ArticleDiagnostics (Basel, Switzerland)2022
Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- MRI-based radiomics nomogram to predict complete response to definitive chemoradiation in patients with anal cancer.The British journal of radiology · 2025Article
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Radiomic score is prognostic in clinical stage I lung adenocarcinoma ≤2 cm undergoing surgery.Journal of thoracic disease · 2024Article
- Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study.Journal of the National Cancer Center · 2024Article
- CT-based radiomic consensus clustering association with tumor biological behavior in clinical stage IA adenocarcinoma: a retrospective study.Translational lung cancer research · 2024Article
- A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.Journal of thoracic disease · 2024Article
- Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer.Biomedicines · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
objectivesTo investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). MATERIALS AND
methodsThis study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction.
resultsSix radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built.
conclusionThe radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine.
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