ArticleInternational journal of radiation oncology, biology, physics2022
Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.
Article in International journal of radiation oncology, biology, physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- Deep learning-based computed tomography quantification integrated with circulating tumor cells for prognostic evaluation in stage I lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.Journal of imaging informatics in medicine · 2026Article
- Integrating pretreatment CT radiomics and circulating tumor cells using machine learning to predict survival in hepatocellular carcinoma.Frontiers in pharmacology · 2026Article
- Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center Study with Implications for Personalized Treatment.OncoTargets and therapy · 2026Article
- Current and Future Applications of PET Radiomics in Radiation Oncology.PET clinics · 2025Review
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer.Breast cancer (Tokyo, Japan) · 2024Article
- Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?Frontiers in oncology · 2024Article
- Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer.Physics and imaging in radiation oncology · 2023Article
- Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.Frontiers in oncology · 2023Article
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.Diagnostics (Basel, Switzerland) · 2022Review
- An Immunological Perspective of Circulating Tumor Cells as Diagnostic Biomarkers and Therapeutic Targets.Life (Basel, Switzerland) · 2022Review
- Article
- Artificial Intellgence in the Era of Precision Oncological Imaging.Technology in cancer research & treatmentReview
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
purposeWe develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic marker for predicting recurrence outcomes of early-stage (ES) non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). METHODS AND MATERIALS: A cohort of 421 NSCLC patients was used to train a DL model for gleaning informative imaging features from computed tomography (CT) data. The learned imaging features were optimized on a cohort of 98 ES-NSCLC patients treated with SBRT for predicting individual patient recurrence risks by building DL models on CT data and clinical measures. These DL models were validated on the third cohort of 60 ES-NSCLC patients treated with SBRT to predict recurrent risks and stratify patients into subgroups with distinct outcomes in conjunction with CTC counts.
resultsThe DL model obtained a concordance-index of 0.880 (95% confidence interval, 0.879-0.881). Patient subgroups with low and high DL risk scores had significantly different recurrence outcomes (P = 3.5e-04). The integration of DL risk scores and CTC measures identified 4 subgroups of patients with significantly different risks of recurrence (χ
conclusionsIn this first-ever study integrating DL radiomics models and CTC counts, our results suggested that this integration improves patient stratification compared with either imagining data or CTC measures alone in predicting recurrence outcomes for patients treated with SBRT for ES-NSCLC.
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