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Radiotherapy, Intensity-Modulated

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Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

Journal of applied clinical medical physics 39611881
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...
Aged Cone-Beam Computed Tomography Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Lung Neoplasms Male Middle Aged Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal of applied clinical medical physics 39704607
PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential...
Adult Aged Breast Neoplasms Cardiotoxicity Echocardiography Female Humans Machine Learning Middle Aged Organs at Risk Radiation Injuries Radiomics Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Unilateral Breast Neoplasms
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Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer.

Physical and engineering sciences in medicine 39693039
This study examined the characteristics of the broad model (KBP) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) plans for prostate cancer in 30 patients at two institutions. KBP, trained using 561 prostate cancer V...
Humans Knowledge Bases Male Prostatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 39647528
BACKGROUND AND PURPOSE: Fast, high-quality deep learning (DL) prediction of patient-specific 3D dose distributions can enable instantaneous treatment planning (IP), in which the treating physician can evaluate the dose and approve the plan immediatel...
Deep Learning Humans Male Prostatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 39645201
PURPOSE: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom i...
Adult Aged Deep Learning Female Humans Magnetic Resonance Imaging Male Middle Aged Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Neoplasm Staging Prognosis Radiotherapy, Intensity-Modulated Retrospective Studies
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Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.

Journal of applied clinical medical physics 39641663
BACKGROUND AND OBJECTIVE: The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get bene...
Adult Algorithms Breast Neoplasms Breath Holding Feasibility Studies Female Heart Humans Middle Aged Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Supervised Machine Learning Unilateral Breast Neoplasms
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Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

Journal of applied clinical medical physics 39626092
PURPOSE: Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment p...
Algorithms Artificial Intelligence Head and Neck Neoplasms Humans Organs at Risk Precision Medicine Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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Breast radiotherapy planning: A decision-making framework using deep learning.

Medical physics 39625151
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...
Breast Neoplasms Decision Making Deep Learning Female Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Radiotherapy, Intensity-Modulated Retrospective Studies
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Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.

Journal of applied clinical medical physics 39616630
OBJECTIVES: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.
Algorithms Head and Neck Neoplasms Humans Knowledge Bases Organs at Risk Quality Assurance, Health Care Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.

Journal of applied clinical medical physics 39616629
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
Deep Learning Feasibility Studies Humans Imaging, Three-Dimensional Male Observer Variation Organs at Risk Prostate Prostatic Neoplasms Radiometry Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies Tomography, X-Ray Computed
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