ArticleExperimental and therapeutic medicine2019
Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy.
Article in Experimental and therapeutic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Pilot study: radiomic analysis for predicting treatment response to whole-brain radiotherapy combined temozolomide in lung cancer brain metastases.Frontiers in oncology · 2024Article
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram.Frontiers in oncology · 2023Article
- CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.Frontiers in oncology · 2023Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Molecular Characterization and Therapeutic Approaches to Small Cell Lung Cancer: Imaging Implications.Radiology · 2022Review
- Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.Diagnostics (Basel, Switzerland) · 2022Review
- Article
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis.Journal of oncology · 2022Article
- Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.Frontiers in medicine · 2022Article
- Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources.Computational intelligence and neuroscience · 2022Article
- Integrative nomogram of intratumoral, peritumoral, and lymph node radiomic features for prediction of lymph node metastasis in cT1N0M0 lung adenocarcinomas.Scientific reports · 2021Article
- [Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- The application of artificial intelligence and radiomics in lung cancer.Precision clinical medicine · 2020Review
- Radiomics: an overview in lung cancer management-a narrative review.Annals of translational medicine · 2020Review
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10 authors.
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Abstract
The aim of the present study was to investigate the utility of a computed tomography (CT)-based radiomics signature for the early prediction of the tumor response of small cell lung cancer (SCLC) patients to chemotherapy. A dataset including 92 patients from a clinical trial was retrospectively assembled. All of the patients received the standard first-line regimen of etoposide and cisplatin. According to the Response Evaluation Criteria in Solid Tumors 1.1, the patients were divided into two groups: Response and no response groups. A total of 21 radiomics features were extracted from CT images prior to and after two cycles of chemotherapy and a radiomics signature was constructed via a binary logistic regression model. The area under the receiver operating characteristics curve (AUC) was determined to evaluate the performance of the radiomics signature to predict the response to chemotherapy. The clinicopathological factors associated with chemotherapy in patients with SCLC were also evaluated, and a predictive model was established using a binary logistic regression analysis. The 21 radiological features were used to establish a radiomics signature that was significantly associated with the efficacy of SCLC chemotherapy (P<0.05). The performance of the radiomics signature to predict the chemotherapy efficacy (AUC=0.797) was better than that of the model using clinicopathological parameters (AUC=0.670). Therefore, the present study demonstrated that radiomics features may be promising prognostic imaging biomarkers to predict the response of SCLC patients to chemotherapy and may thus be utilized to guide appropriate treatment planning.
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