ArticleTranslational lung cancer research2020
Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma.
Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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32 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.BMC cancer · 2022Trial
- A prognostic PET radiomic model for risk stratification in non-small cell lung cancer: integrating radiogenomics and clinical features to predict survival and uncover tumor biology insights.Journal of cancer research and clinical oncology · 2025Article
- Risk Stratification and Adjuvant Chemotherapy for High-Risk Stage IA Lung Adenocarcinoma: The Unmet Needs.Thoracic cancer · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- [Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Nomogram for predicting prognosis and identifying chemotherapy beneficiaries for completely resected stage I invasive mucinous lung adenocarcinoma.Translational lung cancer research · 2024Article
- Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans.Research square · 2024Article
- Value of adjuvant chemotherapy for patients with pT2N0M0 non-small cell lung cancer receiving radical resection.Thoracic cancer · 2024Article
- Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less.Frontiers in oncology · 2024Article
- Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans.Frontiers in oncology · 2024Article
- Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.European radiology · 2023Article
- A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).La Radiologia medica · 2023Article
- Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer.Frontiers in oncology · 2023Article
- Preoperative prognostic prediction for stage I lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system.Frontiers in oncology · 2023Article
- CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing.Frontiers in medicine · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRobust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT).
methodsUsing radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed.
resultsThe radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P<0.001). The GSEA presented that the signature was highly correlated to characteristic metabolic process and immune system during cancer progression. Multivariable analysis revealed that age (P=0.031), pathologic TNM stage (P=0.043), histologic subtype (P=0.010) and the signature (P<0.001) were independently associated with patients' DFS. The integrated radiomics nomogram showed good discrimination performance, as well as good calibration and clinical utility, for DFS prediction in the validation cohort. We further found that the patients with high points (point ≥8.788) defined by the radiomics nomogram obtained a significant favorable response to ACT (P=0.04) while patients with low points (point <8.788) showed no survival difference (P=0.7).
conclusionsThe radiomics nomogram could be used for prognostic prediction and ACT benefits identification for patient with resected stage I lung adenocarcinoma.
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