scholarly journals Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110150
Author(s):  
Shuanhu Wang ◽  
Yakui Liu ◽  
Yi Shi ◽  
Jiajia Guan ◽  
Mulin Liu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Curative Resection ◽  
Predictive Accuracy ◽  
External Validation ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Calibration Plot ◽  
Metastatic Colon Cancer ◽  
Validation Set

Objective To develop and externally validate a prognostic nomogram to predict overall survival (OS) in patients with resectable colon cancer. Methods Data for 50,996 patients diagnosed with non-metastatic colon cancer were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were assigned randomly to the training set (n = 34,168) or validation set (n = 16,828). Independent prognostic factors were identified by multivariate Cox proportional hazards regression analysis and used to construct the nomogram. Harrell’s C-index and calibration plots were calculated using the SEER validation set. Additional external validation was performed using a Chinese dataset (n = 342). Results Harrell’s C-index of the nomogram for OS in the SEER validation set was 0.71, which was superior to that using the 7th edition of the American Joint Committee on Cancer TNM staging (0.59). Calibration plots showed consistency between actual observations and predicted 1-, 3-, and 5-year survival. Harrell’s C-index (0.72) and calibration plot showed excellent predictive accuracy in the external validation set. Conclusions We developed a nomogram to predict OS after curative resection for colon cancer. Validation using the SEER and external datasets revealed good discrimination and calibration. This nomogram may help predict individual survival in patients with colon cancer.

scholarly journals Contemporary Validation of a Nomogram Predicting Colon Cancer Recurrence, Revealing All-Stage Improved Outcomes

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Tsuyoshi Konishi ◽  
Yoshifumi Shimada ◽  
Meier Hsu ◽  
Iris H Wei ◽  
Emmanouil Pappou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Curative Resection ◽  
Predictive Accuracy ◽  
Cancer Recurrence ◽  
Recurrence Risk ◽  
Treatment Modalities ◽  
Cancer Center ◽  
Calibration Plot ◽  
Concordance Index ◽  
Colon Cancer Recurrence

Abstract Background The Memorial Sloan Kettering Cancer Center (MSK) colon cancer recurrence nomogram is a risk calculator that provides patients and clinicians with individualized prediction of recurrence following curative resection of colon cancer. Although validated on multiple separate cohorts, the nomogram requires periodic updating as patient care changes over time. The aim of this study was to evaluate the nomogram’s accuracy in a contemporary cohort and modify the tool to reflect improvements in outcome related to advances in colon cancer therapy. Methods A contemporary patient cohort was compiled, including consecutive colon cancer patients undergoing curative resection for stage I–III colon adenocarcinoma at MSK from 2007 to 2014. The nomogram’s predictive accuracy was assessed by concordance index and calibration plots of predicted vs actual freedom from recurrence at 5 years after surgery. Results Data from a total of 999 eligible patients with complete records were used for validation. Median follow-up among survivors was 37 months. The concordance index was 0.756 (95% confidence interval = 0.707 to 0.805), indicating continued discriminating power, but the calibration plot revealed that the nomogram overestimated recurrence risk. Recalibration of the nomogram by estimating a new baseline freedom-from-recurrence function restored the nomogram’s accuracy. Conclusion The updated nomogram retains the original nomogram’s variables but includes a lower baseline estimation of recurrence risk, reflecting improvements in outcomes for all stages of colon cancer, likely resulting from advances in imaging and integration of multiple treatment modalities.

scholarly journals A novel nomogram based on cardia invasion and chemotherapy to predict postoperative overall survival of gastric cancer patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hanjun Mo ◽  
Pengfei Li ◽  
Sunfang Jiang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Surgical Resection ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Calibration Plot ◽  
Training Set ◽  
Validation Set

Abstract Background We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection. Methods A total of 6543 patients diagnosed with primary GC during 2004–2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We grouped patients diagnosed during 2004–2012 into a training set (n = 4528) and those diagnosed during 2013–2016 into an external validation set (n = 2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot. Results The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia invasion was 0.762 (P < 0.001) and that of chemotherapy was 0.556 (P < 0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P < 0.001); the C-index was also better in the validation set (0.748 vs 0.707, P < 0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance. Conclusions We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia invasion and chemotherapy in predicting the survival rate of GC patients.

scholarly journals Real-World Implications of Nonbiological Factors with Staging, Prognosis and Clinical Management in Colon Cancer

Cancers ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 263 ◽  
Author(s):  
Qi Liu ◽  
Dakui Luo ◽  
Sanjun Cai ◽  
Qingguo Li ◽  
Xinxiang Li
Keyword(s):  
Colon Cancer ◽  
Proportional Hazards ◽  
Staging System ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Metastatic Colon Cancer ◽  
Cancer Specific Survival ◽  
Increased Risk ◽  
Tnm Staging System ◽  
Risk Of Cancer

Background: The present study analyzed the nonbiological factors (NBFs) together with the American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) staging system to generate a refined, risk-adapted stage for the clinical treatment of colon cancer. Methods: Eligible patients (N = 28,818) with colon cancer between 1 January 2010 and 31 December 2014, were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier curves and Cox proportional hazards regression, analyzed the probabilities of cancer-specific survival (CSS) in patients with colon cancer, with different NBF-TNM stages. Results: Insurance status, marital status, and median household income were significant prognostic NBFs in the current study (p < 0.05). The concordance index of NBF-TNM stage was 0.857 (95% confidence interval (CI) = 0.8472–0.8668). Multivariate Cox analyses, indicated that NBF1-stage was independently associated with a 50.4% increased risk of cancer-specific mortality in colon cancer (p < 0.001), which increased to 77.1% in non-metastatic colon cancer. NBF0-stage improved in CSS as compared to the NBF1-stage in the respective stages (p < 0.05). Conclusions: The new proposed NBF-stage was an independent prognostic factor in colon cancer. Effect of NBFs on the survival of colon cancer necessitates further clinical attention. Moreover, the incorporation of NBF-stage into the AJCC TNM staging system is essential for prognostic prediction, and clinical guidance of adjuvant chemotherapy in stage II and III colon cancer.

scholarly journals A novel nomogram based on cardia and chemotherapy to predict postoperative overall survival of gastric cancer patients

2021 ◽  
Author(s):  
Hanjun Mo ◽  
Pengfei Li ◽  
Sunfang Jiang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Surgical Resection ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Calibration Plot ◽  
Training Set ◽  
Validation Set

Abstract Background: We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection and explored the roles of cardia and chemotherapy.Methods: A total of 6543 patients diagnosed with primary GC during 2004-2016 were collected from the Surveillance, Epidemiology and End Results (SEER) database. We grouped patients diagnosed during 2004-2012 into a training set (n=4528) and those diagnosed during 2013-2016 into an external validation set (n=2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell’s C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot.Results: The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia was 0.762 (P<0.001), and that of chemotherapy was 0.556 (P<0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell’s C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P<0.001); the C-index was also better in the validation set (0.748 vs 0.707, P<0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance.Conclusions: We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia and chemotherapy in predicting the survival rate of GC patients.

scholarly journals Survival rates and prognostic factors in right- and left-sided colon cancer stage I–IV: an unselected retrospective single-center trial

2021 ◽  
Author(s):  
Claudius E. Degro ◽  
Richard Strozynski ◽  
Florian N. Loch ◽  
Christian Schineis ◽  
Fiona Speichinger ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Blood Level ◽  
Proportional Hazards ◽  
Survival Rates ◽  
Cox Proportional Hazards ◽  
Stage I ◽  
Cancer Stage ◽  
Single Center ◽  
Significant Difference

Abstract Purpose Colorectal cancer revealed over the last decades a remarkable shift with an increasing proportion of a right- compared to a left-sided tumor location. In the current study, we aimed to disclose clinicopathological differences between right- and left-sided colon cancer (rCC and lCC) with respect to mortality and outcome predictors. Methods In total, 417 patients with colon cancer stage I–IV were analyzed in the present retrospective single-center study. Survival rates were assessed using the Kaplan–Meier method and uni/multivariate analyses were performed with a Cox proportional hazards regression model. Results Our study showed no significant difference of the overall survival between rCC and lCC stage I–IV (p = 0.354). Multivariate analysis revealed in the rCC cohort the worst outcome for ASA (American Society of Anesthesiologists) score IV patients (hazard ratio [HR]: 16.0; CI 95%: 2.1–123.5), CEA (carcinoembryonic antigen) blood level > 100 µg/l (HR: 3.3; CI 95%: 1.2–9.0), increased lymph node ratio of 0.6–1.0 (HR: 5.3; CI 95%: 1.7–16.1), and grade 4 tumors (G4) (HR: 120.6; CI 95%: 6.7–2179.6) whereas in the lCC population, ASA score IV (HR: 8.9; CI 95%: 0.9–91.9), CEA blood level 20.1–100 µg/l (HR: 5.4; CI 95%: 2.4–12.4), conversion to laparotomy (HR: 14.1; CI 95%: 4.0–49.0), and severe surgical complications (Clavien-Dindo III–IV) (HR: 2.9; CI 95%: 1.5–5.5) were identified as predictors of a diminished overall survival. Conclusion Laterality disclosed no significant effect on the overall prognosis of colon cancer patients. However, group differences and distinct survival predictors could be identified in rCC and lCC patients.

scholarly journals QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Medicines ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Junko Nagai ◽  
Mai Imamura ◽  
Hiroshi Sakagami ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Anticancer Drugs ◽  
Predictive Accuracy ◽  
Cell Cancer ◽  
External Validation ◽  
Selective Toxicity ◽  
Normal Cells ◽  
Validation Set ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.

Multi-Institutional Validation of a New Renal Cancer–Specific Survival Nomogram

2007 ◽  
Vol 25 (11) ◽  
pp. 1316-1322 ◽  
Author(s):  
Pierre I. Karakiewicz ◽  
Alberto Briganti ◽  
Felix K.-H. Chun ◽  
Quoc-Dien Trinh ◽  
Paul Perrotte ◽  
...  
Keyword(s):  
Renal Cancer ◽  
Proportional Hazards ◽  
Predictive Accuracy ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Fuhrman Grade ◽  
Histologic Subtype ◽  
Cancer Specific Mortality

Purpose We tested the hypothesis that the prediction of renal cancer–specific survival can be improved if traditional predictor variables are used within a prognostic nomogram. Patients and Methods Two cohorts of patients treated with either radical or partial nephrectomy for renal cortical tumors were used: one (n = 2,530) for nomogram development and for internal validation (200 bootstrap resamples), and a second (n = 1,422) for external validation. Cox proportional hazards regression analyses modeled the 2002 TNM stages, tumor size, Fuhrman grade, histologic subtype, local symptoms, age, and sex. The accuracy of the nomogram was compared with an established staging scheme. Results Cancer-specific mortality was observed in 598 (23.6%) patients, whereas 200 (7.9%) died as a result of other causes. Follow-up ranged from 0.1 to 286 months (median, 38.8 months). External validation of the nomogram at 1, 2, 5, and 10 years after nephrectomy revealed predictive accuracy of 87.8%, 89.2%, 86.7%, and 88.8%, respectively. Conversely, the alternative staging scheme predicting at 2 and 5 years was less accurate, as evidenced by 86.1% (P = .006) and 83.9% (P = .02) estimates. Conclusion The new nomogram is more contemporary, provides predictions that reach further in time and, compared with its alternative, which predicts at 2 and 5 years, generates 3.1% and 2.8% more accurate predictions, respectively.

scholarly journals M2 Macrophage-based Prognostic Nomogram for Gastric Cancer After Surgical Resection: Original Article

2020 ◽  
Author(s):  
Jianwen Hu ◽  
Yongchen Ma ◽  
Ju Ma ◽  
Yanpeng Yang ◽  
Yingze Ning ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Curative Resection ◽  
Patient Survival ◽  
Tnm Staging ◽  
M2 Macrophage ◽  
Concordance Index ◽  
Age And Gender ◽  
Cd163 Expression ◽  
And Gender

Abstract Background: A good prediction model is useful to accurately predict patient prognosis. Tumor–node–metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown infiltration of M2 macrophages in many tumors, which indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging. The present study aimed to develop and validate a nomogram to predict survival in patients with gastric cancer by combining TNM staging and the degree of M2 macrophage infiltration.Methods: Patients undergoing curative resection for gastric cancer between 2008 and 2013 at our hospital were enrolled and assigned into either the training set or the validation set. M2 macrophage markers were evaluated by immunohistochemical staining. A stepwise method was applied to screen variables associated with patient survival time, and a nomogram was constructed to predict patient survival. Concordance index, calibration curve, and decision curve analysis were used to evaluate the discrimination, calibration, and clinical benefit of the model.Results: A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram. In the training, validation, and overall datasets, the concordance index was >0.6. The model showed a high degree of discrimination in all three datasets. The five-year survival calibration curves were a very good fit with standard curves in all three datasets, and the model demonstrated good clinical benefit. The prognostic abilities had threshold probabilities of 10%–38% for one-year survival, 10%–75% for three-year survival, and 35%–80% for five-year survival.Conclusions: We combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict five-year overall survival after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

scholarly journals A Lymph Node Classification-based cancer-specific Survival Predicting Model in Patients with Ampullary cancer: A Derivation and Validation Study

2021 ◽  
Author(s):  
Jixiang Zhao ◽  
Yunzhi Zou ◽  
Xin Huang ◽  
Xiong Zhou ◽  
Xiaojiang Zhou ◽  
...  
Keyword(s):  
Lymph Node ◽  
External Validation ◽  
Tnm Staging ◽  
Ampullary Cancer ◽  
Tnm Stage ◽  
Calibration Plot ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis ◽  
Adaptive Partitioning

Abstract Background: Current lymph node (LN) staging is controversial in predicting the survival of ampullary cancer (AC). We aimed to develop an alternative LN-classification-based nomogram to individualize AC prognosis.Methods: Using the data of patients diagnosed with AC between 2004 and 2015 from the SEER database, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning algorithm. A nomogram predicting the survival of AC patients was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis (DCA), and was compared to the 7th TNM stage.Results: We included 2341 patients with detailed information. The optimal cut-off for examined LN number was 12, while the cut-off value for positive LNs was 0 and 4. The C-index for the nomogram was higher than that of the 7th TNM staging (internal: 0.686; 95% CI, 0.584-0.773 vs. 0.616; 95% CI, 0.533-0.754, P < 0.001; external: 0.713; 95% CI, 0.651-0.784 vs. 0.647; 95% CI, 0.551-0.719, P < 0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort; the nomogram showed a greater benefit across the period of follow-up than did the 7th TNM stage.Conclusion: We found that examined LNs that were more than 12 were beneficial for prognosis of patients. We also modified the current N staging into three groups based on number of metastatic LNs: N0, no LN metastasis; N1, 1–4 metastatic LNs; N2, >=5 metastatic LNs. A nomogram with greater benefit for predicting the survival of patients with AC than TNM staging was constructed.

scholarly journals Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Le Kuai ◽  
Ying Zhang ◽  
Ying Luo ◽  
Wei Li ◽  
Xiao-dong Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Large Scale ◽  
Prediction Models ◽  
External Validation ◽  
Histological Type ◽  
Metastatic Colon Cancer ◽  
Tumor Differentiation ◽  
Tnm System ◽  
Prognostic Predictors ◽  
Tumor Deposit

ObjectiveA proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure.MethodsUsing the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves.ResultsHistological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system.ConclusionThis study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.

Sign in / Sign up

Export Citation Format

Share Document