Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery

Qiu, Qingtao; Duan, Jinghao; Deng, Hongkui; Han, Zhujun; Gu, Jinyan; Yue, Ning J.; Yin, Yong

doi:10.3389/fonc.2020.01398

Cited by 48 publications

(37 citation statements)

References 39 publications

(53 reference statements)

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“…The application of radiomics has been extensively studied in esophageal cancer (28,29), non-small cell lung cancer (30), breast cancer (31), nasopharyngeal carcinoma (32), Glioblastoma (33), and rectal cancer (34), which indicates the potential of radiomics for predicting the efficacy of treatment or patient prognosis. Radiotherapy-orientated CT imaging must be acquired prior to SBRT treatment of HCC with PVTT.…”

Section: Discussionmentioning

confidence: 99%

Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy

Shui

Sun

et al. 2020

Front. Oncol.

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Introduction: This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) treated with stereotactic body radiotherapy (SBRT). Materials and Methods: The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the model. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results: A total of four radiomic features and six clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [area under the curve (AUC) = 0.859 (CI: 0.770-0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641-0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion: This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to assess individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

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Section: Discussionmentioning

confidence: 99%

Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy

Shui

Sun

et al. 2020

Front. Oncol.

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“…In the current study, we developed and validated two prognostic nomograms, one for predicting overall survival and the other for predicting disease-specific survival of oral and oropharyngeal MEC. Clinicians can conveniently predict the 3-year, 5-year and In recent years, more and more prognostic nomograms have been constructed and utilized for prediction of prognosis and clinical decision-making for different types of carcinomas 5,[8][9][10][11][12] . However, because of the low incidence of mucoepidermoid carcinoma, research focusing on this tumor is very rare.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

Liu

Tong

et al. 2020

Preprint

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Background: Due to the low incidence of mucoepidermoid carcinoma, there lacks sufficient studies for determining optimal treatment and predicting prognosis. The purpose of this study was to develop prognostic nomograms, to predict overall survival and disease-specific survival (DSS) of oral and oropharyngeal mucoepidermoid carcinoma patients, using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Methods: Clinicopathological and follow-up data of patients diagnosed with oral and oropharyngeal mucoepidermoid carcinoma between 2004 and 2017 were collected from the SEER database. The Kaplan-Meier method with the log-rank test was employed to identify single prognostic factors. Multivariate Cox regression was utilized to identify independent prognostic factors. C-index, area under the ROC curve (AUC) and calibration curves were used to assess performance of the prognostic nomograms. Results: A total of 1230 patients with oral and oropharyngeal mucoepidermoid carcinoma were enrolled in the present study. After multivariate Cox regression analysis, age, sex, tumor subsite, T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for overall survival. T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for disease-specific survival. Nomograms were constructed to predict the overall survival and disease-specific survival based on the independent prognostic factors. The fitted nomograms possessed excellent prediction accuracy, with a C-index of 0.899 for OS prediction and 0.893 for DSS prediction. Internal validation by computing the bootstrap calibration plots, using the validation set, indicated excellent performance by the nomograms. Conclusion: The prognostic nomograms developed, based on individual clinicopathological characteristics, in the present study, accurately predicted the overall survival and disease-specific survival of patients with oral and oropharyngeal mucoepidermoid carcinoma.

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“…ML methods have shown the potential to act as prognostic tools for risk stratification in ECs. There are seven studies in the prognostic group [ 111 , 112 , 113 , 114 , 115 , 116 , 117 ], including three PET, three CT, and one PET-CT studies. Patients in different risk groups had a significant or borderline significant difference in survival outcomes.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

“…Chen et al [ 112 ] constructed a scoring system based on both clinical and PET radiomics features and enabled better stratification of patients into different long-term prognosis. Qiu et al [ 111 ] developed three prognostic models, the nomogram based on both radiomics and clinical features achieved optimal performance with a C-index of 0.72 in the validation set.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

“…Four studies [ 88 , 109 , 113 , 115 ] applied deep learning networks in the model construction. Incorporation of clinical features into data-based ML models could improve the prediction accuracy in 11 studies [ 88 , 104 , 106 , 110 , 111 , 112 , 113 , 116 , 119 , 120 ]. The majority of studies [ 83 , 88 , 100 , 101 , 102 , 103 , 105 , 111 , 112 , 113 , 114 , 119 , 121 , 122 , 123 ] evaluated SCC patients and some [ 104 , 106 , 107 , 108 , 109 , 110 , 115 , 116 ] mainly focused on adenocarcinoma.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

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Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Xie

Pang

Chan

et al. 2021

Cancers

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Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

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Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery

Cited by 48 publications

References 39 publications

Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy

Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy

Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

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