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Spaanderman D, Hakkesteegt S, Hanff D, Schut A, Schiphouwer L, Vos M, Messiou C, Doran S, Jones R, Hayes A, Nardo L, Abdelhafez Y, Moawad A, Elsayes K, Lee S, Link T, Niessen W, van Leenders G, Visser J, Klein S, Grünhagen D, Verhoef C, Starmans M. Multi-center external validation of an automated method segmenting and differentiating atypical lipomatous tumors from lipomas using radiomics and deep-learning on MRI. EClinicalMedicine 2024; 76:102802. [PMID: 39351025 PMCID: PMC11440245 DOI: 10.1016/j.eclinm.2024.102802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/09/2024] [Accepted: 08/12/2024] [Indexed: 10/04/2024] Open
Abstract
Background As differentiating between lipomas and atypical lipomatous tumors (ALTs) based on imaging is challenging and requires biopsies, radiomics has been proposed to aid the diagnosis. This study aimed to externally and prospectively validate a radiomics model differentiating between lipomas and ALTs on MRI in three large, multi-center cohorts, and extend it with automatic and minimally interactive segmentation methods to increase clinical feasibility. Methods Three study cohorts were formed, two for external validation containing data from medical centers in the United States (US) collected from 2008 until 2018 and the United Kingdom (UK) collected from 2011 until 2017, and one for prospective validation consisting of data collected from 2020 until 2021 in the Netherlands. Patient characteristics, MDM2 amplification status, and MRI scans were collected. An automatic segmentation method was developed to segment all tumors on T1-weighted MRI scans of the validation cohorts. Segmentations were subsequently quality scored. In case of insufficient quality, an interactive segmentation method was used. Radiomics performance was evaluated for all cohorts and compared to two radiologists. Findings The validation cohorts included 150 (54% ALT), 208 (37% ALT), and 86 patients (28% ALT) from the US, UK and NL. Of the 444 cases, 78% were automatically segmented. For 22%, interactive segmentation was necessary due to insufficient quality, with only 3% of all patients requiring manual adjustment. External validation resulted in an AUC of 0.74 (95% CI: 0.66, 0.82) in US data and 0.86 (0.80, 0.92) in UK data. Prospective validation resulted in an AUC of 0.89 (0.83, 0.96). The radiomics model performed similar to the two radiologists (US: 0.79 and 0.76, UK: 0.86 and 0.86, NL: 0.82 and 0.85). Interpretation The radiomics model extended with automatic and minimally interactive segmentation methods accurately differentiated between lipomas and ALTs in two large, multi-center external cohorts, and in prospective validation, performing similar to expert radiologists, possibly limiting the need for invasive diagnostics. Funding Hanarth fonds.
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Affiliation(s)
- D.J. Spaanderman
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - S.N. Hakkesteegt
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute Erasmus University Medical Center, Rotterdam, the Netherlands
| | - D.F. Hanff
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - A.R.W. Schut
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute Erasmus University Medical Center, Rotterdam, the Netherlands
| | - L.M. Schiphouwer
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - M. Vos
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute Erasmus University Medical Center, Rotterdam, the Netherlands
| | - C. Messiou
- The Royal Marsden Hospital and The Institute of Cancer Research London, United Kingdom
| | - S.J. Doran
- The Royal Marsden Hospital and The Institute of Cancer Research London, United Kingdom
| | - R.L. Jones
- The Royal Marsden Hospital and The Institute of Cancer Research London, United Kingdom
| | - A.J. Hayes
- The Royal Marsden Hospital and The Institute of Cancer Research London, United Kingdom
| | - L. Nardo
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Y.G. Abdelhafez
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - A.W. Moawad
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Diagnostic Radiology, Mercy Catholic Medical Center, Darby, PA, USA
| | - K.M. Elsayes
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - S. Lee
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - T.M. Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - W.J. Niessen
- Faculty of Medical Sciences, University of Groningen, Groningen, the Netherlands
| | | | - J.J. Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - S. Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - D.J. Grünhagen
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute Erasmus University Medical Center, Rotterdam, the Netherlands
| | - C. Verhoef
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M.P.A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
- Department of Pathology, Erasmus MC, Rotterdam, the Netherlands
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2
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Peters S, Hertel T. ESTRO congress and AI: (No) more questions! Radiother Oncol 2024; 199:110428. [PMID: 39029589 DOI: 10.1016/j.radonc.2024.110428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 07/01/2024] [Indexed: 07/21/2024]
Affiliation(s)
- Samuel Peters
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
| | - Tanja Hertel
- Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland
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Populaire P, Marini B, Poels K, Svensson S, Sterpin E, Fredriksson A, Haustermans K. Autodelineation methods in a simulated fully automated proton therapy workflow for esophageal cancer. Phys Imaging Radiat Oncol 2024; 32:100646. [PMID: 39381611 PMCID: PMC11460496 DOI: 10.1016/j.phro.2024.100646] [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: 08/05/2024] [Revised: 09/06/2024] [Accepted: 09/10/2024] [Indexed: 10/10/2024] Open
Abstract
Background and purpose Proton Online Adaptive RadioTherapy (ProtOnART) harnesses the dosimetric advantage of protons and immediately acts upon anatomical changes. Here, we simulate the clinical application of delineation and planning within a ProtOnART-workflow for esophageal cancer. We aim to identify the most appropriate technique for autodelineation and evaluate full automation by replanning on autodelineated contours. Materials and methods We evaluated 15 patients who started treatment between 11-2022 and 01-2024, undergoing baseline and three repeat computed tomography (CT) scans in treatment position. Quantitative and qualitative evaluations compared different autodelineation methods. For Organs-at-risk (OAR) deep learning segmentation (DLS), rigid and deformable propagation from baseline to repeat CT-scans were considered. For the clinical target volume (CTV), rigid and three deformable propagation methods (default, heart as controlling structure and with focus region) were evaluated. Adaptive treatment plans with 7 mm (ATP7mm) and 3 mm (ATP3mm) setup robustness were generated using best-performing autodelineated contours. Clinical acceptance of ATPs was evaluated using goals encompassing ground-truth CTV-coverage and OAR-dose. Results Deformation was preferred for autodelineation of heart, lungs and spinal cord. DLS was preferred for all other OARs. For CTV, deformation with focus region was the preferred method although the difference with other deformation methods was small. Nominal ATPs passed evaluation goals for 87 % of ATP7mm and 67 % of ATP3mm. This dropped to respectively 2 % and 29 % after robust evaluation. Insufficient CTV-coverage was the main reason for ATP-rejection. Conclusion Autodelineation aids a ProtOnART-workflow for esophageal cancer. Currently available tools regularly require manual annotations to generate clinically acceptable ATPs.
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Affiliation(s)
- Pieter Populaire
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
- University Hospital Leuven, Department of Radiation Oncology, Leuven, Belgium
| | - Beatrice Marini
- Humanitas University, Department of Biomedical Sciences, Milan, Italy
- Humanitas Research Hospital IRCCS, Department of Radiotherapy and Radiosurgery, Milan, Italy
| | - Kenneth Poels
- University Hospital Leuven, Department of Radiation Oncology, Leuven, Belgium
| | | | - Edmond Sterpin
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, UCLouvain, Belgium
| | | | - Karin Haustermans
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
- University Hospital Leuven, Department of Radiation Oncology, Leuven, Belgium
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Tsui T, Podgorsak A, Roeske JC, Small W, Refaat T, Kang H. Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures. J Appl Clin Med Phys 2024; 25:e14461. [PMID: 39092893 DOI: 10.1002/acm2.14461] [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: 01/24/2024] [Revised: 04/09/2024] [Accepted: 06/23/2024] [Indexed: 08/04/2024] Open
Abstract
The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p = 0.17) and SCV95% (p = 0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p = 0.02). For post-mastectomy plans, AxNV95% (p = 0.35) and SCV95% (p = 0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation <5% when DSC > 0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p = 0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.
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Affiliation(s)
- Tiffany Tsui
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Alexander Podgorsak
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
| | - John C Roeske
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - William Small
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Tamer Refaat
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
| | - Hyejoo Kang
- Department of Radiation Oncology, Loyola University Chicago, Stritch School of Medicine, Maywood, Illinois, USA
- Department of Radiation Oncology, Cardinal Bernard Cancer Center, Maywood, Illinois, USA
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Wen F, Chen Z, Wang X, Dou M, Yang J, Yao Y, Shen Y. Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application. J Appl Clin Med Phys 2024; 25:e14482. [PMID: 39120487 DOI: 10.1002/acm2.14482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/30/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. METHODS Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. RESULTS In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001). CONCLUSION Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.
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Affiliation(s)
- Feng Wen
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhebin Chen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Meng Dou
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jialuo Yang
- Department of Medicine Oncology, Shifang people's Hospital, Shifang, China
| | - Yu Yao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yali Shen
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Callens D, Malone C, Carver A, Fiandra C, Gooding MJ, Korreman SS, Matos Dias J, Popple RA, Rocha H, Crijns W, Cardenas CE. Is full-automation in radiotherapy treatment planning ready for take off? Radiother Oncol 2024; 201:110546. [PMID: 39326522 DOI: 10.1016/j.radonc.2024.110546] [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: 04/29/2024] [Revised: 09/03/2024] [Accepted: 09/23/2024] [Indexed: 09/28/2024]
Abstract
Radiotherapy treatment planning is undergoing a transformation with the increasing integration of automation. This transition draws parallels with the aviation industry, which has a long-standing history of addressing challenges and opportunities introduced by automated systems. Both fields witness a shift from manual operations to systems capable of operating independently, raising questions about the risks and evolving role of humans within automated workflows. In response to this shift, a working group assembled during the ESTRO Physics Workshop 2023, reflected on parallels to draw lessons for radiotherapy. A taxonomy is proposed, leveraging insights from aviation, that outlines the observed levels of automation within the context of radiotherapy and their corresponding implications for human involvement. Among the common identified risks associated with automation integration are complacency, overreliance, attention tunneling, data overload, a lack of transparency and training. These risks require mitigation strategies. Such strategies include ensuring role complementarity, introducing checklists and safety requirements for human-automation interaction and using automation for cognitive unload and workflow management. Focusing on already automated processes, such as dose calculation and auto-contouring as examples, we have translated lessons learned from aviation. It remains crucial to strike a balance between automation and human involvement. While automation offers the potential for increased efficiency and accuracy, it must be complemented by human oversight, expertise, and critical decision-making. The irreplaceable value of human judgment remains, particularly in complex clinical situations. Learning from aviation, we identify a need for human factors engineering research in radiation oncology and a continued requirement for proactive incident learning.
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Affiliation(s)
- Dylan Callens
- Laboratory of Experimental Radiotherapy, Catholic University of Leuven, Leuven, Belgium; Department of Radiation Oncology, University Hospitals of Leuven, Leuven, Belgium.
| | - Ciaran Malone
- St.Luke's Radiation Oncology Network, Dublin, Ireland
| | - Antony Carver
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Mark J Gooding
- Inpictura Ltd, Abingdon, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Stine S Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Joana Matos Dias
- Faculty of Economics and INESC Coimbra, University of Coimbra, Coimbra, Portugal
| | - Richard A Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Humberto Rocha
- CeBER, Faculty of Economics, University of Coimbra, Coimbra, Portugal
| | - Wouter Crijns
- Laboratory of Experimental Radiotherapy, Catholic University of Leuven, Leuven, Belgium; Department of Radiation Oncology, University Hospitals of Leuven, Leuven, Belgium
| | - Carlos E Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
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Zossou VBS, Rodrigue Gnangnon FH, Biaou O, de Vathaire F, Allodji RS, Ezin EC. Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01192-w. [PMID: 39227538 DOI: 10.1007/s10278-024-01192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 09/05/2024]
Abstract
Liver cancer, a leading cause of cancer mortality, is often diagnosed by analyzing the grayscale variations in liver tissue across different computed tomography (CT) images. However, the intensity similarity can be strong, making it difficult for radiologists to visually identify hepatocellular carcinoma (HCC) and metastases. It is crucial for the management and prevention strategies to accurately differentiate between these two liver cancers. This study proposes an automated system using a convolutional neural network (CNN) to enhance diagnostic accuracy to detect HCC, metastasis, and healthy liver tissue. This system incorporates automatic segmentation and classification. The liver lesions segmentation model is implemented using residual attention U-Net. A 9-layer CNN classifier implements the lesions classification model. Its input is the combination of the results of the segmentation model with original images. The dataset included 300 patients, with 223 used to develop the segmentation model and 77 to test it. These 77 patients also served as inputs for the classification model, consisting of 20 HCC cases, 27 with metastasis, and 30 healthy. The system achieved a mean Dice score of 87.65 % in segmentation and a mean accuracy of 93.97 % in classification, both in the test phase. The proposed method is a preliminary study with great potential in helping radiologists diagnose liver cancers.
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Affiliation(s)
- Vincent-Béni Sèna Zossou
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, CESP, Équipe Radiation Epidemiology, 94805, Villejuif, France.
- Centre de recherche en épidémiologie et santé des populations (CESP), U1018, Institut national de la santé et de la recherche médicale (INSERM), 94805, Villejuif, France.
- Department of Clinical Research, Radiation Epidemiology Team, Gustave Roussy, 94805, Villejuif, France.
- Ecole Doctorale Sciences de l'Ingénieur, Université d'Abomey-Calavi, BP 526, Abomey-Calavi, Benin.
| | | | - Olivier Biaou
- Faculté des Sciences de la Santé, Université d'Abomey-Calavi, BP 188, Cotonou, Benin
- Department of Radiology, CNHU-HKM, 1213, Cotonou, Benin
| | - Florent de Vathaire
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, CESP, Équipe Radiation Epidemiology, 94805, Villejuif, France
- Centre de recherche en épidémiologie et santé des populations (CESP), U1018, Institut national de la santé et de la recherche médicale (INSERM), 94805, Villejuif, France
- Department of Clinical Research, Radiation Epidemiology Team, Gustave Roussy, 94805, Villejuif, France
| | - Rodrigue S Allodji
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, CESP, Équipe Radiation Epidemiology, 94805, Villejuif, France
- Centre de recherche en épidémiologie et santé des populations (CESP), U1018, Institut national de la santé et de la recherche médicale (INSERM), 94805, Villejuif, France
- Department of Clinical Research, Radiation Epidemiology Team, Gustave Roussy, 94805, Villejuif, France
| | - Eugène C Ezin
- Institut de Formation et de Recherche en Informatique, Université d'Abomey-Calavi, BP 526, Cotonou, Benin
- Institut de Mathématiques et de Sciences Physiques, Université d'Abomey-Calavi, 613, Dangbo, Benin
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Zhang Y, Amjad A, Ding J, Sarosiek C, Zarenia M, Conlin R, Hall WA, Erickson B, Paulson E. Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning. Pract Radiat Oncol 2024:S1879-8500(24)00204-2. [PMID: 39233005 DOI: 10.1016/j.prro.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/07/2024] [Accepted: 07/30/2024] [Indexed: 09/06/2024]
Abstract
PURPOSE The current commonly used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC) method by combining multiple quantitative metrics for clinical usability-oriented contour quality evaluation for deep learning-based auto-segmentation (DLAS). METHODS AND MATERIALS The CQC was designed to categorize contours on slices as acceptable, minor edit, or major edit based on the expected editing effort/time with supervised ensemble tree classification models using 7 quantitative metrics. Organ-specific models were trained for 5 abdominal organs (pancreas, duodenum, stomach, small, and large bowels) using 50 magnetic resonance imaging (MRI) data sets. Twenty additional MRI and 9 computed tomography (CT) data sets were employed for testing. Interobserver variation (IOV) was assessed among 6 observers and consensus labels were established through majority vote for evaluation. The CQC was also compared with a threshold-based baseline approach. RESULTS For the 5 organs, the average area under the curve was 0.982 ± 0.01 and 0.979 ± 0.01, the mean accuracy was 95.8% ± 1.7% and 94.3% ± 2.1%, and the mean risk rate was 0.8% ± 0.4% and 0.7% ± 0.5% for MRI and CT testing data set, respectively. The CQC results closely matched the IOV results (mean accuracy of 94.2% ± 0.8% and 94.8% ± 1.7%) and were significantly higher than those obtained using the threshold-based method (mean accuracy of 80.0% ± 4.7%, 83.8% ± 5.2%, and 77.3% ± 6.6% using 1, 2, and 3 metrics). CONCLUSIONS The CQC models demonstrated high performance in classifying the quality of contour slices. This method can address the limitations of existing metrics and offers an intuitive and comprehensive solution for clinically oriented evaluation and comparison of DLAS systems.
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Affiliation(s)
- Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jie Ding
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, Georgia
| | - Christina Sarosiek
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mohammad Zarenia
- Department of Radiation Medicine, MedStar Georgetown University Hospital, Washington, District of Columbia
| | - Renae Conlin
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - William A Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
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Chlap P, Min H, Dowling J, Field M, Cloak K, Leong T, Lee M, Chu J, Tan J, Tran P, Kron T, Sidhom M, Wiltshire K, Keats S, Kneebone A, Haworth A, Ebert MA, Vinod SK, Holloway L. Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets. Comput Med Imaging Graph 2024; 116:102403. [PMID: 38878632 DOI: 10.1016/j.compmedimag.2024.102403] [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: 08/02/2023] [Revised: 03/17/2024] [Accepted: 05/21/2024] [Indexed: 09/02/2024]
Abstract
BACKGROUND AND OBJECTIVES Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not. METHODS We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics. RESULTS The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient. CONCLUSIONS We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.
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Affiliation(s)
- Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia.
| | - Hang Min
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; CSIRO Australian e-Health Research Centre, Herston, Australia
| | - Jason Dowling
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; CSIRO Australian e-Health Research Centre, Herston, Australia
| | - Matthew Field
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia
| | - Kirrily Cloak
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia
| | - Trevor Leong
- Peter MacCallum Cancer Centre, Melbourne, Australia; The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Mark Lee
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Julie Chu
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Jennifer Tan
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Phillip Tran
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Tomas Kron
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Mark Sidhom
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia
| | | | - Sarah Keats
- Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia
| | - Andrew Kneebone
- University of Sydney, Institute of Medical Physics, Sydney, Australia; Northern Sydney Cancer Centre, Sydney, Australia
| | - Annette Haworth
- University of Sydney, Institute of Medical Physics, Sydney, Australia
| | - Martin A Ebert
- School of Physics, Mathematics, and Computing, The University of Western Australia, Crawley, Australia; Department of Radiation Oncology, Sir Charles Gardiner Hospital, Nedlands, Australia; School of Medicine and Population Health, University of Wisconsin, Madison, WI, USA
| | - Shalini K Vinod
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia
| | - Lois Holloway
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; University of Sydney, Institute of Medical Physics, Sydney, Australia
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10
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Kim YW, Biggs S, Claridge Mackonis E. Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation. Phys Eng Sci Med 2024; 47:1123-1140. [PMID: 39222214 PMCID: PMC11408550 DOI: 10.1007/s13246-024-01434-9] [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: 08/29/2023] [Accepted: 04/30/2024] [Indexed: 09/04/2024]
Abstract
Manual contouring of organs at risk (OAR) is time-consuming and subject to inter-observer variability. AI-based auto-contouring is proposed as a solution to these problems if it can produce clinically acceptable results. This study investigated the performance of multiple AI-based auto-contouring systems in different OAR segmentations. The auto-contouring was performed using seven different AI-based segmentation systems (Radiotherapy AI, Limbus AI version 1.5 and 1.6, Therapanacea, MIM, Siemens AI-Rad Companion and RadFormation) on a total of 42 clinical cases with varying anatomical sites. Volumetric and surface dice similarity coefficients and maximum Hausdorff distance (HD) between the expert's contours and automated contours were calculated to evaluate their performance. Radiotherapy AI has shown better performance than other software in most tested structures considered in the head and neck, and brain cases. No specific software had shown overall superior performance over other software in lung, breast, pelvis and abdomen cases. Each tested AI system was able to produce comparable contours to the experts' contours of organs at risk which can potentially be used for clinical use. A reduced performance of AI systems in the case of small and complex anatomical structures was found and reported, showing that it is still essential to review each contour produced by AI systems for clinical uses. This study has also demonstrated a method of comparing contouring software options which could be replicated in clinics or used for ongoing quality assurance of purchased systems.
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Affiliation(s)
- Young Woo Kim
- Department of Radiation Oncology, Chris O'Brien Lifehouse, Sydney, NSW, Australia
| | | | - Elizabeth Claridge Mackonis
- Department of Radiation Oncology, Chris O'Brien Lifehouse, Sydney, NSW, Australia.
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.
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11
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Kouzy R, Ludmir EB, Hoffman KE, Jhingran A, Kuban DA. In Reply to Akhtar et al. Pract Radiat Oncol 2024; 14:466-467. [PMID: 39218529 DOI: 10.1016/j.prro.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 05/06/2024] [Indexed: 09/04/2024]
Affiliation(s)
- Ramez Kouzy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ethan B Ludmir
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Karen E Hoffman
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Deborah A Kuban
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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12
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Tegtmeier RC, Kutyreff CJ, Smetanick JL, Hobbis D, Laughlin BS, Toesca DAS, Clouser EL, Rong Y. Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators. Pract Radiat Oncol 2024; 14:e383-e394. [PMID: 38325548 DOI: 10.1016/j.prro.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions for intact prostate and prostate bed treatments. METHODS AND MATERIALS DLAS models were trained using 116 iCBCT data sets with manually delineated organs at risk (bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT data sets were used for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared with those obtained for an additional DLAS-based model trained on planning computed tomography (pCT) data sets and for a deformable image registration (DIR)-based automatic contour propagation method. RESULTS In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all organs at risk and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes of interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, and approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines. CONCLUSIONS The proposed method presents the potential for improved segmentation accuracy and efficiency compared with the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.
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Affiliation(s)
- Riley C Tegtmeier
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | | | | | - Dean Hobbis
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona; Department of Radiation Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Brady S Laughlin
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | | | - Edward L Clouser
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
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13
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Cho B. Advancing radiation therapy through smartwatch technology to monitor anxiety. Radiat Oncol J 2024; 42:169-170. [PMID: 39354819 DOI: 10.3857/roj.2024.00577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Accepted: 09/11/2024] [Indexed: 10/03/2024] Open
Affiliation(s)
- Byungchul Cho
- Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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14
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Rabe M, Kurz C, Thummerer A, Landry G. Artificial intelligence for treatment delivery: image-guided radiotherapy. Strahlenther Onkol 2024:10.1007/s00066-024-02277-9. [PMID: 39138806 DOI: 10.1007/s00066-024-02277-9] [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: 03/01/2024] [Accepted: 07/07/2024] [Indexed: 08/15/2024]
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
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Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Adrian Thummerer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between the DKFZ and the LMU University Hospital Munich, Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
- Bavarian Cancer Research Center (BZKF), Marchioninistraße 15, 81377, Munich, Bavaria, Germany.
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15
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Erdur AC, Rusche D, Scholz D, Kiechle J, Fischer S, Llorián-Salvador Ó, Buchner JA, Nguyen MQ, Etzel L, Weidner J, Metz MC, Wiestler B, Schnabel J, Rueckert D, Combs SE, Peeken JC. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives. Strahlenther Onkol 2024:10.1007/s00066-024-02262-2. [PMID: 39105745 DOI: 10.1007/s00066-024-02262-2] [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: 01/21/2024] [Accepted: 06/13/2024] [Indexed: 08/07/2024]
Abstract
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
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Affiliation(s)
- Ayhan Can Erdur
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
| | - Daniel Rusche
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Daniel Scholz
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Johannes Kiechle
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
| | - Stefan Fischer
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
| | - Óscar Llorián-Salvador
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department for Bioinformatics and Computational Biology - i12, Technical University of Munich, Boltzmannstraße 3, 85748, Garching, Bavaria, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz (JGU), Hüsch-Weg 15, 55128, Mainz, Rhineland-Palatinate, Germany
| | - Josef A Buchner
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Mai Q Nguyen
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
| | - Jonas Weidner
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Julia Schnabel
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Bavaria, Germany
- School of Biomedical Engineering & Imaging Sciences, King's College London, Strand, WC2R 2LS, London, London, UK
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Faculty of Engineering, Department of Computing, Imperial College London, Exhibition Rd, SW7 2BX, London, London, UK
| | - Stephanie E Combs
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
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16
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Nai R, Wang K, Li X, Du S, E T, Xiao H, Quan S, Zhang Y, Yu J, Li J, Zhang X, Wang X. Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning. Med Phys 2024; 51:5457-5467. [PMID: 38477634 DOI: 10.1002/mp.17025] [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: 09/20/2023] [Revised: 02/23/2024] [Accepted: 03/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. PURPOSE The ureter was quantitatively measured on 3D MRU images using a deep learning model. METHODS A retrospective cohort of 445 3D MRU scans (443 patients, 52 ± 18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55 ± 21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. RESULTS In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p < 0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p < 0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p = 0.08) or in the non-dilated (p = 0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p > 0.05). CONCLUSION The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.
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Affiliation(s)
- Rile Nai
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University Beijing, Beijing, China
| | - Xiaoqing Li
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Shangsong Du
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Tuya E
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - He Xiao
- Department of Radiology, Beijing Changping Hospital, Beijing, China
| | - Shuo Quan
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Junhua Yu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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17
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Behzadipour M, Palta J, Ma T, Yuan L, Kim S, Kirby S, Torkelson L, Baker J, Koenig T, Khalifa MA, Hawranko R, Richeson D, Fields E, Weiss E, Song WY. Optimization of treatment workflow for 0.35T MR-Linac system. J Appl Clin Med Phys 2024; 25:e14393. [PMID: 38742819 PMCID: PMC11302807 DOI: 10.1002/acm2.14393] [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: 12/06/2023] [Revised: 03/15/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024] Open
Abstract
PURPOSE This study presents a novel and comprehensive framework for evaluating magnetic resonance guided radiotherapy (MRgRT) workflow by integrating the Failure Modes and Effects Analysis (FMEA) approach with Time-Driven Activity-Based Costing (TDABC). We assess the workflow for safety, quality, and economic implications, providing a holistic understanding of the MRgRT implementation. The aim is to offer valuable insights to healthcare practitioners and administrators, facilitating informed decision-making regarding the 0.35T MRIdian MR-Linac system's clinical workflow. METHODS For FMEA, a multidisciplinary team followed the TG-100 methodology to assess the MRgRT workflow's potential failure modes. Following the mitigation of primary failure modes and workflow optimization, a treatment process was established for TDABC analysis. The TDABC was applied to both MRgRT and computed tomography guided RT (CTgRT) for typical five-fraction stereotactic body RT (SBRT) treatments, assessing total workflow and costs associated between the two treatment workflows. RESULTS A total of 279 failure modes were identified, with 31 categorized as high-risk, 55 as medium-risk, and the rest as low-risk. The top 20% risk priority numbers (RPN) were determined for each radiation oncology care team member. Total MRgRT and CTgRT costs were assessed. Implementing technological advancements, such as real-time multi leaf collimator (MLC) tracking with volumetric modulated arc therapy (VMAT), auto-segmentation, and increasing the Linac dose rate, led to significant cost savings for MRgRT. CONCLUSION In this study, we integrated FMEA with TDABC to comprehensively evaluate the workflow and the associated costs of MRgRT compared to conventional CTgRT for five-fraction SBRT treatments. FMEA analysis identified critical failure modes, offering insights to enhance patient safety. TDABC analysis revealed that while MRgRT provides unique advantages, it may involve higher costs. Our findings underscore the importance of exploring cost-effective strategies and key technological advancements to ensure the widespread adoption and financial sustainability of MRgRT in clinical practice.
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Affiliation(s)
- Mojtaba Behzadipour
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tianjun Ma
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Lulin Yuan
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Siyong Kim
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Suzanne Kirby
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Laurel Torkelson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - James Baker
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Tammy Koenig
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Mateb Al Khalifa
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert Hawranko
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Dylan Richeson
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Emma Fields
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Elisabeth Weiss
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William Y. Song
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
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Zhu J, Yan J, Zhang J, Yu L, Song A, Zheng Z, Chen Y, Wang S, Chen Q, Liu Z, Zhang F. Automatic segmentation of high-risk clinical target volume and organs at risk in brachytherapy of cervical cancer with a convolutional neural network. Cancer Radiother 2024; 28:354-364. [PMID: 39147623 DOI: 10.1016/j.canrad.2024.03.002] [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: 05/17/2023] [Revised: 11/26/2023] [Accepted: 03/14/2024] [Indexed: 08/17/2024]
Abstract
PURPOSE This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes and organs at risk in image-guided adaptive brachytherapy for cervical cancer. MATERIALS AND METHODS A novel SERes-u-net was trained and tested using CT scans from 98 patients with locally advanced cervical cancer who underwent image-guided adaptive brachytherapy. The Dice similarity coefficient, 95th percentile Hausdorff distance, and clinical assessment were used for evaluation. RESULTS The mean Dice similarity coefficients of our model were 80.8%, 91.9%, 85.2%, 60.4%, and 82.8% for the high-risk clinical target volumes, bladder, rectum, sigmoid, and bowel loops, respectively. The corresponding 95th percentile Hausdorff distances were 5.23mm, 4.75mm, 4.06mm, 30.0mm, and 20.5mm. The evaluation results revealed that 99.3% of the convolutional neural networks-generated high-risk clinical target volumes slices were acceptable for oncologist A and 100% for oncologist B. Most segmentations of the organs at risk were clinically acceptable, except for the 25% sigmoid, which required significant revision in the opinion of oncologist A. There was a significant difference in the clinical evaluation of convolutional neural networks-generated high-risk clinical target volumes between the two oncologists (P<0.001), whereas the score differences of the organs at risk were not significant between the two oncologists. In the consistency evaluation, a large discrepancy was observed between senior and junior clinicians. About 40% of SERes-u-net-generated contours were thought to be better by junior clinicians. CONCLUSION The high-risk clinical target volumes and organs at risk of cervical cancer generated by the proposed convolutional neural networks model can be used clinically, potentially improving segmentation consistency and efficiency of contouring in image-guided adaptive brachytherapy workflow.
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Affiliation(s)
- J Zhu
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - J Yan
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - J Zhang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - L Yu
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - A Song
- Department of Radiation Oncology, Cangzhou Central Hospital, Cangzhou, Hebei 061001, China
| | - Z Zheng
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Y Chen
- MedMind Technology Co., Ltd., Beijing 100730, China
| | - S Wang
- MedMind Technology Co., Ltd., Beijing 100730, China
| | - Q Chen
- MedMind Technology Co., Ltd., Beijing 100730, China
| | - Z Liu
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
| | - F Zhang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
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Marquez B, Wooten ZT, Salazar RM, Peterson CB, Fuentes DT, Whitaker TJ, Jhingran A, Pollard-Larkin J, Prajapati S, Beadle B, Cardenas CE, Netherton TJ, Court LE. Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues. Diagnostics (Basel) 2024; 14:1632. [PMID: 39125508 PMCID: PMC11311423 DOI: 10.3390/diagnostics14151632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.
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Affiliation(s)
- Barbara Marquez
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | | | - Ramon M. Salazar
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Christine B. Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David T. Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - T. J. Whitaker
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
| | - Anuja Jhingran
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Julianne Pollard-Larkin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Surendra Prajapati
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Beth Beadle
- Department of Radiation Oncology–Radiation Therapy, Stanford University, Stanford, CA 94305, USA;
| | - Carlos E. Cardenas
- Department of Radiation Oncology, The University of Alabama, Birmingham, AL 35294, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.M.S.); (T.J.W.); (J.P.-L.); (L.E.C.)
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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20
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Kumar K, Yeo AU, McIntosh L, Kron T, Wheeler G, Franich RD. Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults? Int J Radiat Oncol Biol Phys 2024; 119:1297-1306. [PMID: 38246249 DOI: 10.1016/j.ijrobp.2024.01.201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/10/2023] [Accepted: 01/07/2024] [Indexed: 01/23/2024]
Abstract
PURPOSE Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adaptive radiation therapy and radiation therapy planning. However, their performance on pediatric computed tomography (CT) data and cross-scanner compatibility remain unclear. This study aimed to evaluate the performance of AI-based auto-segmentation models trained on adult CT data when applied to pediatric data sets and explore the improvement in performance gained by including pediatric training data. It also examined their ability to accurately segment CT data acquired from different scanners. METHODS AND MATERIALS Using the nnU-Net framework, segmentation models were trained on data sets of adult, pediatric, and combined CT scans for 7 pelvic/thoracic organs. Each model was trained on 290 to 300 cases per category and organ. Training data sets included a combination of clinical data and several open repositories. The study incorporated a database of 459 pediatric (0-16 years) CT scans and 950 adults (>18 years), ensuring all scans had human expert ground-truth contours of the selected organs. Performance was evaluated based on Dice similarity coefficients (DSC) of the model-generated contours. RESULTS AI models trained exclusively on adult data underperformed on pediatric data, especially for the 0 to 2 age group: mean DSC was below 0.5 for the bladder and spleen. The addition of pediatric training data demonstrated significant improvement for all age groups, achieving a mean DSC of above 0.85 for all organs in every age group. Larger organs like the liver and kidneys maintained consistent performance for all models across age groups. No significant difference emerged in the cross-scanner performance evaluation, suggesting robust cross-scanner generalization. CONCLUSIONS For optimal segmentation across age groups, it is important to include pediatric data in the training of segmentation models. The successful cross-scanner generalization also supports the real-world clinical applicability of these AI models. This study emphasizes the significance of data set diversity in training robust AI systems for medical image interpretation tasks.
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Affiliation(s)
- Kartik Kumar
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Adam U Yeo
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Lachlan McIntosh
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia
| | - Tomas Kron
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - Greg Wheeler
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Rick D Franich
- Physical Sciences Department, Peter MacCallum Cancer Centre, Victoria, Australia; School of Science, RMIT University, Melbourne, Victoria, Australia.
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21
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Skett S, Patel T, Duprez D, Gupta S, Netherton T, Trauernicht C, Aldridge S, Eaton D, Cardenas C, Court LE, Smith D, Aggarwal A. Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images. Phys Imaging Radiat Oncol 2024; 31:100637. [PMID: 39297080 PMCID: PMC11408859 DOI: 10.1016/j.phro.2024.100637] [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: 04/04/2024] [Revised: 08/21/2024] [Accepted: 08/21/2024] [Indexed: 09/21/2024] Open
Abstract
Background and purpose In many clinics, positron-emission tomography is unavailable and clinician time extremely limited. Here we describe a deep-learning model for autocontouring gross disease for patients undergoing palliative radiotherapy for primary lung lesions and/or hilar/mediastinal nodal disease, based only on computed tomography (CT) images. Materials and methods An autocontouring model (nnU-Net) was trained to contour gross disease in 379 cases (352 training, 27 test); 11 further test cases from an external centre were also included. Anchor-point-based post-processing was applied to remove extraneous autocontoured regions. The autocontours were evaluated quantitatively in terms of volume similarity (Dice similarity coefficient [DSC], surface Dice coefficient, 95th percentile Hausdorff distance [HD95], and mean surface distance), and scored for usability by two consultant oncologists. The magnitude of treatment margin needed to account for geometric discrepancies was also assessed. Results The anchor point process successfully removed all erroneous regions from the autocontoured disease, and identified two cases to be excluded from further analysis due to 'missed' disease. The average DSC and HD95 were 0.8 ± 0.1 and 10.5 ± 7.3 mm, respectively. A 10-mm uniform margin-distance applied to the autocontoured region was found to yield "full coverage" (sensitivity > 0.99) of the clinical contour for 64 % of cases. Ninety-seven percent of evaluated autocontours were scored by both clinicians as requiring no or minor edits. Conclusions Our autocontouring model was shown to produce clinically usable disease outlines, based on CT alone, for approximately two-thirds of patients undergoing lung radiotherapy. Further work is necessary to improve this before clinical implementation.
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Affiliation(s)
- Stephen Skett
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Tina Patel
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Didier Duprez
- Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Sunnia Gupta
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Tucker Netherton
- The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Christoph Trauernicht
- Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Sarah Aldridge
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - David Eaton
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Carlos Cardenas
- University of Alabama at Birmingham Hazelrig-Salter Radiation Oncology Center, Birmingham, AL, United States
| | - Laurence E Court
- The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Daniel Smith
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Ajay Aggarwal
- Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
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22
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Wahid KA, Cardenas CE, Marquez B, Netherton TJ, Kann BH, Court LE, He R, Naser MA, Moreno AC, Fuller CD, Fuentes D. Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification. Adv Radiat Oncol 2024; 9:101521. [PMID: 38799110 PMCID: PMC11111585 DOI: 10.1016/j.adro.2024.101521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/26/2024] [Indexed: 05/29/2024] Open
Affiliation(s)
- Kareem A. Wahid
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Barbara Marquez
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Benjamin H. Kann
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Amy C. Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Singh S, Singh R, Kumar S, Suri A. A Narrative Review on 3-Dimensional Visualization Techniques in Neurosurgical Education, Simulation, and Planning. World Neurosurg 2024; 187:46-64. [PMID: 38580090 DOI: 10.1016/j.wneu.2024.03.134] [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: 09/20/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND High-fidelity visualization of anatomical organs is crucial for neurosurgical education, simulation, and planning. This becomes much more important for minimally invasive neurosurgical procedures. Realistic anatomical visualization can allow resident surgeons to learn visual cues and orient themselves with the complex 3-dimensional (3D) anatomy. Achieving full fidelity in 3D medical visualization is an active area of research; however, the prior reviews focus on the application area and lack the underlying technical principles. Accordingly, the present study attempts to bridge this gap by providing a narrative review of the techniques used for 3D visualization. METHODS We conducted a literature review on 3D medical visualization technology from 2018 to 2023 using the PubMed and Google Scholar search engines. The cross-referenced manuscripts were extensively studied to find literature that discusses technology relevant to 3D medical visualization. We also compiled and ran software applications that were accessible to us in order to better understand them. RESULTS We present the underlying fundamental technology used in 3D medical visualization in the context of neurosurgical education, simulation, and planning. Further, we discuss and categorize a few important applications based on the 3D visualization techniques they use. CONCLUSIONS The visualization of virtual human organs has not yet achieved a level of realism close to reality. This gap is largely due to the interdisciplinary nature of this research, population diversity, and validation complexities. With the advancements in computational resources and automation of 3D visualization pipelines, next-gen applications may offer enhanced medical 3D visualization fidelity.
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Affiliation(s)
- Sukhraj Singh
- Amar Nath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, New Delhi, India.
| | - Ramandeep Singh
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.
| | - Subodh Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.
| | - Ashish Suri
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.
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Zeverino M, Piccolo C, Marguet M, Jeanneret-Sozzi W, Bourhis J, Bochud F, Moeckli R. Sensitivity of automated and manual treatment planning approaches to contouring variation in early-breast cancer treatment. Phys Med 2024; 123:103402. [PMID: 38875932 DOI: 10.1016/j.ejmp.2024.103402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 05/24/2024] [Accepted: 06/05/2024] [Indexed: 06/16/2024] Open
Abstract
PURPOSE One of the advantages of integrating automated processes in treatment planning is the reduction of manual planning variability. This study aims to assess whether a deep-learning-based auto-planning solution can also reduce the contouring variation-related impact on the planned dose for early-breast cancer treatment. METHODS Auto- and manual plans were optimized for 20 patients using both auto- and manual OARs, including both lungs, right breast, heart, and left-anterior-descending (LAD) artery. Differences in terms of recalculated dose (ΔDrcM,ΔDrcA) and reoptimized dose (ΔDroM,ΔDroA) for manual (M) and auto (A)-plans, were evaluated on manual structures. The correlation between several geometric similarities and dose differences was also explored (Spearman's test). RESULTS Auto-contours were found slightly smaller in size than manual contours for right breast and heart and more than twice larger for LAD. Recalculated dose differences were found negligible for both planning approaches except for heart (ΔDrcM=-0.4 Gy, ΔDrcA=-0.3 Gy) and right breast (ΔDrcM=-1.2 Gy, ΔDrcA=-1.3 Gy) maximum dose. Re-optimized dose differences were considered equivalent to recalculated ones for both lungs and LAD, while they were significantly smaller for heart (ΔDroM=-0.2 Gy, ΔDroA=-0.2 Gy) and right breast (ΔDroM =-0.3 Gy, ΔDroA=-0.9 Gy) maximum dose. Twenty-one correlations were found for ΔDrcM,A (M=8,A=13) that reduced to four for ΔDroM,A (M=3,A=1). CONCLUSIONS The sensitivity of auto-planning to contouring variation was found not relevant when compared to manual planning, regardless of the method used to calculate the dose differences. Nonetheless, the method employed to define the dose differences strongly affected the correlation analysis resulting highly reduced when dose was reoptimized, regardless of the planning approach.
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Affiliation(s)
- Michele Zeverino
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Consiglia Piccolo
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maud Marguet
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Wendy Jeanneret-Sozzi
- Radiation Oncology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean Bourhis
- Radiation Oncology Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Francois Bochud
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Raphaël Moeckli
- Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Temple SWP, Rowbottom CG. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. J Appl Clin Med Phys 2024; 25:e14273. [PMID: 38263866 PMCID: PMC11163497 DOI: 10.1002/acm2.14273] [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: 10/04/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024] Open
Abstract
PURPOSE Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-observer variability. There has been little research investigating gross failure rates and failure modes of such systems. METHOD 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI-generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI-based commercial system was applied to 500 H&N patients. AI-generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI-based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub-types (setup position, anatomy, image artefacts and unknown). RESULTS There were 24 true failures of the AI-based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). CONCLUSION True failures of the AI-based system were predominantly associated with a non-standard element within the CT scan. It is likely that these non-standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI-based system in the H&N region for the OARs investigated was low (∼1%).
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Affiliation(s)
- Simon W. P. Temple
- Medical Physics DepartmentThe Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
| | - Carl G. Rowbottom
- Medical Physics DepartmentThe Clatterbridge Cancer Centre NHS Foundation TrustLiverpoolUK
- Department of PhysicsUniversity of LiverpoolLiverpoolUK
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Ferreira Silvério N, van den Wollenberg W, Betgen A, Wiersema L, Marijnen C, Peters F, van der Heide UA, Simões R, Janssen T. Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer. Adv Radiat Oncol 2024; 9:101483. [PMID: 38706833 PMCID: PMC11066509 DOI: 10.1016/j.adro.2024.101483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/11/2024] [Indexed: 05/07/2024] Open
Abstract
Purpose Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy. Methods and Materials The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution. Results The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage. Conclusions Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.
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Affiliation(s)
| | | | - Anja Betgen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Lisa Wiersema
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Corrie Marijnen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Femke Peters
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Uulke A. van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rita Simões
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tomas Janssen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Zhong H, Kainz KK, Paulson ES. Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy. J Appl Clin Med Phys 2024; 25:e14358. [PMID: 38634799 PMCID: PMC11163488 DOI: 10.1002/acm2.14358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 03/03/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
PURPOSE We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. MATERIALS AND METHODS Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. RESULTS For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. CONCLUSIONS Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.
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Affiliation(s)
- Hualiang Zhong
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Kristofer K. Kainz
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Eric S. Paulson
- Department of Radiation OncologyMedical College of WisconsinMilwaukeeWisconsinUSA
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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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29
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He K, Peng B, Yu W, Liu Y, Liu S, Cheng J, Dai Y. A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation. Bioengineering (Basel) 2024; 11:427. [PMID: 38790294 PMCID: PMC11118222 DOI: 10.3390/bioengineering11050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/14/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model's ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.
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Affiliation(s)
- Keyi He
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
- The School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Weibo Yu
- The School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Surui Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Jian Cheng
- State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing 100191, China
- International Innovation Institute, Beihang University, 166 Shuanghongqiao Street, Pingyao Town, Yuhang District, Hangzhou 311115, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
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Delgadillo R, Deana AM, Ford JC, Studenski MT, Padgett KR, Abramowitz MC, Pra AD, Spieler BO, Dogan N. Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer. Sci Rep 2024; 14:9563. [PMID: 38671043 PMCID: PMC11053114 DOI: 10.1038/s41598-024-60281-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.
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Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Anthony M Deana
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
- Varian Medical Systems, Advanced Oncology Solutions, Avon, IN, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Matthew T Studenski
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Benjamin O Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA.
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Philip MM, Watts J, Moeini SNM, Musheb M, McKiddie F, Welch A, Nath M. Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images. Phys Med Biol 2024; 69:095005. [PMID: 38530298 DOI: 10.1088/1361-6560/ad37ea] [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: 11/07/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.
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Affiliation(s)
- Mahima Merin Philip
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Jessica Watts
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | | | - Mohammed Musheb
- National Health Service Highland, Inverness IV2 3BW, United Kingdom
| | - Fergus McKiddie
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Mintu Nath
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
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Le Bao V, Haworth A, Dowling J, Walker A, Arumugam S, Jameson M, Chlap P, Wiltshire K, Keats S, Cloak K, Sidhom M, Kneebone A, Holloway L. Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy. Phys Med Biol 2024; 69:085008. [PMID: 38471173 DOI: 10.1088/1361-6560/ad3325] [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: 10/19/2023] [Accepted: 03/12/2024] [Indexed: 03/14/2024]
Abstract
Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.
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Affiliation(s)
- Viet Le Bao
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
| | - Jason Dowling
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Amy Walker
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Sankar Arumugam
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Michael Jameson
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia
- GenesisCare, Sydney, NSW, Australia
| | - Phillip Chlap
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirsty Wiltshire
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Sarah Keats
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirrily Cloak
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Mark Sidhom
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | | | - Lois Holloway
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
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Shan G, Yu S, Lai Z, Xuan Z, Zhang J, Wang B, Ge Y. A Review of Artificial Intelligence Application for Radiotherapy. Dose Response 2024; 22:15593258241263687. [PMID: 38912333 PMCID: PMC11193352 DOI: 10.1177/15593258241263687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/03/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Purpose Artificial intelligence (AI) is a technique which tries to think like humans and mimic human behaviors. It has been considered as an alternative in a lot of human-dependent steps in radiotherapy (RT), since the human participation is a principal uncertainty source in RT. The aim of this work is to provide a systematic summary of the current literature on AI application for RT, and to clarify its role for RT practice in terms of clinical views. Materials and Methods A systematic literature search of PubMed and Google Scholar was performed to identify original articles involving the AI applications in RT from the inception to 2022. Studies were included if they reported original data and explored the clinical applications of AI in RT. Results The selected studies were categorized into three aspects of RT: organ and lesion segmentation, treatment planning and quality assurance. For each aspect, this review discussed how these AI tools could be involved in the RT protocol. Conclusions Our study revealed that AI was a potential alternative for the human-dependent steps in the complex process of RT.
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Affiliation(s)
- Guoping Shan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
- Zhejiang Cancer Hospital, Hangzhou, China
| | - Shunfei Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhongjun Lai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhiqiang Xuan
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jie Zhang
- Zhejiang Cancer Hospital, Hangzhou, China
| | | | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
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Kraus AC, Iqbal Z, Cardan RA, Popple RA, Stanley DN, Shen S, Pogue JA, Wu X, Lee K, Marcrom S, Cardenas CE. Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies. Adv Radiat Oncol 2024; 9:101417. [PMID: 38435965 PMCID: PMC10906166 DOI: 10.1016/j.adro.2023.101417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/30/2023] [Indexed: 03/05/2024] Open
Abstract
Purpose The use of deep learning to auto-contour organs at risk (OARs) in gynecologic radiation treatment is well established. Yet, there is limited data investigating the prospective use of auto-contouring in clinical practice. In this study, we assess the accuracy and efficiency of auto-contouring OARs for computed tomography-based brachytherapy treatment planning of gynecologic malignancies. Methods and Materials An inhouse contouring tool automatically delineated 5 OARs in gynecologic radiation treatment planning: the bladder, small bowel, sigmoid, rectum, and urethra. Accuracy of each auto-contour was evaluated using a 5-point Likert scale: a score of 5 indicated the contour could be used without edits, while a score of 1 indicated the contour was unusable. During scoring, automated contours were edited and subsequently used for treatment planning. Dice similarity coefficient, mean surface distance, 95% Hausdorff distance, Hausdorff distance, and dosimetric changes between original and edited contours were calculated. Contour approval time and total planning time of a prospective auto-contoured (AC) cohort were compared with times from a retrospective manually contoured (MC) cohort. Results Thirty AC cases from January 2022 to July 2022 and 31 MC cases from July 2021 to January 2022 were included. The mean (±SD) Likert score for each OAR was the following: bladder 4.77 (±0.58), small bowel 3.96 (±0.91), sigmoid colon 3.92 (±0.81), rectum 4.6 (±0.71), and urethra 4.27 (±0.78). No ACs required major edits. All OARs had a mean Dice similarity coefficient > 0.86, mean surface distance < 0.48 mm, 95% Hausdorff distance < 3.2 mm, and Hausdorff distance < 10.32 mm between original and edited contours. There was no significant difference in dose-volume histogram metrics (D2.0 cc/D0.1 cc) between original and edited contours (P values > .05). The average time to plan approval in the AC cohort was 19% less than the MC cohort. (AC vs MC, 117.0 + 18.0 minutes vs 144.9 ± 64.5 minutes, P = .045). Conclusions Automated contouring is useful and accurate in clinical practice. Auto-contouring OARs streamlines radiation treatment workflows and decreases time required to design and approve gynecologic brachytherapy plans.
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Affiliation(s)
- Abigayle C. Kraus
- Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Zohaib Iqbal
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Rex A. Cardan
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Richard A. Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Dennis N. Stanley
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Sui Shen
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Joel A. Pogue
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Xingen Wu
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kevin Lee
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Samuel Marcrom
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
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Huang K, Chung C, Ludmir EB, Zhang L, Owens CA, Vega JGL, Duryea J, Zhao Y, Chen X, Fuentes D, Cardenas CE, Briere TM, Beddar S, Court LE, Das P. Automatic end-to-end VMAT treatment planning for rectal cancers. J Appl Clin Med Phys 2024; 25:e14259. [PMID: 38317597 PMCID: PMC11005975 DOI: 10.1002/acm2.14259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.
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Affiliation(s)
- Kai Huang
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Christine Chung
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Ethan B. Ludmir
- Department of Gastrointestinal Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Lifei Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Constance A. Owens
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jean Gumma‐De La Vega
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jack Duryea
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Yao Zhao
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Xinru Chen
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - David Fuentes
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
| | - Tina Marie Briere
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Sam Beddar
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Prajnan Das
- Department of Gastrointestinal Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, Nie X, Yu J, Tong J, Liao X. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging 2024; 24:36. [PMID: 38486342 PMCID: PMC10938723 DOI: 10.1186/s40644-024-00682-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
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Affiliation(s)
- Haiqing Fan
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yilin Luo
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Fang Gu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Bin Tian
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Yongqin Xiong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Guipeng Wu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Nie
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Jing Yu
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Juan Tong
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China
| | - Xin Liao
- Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
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Zhang Y, Zhu T, Zheng Y, Xiong Y, Liu W, Zeng W, Tang W, Liu C. Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis. Clin Oral Investig 2024; 28:186. [PMID: 38430334 DOI: 10.1007/s00784-024-05586-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
OBJECTIVES Temporomandibular disorders (TMDs) are the second most common musculoskeletal condition which are challenging tasks for most clinicians. Recent research used machine learning (ML) algorithms to diagnose TMDs intelligently. This study aimed to systematically evaluate the quality of these studies and assess the diagnostic accuracy of existing models. MATERIALS AND METHODS Twelve databases (Europe PMC, Embase, etc.) and two registers were searched for published and unpublished studies using ML algorithms on medical images. Two reviewers extracted the characteristics of studies and assessed the methodological quality using the QUADAS-2 tool independently. RESULTS A total of 28 studies (29 reports) were included: one was at unclear risk of bias and the others were at high risk. Thus the certainty of evidence was quite low. These studies used many types of algorithms including 8 machine learning models (logistic regression, support vector machine, random forest, etc.) and 15 deep learning models (Resnet152, Yolo v5, Inception V3, etc.). The diagnostic accuracy of a few models was relatively satisfactory. The pooled sensitivity and specificity were 0.745 (0.660-0.814) and 0.770 (0.700-0.828) in random forest, 0.765 (0.686-0.829) and 0.766 (0.688-0.830) in XGBoost, and 0.781 (0.704-0.843) and 0.781 (0.704-0.843) in LightGBM. CONCLUSIONS Most studies had high risks of bias in Patient Selection and Index Test. Some algorithms are relatively satisfactory and might be promising in intelligent diagnosis. Overall, more high-quality studies and more types of algorithms should be conducted in the future. CLINICAL RELEVANCE We evaluated the diagnostic accuracy of the existing models and provided clinicians with much advice about the selection of algorithms. This study stated the promising orientation of future research, and we believe it will promote the intelligent diagnosis of TMDs.
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Affiliation(s)
- Yunan Zhang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Tao Zhu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Yunhao Zheng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Yutao Xiong
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Zeng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China
| | - Wei Tang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
| | - Chang Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
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Da Mutten R, Zanier O, Theiler S, Ryu SJ, Regli L, Serra C, Staartjes VE. Whole Spine Segmentation Using Object Detection and Semantic Segmentation. Neurospine 2024; 21:57-67. [PMID: 38317546 PMCID: PMC10992645 DOI: 10.14245/ns.2347178.589] [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: 11/01/2023] [Revised: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 02/07/2024] Open
Abstract
OBJECTIVE Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis. METHODS Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets. RESULTS Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively. CONCLUSION We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.
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Affiliation(s)
- Raffaele Da Mutten
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Olivier Zanier
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Sven Theiler
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Seung-Jun Ryu
- Department of Neurosurgery, Daejeon Eulji University Hospital, Eulji University Medical School, Daejeon, Korea
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Victor E. Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zürich, University of Zürich, Zürich, Switzerland
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Vagni M, Tran HE, Romano A, Chiloiro G, Boldrini L, Zormpas-Petridis K, Kawula M, Landry G, Kurz C, Corradini S, Belka C, Indovina L, Gambacorta MA, Placidi L, Cusumano D. Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models. Phys Med 2024; 119:103297. [PMID: 38310680 DOI: 10.1016/j.ejmp.2024.103297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/06/2024] Open
Abstract
PURPOSE Manual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow. METHODS 3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients. RESULTS In the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs' volumetric segmentation for a single patient. CONCLUSIONS The proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.
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Affiliation(s)
- Marica Vagni
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Huong Elena Tran
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Maria Kawula
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Department of Radiation Oncology, Munich, Germany
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.
| | - Davide Cusumano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia, SS, Italy
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Maes D, Gates EDH, Meyer J, Kang J, Nguyen BNT, Lavilla M, Melancon D, Weg ES, Tseng YD, Lim A, Bowen SR. Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring. Pract Radiat Oncol 2024; 14:e150-e158. [PMID: 37935308 DOI: 10.1016/j.prro.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics. METHODS AND MATERIALS Curated data included 121 patient planning computed tomography (CT) scans with a total of 859 clinically approved contours used for treatment from 4 clinics. Regions of interest (ROIs) were generated with 3 commercial AI-based automated contouring software solutions (AI1, AI2, AI3) spanning the following disease sites: brain, head and neck (H&N), thorax, abdomen, and pelvis. Quantitative agreement between AI-generated and clinical contours was measured by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative assessment was performed by multiple experts scoring blinded AI-contours using a Likert scale. Workflow and usability surveying was also conducted. RESULTS AI1, AI2, and AI3 contours had high quantitative agreement in 27.8%, 32.8%, and 34.1% of cases (DSC >0.9), performing well in pelvis (median DSC = 0.86/0.88/0.91) and thorax (median DSC = 0.91/0.89/0.91). All 3 solutions had low quantitative agreement in 7.4%, 8.8%, and 6.1% of cases (DSC <0.5), performing worse in brain (median DSC = 0.65/0.78/0.75) and H&N (median DSC = 0.76/0.80/0.81). Qualitatively, AI1 and AI2 contours were acceptable (rated 1-2) with at most minor edits in 70.7% and 74.6% of ROIs (2906 ratings), higher for abdomen (AI1: 79.2%) and thorax (AI2: 90.2%), and lower for H&N (29.0/35.6%). An end-user survey showed strong user preference for full automation and mixed preferences for accuracy versus total number of structures generated. CONCLUSIONS Our evaluation method provided a comprehensive analysis of both quantitative and qualitative measures of commercially available pretrained AI autocontouring algorithms. The evaluation framework served as a roadmap for clinical integration that aligned with user workflow preference.
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Affiliation(s)
- Dominic Maes
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington.
| | - Evan D H Gates
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Juergen Meyer
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - John Kang
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Bao-Ngoc Thi Nguyen
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Myra Lavilla
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Dustin Melancon
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Emily S Weg
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Yolanda D Tseng
- Department of Radiation Oncology, University of Washington, Seattle, Washington; Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Andrew Lim
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington; Department of Radiation Oncology, University of Southern California, Los Angeles, California
| | - Stephen R Bowen
- Department of Radiation Oncology, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Radiation Oncology, University of Washington, Seattle, Washington; Department of Radiology, University of Washington, Seattle, Washington
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Mikhailov I, Chauveau B, Bourdel N, Bartoli A. A deep learning-based interactive medical image segmentation framework with sequential memory. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108038. [PMID: 38271792 DOI: 10.1016/j.cmpb.2024.108038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/22/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND OBJECTIVE Image segmentation is an essential component in medical image analysis. The case of 3D images such as MRI is particularly challenging and time consuming. Interactive or semi-automatic methods are thus highly desirable. However, existing methods do not exploit the typical sequentiality of real user interactions. This is due to the interaction memory used in these systems, which discards ordering. In contrast, we argue that the order of the user corrections should be used for training and lead to performance improvements. METHODS We contribute to solving this problem by proposing a general multi-class deep learning-based interactive framework for image segmentation, which embeds a base network in a user interaction loop with a user feedback memory. We propose to model the memory explicitly as a sequence of consecutive system states, from which the features can be learned, generally learning from the segmentation refinement process. Training is a major difficulty owing to the network's input being dependent on the previous output. We adapt the network to this loop by introducing a virtual user in the training process, modelled by dynamically simulating the iterative user feedback. RESULTS We evaluated our framework against existing methods on the complex task of multi-class semantic instance female pelvis MRI segmentation with 5 classes, including up to 27 tumour instances, using a segmentation dataset collected in our hospital, and on liver and pancreas CT segmentation, using public datasets. We conducted a user evaluation, involving both senior and junior medical personnel in matching and adjacent areas of expertise. We observed an annotation time reduction with 5'56" for our framework against 25' on average for classical tools. We systematically evaluated the influence of the number of clicks on the segmentation accuracy. A single interaction round our framework outperforms existing automatic systems with a comparable setup. We provide an ablation study and show that our framework outperforms existing interactive systems. CONCLUSIONS Our framework largely outperforms existing systems in accuracy, with the largest impact on the smallest, most difficult classes, and drastically reduces the average user segmentation time with fast inference at 47.2±6.2 ms per image.
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Affiliation(s)
- Ivan Mikhailov
- EnCoV, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, 63000, France; SurgAR, 22 All. Alan Turing, Clermont-Ferrand, 63000, France.
| | - Benoit Chauveau
- SurgAR, 22 All. Alan Turing, Clermont-Ferrand, 63000, France; CHU de Clermont-Ferrand, Clermont-Ferrand, 63000, France
| | - Nicolas Bourdel
- EnCoV, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, 63000, France; SurgAR, 22 All. Alan Turing, Clermont-Ferrand, 63000, France; CHU de Clermont-Ferrand, Clermont-Ferrand, 63000, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, 63000, France; SurgAR, 22 All. Alan Turing, Clermont-Ferrand, 63000, France; CHU de Clermont-Ferrand, Clermont-Ferrand, 63000, France
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Xu T, Zhang XY, Yang N, Jiang F, Chen GQ, Pan XF, Peng YX, Cui XW. A narrative review on the application of artificial intelligence in renal ultrasound. Front Oncol 2024; 13:1252630. [PMID: 38495082 PMCID: PMC10943690 DOI: 10.3389/fonc.2023.1252630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/12/2023] [Indexed: 03/19/2024] Open
Abstract
Kidney disease is a serious public health problem and various kidney diseases could progress to end-stage renal disease. The many complications of end-stage renal disease. have a significant impact on the physical and mental health of patients. Ultrasound can be the test of choice for evaluating the kidney and perirenal tissue as it is real-time, available and non-radioactive. To overcome substantial interobserver variability in renal ultrasound interpretation, artificial intelligence (AI) has the potential to be a new method to help radiologists make clinical decisions. This review introduces the applications of AI in renal ultrasound, including automatic segmentation of the kidney, measurement of the renal volume, prediction of the kidney function, diagnosis of the kidney diseases. The advantages and disadvantages of the applications will also be presented clinicians to conduct research. Additionally, the challenges and future perspectives of AI are discussed.
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Affiliation(s)
- Tong Xu
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xian-Ya Zhang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Na Yang
- Department of Ultrasound, Affiliated Hospital of Jilin Medical College, Jilin, China
| | - Fan Jiang
- Department of Medical Ultrasound, The Second Hospital of Anhui Medical University, Hefei, China
| | - Gong-Quan Chen
- Department of Medical Ultrasound, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Xiao-Fang Pan
- Health Medical Department, Dalian Municipal Central Hospital, Dalian, China
| | - Yue-Xiang Peng
- Department of Ultrasound, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Werny MJ, Meirer F, Weckhuysen BM. Visualizing the Structure, Composition and Activity of Single Catalyst Particles for Olefin Polymerization and Polyolefin Decomposition. Angew Chem Int Ed Engl 2024; 63:e202306033. [PMID: 37782261 DOI: 10.1002/anie.202306033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/03/2023]
Abstract
The structural and morphological characterization of individual catalyst particles for olefin polymerization, as well as for the reverse process of polyolefin decomposition, can provide an improved understanding for how these catalyst materials operate under relevant reaction conditions. In this review, we discuss an emerging analytical toolbox of 2D and 3D chemical imaging techniques that is suitable for investigating the chemistry and reactivity of related catalyst systems. While synchrotron-based X-ray microscopy still provides unparalleled spatial resolutions in 2D and 3D, a number of laboratory-based techniques, most notably focused ion beam-scanning electron microscopy, confocal fluorescence microscopy, infrared photoinduced force microscopy and laboratory-based X-ray nano-computed tomography, have helped to significantly expand the arsenal of analytical tools available to scientists in heterogeneous catalysis and polymer science. In terms of future research, the review outlines the role and impact of in situ and operando (spectro-)microscopy experiments, involving sophisticated reactors as well as online reactant and product analysis, to obtain real-time information on the formation, decomposition, and mobility of polymer phases within single catalyst particles. Furthermore, the potential of fluorescence microscopy, X-ray microscopy and optical microscopy is highlighted for the high-throughput characterization of olefin polymerization and polyolefin decomposition catalysts. By combining these chemical imaging techniques with, for example, chemical staining methodologies, selective probe molecules as well as particle sorting approaches, representative structure-activity relationships can be derived at the level of single catalyst particles.
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Affiliation(s)
- Maximilian J Werny
- Inorganic Chemistry and Catalysis, Institute for Sustainable and Circular Chemistry and Debye Institute for Nanomaterials Science, Utrecht University, Universiteitsweg 99, 3584, CG Utrecht, The Netherlands
- Dutch Polymer Institute (DPI), P.O. Box 902, 5600, AX Eindhoven, The Netherlands
| | - Florian Meirer
- Inorganic Chemistry and Catalysis, Institute for Sustainable and Circular Chemistry and Debye Institute for Nanomaterials Science, Utrecht University, Universiteitsweg 99, 3584, CG Utrecht, The Netherlands
| | - Bert M Weckhuysen
- Inorganic Chemistry and Catalysis, Institute for Sustainable and Circular Chemistry and Debye Institute for Nanomaterials Science, Utrecht University, Universiteitsweg 99, 3584, CG Utrecht, The Netherlands
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Schmidt EK, Krishnan C, Onuoha E, Gregory AV, Kline TL, Mrug M, Cardenas C, Kim H. Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data. Clin Imaging 2024; 106:110068. [PMID: 38101228 DOI: 10.1016/j.clinimag.2023.110068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD). METHODS We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC). RESULTS The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1-2 % higher than those by the models trained with single institutional data (0.90-0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2-20 % higher than those by the models trained with single institutional data (0.66-0.86). CONCLUSION The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation.
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Affiliation(s)
- Emma K Schmidt
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Chetana Krishnan
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ezinwanne Onuoha
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | | | - Timothy L Kline
- Department of Radiology, Mayo Clinic, Rochester, MN 55902, USA
| | - Michal Mrug
- Department of Veterans Affairs Medical Center, Birmingham, AL 35233, USA; Department of Nephrology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Carlos Cardenas
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Harrison Kim
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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Kawamura M, Kamomae T, Yanagawa M, Kamagata K, Fujita S, Ueda D, Matsui Y, Fushimi Y, Fujioka T, Nozaki T, Yamada A, Hirata K, Ito R, Fujima N, Tatsugami F, Nakaura T, Tsuboyama T, Naganawa S. Revolutionizing radiation therapy: the role of AI in clinical practice. JOURNAL OF RADIATION RESEARCH 2024; 65:1-9. [PMID: 37996085 PMCID: PMC10803173 DOI: 10.1093/jrr/rrad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/25/2023] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.
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Affiliation(s)
- Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Takeshi Kamomae
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3, Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kitaku, Okayama, 700-8558, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Faculty of Medicine, Hokkaido University, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Kita15, Nishi7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo, Chuo-ku, Kumamoto, 860-8556, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, 565-0871, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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Maroongroge S, Mohamed ASR, Nguyen C, Guma De la Vega J, Frank SJ, Garden AS, Gunn BG, Lee A, Mayo L, Moreno A, Morrison WH, Phan J, Spiotto MT, Court LE, Fuller CD, Rosenthal DI, Netherton TJ. Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy. Phys Imaging Radiat Oncol 2024; 29:100540. [PMID: 38356692 PMCID: PMC10864833 DOI: 10.1016/j.phro.2024.100540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Background and Purpose Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. Materials and Methods CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. Results Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. Conclusions An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits.
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Affiliation(s)
- Sean Maroongroge
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Abdallah SR. Mohamed
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Callistus Nguyen
- Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Jean Guma De la Vega
- Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Steven J. Frank
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Adam S. Garden
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Brandon G. Gunn
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Anna Lee
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Lauren Mayo
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Amy Moreno
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - William H. Morrison
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Jack Phan
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Michael T. Spiotto
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Laurence E. Court
- Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Clifton D. Fuller
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - David I. Rosenthal
- Department of Radiation Oncology, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
| | - Tucker J. Netherton
- Department of Radiation Physics, Division of Radiation Oncology, University of Texas MD Anderson Cancer Center, United States
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Paudyal R, Jiang J, Han J, Diplas BH, Riaz N, Hatzoglou V, Lee N, Deasy JO, Veeraraghavan H, Shukla-Dave A. Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae004. [PMID: 38476956 PMCID: PMC10928808 DOI: 10.1093/bjrai/ubae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/14/2024]
Abstract
Objectives Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant. Results No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively. Conclusions The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC. Advances in knowledge First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - James Han
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Bill H Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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Kishigami Y, Nakamura M, Okamoto H, Takahashi A, Iramina H, Sasaki M, Kawata K, Igaki H. Organ-contour-driven auto-matching algorithm in image-guided radiotherapy. J Appl Clin Med Phys 2024; 25:e14220. [PMID: 37994694 PMCID: PMC10795436 DOI: 10.1002/acm2.14220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/02/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE This study aimed to demonstrate the potential clinical applicability of an organ-contour-driven auto-matching algorithm in image-guided radiotherapy. METHODS This study included eleven consecutive patients with cervical cancer who underwent radiotherapy in 23 or 25 fractions. Daily and reference magnetic resonance images were converted into mesh models. A weight-based algorithm was implemented to optimize the distance between the mesh model vertices and surface of the reference model during the positioning process. Within the cost function, weight parameters were employed to prioritize specific organs for positioning. In this study, three scenarios with different weight parameters were prepared. The optimal translation and rotation values for the cervix and uterus were determined based on the calculated translations alone or in combination with rotations, with a rotation limit of ±3°. Subsequently, the coverage probabilities of the following two planning target volumes (PTV), an isotropic 5 mm and anisotropic margins derived from a previous study, were evaluated. RESULTS The percentage of translations exceeding 10 mm varied from 9% to 18% depending on the scenario. For small PTV sizes, more than 80% of all fractions had a coverage of 80% or higher. In contrast, for large PTV sizes, more than 90% of all fractions had a coverage of 95% or higher. The difference between the median coverage with translational positioning alone and that with both translational and rotational positioning was 1% or less. CONCLUSION This algorithm facilitates quantitative positioning by utilizing a cost function that prioritizes organs for positioning. Consequently, consistent displacement values were algorithmically generated. This study also revealed that the impact of rotational corrections, limited to ±3°, on PTV coverage was minimal.
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Affiliation(s)
- Yukako Kishigami
- Department of Advanced Medical PhysicsGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Mitsuhiro Nakamura
- Department of Advanced Medical PhysicsGraduate School of MedicineKyoto UniversityKyotoJapan
| | - Hiroyuki Okamoto
- Radiation Safety and Quality Assurance DivisionNational Cancer Center HospitalTokyoJapan
| | - Ayaka Takahashi
- Department of Radiation OncologyNational Cancer Center HospitalTokyoJapan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image‐Applied TherapyKyoto UniversityKyotoJapan
| | - Makoto Sasaki
- Division of Clinical Radiology ServiceKyoto University HospitalKyotoJapan
| | - Kohei Kawata
- Department of Radiation Oncology and Image‐Applied TherapyKyoto UniversityKyotoJapan
| | - Hiroshi Igaki
- Department of Radiation OncologyNational Cancer Center HospitalTokyoJapan
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McDonald BA, Cardenas CE, O'Connell N, Ahmed S, Naser MA, Wahid KA, Xu J, Thill D, Zuhour RJ, Mesko S, Augustyn A, Buszek SM, Grant S, Chapman BV, Bagley AF, He R, Mohamed ASR, Christodouleas J, Brock KK, Fuller CD. Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers. Med Phys 2024; 51:278-291. [PMID: 37475466 PMCID: PMC10799175 DOI: 10.1002/mp.16582] [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: 10/03/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.
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Affiliation(s)
- Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Sara Ahmed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mohamed A Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | - Raed J Zuhour
- Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Shane Mesko
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander Augustyn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Samantha M Buszek
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Stephen Grant
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Bhavana V Chapman
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander F Bagley
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Wang Z, Liu C, Zhang Z, Deng Y, Xiao M, Zhang Z, Dekker A, Wang S, Liu Y, Qian L, Zhang Z, Traverso A, Feng Y. Real-time auto-segmentation of the ureter in video sequences of gynaecological laparoscopic surgery. Int J Med Robot 2023:e2604. [PMID: 38115728 DOI: 10.1002/rcs.2604] [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: 08/30/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Ureteral injury is common during gynaecological laparoscopic surgery. Real-time auto-segmentation can assist gynaecologists in identifying the ureter and reduce intraoperative injury risk. METHODS A deep learning segmentation model was crafted for ureter recognition in surgical videos, utilising 3368 frames from 11 laparoscopic surgeries. Class activation maps enhanced the model's interpretability, showing its areas. The model's clinical relevance was validated through an End-User Turing test and verified by three gynaecological surgeons. RESULTS The model registered a Dice score of 0.86, a Hausdorff 95 distance of 22.60, and processed images in 0.008 s on average. In complex surgeries, it pinpointed the ureter's position in real-time. Fifty five surgeons across eight institutions found the model's accuracy, specificity, and sensitivity comparable to human performance. Yet, artificial intelligence experience influenced some subjective ratings. CONCLUSIONS The model offers precise real-time ureter segmentation in laparoscopic surgery and can be a significant tool for gynaecologists to mitigate ureteral injuries.
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Affiliation(s)
- Zhixiang Wang
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Chongdong Liu
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhen Zhang
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yupeng Deng
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Meizhu Xiao
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhiqiang Zhang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Shuzhen Wang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yujiang Liu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - LinXue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenyu Zhang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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