Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer

Lu, Nian; Zhang, Wei-Jing; Lu, Dongsi; Chen, Junying; Zhu, Yujia; Zhang, Shenghai; Fu, Jianhua; Yin, Shaohan; Li, Zhicheng; Xie, Conghua

doi:10.1016/j.cmpb.2021.106287

Cited by 27 publications

(14 citation statements)

References 30 publications

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“…Their study was based on the mixed cohort of both ADC and ESCC patients. Lu et al also showed a similar result in predicting OS in patients with ESCC based on a CT tumor radiomics signature (C-Index 0.63, 95% CI 0.578–0.69), although the nomogram based on the radiomics and clinicopathological risk factors in their study showed a slightly better prediction with a C-index of 0.73 (95% CI 0.69–0.78) [ 35 ]. A CT subregion-based radiomics survival prediction model developed by Xie et al had a similar C-index (0.71; 95% CI 0.63–0.78) [ 20 ].…”

Section: Discussionmentioning

confidence: 85%

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

Jayaprakasam

Gibbs

Gangai

et al. 2022

Cancers

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This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category—70.4%, 70.4%, and 81.5%, respectively; cN category—69.0%, 86.2%, and 86.2%, respectively; PET response—60.0%, 66.7%, and 70.0%, respectively; PFS—60.7%, 75.0%, and 75.0%, respectively; and OS—51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

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Section: Discussionmentioning

confidence: 85%

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

Jayaprakasam

Gibbs

Gangai

et al. 2022

Cancers

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“…What is more, using radiomics, Kao and Hsu discovered that it was possible to predict the full pathological response after neoadjuvant chemoradiotherapy in esophageal cancer [ 12 ]. The highest discriminative ability in OS prediction for ESCC patients with a C-index of 0.700 or above was shown to be attained by the integrating primary tumor and lymph node CT radiomics-clinical nomogram, according to research by Lu et al, and improved the calibration and classification for OS prediction compared with the clinical nomogram (C-index: 0.594-0.604) [ 14 ]. These previous studies showed that the radiomics signature outperformed other methods for evaluating esophageal diseases.…”

Section: Discussionmentioning

confidence: 99%

A CT‐Based Radiomics Nomogram Model for Differentiating Primary Malignant Melanoma of the Esophagus from Esophageal Squamous Cell Carcinoma

Shi

Zhu

Yan

et al. 2023

BioMed Research International

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Objective. The diagnosis of primary malignant melanoma of the esophagus (PMME) before treatment is essential for clinical decision-making. However, PMME may be misdiagnosed as esophageal squamous cell carcinoma (ESCC) sometimes. This research is aimed at devising a radiomics nomogram model of CT for distinguishing PMME from ESCC. Methods. In this retrospective analysis, 122 individuals with proven pathologically PMME ( n = 28 ) and ESCC ( n = 94 ) were registered from our hospital. PyRadiomics was applied to derive radiomics features from plain and enhanced CT images after resampling image into an isotropic resolution of 0.625 × 0.625 × 0.625 m m 3 . The diagnostic efficiency of the model was evaluated by an independent validation group. Results. For the purpose of differentiation between PMME and ESCC, a radiomics model was constructed using 5 radiomics features obtained from nonenhanced CT and 4 radiomics features derived from enhanced CT. A radiomics model including multiple radiomics features showed excellent discrimination efficiency with AUCs of 0.975 and 0.906 in the primary and validation cohorts, respectively. Then, a radiomics nomogram model was developed. The decision curve analysis has shown remarkable performance of this nomogram model for distinguishing PMME from ESCC. Conclusions. The proposed radiomics nomogram model based on CT could be used for distinguishing PMME from ESCC. Moreover, this model also contributed to helping clinicians determine an appropriate treatment strategy for esophageal neoplasms.

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“…In the training cohort and the validation cohort, compared with the nomograms of the radiomic characteristics and of clinical risk factors, the nomogram combining the radiomic characteristics and clinical risk factors had optimal performance. Other studies have shown that combining the radiomic characteristics of primary tumors and regional lymph nodes with clinical-pathological factors can improve OS prediction[ 50 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Qi¹,

Zhou²,

Zhao³

et al. 2022

World J Gastroenterol

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Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

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Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer

Cited by 27 publications

References 30 publications

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?

A CT‐Based Radiomics Nomogram Model for Differentiating Primary Malignant Melanoma of the Esophagus from Esophageal Squamous Cell Carcinoma

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

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