Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non–Small Cell Lung Cancer

Wang, Tingting; She, Yunlang; Yang, Yang; Liu, Xinyue; Chen, Shouyu; Zhong, Yifan; Deng, Jiajun; Zhao, Mengmeng; Sun, Xuejun; Xie, Dong; Chen, Chang

doi:10.1148/radiol.2021210109

Cited by 87 publications

(66 citation statements)

References 35 publications

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“…Thereafter, all VOIs are generated automatically by computing devices, based on the label points. After segmentation, the peritumoral VOIs were created at a distance of 15 mm outside of the lesions according to the morphology by two radiologists [ 19 ]. The results were identified by one experienced radiologist (Ji, with over twenty years of experience in lung diagnosis).…”

Section: Methodsmentioning

confidence: 99%

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

Zhang

et al. 2022

BMC Med Imaging

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Objectives To construct a noninvasive radiomics model for evaluating the pathological degree and an individualized treatment strategy for patients with the manifestation of ground glass nodules (GGNs) on CT images. Methods The retrospective primary cohort investigation included patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semiautomatically, and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by ANOVA, Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, a random forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features. Results In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Three intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.960 (0.963 for AAH/AIS, 0.940 for MIA, 0.978 for IAC) in the training set and 0.944 (0.955 for AAH/AIS, 0.952 for MIA, 0.926 for IAC) in the testing set for evaluation of the GGNs. Conclusion The triple classification based on intra- and peritumoral radiomics features derived from the noncontrast CT images had satisfactory performance and may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing of individualized treatment strategies.

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Section: Methodsmentioning

confidence: 99%

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

Zhang

et al. 2022

BMC Med Imaging

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“…For each CT image, we extracted a wide range of features from the segmented cancer region according to the radiomic features described by the Imaging Biomarker Standardization Initiative (IBSI), including intensity features, shape features, texture features, and wavelet features [ 8 ]. Intensity features use first-order statistics, such as energy and entropy, to quantify the tumor intensity characteristics.…”

Section: Methodsmentioning

confidence: 99%

“…Yang et al developed a radiomics nomogram by combining the optimized radiomics signatures of CT images and clinical predictors to assess the overall survival of patients with NSCLC [ 7 ]. Wang et al demonstrated that a radiomics signature with multiregional features could help to stratify the survival risk of patients with clinical stage and pathologic stage IA pure-solid NSCLC [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis

Chen

Yin

et al. 2022

Journal of Oncology

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Purpose. The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model. Methods. A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors. Results. The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data. Conclusions. Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients.

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“…Thereafter, the entire VOIs are generated automatically by computing devices, based on the label points. After the segmentation, the peritumoral VOIs were created at a distance of 15mm outside of the lesions according to the morphology by two radiologists [19]. The results were identi ed by one experienced radiologist (Ji, with over twenty years of experience in lung diagnosis).…”

Section: Region Of Interest Segmentationmentioning

confidence: 99%

A Triple-Classification for The Evaluation of Lung Nodules Manifesting as Pure Ground-Glass Sign: A CT-Based Radiomic Analysis

Zhang

et al. 2022

Preprint

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Objectives: To construct a noninvasive radiomics model for evaluation of pathologic degree and individually treated strategy for patients with the manifestation of imageology-ground glass nodules (GGNs) on CT images.Methods: The retrospective primary cohort investigation included the patients with GGNs on CT images who underwent resection between June 2015 and June 2020. The intratumoral regions of interest were segmented semi-automatically and radiomics features were extracted from the intratumoral and peritumoral regions. After feature selection by Max-Relevance and Min-Redundancy (mRMR) and Least Absolute Shrinkage and Selection Operator (Lasso) regression, Random Forest (RF) model was generated. Receiver operating characteristic (ROC) analysis was calculated to evaluate each classification. Shapley additive explanations (SHAP) was applied to interpret the radiomics features.Results: In this study, 241 patients including atypical adenomatous hyperplasia (AAH) or adenocarcinoma in situ (AIS) (n = 72), minimally invasive adenocarcinoma (MIA) (n = 83) and invasive adenocarcinoma (IAC) (n = 86) were selected for radiomics analysis. Four intratumoral radiomics features and one peritumoral feature were finally identified by the triple RF classifier with an average area under the curve (AUC) of 0.951 (0.953 for AAH/AIS, 0.934 for MIA, 0.965 for IAC, in the training set) and 0.935 (0.938 for AAH/AIS, 0.940 for MIA, 0.926 for IAC, in the validation set) for evaluation of the GGNs.Conclusion: The triple classification based on intra- and peritumoral radiomics features derived from the non-contrast CT images had a satisfactory performance, which may be used as a noninvasive tool for preoperative evaluation of the pure ground-glass nodules and developing individualized treatment strategy.

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Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non–Small Cell Lung Cancer

Cited by 87 publications

References 35 publications

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis

Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis

A Triple-Classification for The Evaluation of Lung Nodules Manifesting as Pure Ground-Glass Sign: A CT-Based Radiomic Analysis

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