ArticleMedical physics2021
Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.European radiology · 2023Trial
- Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.Scientific reports · 2026Article
- Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases.Radiation oncology (London, England) · 2025Article
- Dual-radiomics based on SHapley additive explanations for predicting hematologic toxicity in concurrent chemoradiotherapy patients.Discover oncology · 2025Article
- Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer.Translational cancer research · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Article
- Review
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- Development and validation of a lung biological equivalent dose-based multiregional radiomic model for predicting symptomatic radiation pneumonitis after SBRT in lung cancer patients.Frontiers in oncology · 2024Article
- Predicting grade II-IV bone marrow suppression in patients with cervical cancer based on radiomics and dosiomics.Frontiers in oncology · 2024Article
- A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer.Scientific reports · 2023Article
- Perioperative outcomes of robotic-assisted versus video-assisted thoracoscopic lobectomy: A propensity score matched analysis.Thoracic cancer · 2023Article
- CT radiomic predictors of local relapse after SBRT for lung oligometastases from colorectal cancer: a single institute pilot study.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Development and verification of radiomics framework for computed tomography image segmentation.Medical physics · 2022Article
- Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model.Journal of personalized medicine · 2022Article
- Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.European radiology · 2022Article
- Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer.Current oncology (Toronto, Ont.) · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
purposeThe aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND
methodsImage features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (Rad
resultsComplete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a Rad
conclusionAccording to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.
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