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Yang H, Liu J, Yang N, Fu Q, Wang Y, Ye M, Tao S, Liu X, Li Q. Enhancing metastatic colorectal cancer prediction through advanced feature selection and machine learning techniques. Int Immunopharmacol 2024; 142:113033. [PMID: 39226823 DOI: 10.1016/j.intimp.2024.113033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 08/15/2024] [Accepted: 08/25/2024] [Indexed: 09/05/2024]
Abstract
BACKGROUND AND AIMS Colorectal cancer (CRC) is the third most prevalent cancer globally, posing a significant challenge due to its high rate of metastasis. Approximately 20% of patients with CRC present with distant metastases at diagnosis, and over 50% develop metastases within five years. Accurate prediction of metastasis is crucial for improving survival outcomes in patients with CRC. METHODS This study introduces an innovative cost-sensitive fast correlation-based filter (CS-FCBF) algorithm for feature selection, integrated with machine learning techniques to predict metastatic CRC. The CS-FCBF algorithm effectively reduced the number of genomic features from 184 to 9 critical genes: CXCL9, C2CD4B, RGCC, GFI1, BEX2, CXCL3, FOXQ1, PBK, and PLAG1. The methodology combined in vitro, in vivo, and analysis of publicly available single-cell RNA-seq datasets to validate the findings. RESULTS The application of the CS-FCBF algorithm led to a significant improvement in prediction model performance, with an average 21.16% increase in the area under the precision-recall curve. The nine identified genes hold potential as diagnostic biomarkers and therapeutic targets for metastatic CRC. CONCLUSIONS This study highlights the critical role of advanced feature selection methods, combined with machine learning, in addressing the challenge of class imbalance in medical diagnosis, particularly for CRC. Early detection of metastasis is vital, and the identified genes underscore their importance in the metastatic process of CRC. The methodology applied here offers valuable insights and paves the way for future research in other cancers or diseases that face similar diagnostic challenges.
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Affiliation(s)
- Hui Yang
- Central Laboratory, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui, China; Anhui Province Key Laboratory of Non-coding RNA Basic and Clinical Transformation, Wuhu, Anhui, China
| | - Jun Liu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui, China
| | - Na Yang
- Department of Critical Care Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui, China; Clinical Research Center for Critical Respiratory Medicine of Anhui Province, Wuhu, Anhui, China
| | - Qingsheng Fu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui, China
| | - Yingying Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui 241001, China
| | - Mingquan Ye
- Research Center of Health Big Data Mining and Applications, School of Medical Information, Wannan Medical College, Wuhu, Anhui, China
| | - Shaoneng Tao
- Department of Nuclear Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui 241001, China.
| | - Xiaocen Liu
- Department of Nuclear Medicine, The First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, Anhui 241001, China.
| | - Qingqing Li
- Research Center of Health Big Data Mining and Applications, School of Medical Information, Wannan Medical College, Wuhu, Anhui, China.
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Deng B, Wang Q, Liu Y, Yang Y, Gao X, Dai H. A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer. Abdom Radiol (NY) 2024; 49:1850-1860. [PMID: 38349392 DOI: 10.1007/s00261-023-04164-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 12/10/2023] [Accepted: 12/16/2023] [Indexed: 06/29/2024]
Abstract
PURPOSE To develop and validate a nomogram for the preoperative diagnosis of T2 and T3 stage rectal cancer using MRI radiomics features of mesorectal fat. METHODS The data of 288 patients with T2 and T3 stage rectal cancer were retrospectively collected. Radiomics features were extracted from the lesion region of interest (ROI) in the MRI high-resolution T2WI, apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences. After using ICC inter-group consistency analysis and Pearson correlation analysis to reduce dimensions, LASSO regression analysis was performed to select features and calculate Rad-score for each sequence. Then, Combined_Radscore and nomogram were constructed based on the LASSO-selected features and clinical data for each sequence. Receiver operating characteristic curve (ROC) area under the curve (AUC) was used to evaluate the performance of the Rad-score model and nomogram. Decision curve analysis (DCA) was performed to evaluate the clinical usability of the radiomics nomogram, which were combined with calibration curves to evaluate the prediction accuracy. RESULTS The nomogram based on MRI-report T status and Combined_Radscore achieved AUCs of 0.921 and 0.889 in the training and validation cohorts, respectively. CONCLUSION The nomogram can be stated that the radiomics nomogram based on multi-sequence MRI imaging of the mesorectal fat has excellent diagnosing performance for preoperative differentiation of T2 and T3 stage rectal cancer.
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Affiliation(s)
- Bo Deng
- Department of Radiology, Shanghai Fifth Rehabilitation Hospital, Shanghai, China
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qian Wang
- Department of Radiology, Shanghai Fifth Rehabilitation Hospital, Shanghai, China
| | - Yuanqing Liu
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yanwei Yang
- Magnetic Resonance Room of Orthopedics Department, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaolong Gao
- Department of Radiology, Luodian Hospital, Shanghai University Medical College, Baoshan District, Shanghai, China.
| | - Hui Dai
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China.
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Chen KA, Goffredo P, Hu D, Joisa CU, Guillem JG, Gomez SM, Kapadia MR. Estimating Risk of Locoregional Failure and Overall Survival in Anal Cancer Following Chemoradiation: A Machine Learning Approach. J Gastrointest Surg 2023; 27:1925-1935. [PMID: 37407899 PMCID: PMC10528925 DOI: 10.1007/s11605-023-05755-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/03/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Optimal treatment of anal squamous cell carcinoma (ASCC) is definitive chemoradiation. Patients with persistent or recurrent disease require abdominoperineal resection (APR). Current models for predicting need for APR and overall survival are limited by low accuracy or small datasets. This study sought to use machine learning (ML) to develop more accurate models for locoregional failure and overall survival for ASCC. METHODS This study used the National Cancer Database from 2004-2018, divided into training, validation, and test sets. We included patients with stage I-III ASCC who underwent chemoradiation. Our primary outcomes were need for APR and 3-year overall survival. Random forest (RF), gradient boosting (XGB), and neural network (NN) ML-based models were developed and compared with logistic regression (LR). Accuracy was assessed using area under the receiver operating characteristic curve (AUROC). RESULTS APR was required in 5.3% (1,015/18,978) of patients. XGB performed best with AUROC of 0.813, compared with 0.691 for LR. Tumor size, lymphovascular invasion, and tumor grade showed the strongest influence on model predictions. Mortality was 23.6% (7,988/33,834). AUROC for XGB and LR were similar at 0.766 and 0.748, respectively. For this model, age, radiation dose, sex, and insurance status were the most influential variables. CONCLUSIONS We developed and internally validated machine learning-based models for predicting outcomes in ASCC and showed higher accuracy versus LR for locoregional failure, but not overall survival. After external validation, these models may assist clinicians with identifying patients with ASCC at high risk of treatment failure.
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Affiliation(s)
- Kevin A Chen
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA
| | - Paolo Goffredo
- Division of Colon & Rectal Surgery, Department of Surgery, University of Minnesota, 420 Delaware St SE, MN, 55455, Minneapolis, USA
| | - David Hu
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, 27599-7420, USA
| | - Chinmaya U Joisa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Jose G Guillem
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA
| | - Shawn M Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599, USA
| | - Muneera R Kapadia
- Divison of Gastrointestinal Surgery, Department of Surgery, University of North Carolina at Chapel Hill, 100 Manning Drive, 4038 Burnett Womack Building, CB #7050, Chapel Hill, NC, 27599, USA.
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Spinelli A, Carrano FM, Laino ME, Andreozzi M, Koleth G, Hassan C, Repici A, Chand M, Savevski V, Pellino G. Artificial intelligence in colorectal surgery: an AI-powered systematic review. Tech Coloproctol 2023; 27:615-629. [PMID: 36805890 DOI: 10.1007/s10151-023-02772-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/07/2023] [Indexed: 02/23/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
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Affiliation(s)
- A Spinelli
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy.
| | - F M Carrano
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M E Laino
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Andreozzi
- Department of Clinical Medicine and Surgery, University "Federico II" of Naples, Naples, Italy
| | - G Koleth
- Department of Gastroenterology and Hepatology, Hospital Selayang, Selangor, Malaysia
| | - C Hassan
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - A Repici
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Chand
- Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
| | - V Savevski
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - G Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
- Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona UAB, Barcelona, Spain
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Liu G, Yang Z, Wang D. A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results. Cancer Sci 2023; 114:1131-1141. [PMID: 36285478 PMCID: PMC9986069 DOI: 10.1111/cas.15624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 12/25/2022] Open
Abstract
This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log-rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3- and 5-year survival. We also grouped the patients into new stages by clustering their 5-year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3- and 5-year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan-Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3-year survival, and 0.851 and 0.787 for 5-year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.
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Affiliation(s)
- Guangcong Liu
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
| | - Zhuo Yang
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
| | - Danbo Wang
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute Shenyang, Shenyang, People's Republic of China
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Chi XJ, Song YB, Liu DH, Wei LQ, An X, Feng ZZ, Lan XH, Lan D, Huang C. Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm. Digit Health 2023; 9:20552076231203902. [PMID: 37766908 PMCID: PMC10521306 DOI: 10.1177/20552076231203902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Background Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients. Methods The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package. Results High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848-0.910). Conclusion The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.
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Affiliation(s)
- Xiao-jv Chi
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning, China
| | - Yi-bei Song
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning, China
| | - Deng-he Liu
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning, China
| | - Li-qiang Wei
- Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning, China
| | - Xin An
- The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zi-zhen Feng
- The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiao-hua Lan
- The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong Lan
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chao Huang
- School of Information and Management, Guangxi Medical University, Nanning, China
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Sakamoto T, Goto T, Fujiogi M, Kawarai Lefor A. Machine learning in gastrointestinal surgery. Surg Today 2021; 52:995-1007. [PMID: 34559310 DOI: 10.1007/s00595-021-02380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/03/2021] [Indexed: 12/11/2022]
Abstract
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
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Affiliation(s)
- Takashi Sakamoto
- Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan. .,Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.,TXP Medical Co. Ltd, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 114-8485, Japan
| | - Michimasa Fujiogi
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Department of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Alan Kawarai Lefor
- Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, 3290498, Japan
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Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18157734. [PMID: 34360026 PMCID: PMC8345484 DOI: 10.3390/ijerph18157734] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. MATERIALS AND METHODS A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran's three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. RESULTS The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650-0.734), as well as 0.627 (0.670, 0.686) in the test data. CONCLUSION We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.
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