Commun. 5:3530. DOI: 10.1038/ncomms4530. PMID: 24667486.

62. Ahana P, Kavitha G. 2022; Radiomic features based severity prediction in dementia MR images using hybrid SSA-PSO optimizer and multi-class SVM classifier. IRBM. 43:549–560. DOI: 10.1016/j.irbm.2022.05.003.

63. Awad M, Khanna R. Awad M, Khanna R, editors. 2015. Support vector machines for classification. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. p. 39–66. Apress;DOI: 10.1007/978-1-4302-5990-9_3.

64. Prasad AM, Iverson LR, Liaw A. 2006; Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 9:181–199. DOI: 10.1007/s10021-005-0054-1.