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Ismail M, Hanifa MAM, Mahidin EIM, Manan HA, Yahya N. Cone beam computed tomography (CBCT) and megavoltage computed tomography (MVCT)-based radiomics in head and neck cancers: a systematic review and radiomics quality score assessment. Quant Imaging Med Surg 2024; 14:6963-6977. [PMID: 39281127 PMCID: PMC11400681 DOI: 10.21037/qims-24-334] [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: 03/01/2024] [Accepted: 06/27/2024] [Indexed: 09/18/2024]
Abstract
Background Cone beam computed tomography (CBCT) and megavoltage computed tomography (MVCT)-based images demonstrate measurable radiomics features that are potentially prognostic. This study aims to systematically synthesize the current research applying radiomics in head and neck cancers for outcome prediction and to assess the radiomics quality score (RQS) of the studies. Methods A systematic search was performed to identify available studies on PubMed, Web of Science, and Scopus databases. Studies related to radiomics in oncology/radiotherapy fields and based on predefined Patient, Intervention, Comparator, Outcome, and Study design (PICOS) criteria were included. The methodological quality of the included study was evaluated independently by two reviewers according to the RQS. The Mann-Whitney U test was performed according to subgroups. The P values <0.05 were considered statistically significant. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines were adhered to. Results From a total of 743 identified studies, six original studies were eligible for inclusion in the systematic review (median =97 patients). The intraclass correlation coefficient (ICC) for inter-reviewer on total RQS was excellent with 0.99 [95% confidence interval (CI) of 0.946< ICC <0.999]. There were no significant differences in the analyses between each RQS domain and subgroup components (P always >0.05). Numerically higher RQS domains score for publication year ≤2022 than 2023 and number of patients > median than ≤ median but not statistically significant. Conclusions The number of radiomics studies involving CBCT and MVCT is still very limited. Self-reported RQS assessments should be encouraged for all radiomics studies.
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Affiliation(s)
- Mahayu Ismail
- Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur, Malaysia
- Department of Radiotherapy and Oncology, Hospital Kuala Lumpur, Jalan Pahang, Kuala Lumpur, Malaysia
| | - Mohd Ariff Mohamed Hanifa
- Department of Radiotherapy and Oncology, Hospital Kuala Lumpur, Jalan Pahang, Kuala Lumpur, Malaysia
| | - Eznal Izwadi Mohd Mahidin
- Department of Radiotherapy and Oncology, Hospital Kuala Lumpur, Jalan Pahang, Kuala Lumpur, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur, Malaysia
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [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: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Adachi T, Nakamura M, Iramina H, Matsumoto K, Ishihara Y, Tachibana H, Kurokawa S, Cho S, Tanaka K, Fukumoto K, Nishiyama T, Kito S, Mizowaki T. Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study. Med Phys 2023; 50:5585-5596. [PMID: 36932977 DOI: 10.1002/mp.16376] [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: 10/07/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Kazushige Matsumoto
- Department of Radiology, National Hospital Organization Kyoto Medical Center, Fushimi-ku, Kyoto, Japan
| | - Yoshitomo Ishihara
- Department of Radiation Oncology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Hidenobu Tachibana
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shogo Kurokawa
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - SangYong Cho
- Division of Radiation Oncology, Chiba Cancer Center, Chuo-ku, Chiba, Japan
| | - Kazunori Tanaka
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Kenta Fukumoto
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Tomohiro Nishiyama
- Department of Radiation Oncology, Kyoto-Katsura Hospital, Nishikyo-ku, Kyoto, Japan
| | - Satoshi Kito
- Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Bunkyo-ku, Tokyo, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
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Abe K, Kadoya N, Ito K, Tanaka S, Nakajima Y, Hashimoto S, Suda Y, Uno T, Jingu K. Evaluation of the MVCT-based radiomic features as prognostic factor in patients with head and neck squamous cell carcinoma. BMC Med Imaging 2023; 23:102. [PMID: 37528392 PMCID: PMC10391970 DOI: 10.1186/s12880-023-01055-w] [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: 12/23/2022] [Accepted: 07/18/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Megavoltage computed tomography (MVCT) images acquired during each radiotherapy session may be useful for delta radiomics. However, no studies have examined whether the MVCT-based radiomics has prognostic power. Therefore, the purpose of this study was to examine the prognostic power of the MVCT-based radiomics for head and neck squamous cell carcinoma (HNSCC) patients. METHODS 100 HNSCC patients who received definitive radiotherapy were analyzed and divided into two groups: training (n = 70) and test (n = 30) sets. MVCT images obtained using TomoTherapy for the first fraction of radiotherapy and planning kilovoltage CT (kVCT) images obtained using Aquilion LB CT scanner were analyzed. Primary gross tumor volume (GTV) was propagated from kVCT to MVCT images using rigid registration, and 107 radiomic features were extracted from the GTV in MVCT and kVCT images. Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to examine the association between overall survival (OS) and rad score calculated for each patient by weighting the feature value through the coefficient when features were selected. Then, the predictive values of MVCT-based and kVCT-based rad score and patient-, treatment-, and tumor-specific factors were evaluated. RESULTS C-indices of the rad score for MVCT- and kVCT-based radiomics were 0.667 and 0.685, respectively. The C-indices of 6 clinical factors were 0.538-0.622. The 3-year OS was significantly different between high- and low-risk groups according to the MVCT-based rad score (50% vs. 83%; p < 0.01). CONCLUSIONS Our results suggested that MVCT-based radiomics had stronger prognostic power than any single clinical factor and was a useful prognostic factor when predicting OS in HNSCC patients.
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Affiliation(s)
- Kota Abe
- Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Kei Ito
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
- Department of Radiological Sciences, Komazawa University, 1-23-1 komazawa, Setagaya-ku, Tokyo, 154-8525, Japan
| | - Shimpei Hashimoto
- Saitama Prefectural Cancer Center, 780 large section of a town Omuro, Ina-machi, Kitaadachi- gun, Saitama, 362-0806, Japan
| | - Yuhi Suda
- Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan
| | - Takashi Uno
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo- machi, Aoba-ku, Sendai, 980-8574, Japan
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Berger T, Noble DJ, Shelley LE, McMullan T, Bates A, Thomas S, Carruthers LJ, Beckett G, Duffton A, Paterson C, Jena R, McLaren DB, Burnet NG, Nailon WH. Predicting radiotherapy-induced xerostomia in head and neck cancer patients using day-to-day kinetics of radiomics features. Phys Imaging Radiat Oncol 2022; 24:95-101. [PMID: 36386445 PMCID: PMC9647222 DOI: 10.1016/j.phro.2022.10.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
Background and purpose The images acquired during radiotherapy for image-guidance purposes could be used to monitor patient-specific response to irradiation and improve treatment personalisation. We investigated whether the kinetics of radiomics features from daily mega-voltage CT image-guidance scans (MVCT) improve prediction of moderate-to-severe xerostomia compared to dose/volume parameters in radiotherapy of head-and-neck cancer (HNC). Materials and Methods All included HNC patients (N = 117) received 30 or more fractions of radiotherapy with daily MVCTs. Radiomics features were calculated on the contra-lateral parotid glands of daily MVCTs. Their variations over time after each complete week of treatment were used to predict moderate-to-severe xerostomia (CTCAEv4.03 grade ≥ 2) at 6, 12 and 24 months post-radiotherapy. After dimensionality reduction, backward/forward selection was used to generate combinations of predictors.Three types of logistic regression model were generated for each follow-up time: 1) a pre-treatment reference model using dose/volume parameters, 2) a combination of dose/volume and radiomics-based predictors, and 3) radiomics-based predictors. The models were internally validated by cross-validation and bootstrapping and their performance evaluated using Area Under the Curve (AUC) on separate training and testing sets. Results Moderate-to-severe xerostomia was reported by 46 %, 33 % and 26 % of the patients at 6, 12 and 24 months respectively. The selected models using radiomics-based features extracted at or before mid-treatment outperformed the dose-based models with an AUCtrain/AUCtest of 0.70/0.69, 0.76/0.74, 0.86/0.86 at 6, 12 and 24 months, respectively. Conclusion Our results suggest that radiomics features calculated on MVCTs from the first half of the radiotherapy course improve prediction of moderate-to-severe xerostomia in HNC patients compared to a dose-based pre-treatment model.
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Affiliation(s)
- Thomas Berger
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - David J. Noble
- The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Leila E.A. Shelley
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Thomas McMullan
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Amy Bates
- The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Simon Thomas
- Department of Medical Physics and Clinical Engineering, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Linda J. Carruthers
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - George Beckett
- Edinburgh Parallel Computing Centre, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Aileen Duffton
- Beatson West of Scotland Cancer Centre, Great Western Road, Glasgow G12 0YN, UK
| | - Claire Paterson
- Beatson West of Scotland Cancer Centre, Great Western Road, Glasgow G12 0YN, UK
| | - Raj Jena
- The University of Cambridge, Department of Oncology, Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0QQ, UK
| | - Duncan B. McLaren
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Neil G. Burnet
- The Christie NHS Foundation Trust, Wilmslow Road, Manchester, M20 4BX, UK
| | - William H. Nailon
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh EH4 2XU, UK
- School of Engineering, the University of Edinburgh, the King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
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Peng X, Yang S, Zhou L, Mei Y, Shi L, Zhang R, Shan F, Liu L. Repeatability and Reproducibility of Computed Tomography Radiomics for Pulmonary Nodules: A Multicenter Phantom Study. Invest Radiol 2022; 57:242-253. [PMID: 34743134 PMCID: PMC8903219 DOI: 10.1097/rli.0000000000000834] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/31/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. OBJECTIVES The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. MATERIALS AND METHODS A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. RESULTS A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate <0.001). Finally, we obtained a subset of 19 stable, informative, and nonredundant RFs under all 3 sources of variability. These RFs exhibited clinical effectiveness and showed better prediction performance than unstable RFs in the validation dataset (P = 0.017, Delong test). CONCLUSIONS The stability of RFs was affected to different degrees by test-retest and differences in CT manufacturers and models and CT acquisition and reconstruction parameters, but the influences of these factors showed relatively consistent patterns. We also obtained a subset of 19 stable, informative, and nonredundant RFs that should be preferably used to enhance the generalizability of further radiomics research.
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Affiliation(s)
- Xueqing Peng
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
| | - Shuyi Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai
- Shanghai Institute of Medical Imaging, Shanghai
- Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai
| | - Lingxiao Zhou
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, Guangdong Province
| | - Yu Mei
- Shanghai Mental Health Center, Shanghai
| | - Lili Shi
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
| | - Rengyin Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai
| | - Lei Liu
- From the Institutes of Biomedical Sciences, Fudan University, Shanghai
- School of Basic Medical Sciences, Fudan University, Shanghai, China
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Robustness and reproducibility of radiomics in T2 weighted images from magnetic resonance image guided linear accelerator in a phantom study. Phys Med 2022; 96:130-139. [PMID: 35287100 DOI: 10.1016/j.ejmp.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 02/07/2022] [Accepted: 03/04/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Quantitative radiomics features extracted from medical images have been shown to provide value in predicting clinical outcomes. The study for robustness and reproducibility of radiomics features obtained with magnetic resonance image guided linear accelerator (MR-Linac) is insufficient. The objective of this work was to investigate the stability of radiomics features extracted from T2-weighted images of MR-Linac for five common effect factors. MATERIALS AND METHOD In this work, ten jellies, five fruits/vegetables, and a dynamic phantom were used to evaluate the impact of test-retest, intraobserver, varied thicknesses, radiation, and motion. These phantoms were scanned on a 1.5 T MRI system of MR-Linac. For test-retest data, the phantoms were scanned twice with repositioning within 15 min. To assess for intraobserver comparison, the segmentation of MR images was repeated by one observer in a double-blind manner. Three slice thicknesses (1.2 mm, 2.4 mm, and 4.8 mm) were used to select robust features that were insensitive to different thicknesses. The effect of radiation on features was studied by acquiring images when the beam was on. Common movement images of patients during radiotherapy were simulated by a dynamic phantom with five motion states to study the motion effect. A total of 1409 radiomics features, including shape features, first-order features, and texture features, were extracted from the original, wavelet, square, logarithmic, exponential and gradient images. The robustness and reproducibility features were evaluated using the concordance correlation coefficient (CCC). RESULT The intraobserver group had the most robust features (936/1079, 86.7%), while the group of motion effects had the lowest robustness (56/936, 6.0%), followed by the group of different thickness cohorts (374/936, 40.0%). The stability of features in the test-retest and radiation groups was 1072 of 1312 (81.7%) and 810 of 936 (86.5%), respectively. Overall, 25 of 1409 (2.4%) radiomics features remained robust in all five tests, mostly focusing on the image type of the wavelet. The number of stable features extracted from when the beam was on was less than that extracted when the beam was off. Shape features were the most robust of all of the features in all of the groups, excluding the motion group. CONCLUSION Compared with other factors fewer features remained robust to the effect of motion. This result emphasizes the need to consider the effect of respiration motion. The study for T2-weighted images from MR-Linac under different conditions will help us to build a robust predictive model applicable for radiotherapy.
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Li Y, Reyhan M, Zhang Y, Wang X, Zhou J, Zhang Y, Yue NJ, Nie K. The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features. Med Phys 2022; 49:1648-1659. [PMID: 35103332 DOI: 10.1002/mp.15491] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To understand the design of radiomics phantom and material-dependence on repeatability and reproducibility of CT radiomics features METHODS: : A radiomics phantom consisting of various materials with uniformity, textural and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients' images of five disease sites as brain, head-and-neck, breast, liver and lung cases using box-plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test-retest) via the intra-class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). RESULTS Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D-printed textural phantom with fine and rough-grid. It also showed that certain materials, such as polystyrene foam, plaster and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test-retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter-scanner study, shape-related features were the most-reliable category with 94% of features having CCC ≥0.9, while GOH were the least-reliable with only 14.6% meeting the criteria. For the intra-scanner study, the reproducibility of CT-based radiomics features showed material-dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. CONCLUSION We have built a radiomics phantom using materials that cover a wide span of tumor textures seen in oncological CT images. The designed phantom presents a preliminary opportunity for investigating reproducibility of radiomics features and the reproducibility can be material dependent. Thus, in the radiomics quality assurance design, it is important to choose appropriate materials that can provide a close range of radiomics features to patients with specific disease sites dependency taken into consideration. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yanjing Li
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Meral Reyhan
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Jinghao Zhou
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ning J Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
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Liu Y, Yue C, Zhu J, Yu H, Cheng Y, Yin Y, Li B, Dong J. A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy. Front Oncol 2019; 9:362. [PMID: 31134157 PMCID: PMC6524307 DOI: 10.3389/fonc.2019.00362] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 04/18/2019] [Indexed: 01/08/2023] Open
Abstract
Purpose: To propose a novel method to improve the mega-voltage CT (MVCT) image quality for helical TomoTherapy while maintaining the stability on dose calculation. Materials and Methods: The Block-Matching 3D-transform (BM3D) and Discriminative Feature Representation (DFR) methods were combined into a novel BM3D + DFR method for their respective advantages. A phantom (Catphan504) and three serials of clinical (head & neck, chest, and pelvis) MVCT images from 30 patients were acquired using the helical TomoTherapy system. The contrast-to-noise ratio (CNR) and edge detection algorithm (canny) was employed for image quality comparisons between the original and BM3D + DFR enhanced MVCT. A simulated rectangular field of 6 MV X-ray beams were vertically delivered on the original and post-processed MVCT serials of the same CT density phantom, and the dose curves on both serials were compared to test the effects of image enhancement on dose calculation accuracy. Results: In total, 466 transversal MVCT slices were acquired and processed by both BM3D and the proposed BM3D + DFR methods. Compared to the original MVCT image, the BM3D + DFR method presented a remarkable improvement in terms of the soft tissue contrast and noise reduction. For the phantom image, the CNR of the region of interest (ROI) was improved from 1.70 to 4.03. The average CNR of ROIs for 10 patients from each anatomical group, were increased significantly from 1.45 ± 1.51 to 2.09 ± 1.68 for the head & neck (p < 0.001), from 0.92 ± 0.78 to 1.36 ± 0.85 for the chest (p < 0.001), and from 1.12 ± 1.22 to 1.76 ± 1.31 for the pelvis (p < 0.001), respectively. The canny edge detection operator showed that BM3D + DFR provided clearer organ boundaries with less chaos. The root-mean-square of the dosimetry difference on the iso-center passed horizontal dose profile curves and vertical percentage depth dose curves were only 0.09% and 0.06%, respectively. Conclusions: The proposed BM3D + DFR method is feasible to improve the soft tissue contrast for the original MVCT images with coincidence in dose calculation and without compromising resolution. After integration in clinical workflow, the post-processed MVCT may be better applied on image-guided and adaptive helical TomoTherapy.
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Affiliation(s)
- Yaru Liu
- Network-Based Intelligent Computing, University of Jinan, Jinan, China
| | - Chenxi Yue
- Network-Based Intelligent Computing, University of Jinan, Jinan, China
| | - Jian Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, China.,Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haining Yu
- Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, China
| | - Yang Cheng
- Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, China
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, China
| | - Jiwen Dong
- Network-Based Intelligent Computing, University of Jinan, Jinan, China
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