ArticleJournal of cancer research and clinical oncology2022
Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer.
Article in Journal of cancer research and clinical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Diagnostic Accuracy of Artificial Intelligence Models for Differentiation of Squamous Cell Carcinoma and Adenocarcinoma of Lung-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Quantitative peri-lesional densitometry mapping via thin-slice volume rendering enhances differentiation of pneumonic-type lung cancer and inflammatory pneumonia.Frontiers in oncology · 2026Article
- Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer.Oncology letters · 2026Article
- A Study on dynamic functional connectivity and efficacy prediction of SAINT for rapid antidepressant effects and relief of suicidal ideation.Translational psychiatry · 2025Article
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 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
- Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.Oncology letters · 2025Article
- Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- The influence of image selection and segmentation on the extraction of lung cancer imaging radiomics features using 3D-Slicer software.BMC cancer · 2025Article
- Clinical value of intratumoral and peritumoral CT radiomics models for discriminating benign and malignant parotid gland tumors.Frontiers in oncology · 2025Article
- Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.Gland surgery · 2024Article
- Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism.Heliyon · 2024Article
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer.Frontiers in oncology · 2024Article
- Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung.Cancer management and research · 2024Article
- Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.Quantitative imaging in medicine and surgery · 2023Article
- Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT.Journal of cancer research and clinical oncology · 2023Article
- Intratumoral and peritumoral MRI-based radiomics prediction of histopathological grade in soft tissue sarcomas: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.Radiology · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
purposeTo evaluate a new radiomics strategy that incorporates intratumoral and peritumoral features extracted from lung CT images with ensemble learning for pretreatment prediction of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD).
methodsA total of 105 patients (47 LUSC and 58 LUAD) with pretherapy CT scans were involved in this retrospective study, and were divided into training (n = 73) and testing (n = 32) cohorts. Seven categories of radiomics features involving 3078 metrics in total were extracted from the intra- and peritumoral regions of each patient's CT data. Student's t tests in combination with three feature selection methods were adopted for optimal features selection. An ensemble classifier was developed using five common machine learning classifiers with these optimal features. The performance was assessed using both training and testing cohorts, and further compared with that of Visual Geometry Group-16 (VGG-16) deep network for this predictive task.
resultsThe classification models developed using optimal feature subsets determined from intratumoral region and peritumoral region with the ensemble classifier achieved mean area under the curve (AUC) of 0.87, 0.83 in the training cohort and 0.66, 0.60 in the testing cohort, respectively. The model developed by using the optimal feature subset selected from both intra- and peritumoral regions with the ensemble classifier achieved great performance improvement, with AUC of 0.87 and 0.78 in both cohorts, respectively, which are also superior to that of VGG-16 (AUC of 0.68 in the testing cohort).
conclusionsThe proposed new radiomics strategy that extracts image features from the intra- and peritumoral regions with ensemble learning could greatly improve the diagnostic performance for the histological subtype stratification in patients with NSCLC.
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