Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Wang, Jinkui; Zhanghuang, Chenghao; Tan, Xiaojie; Mi, Tao; Liu, Jiayan; Jin, Liming; Li, Mujie; Zhang, Zhaoxia; He, Dalin

doi:10.3389/fpubh.2021.831940

Cited by 9 publications

(10 citation statements)

References 35 publications

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“…Interestingly, in our previous study, married patients had a significantly better prognosis among less malignant tumours [ 43 , 44 ]. We found in this study that marriage also has a certain predictive role in the prognosis of middle-aged PTC patients.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram to predict cancer-specific survival in middle-aged patients with papillary thyroid cancer: A SEER database study

Tang¹,

Zhanghuang²,

Yao³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram to predict cancer-specific survival in middle-aged patients with papillary thyroid cancer: A SEER database study

Tang¹,

Zhanghuang²,

Yao³

et al. 2023

Heliyon

Self Cite

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“…Nomogram as a prediction tool is commonly used to estimate prognosis in oncology and medicine [ 12 ]. By far, it has been used in various cancers, such as liver [ 17 – 19 ], pancreas [ 20 , 21 ], kidney [ 22 , 23 ], and bone [ 24 – 26 ] et al Besides, Bai et al [ 13 ] constructed a nomogram to predict overall survival after gallbladder cancer resection in China. Cai et al constructed a nomogram to predict distant metastasis in T1 and T2 gallbladder cancer [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…Nomogram as a prediction tool is commonly used to estimate prognosis in oncology and medicine [12]. By far, it has been used in various cancers, such as liver [17][18][19], pancreas [20,21], kidney [22,23], and bone [ with gallbladder cancer varies greatly with age, and elderly patients with GBC have higher morbidity and shorter overall survival. Therefore, we developed and validated the nomogram based on patients, tumor characteristics, and treatment methods to estimate the CSS of elderly GBC patients.…”

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Wen

Tang

Wang³

et al. 2022

BMC Gastroenterol

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Background Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients. Method We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram. Result A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system. Conclusion We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

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“…In addition, T-stage and N-stage are also key factors for tumor metastasis. The higher the T-stage and N-stage, the greater the risk of tumor invasion into blood vessels or lymphatic vessels for metastasis and the worse the prognosis ( 36 ). In this study, the largest number of patients were in grade III, accounting for 65.8%, followed by grade II patients, accounting for 25.6%, and the smallest number was in grade IV, accounting for only 2.7% of the total number of patients.…”

Section: Discussionmentioning

confidence: 99%

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

2022

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BackgroundMany patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.MethodsWe recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.ResultsCox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.ConclusionThe nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

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Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma

Cited by 9 publications

References 35 publications

Development and validation of a nomogram to predict cancer-specific survival in middle-aged patients with papillary thyroid cancer: A SEER database study

Development and validation of a nomogram to predict cancer-specific survival in middle-aged patients with papillary thyroid cancer: A SEER database study

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

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