Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma

Tang, Lin; Li, Chao Feng; Li, Jing; Chen, Wen Hui; Chen, Qiu Yan; Yuan, Lifang; Lai, Xiaomin; He, Yun; Xu, Yun; Hu, Dong; Shi, Wen; Peng, Yu; Zhang, Lu; Guo, Shan; Liu, Li Ting; Guo, Ling; Wu, Yu Chun; Luo, Dong Hua; Huang, Pei Yu; Mo, Hao Yuan; Xiang, Yan; Sun, Rui; Chen, Ming Yuan; Hua, Yi; Lv, Xing; Wang, Lin; Zhao, Chong; Cao, Ke; Qian, Chao Nan; Guo, Xiang; Zeng, Yi Xin; Qiang, Hai; Zeng, Mu Sheng

doi:10.1093/jnci/djv291

Cited by 296 publications

(297 citation statements)

References 32 publications

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“…To our knowledge there are no models to predict survival in laryngeal cancer patients. Some survival prediction models exist for other head and neck cancer patients [29, 30]. The study [30] with a similar approach, large training and external validation cohorts found comparable results in model performance.…”

Section: Discussionmentioning

confidence: 98%

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

et al. 2016

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Background and PurposeTo improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset.Materials and MethodsData extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre's (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort).ResultsData mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so.ConclusionsThe technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.

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Section: Discussionmentioning

confidence: 98%

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

et al. 2016

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“…In recent years, nomograms have been constructed in a variety of cancers, and some of them have been found to be more reliable prediction than the traditional staging system 23, 32-34. Moreover it has been validated to compare favorably to the traditional TNM staging systems in many cancers, and thus have been identified as an alternative or even a new standard 22, 35, 36. A number of studies also found that in patients with NSCLC, nomograms were accurate to predict the risk of a node's involvement 37, 38, the individual risk of developing metastasis to the brain and had strong performance in individualize patient decision making 39.…”

Section: Discussionmentioning

confidence: 99%

A Nomogram based on Inflammatory Factors C-Reactive Protein and Fibrinogen to Predict the Prognostic Value in Patients with Resected Non-Small Cell Lung Cancer

et al. 2017

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Purpose: This study aimed to develop an effective nomogram for predicting survival in surgically treated non-small cell lung cancer patients.Methods: We retrospectively evaluated 856 NSCLC in this study. Cox regression analyses were performed to identify significant prognostic factors for developing a nomogram to predict overall survival (OS). The discriminative ability was assessed with the concordance index (C-index).Results: On multivariate analysis of the 856 cohort, independent factors for survival were CRP, fibrinogen, tumor status, nodal status, distant metastasis and clinical stage, which were entered into the nomogram. The C-index of the established nomogram 0.720 (95% CI: 0.671-0.769) was higher than that of the seventh edition TNM staging system 0.689 (95% CI: 0.668-0.709) for predicting OS (P < 0.05). Compared with patients with low CRP levels (< 8.6 g/L) and low fibrinogen levels (< 3.7 g/L), patients with high CRP and fibrinogen levels had shorter OS. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in stage I-IV NSCLC (P < 0.05).Conclusion: A nomogram integrating CRP and fibrinogen, which could be convenient and feasible to obtain from the serum preoperatively, may assist in risk stratification for individual patient with resected NSCLC.

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“…First, an independent large set of thyroid tumors outside of TCGA with multiple forms of genomic measurement is not currently available as an external validation set. Therefore, we carried out an internal validation approach using 2,000 bootstrap resampling, a procedure to reduce prediction error estimation that has been applied in multiple previous studies [59–65]. Another limitation of this study is that we were missing several variables that might be informative, including exposure to radiation, family history of PTC, BMI, and extrathyroidal extension.…”

Section: Discussionmentioning

confidence: 99%

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors

Cheng

Acharya

et al. 2017

Oncotarget

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Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10–10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC.

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Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma

Abstract: The proposed nomogram with or without plasma EBV DNA resulted in more accurate prognostic prediction for NPC patients.

Cited by 296 publications

References 32 publications

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

A Nomogram based on Inflammatory Factors C-Reactive Protein and Fibrinogen to Predict the Prognostic Value in Patients with Resected Non-Small Cell Lung Cancer

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors

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