A nomogram model for predicting prognosis of obstructive colorectal cancer

Lv, Jian; Liu, Yuan yuan; Jia, Yi; He, Jing; Dai, Guang yao; Guo, Peng; Zhao, Zhao long; Zhang, Yan ni; Li, Zhong Xin

doi:10.1186/s12957-021-02445-6

Cited by 44 publications

(28 citation statements)

References 43 publications

(48 reference statements)

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“…We also analyzed the correlation between the patient's serum in terms of total protein, albumin and other laboratory indicators and tumor development, and finally found that low protein levels may facilitate the growth and metastasis of tumor cells, which may ne beneficial to predict the probability of LNM (34). In a retrospective study of 207 cancer patients, Adam et al (24) found that positive lymph nodes are significantly correlated with low albumin levels, and it was concluded that cancer patients are usually accompanied by hypoproteinemia and the subsequent production of ascites and tissue oedema may cause migration of tumor cells, leading to the development of LNM. A related discussion has been found in other studies (35,36).…”

Section: Discussionmentioning

confidence: 99%

New nomogram for predicting lymph node positivity in pancreatic head cancer

et al. 2023

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BackgroundPancreatic cancer is one of the most malignant cancers worldwide, and it mostly occurs in the head of the pancreas. Existing laparoscopic pancreaticoduodenectomy (LPD) surgical techniques have has undergone a learning curve, a wide variety of approaches for the treatment of pancreatic cancer have been proposed, and the operation has matured. At present, pancreatic head cancer has been gradually changing from “surgeons’ evaluation of anatomical resection” to “biologically inappropriate resection”. In this study, the risk of lymph node metastasis in pancreatic head cancer was predicted using common preoperative clinical indicators.MethodsThe preoperative clinical data of 191 patients with pancreatic head cancer who received LPD in the First Affiliated Hospital of Jilin University from May 2016 to December 2021 were obtained. A univariate regression analysis study was conducted, and the indicators with a significance level of P<0.05 were included in the univariate logistic regression analysis into multivariate. Lastly, a nomogram was built based on age, tumor size, leucocyte,albumin(ALB), and lymphocytes/monocytes(LMR). The model with the highest resolution was selected by obtaining the area under a curve. The clinical net benefit of the prediction model was examined using decision curve analyses.Risk stratification was performed by combining preoperative CT scan with existing models.ResultsMultivariate logistic regression analysis found age, tumor size, WBC, ALB, and LMR as five independent factors. A nomogram model was constructed based on the above indicators. The model was calibrated by validating the calibration curve within 1000 bootstrap resamples. The ROC curve achieved an AUC of 0.745(confidence interval of 95%: 0.673-0.816), thus indicating that the model had excellent discriminative skills. DCA suggested that the predictive model achieved a high net benefit in the nearly entire threshold probability range.ConclusionsThis study has been the first to investigate a nomogram for preoperative prediction of lymphatic metastasis in pancreatic head cancer. The result suggests that age, ALB, tumor size, WBC, and LMR are independent risk factors for lymph node metastasis in pancreatic head cancer. This study may provide a novel perspective for the selection of appropriate continuous treatment regimens, the increase of the survival rate of patients with pancreatic head cancer, and the selection of appropriate neoadjuvant therapy patients.

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Section: Discussionmentioning

confidence: 99%

New nomogram for predicting lymph node positivity in pancreatic head cancer

et al. 2023

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“…A nomogram risk prediction model for colorectal cancer patients has been developed, however lacking data on its pathological type and on the prognosis of lymph node positive patients [21][22][23]. Moreover, these reports have not distinguished RSJ from coloretal cancer [24,25].…”

Section: Discussionmentioning

confidence: 99%

A Postsurgical Prognostic Nomogram for Patients with Lymph Node Positive Rectosigmoid Junction Adenocarcinoma

Yanlong

Wang

Yunxiao

2022

Preprint

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Objective: The definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery. Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n=2344) and validation (n=1004) cohorts at a radio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan–Meier method together with the log-rank test. Results: Age, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737-0.765) and validation cohorts (0.750;0.764-0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665-0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan-Meier curves in the low and high groups showed significant differences in patient OS. Conclusions: We established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

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“…Nomograms were widely used for cancer prognosis. Based on age, gender, tumor (T), node (N), metastasis (M) and risk score, a nomogram was constructed using "RMS" package in R. Calibration curves was used to visually predict deviations between actual and predicted survival [17].…”

Section: Construction Of the Nomogrammentioning

confidence: 99%

Construction of a prognostic risk model based on apoptosis-related genes to assess tumor immune microenvironment and predict prognosis in hepatocellular carcinoma

Wang

2022

BMC Gastroenterol

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Background Hepatocellular carcinoma (HCC) is a serious malignant disease with high incidence, high mortality and poor prognosis. This study aimed to establish a novel signature based on apoptosis-related genes (ARGs) to predict the prognosis of HCC. Methods Expression data of HCC from TCGA database and the list of 160 ARGs from MSigDB were downloaded. The genes included in apoptosis-related signature were selected by univariate Cox regression analysis and lasso Cox regression analysis. Subsequently, a prognostic risk model for scoring patients was developed, and then separates patients into two groups. Kaplan–Meier and receiver operating characteristic analysis were performed to evaluate the prognostic value of the model in TCGA, GEO and ICGC databases. The characteristics of immune cell infiltration between two groups of HCC were investigated. Finally, a nomogram was plotted to visualize the prognosis prediction. Results Nine genes (CDC25B, DAP3, ETF1, GSR, LGALS3, MGMT, PPP2R5B, SQSTM1 and VDAC2) were included in the prognostic risk model. Survival was lower in the high-risk group. Surprisingly, the high-risk group was significantly more in immune cell infiltration and with higher immunoscore and stromalscore than in the low-risk group. In addition, the risk score was an independent prognostic factor for HCC. Conclusions Prognostic signature comprising nine ARGs could be used as a potential prognostic factor for HCC. It also provides an important idea for further understanding the immunotherapy of HCC.

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A nomogram model for predicting prognosis of obstructive colorectal cancer

Cited by 44 publications

References 43 publications

New nomogram for predicting lymph node positivity in pancreatic head cancer

New nomogram for predicting lymph node positivity in pancreatic head cancer

A Postsurgical Prognostic Nomogram for Patients with Lymph Node Positive Rectosigmoid Junction Adenocarcinoma

Construction of a prognostic risk model based on apoptosis-related genes to assess tumor immune microenvironment and predict prognosis in hepatocellular carcinoma

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