References
- Edwards BK, Ward E, Kohler BA, Eheman C, Zauber AG, Anderson RN, et al. 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 2010;116:544-573.
- Morgan E, Arnold M, Gini A, Lorenzoni V, Cabasag C, Laversanne M, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut 2023;72:338-344.
- 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;68:394-424.
- Yu X, Zhu L, Liu J, Xie M, Chen J, Li J. Emerging role of immunotherapy for colorectal cancer with liver metastasis. Onco Targets Ther 2020;13:11645-11658.
- Tauriello DV, Calon A, Lonardo E, Batlle E. Determinants of metastatic competency in colorectal cancer. Mol Oncol 2017;11:97-119.
- Engstrand J, Nilsson H, Stromberg C, Jonas E, Freedman J. Colorectal cancer liver metastases-a population-based study on incidence, management and survival. BMC Cancer 2018;18:78.
- Martin J, Petrillo A, Smyth EC, Shaida N, Khwaja S, Cheow H, et al. Colorectal liver metastases: current management and future perspectives. World J Clin Oncol 2020;11:761.
- Hugen N, van de Velde CJH, de Wilt JHW, Nagtegaal ID. Metastatic pattern in colorectal cancer is strongly influenced by histological subtype. Ann Oncol 2014;25:651-657.
- Ivey GD, Johnston FM, Azad NS, Christenson ES, Lafaro KJ, Shubert CR. Current surgical management strategies for colorectal cancer liver metastases. Cancers (Basel) 2022;14:1063.
- Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018;2:719-731.
- Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017;2:230-243.
- Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 2021;21:125.
- Vorontsov E, Cerny M, Regnier P, Di Jorio L, Pal CJ, Lapointe R, et al. Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases. Radiol Artif Intell 2019;1:180014.
- Paredes AZ, Hyer JM, Tsilimigras DI, Moro A, Bagante F, Guglielmi A, et al. A novel machine-learning approach to predict recurrence after resection of colorecta liver metastases. Ann Surg Oncol 2020;27:5139-5147.
- Taghavi M, Trebeschi S, Simoes R, Meek DB, Beckers RC, Lambregts DM, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY) 2021;46:249-256.
- Rompianesi G, Pegoraro F, Ceresa CD, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol 2022;28:108-122.
- Visvikis D, Cheze Le Rest C, Jaouen V, Hatt M. Artificial intelligence, machine (deep) learning and radio (geno) mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging 2019;46:2630-2637.
- Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int J Surg 2021;88:105906.
- Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014;11:e1001744.
- Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 2019;170:W1-W33.
- Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol 2019;19:64.
- Pfob A, Lu SC, Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Med Res Methodol 2022;22:282.
- Bektas M, Tuynman JB, Costa Pereira J, Burchell GL, Van Der Peet DL. Machine learning algorithms for predicting surgical outcomes after colorectal surgery: a systematic review. World J Surg 2022;46:3100-3110.
- Jalali A, Alvarez-Iglesias A, Roshan D, Newell J. Visualising statistical models using dynamic nomograms. PloS One 2019;14:e0225253.
- Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, et al. Machine and deep learning methods for radiomics. Med Phys 2020;47:e185-e202.
- Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-446.
- Liang M, Cai Z, Zhang H, Huang C, Meng Y, Zhao L, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis. Acad Radiol 2019;26:1495-1504.
- Yan Y, Liu H, Mao K, Zhang M, Zhou Q, Yu W, et al. Novel nomograms to predict lymph node metastasis and liver metastasis in patients with early colon carcinoma. J Transl Med 2019;17:193.
- Li Y, Eresen A, Shangguan J, Yang J, Lu Y, Chen D, et al. Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer. Am J Cancer Res 2019;9:2482-2492.
- Lee S, Choe EK, Kim SY, Kim HS, Park KJ, Kim D. 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 2020;21:382.
- Xiao C, Zhou M, Yang X, Wang H, Tang Z, Zhou Z, et al. Accurate prediction of metachronous liver metastasis in stage I-III colorectal cancer patients using deep learning with digital pathological images. Front Oncol 2022;12:844067.
- Hao M, Li H, Wang K, Liu Y, Liang X, Ding L. Predicting metachronous liver metastasis in patients with colorectal cancer: development and assessment of a new nomogram. World J Surg Oncol 2022;20:80.
- Chok AY, Zhao Y, Chen HLR, Tan IE, Chew DHW, Zhao Y, et al. Elderly patients over 80 years undergoing colorectal cancer resection: development and validation of a predictive nomogram for survival. World J Gastrointest Surg 2023;15:892-905.
- Xiao J, Wang Y, Chen J, Xie L, Huang J. Impact of resampling methods and classification models on the imbalanced credit scoring problems. Information Sciences 2021;569:508-526.
- International Joint Conferene on Artificial Intelligence (IJCAI); The 1995 International Joint Conference on AI; 1995 Aug 20-25; Montreal, Canada. Montreal: International Joint Conferene on Artificial Intelligence (IJCAI); 1995:1137-1143.
- Javaid M, Haleem A, Singh RP, Suman R, Rab S. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw 2022;3:58-73.
- Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux P, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA 2017;318:1377-1384.
- Schulz A, Viktil E, Godt JC, Johansen CK, Dormagen JB, Holtedahl JE, et al. Diagnostic performance of CT, MRI and PET/CT in patients with suspected colorectal liver metastases: the superiority of MRI. Acta Radiol 2016;57:1040-1048.
- Niekel MC, Bipat S, Stoker J. 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 2010;257:674-684.
- Forghani R, Savadjiev P, Chatterjee A, Muthukrishnan N, Reinhold C, Forghani B. Radiomics and artificial intelligence for biomarker and prediction model development in oncology. Comput Struct Biotechnol J 2019;17:995-1008.
- Fan L, Fang M, Tu W, Zhang D, Wang Y, Zhou X, et al. Radiomics signature: a biomarker for the preoperative distant metastatic prediction of stage I nonsmall cell lung cancer. Acad Radiol 2019;26:1253-1261.
- Zhang L, Dong D, Li H, Tian J, Ouyang F, Mo X, et al. 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 2019; 40:327-335.
- Coroller TP, Grossmann P, Hou Y, Velazquez ER, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015;114:345-350.
- Busse PJ, Mathur SK. Age-related changes in immune function: effect on airway inflammation. J Allergy Clin Immunol 2010;126:690-699.
- Parcesepe P, Giordano G, Laudanna C, Febbraro A, Pancione M. Cancer-associated immune resistance and evasion of immune surveillance in colorectal cancer. Gastroenterol Res Pract 2016;2016:6261721.
- McConnell BB, Yang VW. The role of inflammation in the pathogenesis of colorectal cancer. Curr Colorectal Cancer Rep 2009;5:69-74.
- Smith HA, Kang Y. The metastasis-promoting roles of tumor-associated immune cells. J Mol Med (Berl) 2013;91:411-429.
- Kitamura T, Qian BZ, Pollard JW. Immune cell promotion of metastasis. Nat Rev Immunol 2015;15:73-86.
- Pancione M, Giordano G, Remo A, Febbraro A, Sabatino L, Manfrin E, et al. Immune escape mechanisms in colorectal cancer pathogenesis and liver metastasis. J Immunol Res 2014;2014:686879.
- Hendifar A, Yang D, Lenz F, Lurje G, Pohl A, Lenz C, et al. Gender disparities in metastatic colorectal cancer survival. Clin Cancer Res 2009;15:6391-6397.
- Press OA, Zhang W, Gordon MA, Yang D, Lurje G, Iqbal S, et al. Gender-related survival differences associated with EGFR polymorphisms in metastatic colon cancer. Cancer Res 2008;68:3037-3042.
- Abancens M, Bustos V, Harvey H, Mcbryan J, Harvey BJ. Sexual dimorphism in colon cancer. Front Oncol 2020;10:607909.
- Roshan MH, Tambo A, Pace NP. The role of testosterone in colorectal carcinoma: pathomechanisms and open questions. EPMA J 2016;7:22.
- Milette S, Hashimoto M, Perrino S, Qi S, Chen M, Ham B, et al. Sexual dimorphism and the role of estrogen in the immune microenvironment of liver metastases. Nat Commun 2019;10:5745.
- Su BB, Shi H, Wan J. Role of serum carcinoembryonic antigen in the detection of colorectal cancer before and after surgical resection. World J Gastroenterol 2012;18:2121-2126.
- Hammarstrom S. The carcinoembryonic antigen (CEA) family: structures, suggested functions and expression in normal and malignant tissues. Sem Cancer Biol 1999;9:67-81.
- Kamphues C, Andreatos N, Kruppa J, Buettner S, Wang J, Sasaki K, et al. 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 2021;123:939-948.
- Fletcher R H. Ca rcinoembr yonic a nt igen. A nn Intern Med 1986;104:66-73.
- Duffy MJ. Carcinoembryonic antigen as a marker for colorectal cancer: is it clinically useful? Clin Chem 2001;47:624-630.
- Hall C, Clarke L, Pal A, Buchwald P, Eglinton T, Wakeman C, et al. A review of the role of carcinoembryonic antigen in clinical practice. Ann Coloproctol 2019;35:294-305.
- Enquist IB, Good Z, Jubb AM, Fuh G, Wang X, Junttila MR, et al. Lymph node-independent liver metastasis in a model of metastatic colorectal cancer. Nat Commun 2014;5:3530.
- Ahana P, Kavitha G. Radiomic features based severity prediction in dementia MR images using hybrid SSA-PSO optimizer and multiclass SVM classifier. IRBM 2022;43:549-560.
- Awad M, Khanna R. Support vector machines for classification. In: Awad M, Khanna R, ed. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Apress, 2015:39-66.
- Prasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 2006;9:181-199.