Clinical and Translational Research Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Jun 27, 2024; 16(6): 1791-1802
Published online Jun 27, 2024. doi: 10.4240/wjgs.v16.i6.1791
Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study
Yin-Jie Zhou, Xin-Hua Xu, Department of Oncology, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang 443000, Hubei Province, China
Zhi-E Tan, Department of Nuclear Medicine, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, Yichang 443000, Hubei Province, China
Wei-Da Zhuang, Department of Athe and Intestinal Surgery, Cancer Hospital of The Chinese Academy of Medical Sciences, Beijing 100021, China
ORCID number: Xin-Hua Xu (0009-0004-3154-2221).
Author contributions: Zhou YJ wrote the manuscript; Tan ZE and Zhuang WD collected the data; and Xu XH guided the study; All authors reviewed, edited, and approved the final manuscript and revised it critically for important intellectual content, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.
Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xin-Hua Xu, MM, Doctor, Department of Oncology, The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People's Hospital, No. 183 Yi Ling Road, Yichang 443000, Hubei Province, China. 2732774352@qq.com
Received: March 8, 2024
Revised: April 29, 2024
Accepted: May 16, 2024
Published online: June 27, 2024
Processing time: 113 Days and 23.2 Hours

Abstract
BACKGROUND

Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation.

AIM

To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC.

METHODS

Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation.

RESULTS

A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively.

CONCLUSION

Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

Key Words: Colorectal tumor, Surveillance epidemiology and end results database, Nomogram analysis, Survival prognosis, Retrospective study

Core Tip: This study utilized an evidence-based approach to analyze cancer-specific survival (CSS) in patients with metastatic colorectal cancer (mCRC). By systematically collecting, integrating, and analyzing relevant data, we explored CSS in mCRC patients and its influencing factors to provide clinicians with more accurate prognostic assessments and treatment decision support. The importance of this study is that it can provide a basis for individualized treatment of mCRC patients and promote the maximization of treatment effects, thereby improving the quality of life and survival rate of patients.



INTRODUCTION

Colorectal cancer (CRC) is one of the most common malignant neoplasms, ranking third in incidence (10.2%) and second in mortality (9.2%)[1-3]. In countries in Eastern Europe, Latin America, and Asia, the incidence and mortality of CRC are increasing annually[4]. There are no obvious signs or symptoms of CRC in the early stages, and more than one-fifth of patients have developed distant metastases at the time of diagnosis[5]. Among patients with CRC, patients with simultaneous metastases have lower survival rates than patients with heterochronous metastases[6]. The most common metastatic organs for CRC are the liver and lung, while bone metastases are rare, and brain metastases occur in only 1% of CRC patients[7]. Although metastatic CRC (mCRC) has the worst prognosis, there are large differences in survival outcomes between patients with different metastatic organs. The 1-year survival rate for patients with liver and lung metastases is greater than 80%, while the 1-year survival rates for patients with bone and brain metastases are 30% and 11%, respectively[8]. Therefore, accurate screening for different risk factors is critical for physicians to predict mCRC outcomes.

Currently, the American Joint Committee on Cancer (AJCC) staging system is the primary method for predicting survival outcomes in patients with mCRC[9]. However, the T stage, N stage, and M stage are the only factors for distinguishing different prognoses, and this scheme is far from satisfactory in terms of prediction accuracy[10]. A nomogram is a visual tool used to predict the probability of an endpoint occurring and to quantify survival risk. According to the different regression coefficients, the columniogram can include significant factors to improve the prediction accuracy. To date, nomograms have been successfully used to predict the prognosis of patients with CRC but have rarely been used for patients with mCRC[11].

Therefore, our goal was to develop a new nomographic model to predict tumor-specific survival for patients with simultaneous mCRC and to divide this model into different risk levels to accurately assess patient prognosis.

MATERIALS AND METHODS
Research subjects

This study obtained all the data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute using SEER Stat software (version 8.3.6). The data were collected and reported using data items and codes recorded by the North American Association of Central Cancer Registries. The inclusion criteria for patients were as follows: (1) Were diagnosed with CRC between 2018 and 2023; (2) were diagnosed with simultaneous metastasis; and (3) had a histological diagnosis. The exclusion criteria were as follows: (1) No patients with distant metastasis; and (2) unknown missing data, such as race, primary tumor site, T stage, N stage, carcinoembryonic antigen (CEA) status, surgical status, and survival time.

The following variables were collected: Race, sex, age at diagnosis, primary site, grade, T stage, N stage, CEA status, distant metastatic status (liver, lung, bone, brain), surgery (primary tumor resection), chemotherapy, cancer-specific survival (CSS), and survival time. CSS was assessed by 1-, 2-, and 3-year survival rates, defined as the time from the date of diagnosis to the date of death or study due to CRC, according to the eighth edition of the AJCC tumor-node-metastasis staging system.

Research method

All eligible patients were randomly divided into training and validation groups (at a ratio of 7:3). The Pearson chi-square test was used to examine demographic differences between all coqueues, training coqueues, and validation coqueues. A multivariate Cox proportional risk model was used to explore independent risk factors for CSS, and a predictive nomogram model was built using a training cohort. The C-index, calibration curve, and decision curve analysis (DCA) were used for internal and external verification.

Nomogram analysis

X-tile software was used to determine the optimal critical value according to the total score of the column graph to establish a risk grading system, and all patients were divided into low-, medium-, and high-risk groups. Kaplan-Meier (K-M) curves of CSS were constructed and compared with a logarithmic rank test. Statistical analysis was performed using SPSS 21.0 statistical software (IBM SPSS Statistics for Windows; Armonk, NY, United States), GraphPad Prism 6 (GraphPad Software), X-Tile software (Yale University), and R Statistical Software 3.6.2 (www.r-project.org/).

Statistical analysis

SPSS 23.0 statistical software was used for analysis. The χ2 test was used for comparison of counting data, and the t test was used for comparison of measurement data. The survival rate was calculated by the life table method, the survival curve was plotted by the K-M method, and comparisons were performed by the log-rank method. Multiple factor analysis was performed by the Cox proportional risk regression model, and P < 0.050 was considered to indicate statistical significance.

RESULTS
Baseline population information

According to the inclusion criteria, a total of 15838 patients eligible for inclusion were included in this study, among whom 11088 (70.0%) patients were randomly assigned to the training cohort and 4750 (30.0%) patients were randomly assigned to the verification cohort. The demographic characteristics of this study population are shown in Table 1.

Table 1 Demographic characteristics, n (%).
Variable
Total cohort (n = 15838)
Training cohort (n = 11088)
Validation cohort (n = 4750)
χ2
P value
Race1.360.507
        Black people2303 (14.5)1590 (14.3)713 (15.0)
        White race12075 (76.2)8480 (76.5)3595 (75.7)
        Other1460 (9.2)1018 (9.2)442 (9.3)
Sex0.0270.869
        Male8560 (54.0)5988 (54.0)2572 (54.1)
        Female7278 (46.0)5100 (46.0)2178 (45.9)
Age at diagnosis (yr)1.730.188
        < 7010 735 (67.8)7480 (67.5)3255 (68.5)
        ≥ 705103 (32.2)3608 (32.5)1495 (31.5)
Primary tumor location0.2640.607
        Colon7063 (44.6)4930 (44.5)2133 (44.9)
        Rectum8775 (55.4)6158 (55.5)2617 (55.1)
Tumor differentiation0.0140.906
        Ⅰ-Ⅱ11127 (70.3)7793 (70.3)3334 (70.2)
        Ⅲ-Ⅳ4711 (29.7)3295 (29.7)1416 (29.8)
T staging0.080.777
        1-22079 (13.1)1461 (13.2)618 (13.0)
        3-413759 (86.9)9627 (86.8)4132 (87.0)
N stage0.5760.448
        04209 (26.6)2966 (26.7)1243 (26.2)
        1-211629 (73.4)8122 (73.3)3507 (73.8)
CEA status2.7210.099
        Masculine12378 (78.2)8705 (78.5)3673 (77.3)
        Feminine3460 (21.8)2383 (21.5)1077 (22.7)
Liver metastases0.2860.593
        No4731 (29.9)3298 (29.7)1433 (30.2)
        Yes11107 (70.1)7790 (70.3)3317 (69.8)
Lung metastases2.2020.138
        No12673 (80.0)8838 (79.7)3835 (80.7)
        Yes3165 (20.0)2250 (20.3)915 (19.3)
Bone metastases0.7240.395
        No15226 (96.1)10669 (96.2)4557 (95.9)
        Yes612 (3.9)419 (3.8)193 (4.1)
Brain metastases1.0320.31
        No15682 (99.0)10973 (99.0)4709 (99.1)
        Yes156 (1.0)115 (1.0)41 (0.9)
Surgical0.0140.906
        No3495 (22.1)2444 (22.0)1051 (22.1)
        Yes12343 (77.9)8644 (78.0)3699 (77.9)
Chemotherapy0.0260.872
        None/unknown4235 (26.7)2969 (26.8)1266 (26.7)
        Yes11603 (73.3)8119 (73.2)3484 (73.3)

In this study, there were 8560 males (54.0%) and 7278 females (46.0%), of which the majority were white (76.2%), 13759 (86.9%) were T3-4, 11629 (73.4%) were N1-2, and CEA was positive (78.2%). The incidence of distant metastasis in the liver, lung, bone, and brain was 11107 (70.1%), 3165 (20.0%), 612 (3.9%), and 156 (1.0%), respectively. A total of 12343 patients (77.9%) received surgery, and 11603 patients (73.3%) received chemotherapy. There was no significant difference between the training cohort and the verification cohort (P > 0.05).

Prediction factor determination

The Cox proportional hazards model was used to identify independent risk factors for CSS. Multivariate analysis revealed that the independent risk factors in the training cohort were race, age at diagnosis, primary site, tumor grade, N stage, CEA status, liver metastasis, lung metastasis, bone metastasis, brain metastasis, surgery, and chemotherapy (Table 2).

Table 2 Multivariate analysis of COX based on training cohorts.
Variable
Multivariate analysis
HR (95%CI)
P value
Race
        Black people1
        White race0.894 (0.834-0.959)0.002
        Other0.835 (0.752-0.928)0.001
Sex
        Male1
        Female0.965 (0.918-1.015)0.17
Age at diagnosis (yr)
        < 701
        ≥ 701.162 (1.099-1.228)< 0.001
Primary tumor location
        Colon1
        Rectum0.715 (0.678-0.754)< 0.001
Tumor differentiation
        Ⅰ-Ⅱ1
        Ⅲ-Ⅳ1.721 (1.630-1.817)< 0.001
T staging
        1-21
        3-41.085 (0.999-1.179)0.053
N stage
        01
        1-21.304 (1.226-1.386)< 0.001
CEA status
        Masculine1
        Feminine0.699 (0.655-0.746)< 0.001
Liver metastases
        No1
        Yes1.406 (1.326-1.490)< 0.001
Lung metastases
        No1
        Yes1.341 (1.260-1.426)< 0.001
Bone metastases
        No1
        Yes1.621 (1.438-1.827)< 0.001
Brain metastases
        No1
        Yes1.718 (1.370-2.155)< 0.001
Surgical
        No1
        Yes0.459 (0.429-0.492)< 0.001
Chemotherapy
        None/unknown1
        Yes0.368 (0.348-0.390)< 0.001

Based on the significant risk factors for CSS, a predictive nomogram model of CSS was established (Figure 1). The regression coefficients and estimates of the training queue are shown in Table 3. The nomogram was evaluated with internal and external validation. The C-index of the column chart was 0.718 (95%CI: 0.712-0.725), and the C-finger number of the verification set was 0.722 (95%CI: 0.711-0.732), indicating good identification ability and better per-formance than TNM staging (C-index: Training set, 0.533, 95%CI: 0.525-0.540; verification set, 0.524, 95%CI: 0.513-0.535). A calibration diagram of the CSS showed good agreement between the predicted and actual values of the training and validation samples, with 1000 bootstrap samples (Figure 2). The DCA curve showed a large net gain between most threshold probabilities at different time points, indicating good potential clinical validity for predicting CSS (Figure 3).

Figure 1
Figure 1 Nomogram for predicting the tumor-specific survival of patients with metastatic colorectal cancer. CEA: Carcinoembryonic antigen; CSS: Cancer-specific survival.
Figure 2
Figure 2 Calibration curves based on cancer-specific survival for metastatic colorectal cancer patients. A-C: Calibration curves based on 1-, 2-, and 3-year cancer-specific survival (CSS) of the training cohort; D-F: Calibration curves based on 1-, 2-, and 3-year CSS of the validation cohort.
Figure 3
Figure 3 The nomogram model predicts the clinical decision curve of cancer-specific survival in metastatic colorectal cancer patients. A-C: Clinical decision curves based on 1-, 2-, and 3-year cancer-specific survival (CSS) in the training cohort; D-F: Clinical decision curves based on 1-, 2-, and 3-year CSS in the validation cohort.
Table 3 Regression coefficients and estimated scores for building a Nomogram prediction model based on a training cohort.
Variable
Nomogram
Regression coefficients
Estimated score
Race
        Black people17.84626118
        White race6.9607467
        Other00
Age at diagnosis (yr)
        < 7000
        ≥ 7014.5583615
Primary tumor location
        Colon32.7688133
        Rectum00
Tumor differentiation
        Ⅰ-Ⅱ00
        Ⅲ-Ⅳ54.3928954
N stage
        000
        1-227.3879427
CEA status
        Masculine35.5605136
        Feminine00
Liver metastases
        No00
        Yes34.1221334
Lung metastases
        No00
        Yes29.096529
Bone metastases
        No00
        Yes49.3078749
Brain metastases
        No00
        Yes54.3587954
Surgical
        No75.07475
        Yes00
Chemotherapy
        None/unknown100100
        Yes00
Range0-524.4740610-524
Score531.434807531
Establishment of the risk classification system

In addition, X-Tile software was used to determine the optimal cutoff value and establish a risk classification system (Figure 4). All patients were classified as low risk (5852/11088, 52.78%, score: 0-164), medium risk (3487/11088, 31.45%, score: 165-247) or high risk (1749/11088, 15.77%, score: 248-524). In theory, the total score ranges from 0 to 524. K-M curves showed that the risk grading system had good layering and differentiation ability for different CSS groups (Table 4, Figure 5).

Figure 4
Figure 4 X-tile software was used to calculate the optimal truncation value and establish a risk classification system. A and B: The optimal cutoff values of the predicted total scores, including the low-risk group (score: 0-164), medium-risk group (score: 165-247) and high-risk group (score: 248-480); C: Kaplan-Meier curves for different risk levels according to the cancer-specific survival of the training cohort.
Figure 5
Figure 5 Kaplan-Meier survival curves for patients with different risk levels were drawn according to their cancer-specific survival. A: Platoon line; B: Training queue; C: Authentication queue.
Table 4 Analyzes tumor-specific survival rates in patients with different risk classes, %.
Variable
Low-risk group
Medium-risk group
High-risk group
(n = 8140)
(n = 4737)
(n = 2013)
1 yr CSS86.1063.0031.50
2 yr CSS67.3038.0016.10
3 yr CSS49.7024.608.90
5 yr CSS31.3014.204.30
Median CSS36 months18 months5 months
95%CI34.987-37.01317.273-18.7274.503-5.497
DISCUSSION

The prognosis of mCRC patients is significantly worse than that of non-mCRC patients. mCRC mortality varies widely from patient to patient, suggesting the importance and necessity of reclassifying the exact risk level based on the AJCC staging system[12-14]. However, due to the limitations of the included factors, the existing prediction models lack individualization and comprehensive evaluation, and the sample sizes of most studies[15-17] are small, which also limits their universal applicability. In this study, we developed a new CSS predictive nomogram based on simultaneous mCRC data from large population cohorts.

We identified predictors of CSS that were consistent with previous studies, including race, age at diagnosis, primary site, grade, N stage, CEA status, liver metastasis, lung metastasis, bone metastasis, brain metastasis, surgery, and chemotherapy[18]. For patients with mCRC, both surgery and chemotherapy are important for improving outcomes, as recommended by the United States National Comprehensive Cancer Network (NCCN) guidelines and the European Society of Medical Oncology guidelines[19]. Modest suggested that the effective rate of first-line systemic treatment is 38% to 65%, and the disease control rate is 81% to 90%[20]. Compared to earlier studies, this column chart is the first to include chemotherapy status as a risk predictor for predicting CSS. The highest score of mCRC patients who did not receive chemotherapy was 100, which was greater than that of mCRC patients who did not receive surgery, indicating that the regression coefficient of the effect of chemotherapy on CSS was greater than that of surgery[21-23]. In addition, patients who did not receive chemotherapy or who did not receive chemotherapy were not separately recorded in the SEER database as confounding risk factors in this study, which may reduce the actual regression coefficient of not receiving chemotherapy[24-26]. According to previous studies[27-29], chemotherapy is positively associated with survival benefits in patients with mCRC, and our study further highlights the unique advantages of simultaneous mCRC chemotherapy.

In addition to chemotherapy, our study revealed that primary tumor resection is also important for prognosis. Several studies[30-32] support this idea in mCRC, especially in patients with liver or lung metastases. The NCCN guidelines recommend that patients with mCRC should be evaluated by a multidisciplinary team and, if possible, that the metastatic disease and primary tumor should be removed. Therefore, primary tumor resection remains controversial for mCRC patients whose metastases cannot be resected. Studies[33-35] have shown that primary tumor resection significantly extends overall survival (OS) in mCRC patients with unresectable metastases (median OS: 13.8 months vs 6.3 months, P = 0.0001). Another study[36] also supported the idea that primary tumor removal resulted in better survival for mCRC patients with unresectable metastases (2-year CSS: 50.2% vs 28.1%, P < 0.001). In conclusion, primary tumor resection has a positive impact on patient survival. As mentioned above, the liver and lungs are the most common sites of CRC metastasis, and bone and brain metastases are very rare. In addition, the prognostic significance of different metastatic organs was inconsistent. The occurrence of brain metastases is often associated with the worst survival, and studies[37-39] have reported that the median survival of CRC patients with brain metastases is 3 to 6 months, that of CRC patients with bone metastases is 5 to 7 months, that of CRC patients with liver metastases is 22.8 months, and that of CRC patients with lung metastases is 36.2 to 49 months. Another study confirmed this idea, with brain metastases having the largest coefficient of impact among the four metastatic organs of CRC. Our study showed that the regression coefficients of CSS in descending order were brain metastasis, bone metastasis, liver metastasis, and lung metastasis. Due to the presence of the blood-brain barrier (BBB) and blood–cerebrospinal fluid barrier (CSF), brain metastases are often the ultimate organs of metastasis for CRC, while other extracranial metastases occur in areas such as the liver and lungs. The BBB and CSF also hinder chemotherapy efficacy, which may be another reason for the poor prognosis.

On the basis of multiple regression analysis, we developed a new nomograph to integrate multiple predictors and help accurately predict the survival of patients with synchronous mCRC. One study constructed a nomogram for predicting the survival of CRC patients. Another study also developed an OS nomogram model for predicting mCRC with strong consistency. Compared to existing predictive models, our column charts integrate more predictive variables, such as chemotherapy and surgery, to provide comprehensive predictions for CSS. In addition, through X-Tile software, we established a risk classification system with an optimal cutoff value that is more accurate and reliable. This approach helps to assess the level of risk in patients with mCRC, allowing for individualized treatment and an accurate prognosis. In addition, we provide estimated points for each important prognostic factor to improve clinical application[40].

There are several limitations to our study. First, this study is a retrospective analysis of existing selection bias. Furthermore, the SEER database does not contain detailed information on chemotherapy regimens or targeted therapies, which hinders further subgroup analysis. Then, the SEER data are used to verify the validity of the column graph prediction, which lacks the verification of real data.

CONCLUSION

In summary, we developed a new nomogram model to predict CSS in patients with synchronous mCRC. The verification of the model showed that the model has good discriminability and consistency. The risk grading system can grade the risk level of mCRC patients, accurately evaluate patient prognosis, and guide treatment.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single-blind

Specialty type: Gastroenterology and hepatology

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade C

Novelty: Grade C

Creativity or Innovation: Grade C

Scientific Significance: Grade C

P-Reviewer: Yadav BS, India S-Editor: Li L L-Editor: A P-Editor: Chen YX

References
1.  Chen J, Feng B, Hu M, Huang F, Chen Y, Ma X, Long W. A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI. BMC Med Imaging. 2023;23:200.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
2.  Park JK, Park JE, Bang S, Jeon HJ, Kim JW, Lee WS. Development and validation of a nomogram for predicting ongoing pregnancy in single vitrified-warmed blastocyst embryo transfer cycles. Front Endocrinol (Lausanne). 2023;14:1257764.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
3.  Zhu S, Liao X, Jiang W, Sun Y, Chen X, Zheng B. Development and validation of a nomogram model for predicting clinical pregnancy in endometriosis patients undergoing fresh embryo transfer. BMC Pregnancy Childbirth. 2023;23:764.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
4.  Garajova I, Balsano R, Tommasi C, Dalla Valle R, Pedrazzi G, Ravaioli M, Spallanzani A, Leonardi F, Santini C, Caputo F, Riefolo M, Giuffrida M, Gelsomino F. Synchronous and metachronous colorectal liver metastases: impact of primary tumor location on patterns of recurrence and survival after hepatic resection. Acta Biomed. 2020;92:e2021061.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 2]  [Reference Citation Analysis (0)]
5.  Xiong F, Sun Q, Wang S, Yao Z, Chen P, Wan C, Zhong H, Zeng Y. A nomogram to assist blastocyst selection in vitrified-warmed embryo transfer cycles. J Obstet Gynaecol Res. 2022;48:1816-1828.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 2]  [Reference Citation Analysis (0)]
6.  Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, Wei W, Dai W, Tang Z, Ding Y, Cai G, Tong T, Meng X, Tian J. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine. 2021;69:103442.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 36]  [Cited by in F6Publishing: 38]  [Article Influence: 12.7]  [Reference Citation Analysis (0)]
7.  Zhang Q, Wang X, Zhang Y, Lu H, Yu Y. Nomogram prediction for the prediction of clinical pregnancy in Freeze-thawed Embryo Transfer. BMC Pregnancy Childbirth. 2022;22:629.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 0.5]  [Reference Citation Analysis (0)]
8.  Feng B, Huang L, Liu Y, Chen Y, Zhou H, Yu T, Xue H, Chen Q, Zhou T, Kuang Q, Yang Z, Chen X, Chen X, Peng Z, Long W. A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma. Front Oncol. 2021;11:802205.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 4]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
9.  Yue M, Ma L, Cao Y, Zhai J. Application of a Nomogram for Predicting the Risk of Subchorionic Hematoma in Early Pregnancy With In Vitro Fertilization-Embryo Transfer/Frozen Embryo Transfer. Front Endocrinol (Lausanne). 2021;12:631097.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 1]  [Article Influence: 0.3]  [Reference Citation Analysis (0)]
10.  Zeng Z, Ma Y, Zeng H, Huang P, Liu W, Jiang M, Xiang X, Deng D, Liao X, Chen P, Chen Y. Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. J Med Virol. 2021;93:434-440.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 15]  [Article Influence: 3.8]  [Reference Citation Analysis (0)]
11.  Wu L, Zheng Y, Liu J, Luo R, Wu D, Xu P, Wu D, Li X. Comprehensive evaluation of the efficacy and safety of LPV/r drugs in the treatment of SARS and MERS to provide potential treatment options for COVID-19. Aging (Albany NY). 2021;13:10833-10852.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 2]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
12.  Jiang X, Liu R, Liao T, He Y, Li C, Guo P, Zhou P, Cao Y, Wei Z. A Predictive Model of Live Birth Based on Obesity and Metabolic Parameters in Patients With PCOS Undergoing Frozen-Thawed Embryo Transfer. Front Endocrinol (Lausanne). 2021;12:799871.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 6]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
13.  Zhang H, Hu S, Xu D, Shen H, Jin H, Yang J, Zhang X. Risk Factors for Carbapenem Resistant Gram Negative Bacteria (CR-GNB) Carriage Upon Admission to the Gastroenterology Department in a Tertiary First Class Hospital of China: Development and Assessment of a New Predictive Nomogram. Infect Drug Resist. 2022;15:7761-7775.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
14.  Sun J, Huang L, Yang Y, Liao H. Risk assessment and clinical prediction model of planned transfer to the ICU after hip arthroplasty in elderly individuals. BMC Surg. 2023;23:305.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
15.  Wu L, Zhong Y, Wu D, Xu P, Ruan X, Yan J, Liu J, Li X. Immunomodulatory Factor TIM3 of Cytolytic Active Genes Affected the Survival and Prognosis of Lung Adenocarcinoma Patients by Multi-Omics Analysis. Biomedicines. 2022;10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
16.  Guo Z, Chen W, Wang Y, Chu R, Xu X, Zhang L, Yan L, Ma J. Nomogram to predict an endometrial thickness above 7.5 mm in the frozen embryo transfer cycle of women with a thin endometrium. Reprod Biomed Online. 2022;44:324-332.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 1]  [Reference Citation Analysis (0)]
17.  Si M, Jiang H, Zhao Y, Qi X, Li R, Long X, Qiao J. Nomogram for Predicting Live Birth after the First Fresh Embryo Transfer in Patients with PCOS Undergoing IVF/ICSI Treatment with the GnRH-Ant Protocol. Diagnostics (Basel). 2023;13.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
18.  Sheng Y, Wang Q, Liu H, Wang Q, Chen W, Xing W. Prognostic nomogram model for selecting between transarterial chemoembolization plus lenvatinib, with and without PD-1 inhibitor in unresectable hepatocellular carcinoma. Br J Radiol. 2024;97:668-679.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
19.  Duan C, Hao D, Cui J, Wang G, Xu W, Li N, Liu X. An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma. J Imaging Inform Med. 2024;37:510-519.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
20.  Wu L, Liu Q, Ruan X, Luan X, Zhong Y, Liu J, Yan J, Li X. Multiple Omics Analysis of the Role of RBM10 Gene Instability in Immune Regulation and Drug Sensitivity in Patients with Lung Adenocarcinoma (LUAD). Biomedicines. 2023;11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
21.  Kim JH, Jee BC, Suh CS, Kim SH. Nomogram to predict ongoing pregnancy using age of women and serum biomarkers after in vitro fertilization cycles. Eur J Obstet Gynecol Reprod Biol. 2014;172:65-69.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 11]  [Article Influence: 1.1]  [Reference Citation Analysis (0)]
22.  Wu Y, Yang R, Lin H, Cao C, Jiao X, Zhang Q. A Validated Model for Individualized Prediction of Live Birth in Patients With Adenomyosis Undergoing Frozen-Thawed Embryo Transfer. Front Endocrinol (Lausanne). 2022;13:902083.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 2]  [Reference Citation Analysis (0)]
23.  Diao L, Cai S, Huang C, Li L, Yu S, Wang L, Liu S, Li Y, Zeng Y. New endometrial immune cell-based score (EI-score) for the prediction of implantation success for patients undergoing IVF/ICSI. Placenta. 2020;99:180-188.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 13]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
24.  Vivanti AJ, Maraux B, Bornes M, Daraï E, Richard F, Rouzier R. Threatened preterm birth: Validation of a nomogram to predict the individual risk of very preterm delivery in a secondary care center. J Gynecol Obstet Hum Reprod. 2019;48:501-507.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 0.2]  [Reference Citation Analysis (0)]
25.  Wu L, Zheng Y, Ruan X, Wu D, Xu P, Liu J, Wu D, Li X. Long-chain noncoding ribonucleic acids affect the survival and prognosis of patients with esophageal adenocarcinoma through the autophagy pathway: construction of a prognostic model. Anticancer Drugs. 2022;33:e590-e603.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 12]  [Article Influence: 6.0]  [Reference Citation Analysis (0)]
26.  Wang R, Dai W, Gong J, Huang M, Hu T, Li H, Lin K, Tan C, Hu H, Tong T, Cai G. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022;15:11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3]  [Cited by in F6Publishing: 62]  [Article Influence: 31.0]  [Reference Citation Analysis (0)]
27.  Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34:2157-2164.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 896]  [Cited by in F6Publishing: 1196]  [Article Influence: 149.5]  [Reference Citation Analysis (0)]
28.  Wu L, Zhong Y, Yu X, Wu D, Xu P, Lv L, Ruan X, Liu Q, Feng Y, Liu J, Li X. Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research. Anticancer Drugs. 2022;33:943-959.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Cited by in F6Publishing: 8]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
29.  Lee JH, Jung S, Park WS, Choe EK, Kim E, Shin R, Heo SC, Lee JH, Kim K, Chai YJ. Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database. Sci Rep. 2019;9:1803.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 22]  [Article Influence: 4.4]  [Reference Citation Analysis (0)]
30.  Yan S, Ding H, Zhao X, Wang J, Deng W. Development and validation of a nomogram for further decision of radical surgery in pT1 colorectal cancer after local resection. Int J Colorectal Dis. 2021;36:1499-1506.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 4]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
31.  Wu L, Li H, Liu Y, Fan Z, Xu J, Li N, Qian X, Lin Z, Li X, Yan J. Research progress of 3D-bioprinted functional pancreas and in vitro tumor models. IJB. 2024;10:1256.  [PubMed]  [DOI]  [Cited in This Article: ]
32.  Liu Y, Meng Y, Zhou C, Liu Y, Tian S, Li J, Dong W. Creation and Validation of a Survival Nomogram Based on Immune-Nutritional Indexes for Colorectal Cancer Patients. J Oncol. 2022;2022:1854812.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1]  [Cited by in F6Publishing: 1]  [Article Influence: 0.5]  [Reference Citation Analysis (0)]
33.  Qin X, Zhao M, Deng W, Huang Y, Cheng Z, Chung JPW, Chen X, Yang K, Chan DYL, Wang H. Development and Validation of a Novel Prognostic Nomogram Combined With Desmoplastic Reaction for Synchronous Colorectal Peritoneal Metastasis. Front Oncol. 2022;12:826830.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
34.  Hong T, Cai D, Jin L, Zhang Y, Lu T, Hua D, Wu X. Development and validation of a nomogram to predict survival after curative resection of nonmetastatic colorectal cancer. Cancer Med. 2020;9:4126-4136.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 7]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
35.  Wu L, Li X, Qian X, Wang S, Liu J, Yan J. Lipid Nanoparticle (LNP) Delivery Carrier-Assisted Targeted Controlled Release mRNA Vaccines in Tumor Immunity. Vaccines (Basel). 2024;12.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
36.  Zhang Y, Zhang Z, Wei L, Wei S. Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer. Front Public Health. 2022;10:1008137.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 3]  [Reference Citation Analysis (0)]
37.  Kajiwara Y, Oka S, Tanaka S, Nakamura T, Saito S, Fukunaga Y, Takamatsu M, Kawachi H, Hotta K, Ikematsu H, Kojima M, Saito Y, Yamada M, Kanemitsu Y, Sekine S, Nagata S, Yamada K, Kobayashi N, Ishihara S, Saitoh Y, Matsuda K, Togashi K, Komori K, Ishiguro M, Kuwai T, Okuyama T, Ohuchi A, Ohnuma S, Sakamoto K, Sugai T, Katsumata K, Matsushita HO, Yamano HO, Eda H, Uraoka T, Akimoto N, Kobayashi H, Ajioka Y, Sugihara K, Ueno H. Nomogram as a novel predictive tool for lymph node metastasis in T1 colorectal cancer treated with endoscopic resection: a nationwide, multicenter study. Gastrointest Endosc. 2023;97:1119-1128.e5.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 9]  [Reference Citation Analysis (0)]
38.  Wu L, Chen X, Zeng Q, Lai Z, Fan Z, Ruan X, Li X, Yan J. NR5A2 gene affects the overall survival of LUAD patients by regulating the activity of CSCs through SNP pathway by OCLR algorithm and immune score. Heliyon. 2024;10:e28282.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
39.  Tang X, Hu N, Huang S, Jiang J, Rao H, Yang X, Yuan Y, Zhang Y, Xia G. Prognostic nomogram for colorectal cancer patients with multi-organ metastases: a Surveillance, Epidemiology, and End Results program database analysis. J Cancer Res Clin Oncol. 2023;149:12131-12143.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
40.  Xie Z, Zhang Q, Wang X, Chen Y, Deng Y, Lin H, Wu J, Huang X, Xu Z, Chi P. Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer. Eur J Surg Oncol. 2023;49:107118.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 1]  [Reference Citation Analysis (0)]