ArticleIEEE transactions on radiation and plasma medical sciences2019
Development of a Fully Cross-Validated Bayesian Network Approach for Local Control Prediction in Lung Cancer.
Article in IEEE transactions on radiation and plasma medical sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.Scientific reports · 2025Article
- Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.Medical physics · 2024Article
- Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach.Scientific reports · 2024Article
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Predictors on outcomes of cardiovascular disease of male patients in Malaysia using Bayesian network analysis.BMJ open · 2023Article
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- Radiation Response Prediction Model Based on Integrated Clinical and Genomic Data Analysis.Cancer research and treatment · 2022Article
- Imaging response assessment for predicting outcomes after bioselection chemotherapy in larynx cancer: A secondary analysis of two prospective trials.Clinical and translational radiation oncology · 2022Article
- Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy.JCO clinical cancer informatics · 2022Article
- A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy.Frontiers in oncology · 2022Article
- Simulation of Logistics Delay in Bayesian Network Control Based on Genetic EM Algorithm.Computational intelligence and neuroscience · 2022Article
- Article
- Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors.Clinical and translational radiation oncology · 2021Article
- Value of PET imaging for radiation therapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2021Review
- A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Article
- Artificial Intelligence for Response Evaluation With PET/CT.Seminars in nuclear medicine · 2021Review
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Authors and funding
9 authors.
Funding
Abstract
The purpose of this study is to demonstrate that a Bayesian network (BN) approach can explore hierarchical biophysical relationships that influence tumor response and predict tumor local control (LC) in non-small-cell lung cancer (NSCLC) patients before and during radiotherapy from a large-scale dataset. Our BN building approach has two steps. First, relevant biophysical predictors influencing LC before and during the treatment are selected through an extended Markov blanket (eMB) method. From this eMB process, the most robust BN structure for LC prediction was found via a wrapper-based approach. Sixty-eight patients with complete feature information were used to identify a full BN model for LC prediction before and during the treatment. Fifty more recent patients with some missing information were reserved for independent testing of the developed pre- and during-therapy BNs. A nested cross-validation (N-CV) was developed to evaluate the performance of the two-step BN approach. An ensemble BN model is generated from the N-CV sampling process to assess its similarity with the corresponding full BN model, and thus evaluate the sensitivity of our BN approach. Our results show that the proposed BN development approach is a stable and robust approach to identify hierarchical relationships among biophysical features for LC prediction. Furthermore, BN predictions can be improved by incorporating during treatment information.
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Registered trials
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 Socratic graph.