4.3 Article

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Extensive upfront validation and testing are needed prior to the clinical implementation of AI-based auto-segmentation tools

Justin Roper et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2023)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer

Chen-Ying Ma et al.

Summary: The study evaluated a deep learning-based auto-segmentation algorithm for contouring clinical target volumes in cervical cancer treatment, showing improved accuracy and efficiency in delineating radiation targets.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process

Jingwei Duan et al.

Summary: This study evaluates the performance of a commercial AI-based contouring model in the automated prostate treatment planning process. The results demonstrate good geometric accuracy and clinical acceptability of the AI contours, and the automated treatment plans based on these contours are comparable to those based on reference contours.

MEDICAL PHYSICS (2022)

Article Biology

Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images

Daisuke Kawahara et al.

Summary: The current study proposes an auto-segmentation model using a stepwise deep neural network on CT images of head and neck cancer. The results show that the stepwise-network outperforms the atlas-based method and conventional U-net, indicating its potential value in improving the efficiency of head and neck radiotherapy treatment planning.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Artificial Intelligence

PSA-Net: Deep learning-based physician style-aware segmentation network for postoperative prostate cancer clinical target volumes

Anjali Balagopal et al.

Summary: The study investigated automatic segmentation of medical images using deep learning algorithms, aiming to determine the consistency and impact of physician styles on treatment outcomes. Results showed that physician styles are learnable and do not significantly affect treatment outcomes. A physician style-aware segmentation network was developed to improve segmentation accuracy.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Biology

Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging

Jeremy M. Webb et al.

Summary: Deep learning, a powerful tool, has rapidly developed in various imaging modalities. Reliability and repeatability are crucial for assisting experts, while high labeling costs raise concerns about the clinical acceptability of errors.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Oncology

Reduction of inter-observer differences in the delineation of the target in spinal metastases SBRT using an automatic contouring dedicated system

Niccolo Giaj-Levra et al.

Summary: This study evaluated an automatic contouring tool for spine SBRT, demonstrating its ability to reduce inter-observer differences in target definition and increase precision in SBRT treatment of the spine.

RADIATION ONCOLOGY (2021)

Article Oncology

Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Seung Yeun Chung et al.

Summary: The study demonstrated the feasibility of using deep learning-based auto-segmentation in breast radiotherapy planning. The correlation between auto-segmented and manual contours was acceptable, with mean DSC higher than 0.80 for all OARs. Additionally, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs.

RADIATION ONCOLOGY (2021)

Article Oncology

Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy

Elaine Cha et al.

Summary: The study evaluated the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning, finding that this technology improved efficiency, although some autocontours still required major clinically significant edits. Geometric indices correlated weakly with contouring time, but had no relationship with quality scores.

RADIOTHERAPY AND ONCOLOGY (2021)

Article Computer Science, Artificial Intelligence

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy

Anjali Balagopal et al.

Summary: Utilizing deep learning to accurately segment post-operative prostate CTVs, training the model to improve segmentation accuracy, using the relationship of surrounding organs for assistance, employing MCDO to estimate model uncertainty for practical clinical use.

MEDICAL IMAGE ANALYSIS (2021)

Review Oncology

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review

Michael Sherer et al.

Summary: Advances in AI-based methods have led to the development of auto-segmentation systems in radiotherapy. However, there is no uniform standard for evaluating their efficacy. Common evaluation techniques include geometric overlap, dosimetric parameters, and clinical rating scales, with many geometric indices showing weak correlation with clinical endpoints. A multi-domain evaluation is necessary to assess the clinical readiness of auto-segmentation for radiation treatment planning.

RADIOTHERAPY AND ONCOLOGY (2021)

Article Oncology

A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy

Xuming Chen et al.

Summary: This study introduced a deep learning-based automatic segmentation algorithm, WBNet, which accurately and efficiently delineates major organs at risk (OARs) on CT images. WBNet outperformed other AS algorithms in terms of DSC values and delineation time, showing great effectiveness in clinical practice.

RADIOTHERAPY AND ONCOLOGY (2021)

Article Biochemistry & Molecular Biology

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

Chris McIntosh et al.

Summary: The study demonstrates the potential impact of machine learning in healthcare delivery, with a random forest algorithm applied to therapeutic radiation therapy planning for prostate cancer. Machine-generated RT plans were found to be clinically acceptable in 89% of cases and were selected over human-generated plans in 72% of head-to-head comparisons, significantly reducing the time required for RT planning.

NATURE MEDICINE (2021)

Article Oncology

Interobserver variability in organ at risk delineation in head and neck cancer

J. van der Veen et al.

Summary: The study found that in radiotherapy for head and neck cancer, although international consensus guidelines for organ at risk delineation exist, only about half of radiation oncologists actually use these guidelines, which partly explains the variability in delineation. The research highlights that guidelines alone are not sufficient to eliminate interobserver variability and more efforts are needed to achieve further treatment standardization, such as utilizing artificial intelligence.

RADIATION ONCOLOGY (2021)

Review Health Care Sciences & Services

The potential of artificial intelligence to improve patient safety: a scoping review

David W. Bates et al.

Summary: Artificial intelligence (AI) is a valuable tool that can be utilized to improve patient safety by predicting, preventing, and detecting adverse events in healthcare. The literature reviewed in this study highlights numerous examples of AI applications in various harm domains, emphasizing the potential for AI to reduce harm occurrence across all areas, especially in cases where current strategies are not effective.

NPJ DIGITAL MEDICINE (2021)

Article Oncology

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

Lisanne V. van Dijk et al.

RADIOTHERAPY AND ONCOLOGY (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Strategies for effective physics plan and chart review in radiation therapy: Report of AAPM Task Group 275

Eric Ford et al.

MEDICAL PHYSICS (2020)

Article Engineering, Biomedical

Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation

Xue Feng et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

Article Oncology

Segmentation of prostate and prostate zones using deep learning A multi-MRI vendor analysis

Olmo Zavala-Romero et al.

STRAHLENTHERAPIE UND ONKOLOGIE (2020)

Article Radiology, Nuclear Medicine & Medical Imaging

Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning

Charles C. Vu et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Physician review of image registration and normal structure delineation

William Tyler Turchan et al.

JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS (2020)

Article Oncology

Automatic Segmentation of the Prostate on CT Images Using Deep Neural Networks (DNN)

Chang Liu et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2019)

Article Oncology

Advances in Auto-Segmentation

Carlos E. Cardenas et al.

SEMINARS IN RADIATION ONCOLOGY (2019)

Article Oncology

Interobserver variability in delineation of target volumes in head and neck cancer

Julie van der Veen et al.

RADIOTHERAPY AND ONCOLOGY (2019)

Article Chemistry, Multidisciplinary

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

Hironao Yamada et al.

ACS CENTRAL SCIENCE (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Multi-observer contouring of male pelvic anatomy: Highly variable agreement across conventional and emerging structures of interest

Dale Roach et al.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2019)

Article Radiology, Nuclear Medicine & Medical Imaging

Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images

Jiazhou Wang et al.

MEDICAL PHYSICS (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy

Yabo Fu et al.

MEDICAL PHYSICS (2018)

Review Oncology

Online Adaptive Radiation Therapy

Stephanie Lim-Reinders et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2017)

Review Radiology, Nuclear Medicine & Medical Imaging

A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology

Shalini K. Vinod et al.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2016)

Article Oncology

Radiation Therapy after Radical Prostatectomy: implications for Clinicians

Fernanda G. Herrera et al.

FRONTIERS IN ONCOLOGY (2016)

Article Oncology

CONE BEAM COMPUTED TOMOGRAPHY-DERIVED ADAPTIVE RADIOTHERAPY FOR RADICAL TREATMENT OF ESOPHAGEAL CANCER

Maria A. Hawkins et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2010)

Article Oncology

Anatomic boundaries of the clinical target volume (prostate bed) after radical prostatectomy

Kirsty L. Wiltshire et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2007)