ArticleTranslational oncology2018
Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features.
Article in Translational oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer.Clinical and translational science · 2025Article
- Reproducibility of the CT radiomic features of pulmonary nodules: the effects of the CT reconstruction algorithm, radiation dose, and contrast agent.Quantitative imaging in medicine and surgery · 2025Article
- A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.Medical & biological engineering & computing · 2024Review
- A deep learning-informed interpretation of why and when dose metrics outside the PTV can affect the risk of distant metastasis in SBRT NSCLC patients.Radiation oncology (London, England) · 2024Article
- Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.Journal of imaging informatics in medicine · 2024Article
- Predicting bone metastasis-free survival in non-small cell lung cancer from preoperative CT via deep learning.NPJ precision oncology · 2024Article
- MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome.Diagnostics (Basel, Switzerland) · 2024Article
- Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.NPJ precision oncology · 2023Review
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
- The Role of Sublobar Resection for the Surgical Treatment of Non-Small Cell Lung Cancer.Current oncology (Toronto, Ont.) · 2023Review
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
- Review
- Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans.Frontiers in medicine · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- What does radiomics do in PD-L1 blockade therapy of NSCLC patients?Thoracic cancer · 2022Review
- Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients.Journal of healthcare engineering · 2022Article
- Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer.Frontiers in oncology · 2022Article
- Structural and functional radiomics for lung cancer.European journal of nuclear medicine and molecular imaging · 2021Review
- A predictive model for pain response following radiotherapy for treatment of spinal metastases.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer.
methodsThis was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature.
resultsFour radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved.
conclusionThe radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.
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Registered trials
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.