Literature DB >> 31569054

A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma.

Kaixuan Yang1, Jiangfang Tian1, Bin Zhang2, Mei Li1, Wenji Xie1, Yating Zou3, Qiaoyue Tan1, Lihui Liu1, Jinbing Zhu1, Arthur Shou4, Guangjun Li5.   

Abstract

OBJECTIVES: To develop a multidimensional nomogram for predicting the progression-free survival (PFS) in patients with locoregionally advanced nasopharyngeal carcinoma (NPC) (stage III-IVa).
MATERIALS AND METHODS: A total of 224 patients with locoregionally advanced NPC (training cohort, n = 149; validation cohort, n = 75) were retrospectively included. We extracted 260 radiomic features from the primary tumor and lymph nodes on the axial contrast-enhanced T1 weighted and T2 weighted MRI. Radiomic signatures of the gross tumor volume (RSnx) and lymph node (RSnd), Dose Volume Histogram (DVH) signature reflecting planning score (PS), and clinical characteristics were included as potential predictors of PFS. The least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and data dimension reduction. A nomogram was developed by incorporating the selected predictors. The C-index and calibration curve were used to assess discrimination and calibration power of the nomogram, respectively.
RESULTS: RSnd, PS, and tumor-node-metastasis (TNM) stage were the independent predictors for PFS (all p < 0.05). The nomogram integrating the three factors achieved a C-index of 0.811 (95% CI: 0.74-0.882) in the validation cohort for predicting PFS, which outperformed than that of the TNM stage alone (C-index, 0.613, 95% CI: 0.532-0.694). Subgroup analysis showed Epstein-Barr virus (EBV) DNA status improved the predictive accuracy of the nomogram (C-index, 0.86, 95% CI: 0.787-0.933).
CONCLUSIONS: The multidimensional nomogram incorporating RSnd, PS, and TNM stage showed high performance for predicting PFS in patients with locoregionally advanced NPC.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Dose volume histogram parameters; Intensity modulated radiotherapy; Locoregionally advanced nasopharyngeal carcinoma; Lymph node; Nomogram; Radiomics

Year:  2019        PMID: 31569054     DOI: 10.1016/j.oraloncology.2019.09.022

Source DB:  PubMed          Journal:  Oral Oncol        ISSN: 1368-8375            Impact factor:   5.337


  21 in total

1.  Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy.

Authors:  Zhiyuan Xu; Li Yang; Qin Liu; Hao Yu; Longhua Chen
Journal:  J Oncol       Date:  2022-06-14       Impact factor: 4.501

2.  Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma.

Authors:  Qiyi Hu; Guojie Wang; Xiaoyi Song; Jingjing Wan; Man Li; Fan Zhang; Qingling Chen; Xiaoling Cao; Shaolin Li; Ying Wang
Journal:  Cancers (Basel)       Date:  2022-06-30       Impact factor: 6.575

3.  A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer.

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Review 4.  Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.

Authors:  Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry
Journal:  Front Oncol       Date:  2021-07-07       Impact factor: 6.244

5.  Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma.

Authors:  Hesong Shen; Yu Wang; Daihong Liu; Rongfei Lv; Yuanying Huang; Chao Peng; Shixi Jiang; Ying Wang; Yongpeng He; Xiaosong Lan; Hong Huang; Jianqing Sun; Jiuquan Zhang
Journal:  Front Oncol       Date:  2020-05-12       Impact factor: 6.244

6.  Risk stratification for nasopharyngeal carcinoma: a real-world study based on locoregional extension patterns and Epstein-Barr virus DNA load.

Authors:  Lu-Lu Zhang; Meng-Yao Huang; Ke-Xin Wang; Di Song; Ting Wang; Li-Yue Sun; Jian-Yong Shao
Journal:  Ther Adv Med Oncol       Date:  2020-06-12       Impact factor: 8.168

7.  Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.

Authors:  Quoc Cuong Le; Hidetaka Arimura; Kenta Ninomiya; Yutaro Kabata
Journal:  Sci Rep       Date:  2020-12-04       Impact factor: 4.379

8.  A Nomogram for the Prognosis of Nasopharyngeal Carcinoma with MR Imaging-Detected Tumor Residue at the End of Intensity-Modulated Radiotherapy.

Authors:  Meng Xu; Chang Liu; Jing Lin Mi; Ren Sheng Wang
Journal:  Cancer Manag Res       Date:  2020-05-25       Impact factor: 3.989

Review 9.  Application of radiomics and machine learning in head and neck cancers.

Authors:  Zhouying Peng; Yumin Wang; Yaxuan Wang; Sijie Jiang; Ruohao Fan; Hua Zhang; Weihong Jiang
Journal:  Int J Biol Sci       Date:  2021-01-01       Impact factor: 6.580

10.  Establishment of a Prognostic Nomogram for Patients With Locoregionally Advanced Nasopharyngeal Carcinoma Incorporating TNM Stage, Post-Induction Chemotherapy Tumor Volume and Epstein-Barr Virus DNA Load.

Authors:  Yu-Ting Jiang; Kai-Hua Chen; Jie Yang; Zhong-Guo Liang; Song Qu; Ling Li; Xiao-Dong Zhu
Journal:  Front Oncol       Date:  2021-06-16       Impact factor: 6.244

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