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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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Weninger L, Ecke J, Jütten K, Clusmann H, Wiesmann M, Merhof D, Na CH. Diffusion MRI anomaly detection in glioma patients. Sci Rep 2023; 13:20366. [PMID: 37990121 PMCID: PMC10663596 DOI: 10.1038/s41598-023-47563-1] [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: 08/17/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.
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Affiliation(s)
- Leon Weninger
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Jarek Ecke
- Department of Electrical Engineering, RWTH Aachen University, Aachen, Germany
| | - Kerstin Jütten
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
| | - Hans Clusmann
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany
| | - Martin Wiesmann
- Department of Neuroradiology, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Faculty of Informatics and Computer Science, University of Regensburg, Regensburg, Germany
- Frauenhofer-Institut für Digitale Medizin, MEVIS, Bremen, Germany
| | - Chuh-Hyoun Na
- Department of Neurosurgery, RWTH Aachen University, Aachen, Germany.
- Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Germany.
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Rodríguez Outeiral R, Bos P, van der Hulst HJ, Al-Mamgani A, Jasperse B, Simões R, van der Heide UA. Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging. Phys Imaging Radiat Oncol 2022; 23:144-149. [PMID: 36035088 PMCID: PMC9405079 DOI: 10.1016/j.phro.2022.08.005] [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: 11/15/2021] [Revised: 07/29/2022] [Accepted: 08/10/2022] [Indexed: 12/05/2022] Open
Abstract
Loss functions that account for class imbalance may not improve the segmentations. Our two-stage segmentation approach can outperform the 3D U-Net. A fully-automatic two-stage approach can perform comparably to a semi-automatic approach.
Background and purpose Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen–Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net.
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Schouten JPE, Noteboom S, Martens RM, Mes SW, Leemans CR, de Graaf P, Steenwijk MD. Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN. Cancer Imaging 2022; 22:8. [PMID: 35033188 PMCID: PMC8761340 DOI: 10.1186/s40644-022-00445-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.
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Affiliation(s)
- Jens P E Schouten
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Samantha Noteboom
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Roland M Martens
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Steven W Mes
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - C René Leemans
- Department of Otolaryngology - Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Martijn D Steenwijk
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands. .,, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
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Wahid KA, Ahmed S, He R, van Dijk LV, Teuwen J, McDonald BA, Salama V, Mohamed AS, Salzillo T, Dede C, Taku N, Lai SY, Fuller CD, Naser MA. Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry. Clin Transl Radiat Oncol 2022; 32:6-14. [PMID: 34765748 PMCID: PMC8570930 DOI: 10.1016/j.ctro.2021.10.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND/PURPOSE Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. MATERIALS/METHODS GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers. RESULTS Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p < 0.05) and for FND for the T2 + Ve and ALL models (p < 0.05). No model demonstrated significant correlations between tumor size and DSC (p > 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p < 0.05), except those that included ADC or Ve as input channels (p > 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05). CONCLUSION DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size.
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Affiliation(s)
- Kareem A. Wahid
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Sara Ahmed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Renjie He
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Brigid A. McDonald
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Vivian Salama
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Abdallah S.R. Mohamed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Travis Salzillo
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Cem Dede
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Nicolette Taku
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Stephen Y. Lai
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Mohamed A. Naser
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
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6
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Samarasinghe G, Jameson M, Vinod S, Field M, Dowling J, Sowmya A, Holloway L. Deep learning for segmentation in radiation therapy planning: a review. J Med Imaging Radiat Oncol 2021; 65:578-595. [PMID: 34313006 DOI: 10.1111/1754-9485.13286] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 06/29/2021] [Indexed: 12/21/2022]
Abstract
Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.
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Affiliation(s)
- Gihan Samarasinghe
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia
| | - Michael Jameson
- Genesiscare, Sydney, New South Wales, Australia.,St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Shalini Vinod
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Matthew Field
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Jason Dowling
- Commonwealth Scientific and Industrial Research Organisation, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
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Oerther B, Buren MV, Klein CM, Kirste S, Nicolay NH, Sprave T, Spohn S, Gunashekar DD, Hagele L, Bielak L, Bock M, Grosu AL, Bamberg F, Benndorf M, Zamboglou C. Predicting Biochemical Failure in Irradiated Patients With Prostate Cancer by Tumour Volume Measured by Multiparametric MRI. In Vivo 2021; 34:3473-3481. [PMID: 33144456 DOI: 10.21873/invivo.12187] [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: 07/15/2020] [Revised: 07/29/2020] [Accepted: 08/05/2020] [Indexed: 11/10/2022]
Abstract
BACKGROUND/AIM We examined the prognostic value of intraprostatic gross tumour volume (GTV) as measured by multiparametric MRI (mpMRI) in patients with prostate cancer following (primary) external beam radiation therapy (EBRT). PATIENTS AND METHODS In a retrospective monocentric study, we analysed patients with prostate cancer (PCa) after EBRT. GTV was delineated in pre-treatment mpMRI (GTV-MRI) using T2-weighted images. Cox-regression analyses were performed considering biochemical failure recurrence-free survival (BRFS) as outcome variable. RESULTS Among 131 patients, after a median follow-up of 57 months, biochemical failure occurred in 27 (21%). GTV-MRI was not correlated with % of positive biopsy cores, Gleason score and initial PSA (all r<0.2) and only moderately correlated with cT stage (r=0.32). In univariate analysis, cT stage, Gleason score and GTV-MRI were higher in subjects with shorter BRFS (p<0.05). GTV-MRI remained a significant predictor for BRFS in multivariate analyses, independent of Gleason score and cT stage. CONCLUSION GTV, defined using mpMRI, provides incremental prognostic value for BRFS, independent of established risk factors. This supports the implementation of imaging-based GTV for risk-stratification, although further validation is needed.
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Affiliation(s)
- Benedict Oerther
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Moritz V Buren
- Department of Urology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christina M Klein
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Nils H Nicolay
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Tanja Sprave
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Simon Spohn
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Deepa Darshini Gunashekar
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Leonard Hagele
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lars Bielak
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Bock
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anca-L Grosu
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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8
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Mehandru N, Hicks WL, Singh AK, Markiewicz MR. Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions. J Oral Maxillofac Surg 2020; 78:2106-2107. [PMID: 32777248 DOI: 10.1016/j.joms.2020.07.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Nikhil Mehandru
- Predoctoral Student, School of Dental Medicine, University at Buffalo, Buffalo, NY
| | - Wesley L Hicks
- Professor and Chair, Department of Head & Neck/Plastic & Reconstructive Surgery, Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - Anurag K Singh
- Professor, Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - Michael R Markiewicz
- Professor and Chair, Department of Oral and Maxillofacial Surgery, Associate Dean for Hospital Affairs, School of Dental Medicine, University at Buffalo, Clinical Professor, Department of Neurosurgery, Divison of Pediatric Surgery, Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, Co-Director- Craniofacial Center of Western New York, John Oishei Children's Hospital, Attending Surgeon, Department of Head & Neck/Plastic & Reconstructive Surgery, Roswell Park Comprehensive Cancer Center, Buffalo, NY.
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Bielak L, Wiedenmann N, Berlin A, Nicolay NH, Gunashekar DD, Hägele L, Lottner T, Grosu AL, Bock M. Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis. Radiat Oncol 2020; 15:181. [PMID: 32727525 PMCID: PMC7392704 DOI: 10.1186/s13014-020-01618-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
Background Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer. Methods Head&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (ktrans, ve) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance. Results The CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSCGTV-T = 5.7% for primary tumor and ΔDSCGTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSCGTV-T = 2.4% for primary tumor and ΔDSCGTV-Ln = 2.2% respectively. Conclusions We developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance. Trial registration The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.
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Affiliation(s)
- Lars Bielak
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. .,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.
| | - Nicole Wiedenmann
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Nils Henrik Nicolay
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Deepa Darshini Gunashekar
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Leonard Hägele
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Lottner
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anca-Ligia Grosu
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Bock
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
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An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI. Sci Rep 2020; 10:8063. [PMID: 32415137 PMCID: PMC7228927 DOI: 10.1038/s41598-020-64912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 04/24/2020] [Indexed: 11/16/2022] Open
Abstract
Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.
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