CT-based radiomics nomogram may predict who can benefit from adaptive radiotherapy in patients with local advanced-NSCLC patients

Zhou, Chao; Hou, Liqiao; Tang, Xinni; Liu, Changxing; Meng, Yinnan; Jia, Haijian; Yang, Haihua; Zhou, Suna

doi:10.1016/j.radonc.2023.109637

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…As an emerging translation field, radiomics investigates the association between quantitative high-dimensional data extracted from imaging examinations and clinical data to construct a prediction model, which eventually improves evidence-based personalized medical decision-making [12,13]. In recent years, radiomics has shown great promise in predicting tumor diagnosis, molecular subtype, therapeutic effect, and survival in patients with various tumor types, including breast cancer [14,15], lung cancer [16,17], and OC [18][19][20]. A systematic review demonstrated that radiomics models have shown promising results as predictors of OS and progression-free survival (PFS) in patients with OC, but larger studies are needed to demonstrate clinical applicability [20].…”

Section: Introductionmentioning

confidence: 99%

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

Zhang,

Cui,

et al. 2024

J Ovarian Res

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Background This study aimed to develop and evaluate radiomics models to predict CD27 expression and clinical prognosis before surgery in patients with serous ovarian cancer (SOC). Methods We used transcriptome sequencing data and contrast-enhanced computed tomography images of patients with SOC from The Cancer Genome Atlas (n = 339) and The Cancer Imaging Archive (n = 57) and evaluated the clinical significance and prognostic value of CD27 expression. Radiomics features were selected to create a recursive feature elimination-logistic regression (RFE-LR) model and a least absolute shrinkage and selection operator logistic regression (LASSO-LR) model for CD27 expression prediction. Results CD27 expression was upregulated in tumor samples, and a high expression level was determined to be an independent protective factor for survival. A set of three and six radiomics features were extracted to develop RFE-LR and LASSO-LR radiomics models, respectively. Both models demonstrated good calibration and clinical benefits, as determined by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The LASSO-LR model performed better than the RFE-LR model, owing to the area under the curve (AUC) values of the ROC curves (0.829 vs. 0.736). Furthermore, the AUC value of the radiomics score that predicted the overall survival of patients with SOC diagnosed after 60 months was 0.788 using the LASSO-LR model. Conclusion The radiomics models we developed are promising noninvasive tools for predicting CD27 expression status and SOC prognosis. The LASSO-LR model is highly recommended for evaluating the preoperative risk stratification for SOCs in clinical applications.

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Section: Introductionmentioning

confidence: 99%

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

Zhang,

Cui,

et al. 2024

J Ovarian Res

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CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

Hou,

Chen,

Zhou

et al. 2024

Clinical and Translational Radiation Oncology

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Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features

Wang,

Zhang,

Liu

et al. 2023

Respir Res

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Background To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma. Methods This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application. Results Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817–0.909), 0.771 (95%CI: 0.713–0.713) and 0.872 (95%CI: 0.829–0.916) in the training set, and 0.849 (95%CI: 0.774–0.924), 0.778 (95%CI: 0.687–0.868) and 0.853 (95%CI: 0.782–0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%. Conclusions Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma.

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CT-based radiomics nomogram may predict who can benefit from adaptive radiotherapy in patients with local advanced-NSCLC patients

Cited by 3 publications

References 35 publications

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics

CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy

Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features

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