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Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
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Luan S, Ou-Yang J, Yang X, Wei W, Xue X, Zhu B. A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization. Phys Med Biol 2024; 69:065005. [PMID: 38373347 DOI: 10.1088/1361-6560/ad2a97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/19/2024] [Indexed: 02/21/2024]
Abstract
Objective.Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize 'incorrect' auto-segmentations.Approach.A total of 586 CT images and labels from nine institutions were used. The OARs included the brainstem, parotid, and mandible. The deep learning generated contours were compared with the manual ground truth delineations. In this study, we proposed a novel contour quality assurance and adaptive optimization (CQA-AO) strategy, which consists of the following three main components: (1) the contour QA module classified the deep learning generated contours as either accepted or unaccepted; (2) the unacceptable contour categories analysis module provided the potential error reasons (five unacceptable category) and locations (attention heatmaps); (3) the adaptive correction of unacceptable contours module integrate vision-language representations and utilize convex optimization algorithms to achieve adaptive correction of 'incorrect' contours.Main results. In the contour QA tasks, the sensitivity (accuracy, precision) of CQA-AO strategy reached 0.940 (0.945, 0.948), 0.962 (0.937, 0.913), and 0.967 (0.962, 0.957) for brainstem, parotid and mandible, respectively. The unacceptable contour category analysis, the(FI,AccI,Fmicro,Fmacro)of CQA-AO strategy reached (0.901, 0.763, 0.862, 0.822), (0.855, 0.737, 0.837, 0.784), and (0.907, 0.762, 0.858, 0.821) for brainstem, parotid and mandible, respectively. After adaptive optimization correction, the DSC values of brainstem, parotid and mandible have been improved by 9.4%, 25.9%, and 13.5%, and Hausdorff distance values decreased by 62%, 70.6%, and 81.6%, respectively.Significance. The proposed CQA-AO strategy, which combines QA of contour and adaptive optimization correction for OARs contouring, demonstrated superior performance compare to conventional methods. This method can be implemented in the clinical contouring procedures and improve the efficiency of delineating and reviewing workflow.
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Affiliation(s)
- Shunyao Luan
- School of Integrated Circuits, Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Jun Ou-Yang
- School of Integrated Circuits, Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xiaofei Yang
- School of Integrated Circuits, Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Benpeng Zhu
- School of Integrated Circuits, Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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Salahuddin S, Buzdar SA, Iqbal K, Azam MA, Strigari L. Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning. Radiol Phys Technol 2024; 17:219-229. [PMID: 38160437 DOI: 10.1007/s12194-023-00768-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.
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Affiliation(s)
- Sana Salahuddin
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy.
| | - Saeed Ahmad Buzdar
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Khalid Iqbal
- Medical Physics Department Shaukat Khanum Memorial Cancer Hospital & Research Center, Lahore, Pakistan
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
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Roberfroid B, Lee JA, Geets X, Sterpin E, Barragán-Montero AM. DIVE-ART: A tool to guide clinicians towards dosimetrically informed volume editions of automatically segmented volumes in adaptive radiation therapy. Radiother Oncol 2024; 192:110108. [PMID: 38272315 DOI: 10.1016/j.radonc.2024.110108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Benjamin Roberfroid
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.
| | - John A Lee
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Xavier Geets
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium; Cliniques universitaires Saint-Luc, Department of Radiation Oncology, Brussels, Belgium
| | - Edmond Sterpin
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium; KU Leuven - Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium
| | - Ana M Barragán-Montero
- Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
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Duan J, Bernard ME, Rong Y, Castle JR, Feng X, Johnson JD, Chen Q. Contour subregion error detection methodology using deep learning auto-segmentation. Med Phys 2023; 50:6673-6683. [PMID: 37793103 DOI: 10.1002/mp.16768] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/26/2023] [Accepted: 09/17/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of flagged cases is a time-consuming and challenging process, and can lead to users overlooking the exact error location. PURPOSE Our aim is to develop and validate a contour QA system that can effectively detect and visualize subregional contour errors, both qualitatively and quantitatively. METHODS/MATERIALS A novel contour subregion error detection (CSED) system was developed using subregional surface distance discrepancies between manual and deep learning auto-segmentation (DLAS) contours. A validation study was conducted using a head and neck public dataset containing 339 cases and evaluated according to knowledge-based pass criteria derived from a clinical training dataset of 60 cases. A blind qualitative evaluation was conducted, comparing the results from the CSED system with manual labels. Subsequently, the CSED-flagged cases were re-examined by a radiation oncologist. RESULTS The CSED system could visualize the diverse types of subregional contour errors qualitatively and quantitatively. In the validation dataset, the CSED system resulted in true positive rates (TPR) of 0.814, 0.800, and 0.771; false positive rates (FPR) of 0.310, 0.267, and 0.298; and accuracies of 0.735, 0.759, and 0.730, for brainstem and left and right parotid contours, respectively. The CSED-assisted manual review caught 13 brainstem, 19 left parotid, and 21 right parotid contour errors missed by conventional human review. The TPR/FPR/accuracy of the CSED-assisted manual review improved to 0.836/0.253/0.784, 0.831/0.171/0.830, and 0.808/0.193/0.807 for each structure, respectively. Further, the time savings achieved through CSED-assisted review improved by 75%, with the time for review taking 24.81 ± 12.84, 26.75 ± 10.41, and 28.71 ± 13.72 s for each structure, respectively. CONCLUSIONS The CSED system enables qualitative and quantitative detection, localization, and visualization of manual segmentation subregional errors utilizing DLAS contours as references. The use of this system has been shown to help reduce the risk of high-risk failure modes resulting from inaccurate organ segmentation.
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Affiliation(s)
- Jingwei Duan
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Mark E Bernard
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - James R Castle
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Xue Feng
- Carina Medical LLC, Lexington, Kentucky, USA
| | - Jeremiah D Johnson
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Quan Chen
- Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA
- Department of Radiation Oncology, City of Hope Comprehensive Cancer Center, Duarte, California, USA
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De Kerf G, Claessens M, Raouassi F, Mercier C, Stas D, Ost P, Dirix P, Verellen D. A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer. Phys Imaging Radiat Oncol 2023; 28:100494. [PMID: 37809056 PMCID: PMC10550805 DOI: 10.1016/j.phro.2023.100494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
Background and Purpose Clinical Artificial Intelligence (AI) implementations lack ground-truth when applied on real-world data. This study investigated how combined geometrical and dose-volume metrics can be used as performance monitoring tools to detect clinically relevant candidates for model retraining. Materials and Methods Fifty patients were analyzed for both AI-segmentation and planning. For AI-segmentation, geometrical (Standard Surface Dice 3 mm and Local Surface Dice 3 mm) and dose-volume based parameters were calculated for two organs (bladder and anorectum) to compare AI output against the clinically corrected structure. A Local Surface Dice was introduced to detect geometrical changes in the vicinity of the target volumes, while an Absolute Dose Difference (ADD) evaluation increased focus on dose-volume related changes. AI-planning performance was evaluated using clinical goal analysis in combination with volume and target overlap metrics. Results The Local Surface Dice reported equal or lower values compared to the Standard Surface Dice (anorectum: (0.93 ± 0.11) vs (0.98 ± 0.04); bladder: (0.97 ± 0.06) vs (0.98 ± 0.04)). The ADD metric showed a difference of (0.9 ± 0.8)Gy for the anorectum D 1 cm 3 . The bladder D 5cm 3 reported a difference of (0.7 ± 1.5)Gy. Mandatory clinical goals were fulfilled in 90 % of the DLP plans. Conclusions Combining dose-volume and geometrical metrics allowed detection of clinically relevant changes, applied to both auto-segmentation and auto-planning output and the Local Surface Dice was more sensitive to local changes compared to the Standard Surface Dice. This monitoring is able to evaluate AI behavior in clinical practice and allows candidate selection for active learning.
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Affiliation(s)
- Geert De Kerf
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
| | - Michaël Claessens
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Centre for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Fadoua Raouassi
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
| | - Carole Mercier
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Centre for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Daan Stas
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Piet Ost
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Centre for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Piet Dirix
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Centre for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Dirk Verellen
- Department of Radiation Oncology, Iridium Netwerk, Wilrijk (Antwerp), Belgium
- Centre for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
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Min H, Dowling J, Jameson MG, Cloak K, Faustino J, Sidhom M, Martin J, Cardoso M, Ebert MA, Haworth A, Chlap P, de Leon J, Berry M, Pryor D, Greer P, Vinod SK, Holloway L. Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation. Radiother Oncol 2023; 186:109794. [PMID: 37414257 DOI: 10.1016/j.radonc.2023.109794] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/19/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND AND PURPOSE Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. MATERIALS AND METHODS The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. RESULTS The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. CONCLUSION To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.
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Affiliation(s)
- Hang Min
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia.
| | - Jason Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Institute of Medical Physics, The University of Sydney, New South Wales, Australia; School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia
| | - Michael G Jameson
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia; GenesisCare, Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia
| | - Joselle Faustino
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Mark Sidhom
- South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jarad Martin
- Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia
| | - Michael Cardoso
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Martin A Ebert
- Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia; School of Physics Mathematics and Computing, University of Western Australia, Perth, Western Australia, Australia
| | - Annette Haworth
- Institute of Medical Physics, The University of Sydney, New South Wales, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jeremiah de Leon
- GenesisCare, Sydney, New South Wales, Australia; Illawarra Cancer Care Centre, Wollongong, Australia
| | - Megan Berry
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - David Pryor
- Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia; Calvary Mater Newcastle Hospital, Radiation Oncology, Newcastle, Australia
| | - Shalini K Vinod
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia; Institute of Medical Physics, The University of Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
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Luan S, Xue X, Wei C, Ding Y, Zhu B, Wei W. Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy. Technol Cancer Res Treat 2023; 22:15330338231157936. [PMID: 36788411 PMCID: PMC9932790 DOI: 10.1177/15330338231157936] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists' labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells' radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours' location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices.
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Affiliation(s)
- Shunyao Luan
- Department of Radiation Oncology, Hubei Cancer
Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan, China,School of Optical and Electronic Information,
Huazhong
University of Science and Technology,
Wuhan, China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer
Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan, China
| | - Changchao Wei
- Department of Radiation Oncology, Hubei Cancer
Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan, China,Key Laboratory of Artificial Micro and Nano-structures of Ministry
of Education, Center for Theoretical Physics, School of Physics and Technology,
Wuhan
University, Wuhan, China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer
Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan, China
| | - Benpeng Zhu
- School of Optical and Electronic Information,
Huazhong
University of Science and Technology,
Wuhan, China,Benpeng Zhu, School of Optical and
Electronic Information, Huazhong University of Science and Technology, Wuhan,
430000, China.
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer
Hospital, Tongji Medical College, Huazhong
University of Science and Technology, Wuhan, China,Wei Wei, Department of Radiation Oncology,
Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science
and Technology, Wuhan, 430079, China.
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9
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Zhang Q, Wang J, Liu G, Zhang W. Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU. Physiol Meas 2023; 44. [PMID: 36599174 DOI: 10.1088/1361-6579/acb03b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/04/2023] [Indexed: 01/05/2023]
Abstract
Objective.Sepsis seriously threatens human life. Early identification of a patient's risk status and appropriate treatment can reduce septic shock risk and mortality. Our purpose is to design and validate an adjunctive therapy system based on deep reinforcement learning (DRL), which can provide treatment recommendations with providence and assess the patient's risk status and treatment options in the early stages.Approach.Data is from the Beth Israel Deaconess Medical Center. The raw data included 53 423 patients from MIMIC-III. Of these, 19 620 eligible samples were screened to form the final cohort. First, the patient's physiological parameters were fed into the DRL therapy strategy recommendation module (TSRM), which provides a forward-looking recommendation for treatment strategy. The recommended strategies were then fed into the reinforcement learning risk assessment module (RAM), which predicts the patient's risk status and treatment strategy from a long-term perspective. The DRL model designed in this paper assists in formulating treatment plans and evaluating treatment risks and patient status through continuous interaction with patient trajectory; this model therefore has the foresight that a supervising deep learning model does not.Main results.The experiment shows that, in the test set for the TSRM, mortality is the lowest when the treatment strategy that is actually implemented is the same as the AI-recommended strategy. Regarding the RAM, it can accurately grasp a patient's deterioration trend, and can reasonably assess a patient's risk status and treatment plans at an early stage. The assessment results of the model were matched with the actual clinical records.Significance.A DRL-based sepsis adjunctive therapy model is proposed. It can prospectively assist physicians in proposing treatment strategies, assess the patient's risk status and treatment methods early on, and detect deterioration trends in advance.
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Affiliation(s)
- Quan Zhang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, People's Republic of China.,General Terminal IC Interdisciplinary Science Center of Nankai University, Nankai University, Tianjin 300350, People's Republic of China
| | - Jianqi Wang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, People's Republic of China.,General Terminal IC Interdisciplinary Science Center of Nankai University, Nankai University, Tianjin 300350, People's Republic of China
| | - Guohua Liu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People's Republic of China.,Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, People's Republic of China.,General Terminal IC Interdisciplinary Science Center of Nankai University, Nankai University, Tianjin 300350, People's Republic of China.,Engineering Research Center of Thin Film Optoelectronics Technology, Ministry of Education, Nankai University, Tianjin 300350, People's Republic of China
| | - Wenjia Zhang
- Tianjin Research Institute for Water Transport Engineering, M.O.T., Tianjin 300456, People's Republic of China
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10
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Claessens M, Oria CS, Brouwer CL, Ziemer BP, Scholey JE, Lin H, Witztum A, Morin O, Naqa IE, Van Elmpt W, Verellen D. Quality Assurance for AI-Based Applications in Radiation Therapy. Semin Radiat Oncol 2022; 32:421-431. [DOI: 10.1016/j.semradonc.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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11
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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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