ArticleFrontiers in oncology2023
Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence.Academic radiology · 2025Article
- Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR/MSI-H colorectal cancer.Journal for immunotherapy of cancer · 2025Article
- Deep Learning-Based DCE-MRI Automatic Segmentation in Predicting Lesion Nature in BI-RADS Category 4.Journal of imaging informatics in medicine · 2025Article
- Radiomics models for predicting occult nodal metastasis in stage cT1a-bN0M0 lung adenocarcinoma: a multicenter study.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning-based integration of DCE-MRI radiomics for STAT3 expression prediction and survival stratification in breast cancer.Frontiers in immunology · 2025Article
- Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.Scientific reports · 2024Article
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13 authors.
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Abstract
Objectives: The purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. Methods: After rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively. Results: Six radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS. Conclusions: Our radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients.
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