ArticleEuropean radiology2022
Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT.
Article in European radiology, 2022. 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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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.European radiology · 2023Pooled it
- Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.La Radiologia medica · 2026Article
- Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics.Cancer medicine · 2026Article
- Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients.Scientific reports · 2026Article
- Tumor rim-specific computed tomography radiomics improves prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.Translational lung cancer research · 2025Article
- A radiomics-based machine learning model and SHAP for predicting spread through air spaces and its prognostic implications in stage I lung adenocarcinoma: a multicenter cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer.Translational lung cancer research · 2025Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma.Journal for immunotherapy of cancer · 2025Article
- CT-based habitat radiomics for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma from invasive adenocarcinoma manifesting as ground-glass nodules: a multicenter study.Frontiers in oncology · 2025Article
- Integration of intratumoral/peritumoral radiomics and deep learning for predicting overall survival in non-small cell lung cancer patients: a multicenter study.Frontiers in oncology · 2025Article
- Preoperative CT-based Intratumoral and Peritumoral Radiomics Prediction for Vasculogenic Mimicry in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Prediction of early lung adenocarcinoma spread through air spaces by machine learning radiomics: a cross-center cohort study.Translational lung cancer research · 2024Article
- CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma.Journal of thoracic disease · 2024Article
- [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
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs).
methodsThis retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]
resultsA total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV
conclusionsA radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]
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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.