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Luo X, Liao W, Zhao Y, Qiu Y, Xu J, He Y, Huang H, Li L, Zhang S, Fu J, Wang G, Zhang S. A multicenter dataset for lymph node clinical target volume delineation of nasopharyngeal carcinoma. Sci Data 2024; 11:1085. [PMID: 39366975 PMCID: PMC11452638 DOI: 10.1038/s41597-024-03890-0] [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: 02/19/2024] [Accepted: 09/17/2024] [Indexed: 10/06/2024] Open
Abstract
The deep learning (DL)-based prediction of accurate lymph node (LN) clinical target volumes (CTVs) for nasopharyngeal carcinoma (NPC) radiotherapy (RT) remains challenging. One of the main reasons is the variability of contours despite standardization processes by expert guidelines in combination with scarce data sharing in the community. Therefore, we retrospectively generated a 262-subjects dataset from four centers to develop the DL models for LN CTVs delineation. This dataset included 440 computed tomography images from different scanning phases, disease stages and treatment strategies. Three clinical expert boards, each comprising two experts (totalling six experts), manually delineated six basic LN CTVs on separate cohorts as the ground truth according to LN involvement and clinical requirements. Several state-of-the-art segmentation algorithms were evaluated on this benchmark, showing promising results for LN CTV segmentation. In conclusion, this work built a multicenter LN CTV segmentation dataset, which may be the first dataset for automatic LN CTV delineation development and evaluation, serving as a benchmark for future research.
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Affiliation(s)
- Xiangde Luo
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute. Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shanghai AI Laboratory, Shanghai, China
| | - Wenjun Liao
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute. Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Yue Zhao
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute. Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Youjing Qiu
- Department of Radiation Oncology, Daguan Hospital of Chengdu Jinjiang, Chengdu, China
| | - Jinfeng Xu
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuan He
- Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Hui Huang
- Cancer center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute. Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Shichuan Zhang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute. Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Jia Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shanghai AI Laboratory, Shanghai, China
| | - Shaoting Zhang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shanghai AI Laboratory, Shanghai, China
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Liu H, McKenzie E, Xu D, Xu Q, Chin RK, Ruan D, Sheng K. MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration. Med Image Anal 2024; 99:103351. [PMID: 39388843 DOI: 10.1016/j.media.2024.103351] [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: 09/29/2023] [Revised: 06/05/2024] [Accepted: 09/16/2024] [Indexed: 10/12/2024]
Abstract
Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.
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Affiliation(s)
- Hengjie Liu
- Physics and Biology in Medicine Graduate Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Elizabeth McKenzie
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Di Xu
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA; Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Qifan Xu
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA; Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Robert K Chin
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Dan Ruan
- Physics and Biology in Medicine Graduate Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ke Sheng
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco, San Francisco, CA, USA; Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.
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Ahmed SBS, Naeem S, Khan AMH, Qureshi BM, Hussain A, Aydogan B, Muhammad W. Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer. Front Artif Intell 2024; 7:1329737. [PMID: 38646416 PMCID: PMC11026659 DOI: 10.3389/frai.2024.1329737] [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: 11/01/2023] [Accepted: 03/25/2024] [Indexed: 04/23/2024] Open
Abstract
Background and purpose We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.
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Affiliation(s)
- Saad Bin Saeed Ahmed
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - Shahzaib Naeem
- Gamma Knife Radiosurgery Center, Dow University of Health Sciences, Karachi, Pakistan
| | | | | | | | - Bulent Aydogan
- Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Wazir Muhammad
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
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Walter A, Hoegen-Saßmannshausen P, Stanic G, Rodrigues JP, Adeberg S, Jäkel O, Frank M, Giske K. Segmentation of 71 Anatomical Structures Necessary for the Evaluation of Guideline-Conforming Clinical Target Volumes in Head and Neck Cancers. Cancers (Basel) 2024; 16:415. [PMID: 38254904 PMCID: PMC11154560 DOI: 10.3390/cancers16020415] [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: 11/28/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep-learning methods depend heavily on consistent training data; thus, State-of-the-Art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on the surrounding anatomical structures. Training strategies that directly follow the construction rules given in the expert guidelines or the possibility of quantifying the conformance of manually drawn contours to the guidelines are still missing. Seventy-one anatomical structures that are relevant to CTV delineation in head- and neck-cancer patients, according to the expert guidelines, were segmented on 104 computed tomography scans, to assess the possibility of automating their segmentation by State-of-the-Art deep learning methods. All 71 anatomical structures were subdivided into three subsets of non-overlapping structures, and a 3D nnU-Net model with five-fold cross-validation was trained for each subset, to automatically segment the structures on planning computed tomography scans. We report the DICE, Hausdorff distance and surface DICE for 71 + 5 anatomical structures, for most of which no previous segmentation accuracies have been reported. For those structures for which prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2 mm margin was larger than 80% for almost all the structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.
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Affiliation(s)
- Alexandra Walter
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (G.S.); (J.P.R.); (O.J.); (K.G.)
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
- Karlsruhe Institute of Technology (KIT), Scientific Computing Center, Zirkel 2, 76131 Karlsruhe, Germany;
| | - Philipp Hoegen-Saßmannshausen
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
- Department of Radiation Oncology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, 69120 Heidelberg, Germany
| | - Goran Stanic
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (G.S.); (J.P.R.); (O.J.); (K.G.)
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
- Faculty of Physics and Astronomy, University of Heidelberg, 69120 Heidelberg, Germany
| | - Joao Pedro Rodrigues
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (G.S.); (J.P.R.); (O.J.); (K.G.)
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
| | - Sebastian Adeberg
- Department of Radiotherapy and Radiation Oncology, Marburg University Hospital, 35043 Marburg, Germany;
- Marburg Ion-Beam Therapy Center (MIT), 35043 Marburg, Germany
- Universitäres Centrum für Tumorerkrankungen (UCT), 35033 Marburg, Germany
| | - Oliver Jäkel
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (G.S.); (J.P.R.); (O.J.); (K.G.)
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
- Heidelberg Ion-Beam Therapy Center (HIT), 69120 Heidelberg, Germany
| | - Martin Frank
- Karlsruhe Institute of Technology (KIT), Scientific Computing Center, Zirkel 2, 76131 Karlsruhe, Germany;
| | - Kristina Giske
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (G.S.); (J.P.R.); (O.J.); (K.G.)
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), 69120 Heidelberg, Germany;
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Abdollahi H, Dehesh T, Abdalvand N, Rahmim A. Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients. Int J Radiat Biol 2023; 99:1669-1683. [PMID: 37171485 DOI: 10.1080/09553002.2023.2214206] [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: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND AIM Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features. METHODS Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pretreatment (CT1), mid-treatment (CT2), post-treatment (CT3), and delta features (ΔCT2-1, ΔCT3-1, ΔCT3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained. RESULTS In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT3 model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT2 & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively. CONCLUSION Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies toward improved therapeutic outcomes.
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Affiliation(s)
- Hamid Abdollahi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Tania Dehesh
- Modelling in Health Research Center, Institute for Future Studies in Health, Kerman University ofMedical Sciences, Kerman, Iran
| | - Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
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Lee D, Alam SR, Jiang J, Zhang P, Nadeem S, Hu YC. Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy. Med Phys 2021; 48:4784-4798. [PMID: 34245602 DOI: 10.1002/mp.15075] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/21/2021] [Accepted: 06/07/2021] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. METHODS To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). RESULTS The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02. CONCLUSION We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sadegh R Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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