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

Yang, Kaixuan; Tian, Jiangfang; Zhang, B.; Li, Mei; Xie, Wenji; Zou, Yating; Tan, Qiaoyue; Liu, L.; Zhu, Junkai; Shou, Arthur; Li, Guangjun

doi:10.1016/j.oraloncology.2019.09.022

Cited by 59 publications

(83 citation statements)

References 33 publications

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“…In this study, because the majority patients have tumors exceeding 10 cm 3 (training cohort: 108/126, test cohort: 51/68), radiomic features were found to obtained more prognostic power, compared to tumor volume. Furthermore, EBV status was reported to be as useful biomarker in prediction of prognosis for nasopharyngeal cancer patients 44,45 . We could not performed this task because EBV status was not available in our dataset.…”

Section: Discussionmentioning

confidence: 99%

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

Arimura

Ninomiya

et al. 2020

Sci Rep

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This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 $$\times$$ × 10–2, 1.85 $$\times$$ × 10–2, 3.17 $$\times$$ × 10–2, and 1.87 $$\times$$ × 10–3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 $$\times$$ × 10–3, 1.28 $$\times$$ × 10–3, and 1.45 $$\times$$ × 10–3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.

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Section: Discussionmentioning

confidence: 99%

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

Arimura

Ninomiya

et al. 2020

Sci Rep

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“…To reduce the dimensionality of radiomics features, Spearman's rank correlation test was initially used to exclude the redundant features (correlation coefficient values ≥0.9), and the remaining features were used for subsequent evaluation. Then the least absolute shrinkage and selection operator (LASSO) algorithm 26 and stepwise logistic regression were performed to identify the top‐ranked and most valuable features, with penalty parameter tuning conducted by 5‐fold cross‐validation. Multivariate logistic regression was applied to construct radiomics models in each MRI sequence.…”

Section: Methodsmentioning

confidence: 99%

Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy

Zhao

Zhang

et al. 2020

Magnetic Resonance Imaging

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Background Preoperative prediction of early recurrence (ER) of hepatocellular carcinoma (HCC) plays a critical role in individualized risk stratification and further treatment guidance. Purpose To investigate the role of radiomics analysis based on multiparametric MRI (mpMRI) for predicting ER in HCC after partial hepatectomy. Study Type Retrospective. Population In all, 113 HCC patients (ER, n = 58 vs. non‐ER, n = 55), divided into training (n = 78) and validation (n = 35) cohorts. Field Strength/Sequence 1.5T or 3.0T, gradient‐recalled‐echo in‐phase T1‐weighted imaging (I‐T1WI) and opposed‐phase T1WI (O‐T1WI), fast spin‐echo T2‐weighted imaging (T2WI), spin‐echo planar diffusion‐weighted imaging (DWI), and gradient‐recalled‐echo contrast‐enhanced MRI (CE‐MRI). Assessment In all, 1146 radiomics features were extracted from each image sequence, and radiomics models based on each sequence and their combination were established via multivariate logistic regression analysis. The clinicopathologic‐radiologic (CPR) model and the combined model integrating the radiomics score with the CPR risk factors were constructed. A nomogram based on the combined model was established. Statistical Tests Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of each model. The potential clinical usefulness was evaluated by decision curve analysis (DCA). Results The radiomics model based on I‐T1WI, O‐T1WI, T2WI, and CE‐MRI sequences presented the best performance among all radiomics models with an area under the ROC curve (AUC) of 0.771 (95% confidence interval (CI): 0.598–0.894) in the validation cohort. The combined nomogram (AUC: 0.873; 95% CI: 0.756–0.989) outperformed the radiomics model and the CPR model (AUC: 0.742; 95% CI: 0.577–0.907). DCA demonstrated that the combined nomogram was clinically useful. Data Conclusion The mpMRI‐based radiomics analysis has potential to predict ER of HCC patients after hepatectomy, which could enhance risk stratification and provide support for individualized treatment planning. Evidence Level 4. Technical Efficacy Stage 4.

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“…We have previously demonstrated that pre‐treatment plasma EBV DNA status helped improving the predictive accuracy of survival outcomes in patients with NPC 18 . Unfortunately, this research failed to justify that pre‐treatment plasma EBV DNA was a significant predictive factor for clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…We collected the following clinical variables from the Hospital Information Systems: age, sex, family history of cancer, cigarette smoking, alcohol consumption, TNM stage, WHO histological type, pre‐treatment EBV DNA, post‐IC EBV DNA, lactate dehydrogenase (LDH), induction chemotherapy cycles, waiting time before radiotherapy (WRT), treatment group, and radiotherapy duration days (RDD). The plasma EBV DNA was quantified by real‐time polymerase chain reaction assay before and after IC 18 …”

Section: Methodsmentioning

confidence: 99%

Nomograms for predicting survival outcomes in intensity‐modulated radiotherapy era of nasopharyngeal carcinoma: A study based on Epstein–Barr virus DNA biological responses

Yang

Zhu

et al. 2021

Head & Neck

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Background Treatment of nasopharyngeal carcinoma (NPC) is evolving toward Intensity‐modulated radiotherapy (IMRT) era, which requires patient‐specific reestimation of survival outcomes in modern health care. Methods A total of 488 detectable pre‐treatment Epstein–Barr virus (EBV) DNA patients (stage II‐IVa) treated with induction chemotherapy (IC) and IMRT were examined (training set, n = 325; validation set, n = 163). Results Concurrent chemotherapy (CC) was still an independent prognosticator for overall survival (OS) and progression‐free survival (PFS). Both nomograms included age, T classification, N classification, post‐IC EBV DNA, and CC. Predictions correlated well with observed 3‐/5‐year OS and PFS. The concordance index was 0.776 (95% confidence interval (CI) 0.69–0.86) for OS and 0.742 (95% CI 0.65–0.83) for PFS in the validation cohort. The nomograms can successfully classify patients into low‐ and high‐risk groups. Conclusion The validated nomograms provided useful prediction of OS and PFS for detectable pre‐treatment EBV DNA patients with NPC in IMRT era.

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

Cited by 59 publications

References 33 publications

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

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

Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy

Nomograms for predicting survival outcomes in intensity‐modulated radiotherapy era of nasopharyngeal carcinoma: A study based on Epstein–Barr virus DNA biological responses

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