Nomograms for predicting overall and cancer-specific survival in patients with papillary renal cell carcinoma: a population-based study using SEER database

Yan, Haicui; Wei, Xiao‐Hong; Wu, Ai-Min; Sha, Yeqin; Li, Xiao; Qi, Feng

doi:10.21037/tau-19-807

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Through the incorporation of diverse prognostic factors, nomograms can determine the probability of a clinical event (e.g., OS or DFS) and thus predict the prognosis of a single patient. Nomograms are necessary in the era of individualized oncological therapy, and several nomograms have been published as adjuncts in prognostic determination in different types of cancer, such as CRC ( 45 ), renal cancer ( 46 ), NSCLC ( 47 ), and gastric cancer ( 48 ). Regarding the HNC, the nomograms have also been constructed to estimate the risk of developing major surgical complications in OSCC patients ( 49 ) and predict the recurrence-free probability in patients with parotid cancer ( 50 ).…”

Section: Discussionmentioning

confidence: 99%

Advanced Lung Cancer Inflammation Index Predicts Survival Outcomes of Patients With Oral Cavity Cancer Following Curative Surgery

et al. 2021

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AimThe aim of our study was to investigate the prognostic value of preoperative advanced lung cancer inflammation index (ALI) and to establish prognostic nomograms for the prediction of survival outcomes in patients with oral cavity squamous cell carcinoma (OSCC).Materials and MethodsA total of 372 patients who received primary curative surgery for OSCC during 2008–2017 at a tertiary referral center were enrolled. We used the receiver operating characteristic curve to determine the optimal cutoff point of ALI. Through a Cox proportional hazards model and Kaplan–Meier analysis, we elucidated the ALI–overall survival (OS) and ALI–disease-free survival (DFS) associations. Prognostic nomograms based on ALI and the results of multivariate analysis were created to predict the OS and DFS. We used the concordance indices (C-indices) and calibration plots to assess the discriminatory and predictive ability.ResultsThe results revealed that the ALI cutoff was 33.6, and 105 and 267 patients had ALI values of <33.6 and ≥33.6, respectively. ALI < 33.6 significantly indicated lower OS (44.0% vs. 80.1%, p < 0.001) and DFS (33.6% vs. 62.8%; p < 0.001). In multivariate analysis, ALI < 33.6 was independently associated with poor OS and DFS (both p < 0.001). The C-indices of established nomograms were 0.773 and 0.674 for OS and DFS, respectively; moreover, the calibration plots revealed good consistency between nomogram-predicted and actual observed OS and DFS.ConclusionALI is a promising prognostic biomarker in patients undergoing primary surgery for OSCC; moreover, ALI-based nomograms may be a useful prognostic tool for individualized OS and DFS estimations.

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Section: Discussionmentioning

confidence: 99%

Advanced Lung Cancer Inflammation Index Predicts Survival Outcomes of Patients With Oral Cavity Cancer Following Curative Surgery

et al. 2021

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“…The advantage is that it can provide better individualized prognostic risk assessments in the form of intuitive graphics, which have de nite value in clinical practice and can provide a reference for individualized clinical decision-making. Thus, such predictive nomograms are widely used in clinical oncology [15][16][17]. All of the nomograms presented showed better discriminatory capacity than did classical staging systems.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomograms to Predict Long-term Oncological Outcomes in Serous Ovarian Cancer

Liu

Zhao

Liu

et al. 2021

Preprint

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Background: Nomograms are statistics-based predictive tools that integrate predictive factors. We developed and validated a nomogram to predict overall survival (OS) in serous ovarian cancer (SOC).Methods: In total, 6957 patients from the SEER database were included in the training group; the external validation group included 1244 SOC patients from two Chinese hospitals. The nomogram was structured on Cox regression analyses and was evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve (AUC), calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram.Results: The independent factors identified by multivariate analysis in the training cohort and selected for the nomogram included age, tumor grade, and FIGO stage. The consistency indexes for OS were 0.689 (95% confidence interval: 0.677–0.701) in the training cohort and 0.639 (95% confidence interval: 0.601–0.670) in the validation cohort; the AUCs were 0.675 and 0.661 in the validation cohorts, respectively. Calibration curves showed good consistency between predicted and actual 3- and 5-year OS. Significant differences were observed in the survival curves of different risk subgroups. The decision-curve analysis indicated our nomogram was superior to the AJCC staging system.Conclusion: We constructed a nomogram to predict long-term OS in SOC and externally verified it in an Asian population. This nomogram showed more accurate survival predictions, which will help provide personalized treatments and follow-up strategies.

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“…13,14 Various nomogram models have been proposed for RCC, but most are for ccRCC or pRCC. [15][16][17][18] For chRCC prognosis prediction, Xie et al analyzed the relation between the clinical and pathological features of Chinese patients and patients' prognosis, and Luzzago et al explored the effect of tumor stage and grade on CSS of ccRCC and non-ccRCC patients. 19,20 However, there are no effective and reliable nomograms available to the prognostic prediction of chRCC patients.…”

Section: Impact Statementmentioning

confidence: 99%

Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

Chen

Geng

Liang

et al. 2020

Exp Biol Med (Maywood)

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This study built and tested two effective nomograms for the purpose of predicting cancer-specific survival and overall survival of chromophobe renal cell carcinoma (chRCC) patients. Multivariate Cox regression analysis was employed to filter independent prognostic factors predictive of cancer-specific survival and overall survival, and the nomograms were built based on a training set incorporating 2901 chRCC patients in a retrospective study (from 2004 to 2015) downloaded from the surveillance, epidemiology, and end results (SEER) database. The nomograms were verified on a validation cohort of 1934 patients, subsequently the performances of the nomograms were examined according to the receiver operating characteristic curve, calibration curves, the concordance (C-index), and decision curve analysis. The results showed that tumor grade, AJCC and N stages, race, marital status, age, histories of chemotherapy, radiotherapy and surgery were the individual prognostic factors for overall survival, and that AJCC, N and SEER stages, histories of surgery, radiotherapy and chemotherapy, age, tumor grade were individual prognostic factors for cancer-specific survival. According to C-indexes, receiver operating characteristic curves, and decision curve analysis outcomes, the nomograms showed a higher accuracy in predicting overall survival and OSS when compared with TNM stage and SEER stage. All the calibration curves were significantly consistent between predictive and validation sets. In this study, the nomograms, which were validated to be highly accurate and applicable, were built to facilitate individualized predictions of the cancer-specific survival and overall survival to patients diagnosed with chRCC between 2004 and 2015.

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Nomograms for predicting overall and cancer-specific survival in patients with papillary renal cell carcinoma: a population-based study using SEER database

Cited by 10 publications

References 31 publications

Advanced Lung Cancer Inflammation Index Predicts Survival Outcomes of Patients With Oral Cavity Cancer Following Curative Surgery

Advanced Lung Cancer Inflammation Index Predicts Survival Outcomes of Patients With Oral Cavity Cancer Following Curative Surgery

Development and Validation of Nomograms to Predict Long-term Oncological Outcomes in Serous Ovarian Cancer

Nomograms-based prediction of overall and cancer-specific survivals for patients with chromophobe renal cell carcinoma

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