ArticleLa Radiologia medica2023
Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Deep learning-based independent lymph node segmentation in esophageal cancer: a precise and efficient approach for radiotherapy planning.Quantitative imaging in medicine and surgery · 2026Article
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- Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.BMC oral health · 2026Article
- Deep ensemble optimized models for probabilistic CTV breast segmentation.Frontiers in artificial intelligence · 2026Article
- Delta-Radiomics Biomarker in Colorectal Cancer Liver Metastases Treated with Cetuximab Plus Avelumab (CAVE Trial).Diagnostics (Basel, Switzerland) · 2025Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
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- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Artificial Intelligence-Assisted Compressed Sensing Technique Accelerates Magnetic Resonance Imaging Simulation for Head and Neck Cancer Radiation Therapy.Advances in radiation oncology · 2025Article
- CT-based auto-segmentation of multiple target volumes for all-in-one radiotherapy in rectal cancer patients.Radiation oncology (London, England) · 2025Article
- Deep learning-based auto-segmentation model for clinical target volume delineation in brachytherapy after parotid cancer surgery.Journal of contemporary brachytherapy · 2025Article
- Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.Radiation oncology (London, England) · 2025Article
- MHAU-Net: a multi-scale hybrid attention U-shaped network for the segmentation of MRI breast tumors.Quantitative imaging in medicine and surgery · 2025Article
- Integrating radiomics and deep learning for enhanced prediction of high-grade patterns in stage IA lung adenocarcinoma.Translational lung cancer research · 2025Article
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Treatments and cancer: implications for radiologists.Frontiers in immunology · 2025Review
- Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.Journal of applied clinical medical physics · 2025Article
- Ultrasound Findings After Breast Cancer Radiation Therapy: Cutaneous, Pleural, Pulmonary, and Cardiac Changes.Korean journal of radiology · 2024Review
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach.Insights into imaging · 2024Article
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8 authors.
Funding
Abstract
purposeThe large variability in tumor appearance and shape makes manual delineation of the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' experience. Whereas deep learning techniques have allowed oncologists to automate the CTV delineation, multi-site tumor analysis is often lacking in the literature. This study aimed to evaluate the deep learning models that automatically contour CTVs of tumors at various sites on computed tomography (CT) images from objective and subjective perspectives. METHODS AND MATERIALS: 577 patients were selected for the present study, including nasopharyngeal (n = 34), esophageal (n = 40), breast-conserving surgery (BCS) (left-sided, n = 71; right-sided, n = 71), breast-radical mastectomy (BRM) (left-sided, n = 43; right-sided, n = 37), cervical (radical radiotherapy, n = 45; postoperative, n = 85), prostate (n = 42), and rectal (n = 109) carcinomas. Manually delineated CTV contours by radiation oncologists are served as ground truth. Four models were evaluated: Flexnet, Unet, Vnet, and Segresnet, which are commercially available in the medical product "AccuLearning AI model training platform". The data were divided into the training, validation, and testing set at a ratio of 5:1:4. The geometric metrics, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated for objective evaluation. For subjective assessment, oncologists rated the segmentation contours of the testing set visually.
resultsHigh correlations were observed between automatic and manual contours. Based on the results of the independent test group, most of the patients achieved satisfactory quantitative results (DSC > 0.8), except for patients with esophageal carcinoma (DSC: 0.62-0.64). The subjective review indicated that 82.65% of predicted CTVs scored either as clinically accepting (8.68%) or requiring minor revision (73.97%), and no patients were scored as rejected.
conclusionThis experimental work demonstrated that auto-generated contours could serve as an initial template to help oncologists save time in CTV delineation. The deep learning-based auto-segmentations achieve acceptable accuracy and show the potential to improve clinical efficiency for radiotherapy of a variety of cancer.
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