ArticleScientific reports2019
Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- Longitudinal bilateral parotid delta-radiomic kinetics and variance signatures for prediction of late xerostomia following radiotherapy.BMC medical imaging · 2026Article
- Structured proxy features for multimodal NSCLC survival prediction from pretreatment CT.Frontiers in artificial intelligence · 2026Article
- Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment.Quantitative imaging in medicine and surgery · 2024Review
- Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.Japanese journal of radiology · 2024Article
- Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.CPT: pharmacometrics & systems pharmacology · 2023Article
- Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.JCO clinical cancer informatics · 2023Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer.Frontiers in oncology · 2022Article
- Computed tomography-based radiomic model predicts radiological response following stereotactic body radiation therapy in early-stage non-small-cell lung cancer and pulmonary oligo-metastases.Radiation oncology journal · 2021Article
- Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.European radiology · 2021Article
- Quality control of radiomic features using 3D-printed CT phantoms.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.International journal of radiation oncology, biology, physics · 2021Article
- A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.