We are analyzing https://link.springer.com/article/10.1007/s00330-022-09109-3.

Title:
Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer | European Radiology
Description:
Objectives To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). Methods In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. Results Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05). Conclusions CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery. Key Points • Compared with GTV, GPTV 3 , and GPTV 6 radiomics models, GPTV 9 radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV 9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.
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article, cancer, lung, google, scholar, radiomics, cell, invasion, nonsmall, peritumoral, analysis, tumor, stage, lymphovascular, gptv, clinical, survival, feng, prediction, nsclc, eur, radiol, intratumoral, nomogram, features, machine, preoperative, lvi, predict, access, vascular, cas, chen, patients, prognostic, wang, nantong, privacy, cookies, content, information, research, imaging, volume, status, ctbased, based, model, open, learning,

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dual-phase dual-energy ct node-negative stage i-iia month download article/chapter small-cell lung cancer kaplan-meier survival analysis cancer imaging archive propensity score-matched analysis preoperative fdg pet/ct pd-l1 correlates weakly institutional review board mol imaging biol radiomics nomogram based lung cancer screening radiomics-based study ct texture analysis machine learning methods peritumoral radiomics nomograms lung cancer lesions gross tumor volume ct-based intratumoral clinical research plan privacy choices/manage cookies solid lung adenocarcinoma ct-based clinical additional scoring formula fdg-pet/ct renal cell carcinoma survival time based 3d texture analysis predict lymphovascular invasion systemic cancer therapies small renal masses full article pdf related subjects microscopic tumor extension european economic area lung adenocarcinoma patients pulmonary lesion based predict hpv status predictive lymph node status affiliated tumor hospital machine learning global cancer statistics lymphovascular invasion increases clinical stage ia visceral pleural invasion diagnosing microvascular invasion clinical independent predictors check access

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         description:To evaluate the predictive value of intratumoral and peritumoral radiomics and radiomics nomogram for preoperative lymphovascular invasion (LVI) status and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). In total, 240 NSCLC patients from our institution were randomly divided into the training cohort (n = 145) and internal validation cohort (n = 95) with a ratio of 6:4, and 65 patients from the Cancer Imaging Archive were enrolled as the external validation cohort. We extracted 1217 CT-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 3, 6, and 9 mm regions (GPTV3, GPTV6, GPTV9). A radiomics nomogram based on clinical independent predictors and radiomics score (Radscore) of the best radiomics model was constructed. The correlation between factors and OS was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. Compared with GTV, GPTV3, and GPTV6 radiomics models, GPTV9 radiomics model exhibited better prediction performance with the AUCs of 0.82, 0.75, and 0.67 in the training, internal validation, and external validation cohorts, respectively. In the clinical model, smoking and clinical stage were independent predictors. The nomogram incorporating independent predictors and GPTV9-Radscore was clinically useful, with the AUCs of 0.89, 0.83, and 0.66 in three cohorts. Pathological LVI, GPTV9-Radscore-predicted, and Nomoscore-predicted LVI were associated with poor OS (p < 0.05). CT-based radiomics nomogram can predict LVI and OS in patients with NSCLC and may help in making personalized treatment strategies before surgery. • Compared with GTV, GPTV 3 , and GPTV 6 radiomics models, GPTV 9 radiomics model showed better prediction performance for LVI status in NSCLC. • The radiomics nomogram based on GPTV 9 radiomics features and clinical independent predictors could effectively predict LVI status and OS in NSCLC and outperformed the clinical model. • The radiomics nomogram had a wider scope of clinical application.
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