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Barten DLJ, van Kesteren Z, Laan JJ, Dassen MG, Westerveld GH, Pieters BR, de Jonge CS, Stoker J, Bel A. Precision assessment of bowel motion quantification using 3D cine-MRI for radiotherapy. Phys Med Biol 2024; 69:04NT01. [PMID: 38232395 DOI: 10.1088/1361-6560/ad1f89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/17/2024] [Indexed: 01/19/2024]
Abstract
Objective. The bowel is an important organ at risk for toxicity during pelvic and abdominal radiotherapy. Identifying regions of high and low bowel motion with MRI during radiotherapy may help to understand the development of bowel toxicity, but the acquisition time of MRI is rather long. The aim of this study is to retrospectively evaluate the precision of bowel motion quantification and to estimate the minimum MRI acquisition time.Approach. We included 22 gynaecologic cancer patients receiving definitive radiotherapy with curative intent. The 10 min pre-treatment 3D cine-MRI scan consisted of 160 dynamics with an acquisition time of 3.7 s per volume. Deformable registration of consecutive images generated 159 deformation vector fields (DVFs). We defined two motion metrics, the 50th percentile vector lengths (VL50) of the complete set of DVFs was used to measure median bowel motion. The 95th percentile vector lengths (VL95) was used to quantify high motion of the bowel. The precision of these metrics was assessed by calculating their variation (interquartile range) in three different time frames, defined as subsets of 40, 80, and 120 consecutive images, corresponding to acquisition times of 2.5, 5.0, and 7.5 min, respectively.Main results. For the full 10 min scan, the minimum motion per frame of 50% of the bowel volume (M50%) ranged from 0.6-3.5 mm for the VL50 motion metric and 2.3-9.0 mm for the VL95 motion metric, across all patients. At 7.5 min scan time, the variation in M50% was less than 0.5 mm in 100% (VL50) and 95% (VL95) of the subsets. A scan time of 5.0 and 2.5 min achieved a variation within 0.5 mm in 95.2%/81% and 85.7%/57.1% of the subsets, respectively.Significance. Our 3D cine-MRI technique quantifies bowel loop motion with 95%-100% confidence with a precision of 0.5 mm variation or less, using a 7.5 min scan time.
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Affiliation(s)
- D L J Barten
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - Z van Kesteren
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - J J Laan
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
| | - M G Dassen
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - G H Westerveld
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus University Medical Center, Department of Radiation Oncology, Rotterdam, The Netherlands
| | - B R Pieters
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C S de Jonge
- Amsterdam UMC location University of Amsterdam, Department of Radiology and Nuclear Medicine, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
| | - J Stoker
- Amsterdam UMC location University of Amsterdam, Department of Radiology and Nuclear Medicine, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam, The Netherlands
| | - A Bel
- Amsterdam UMC location University of Amsterdam, Department of Radiation Oncology, Meibergdreef 9, 1105 AZAmsterdam, The Netherlands
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Zhou Y, Song L, Xia J, Liu H, Xing J, Gao J. Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma. Front Oncol 2023; 13:1225180. [PMID: 37664013 PMCID: PMC10473874 DOI: 10.3389/fonc.2023.1225180] [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: 05/18/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Background Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC. Methods This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration. Results Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively. Conclusion The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.
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Affiliation(s)
- Yue Zhou
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lijie Song
- Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jin Xia
- Department of Oncology, Anyang Tumor Hospital, Anyang, China
| | - Huan Liu
- Advanced Analytics Team, GE Healthcare, Shanghai, China
| | - Jingjing Xing
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Yang Z, Gong J, Li J, Sun H, Pan Y, Zhao L. The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis. Int J Surg 2023; 109:2451-2466. [PMID: 37463039 PMCID: PMC10442126 DOI: 10.1097/js9.0000000000000441] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/01/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. MATERIALS AND METHODS PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. RESULTS Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. CONCLUSIONS Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.
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Affiliation(s)
- Zhi Yang
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Gong
- Department of Radiation Oncology, Xijing Hospital
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital
| | - Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital
| | - Yanglin Pan
- State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases and Xijing Hospital of Digestive Diseases, Air Force Medical University, Xi’an, People’s Republic of China
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital
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Menon N, Guidozzi N, Chidambaram S, Markar SR. Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy. Dis Esophagus 2023; 36:doad034. [PMID: 37236811 PMCID: PMC10789236 DOI: 10.1093/dote/doad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 05/04/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023]
Abstract
Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.
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Affiliation(s)
- Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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5
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Jailin C, Milioni De Carvalho P, Mohamed S, Vancamberg L, Amr Farouk Ibrahim M, Gomaa MM, Kamal RM, Muller S. Deformable registration with intensity correction for CESM monitoring response to Neoadjuvant Chemotherapy. Biomed Phys Eng Express 2023; 9. [PMID: 36758233 DOI: 10.1088/2057-1976/acba9f] [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: 12/16/2022] [Accepted: 02/09/2023] [Indexed: 02/11/2023]
Abstract
This paper proposes a robust longitudinal registration method for Contrast Enhanced Spectral Mammography in monitoring neoadjuvant chemotherapy. Because breast texture intensity changes with the treatment, a non-rigid registration procedure with local intensity compensations is developed. The approach allows registering the low energy images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding recombined images. The difference of registered images, called residual, makes vanishing the breast texture that did not changed between the two exams. Consequently, this registered residual allows identifying local density and iodine changes, especially in the lesion area. The method is validated with a synthetic NAC case where ground truths are available. Then the procedure is applied to 51 patients with 208 CESM image pairs acquired before and after the chemotherapy treatment. The proposed registration converged in all 208 cases. The intensity-compensated registration approach is evaluated with different mathematical metrics and through the repositioning of clinical landmarks (RMSE: 5.9 mm) and outperforms state-of-the-art registration techniques.
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Affiliation(s)
| | | | | | | | | | | | - Rasha Mohammed Kamal
- Baheya Foundation For Early Detection And Treatment Of Breast Cancer, El Haram, Giza, Egypt
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6
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Deng X, Milligan K, Ali-Adeeb R, Shreeves P, Brolo A, Lum JJ, Andrews JL, Jirasek A. Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer. APPLIED SPECTROSCOPY 2022; 76:462-474. [PMID: 34355582 PMCID: PMC9003771 DOI: 10.1177/00037028211035398] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.
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Affiliation(s)
- Xinchen Deng
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Kirsty Milligan
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, The University of British Columbia Kelowna, Canada
| | - Phillip Shreeves
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Alexandre Brolo
- Department of Chemistry, University of Victoria, Victoria, Canada
| | - Julian J. Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Jeffrey L. Andrews
- Department of Statistics, The University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, The University of British Columbia Kelowna, Canada
- Andrew Jirasek, Department of Physics, The University of British Columbia–Okanagan Campus, Kelowna V1V 1V7, Canada.
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7
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Alam S, Veeraraghavan H, Tringale K, Amoateng E, Subashi E, Wu AJ, Crane CH, Tyagi N. Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients. Phys Imaging Radiat Oncol 2022; 21:54-61. [PMID: 35243032 PMCID: PMC8861831 DOI: 10.1016/j.phro.2022.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/02/2022] [Accepted: 02/11/2022] [Indexed: 12/24/2022] Open
Abstract
Background and purpose Stereotactic body radiation therapy (SBRT) of locally advanced pancreatic cancer (LAPC) is challenging due to significant motion of gastrointestinal (GI) organs. The goal of our study was to quantify inter and intrafraction deformations and dose accumulation of upper GI organs in LAPC patients. Materials and methods Five LAPC patients undergoing five-fraction magnetic resonance-guided radiation therapy (MRgRT) using abdominal compression and daily online plan adaptation to 50 Gy were analyzed. A pre-treatment, verification, and post-treatment MR imaging (MRI) for each of the five fractions (75 total) were used to calculate intra and interfraction motion. The MRIs were registered using Large Deformation Diffeomorphic Metric Mapping (LDDMM) deformable image registration (DIR) method and total dose delivered to stomach_duodenum, small bowel (SB) and large bowel (LB) were accumulated. Deformations were quantified using gradient magnitude and Jacobian integral of the Deformation Vector Fields (DVF). Registration DVFs were geometrically assessed using Dice and 95th percentile Hausdorff distance (HD95) between the deformed and physician’s contours. Accumulated doses were then calculated from the DVFs. Results Median Dice and HD95 were: Stomach_duodenum (0.9, 1.0 mm), SB (0.9, 3.6 mm), and LB (0.9, 2.0 mm). Median (max) interfraction deformation for stomach_duodenum, SB and LB was 6.4 (25.8) mm, 7.9 (40.5) mm and 7.6 (35.9) mm. Median intrafraction deformation was 5.5 (22.6) mm, 8.2 (37.8) mm and 7.2 (26.5) mm. Accumulated doses for two patients exceeded institutional constraints for stomach_duodenum, one of whom experienced Grade1 acute and late abdominal toxicity. Conclusion LDDMM method indicates feasibility to measure large GI motion and accumulate dose. Further validation on larger cohort will allow quantitative dose accumulation to more reliably optimize online MRgRT.
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Affiliation(s)
- Sadegh Alam
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Kathryn Tringale
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Emmanuel Amoateng
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham J. Wu
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Christopher H. Crane
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
- Corresponding author at: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 545 East 74th Street, New York, NY 10021, USA.
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Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 2021; 49:2462-2481. [PMID: 34939174 PMCID: PMC9206619 DOI: 10.1007/s00259-021-05658-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/12/2021] [Indexed: 10/24/2022]
Abstract
PURPOSE Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. METHODS A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. RESULTS Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. CONCLUSION A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
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9
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Amugongo LM, Green A, Cobben D, van Herk M, McWilliam A, Osorio EV. Identification of modes of tumor regression in non-small cell lung cancer patients during radiotherapy. Med Phys 2021; 49:370-381. [PMID: 34724228 DOI: 10.1002/mp.15320] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 09/18/2021] [Accepted: 10/19/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. METHODS In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. RESULTS Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. CONCLUSION Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.
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Affiliation(s)
- Lameck Mbangula Amugongo
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Andrew Green
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - David Cobben
- The Clatterbridge Cancer Centre NHS Foundation Trust, Clatterbridge Hospital, Birkenhead, UK
| | - Marcel van Herk
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Alan McWilliam
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
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Malpani R, Petty CW, Bhatt N, Staib LH, Chapiro J. Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions. DIGESTIVE DISEASE INTERVENTIONS 2021; 5:331-337. [PMID: 35005333 PMCID: PMC8740955 DOI: 10.1055/s-0041-1726300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.
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Affiliation(s)
- Rohil Malpani
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Christopher W. Petty
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Neha Bhatt
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Lawrence H. Staib
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
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11
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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: 0] [Impact Index Per Article: 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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Lu L, Sun SH, Yang H, E L, Guo P, Schwartz LH, Zhao B. Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data. ACTA ACUST UNITED AC 2021; 6:223-230. [PMID: 32548300 PMCID: PMC7289249 DOI: 10.18383/j.tom.2020.00017] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We investigated the performance of multiple radiomics feature extractors/software on predicting epidermal growth factor receptor mutation status in 228 patients with non–small cell lung cancer from publicly available data sets in The Cancer Imaging Archive. The imaging and clinical data were split into training (n = 105) and validation cohorts (n = 123). Two of the most cited open-source feature extractors, IBEX (1563 features) and Pyradiomics (1319 features), and our in-house software, Columbia Image Feature Extractor (CIFE) (1160 features), were used to extract radiomics features. Univariate and multivariate analyses were performed sequentially to predict EGFR mutation status using each individual feature extractor. Our univariate analysis integrated an unsupervised clustering method to identify nonredundant and informative candidate features for the creation of prediction models by multivariate analyses. In training, unsupervised clustering-based univariate analysis identified 5, 6, and 4 features from IBEX, Pyradiomics, and CIFE as candidate features, respectively. Multivariate prediction models using these features from IBEX, Pyradiomics, and CIFE yielded similar areas under the receiver operating characteristic curve of 0.68, 0.67, and 0.69. However, in validation, areas under the receiver operating characteristic curve of multivariate prediction models from IBEX, Pyradiomics, and CIFE decreased to 0.54, 0.56 and 0.64, respectively. Different feature extractors select different radiomics features, which leads to prediction models with varying performance. However, correlation between those selected features from different extractors may indicate these features measure similar imaging phenotypes associated with similar biological characteristics. Overall, attention should be paid to the generalizability of individual radiomics features and radiomics prediction models.
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Affiliation(s)
- Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Shawn H Sun
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Hao Yang
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Linning E
- Department of Radiology, Shanxi DAYI Hospital, Taiyuan, Shanxi, China
| | - Pingzhen Guo
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY; and
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Barten DLJ, Laan JJ, Nelissen KJ, Visser J, Westerveld H, Bel A, de Jonge CS, Stoker J, van Kesteren Z. A 3D cine-MRI acquisition technique and image analysis framework to quantify bowel motion demonstrated in gynecological cancer patients. Med Phys 2021; 48:3109-3119. [PMID: 33738805 PMCID: PMC8360025 DOI: 10.1002/mp.14851] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/01/2021] [Accepted: 03/05/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is increasingly used in radiation oncology for target delineation and radiotherapy treatment planning, for example, in patients with gynecological cancers. As a consequence of pelvic radiotherapy, a part of the bowel is irradiated, yielding risk of bowel toxicity. Existing dose-effect models predicting bowel toxicity are inconclusive and bowel motion might be an important confounding factor. The exact motion of the bowel and dosimetric effects of its motion are yet uncharted territories in radiotherapy. In diagnostic radiology methods on the acquisition of dynamic MRI sequences were developed for bowel motility visualization and quantification. Our study aim was to develop an imaging technique based on three-dimensional (3D) cine-MRI to visualize and quantify bowel motion and demonstrate it in a cohort of gynecological cancer patients. METHODS We developed an MRI acquisition suitable for 3D bowel motion quantification, namely a balanced turbo field echo sequence (TE = 1.39 ms, TR = 2.8 ms), acquiring images in 3.7 s (dynamic) with a 1.25 × 1.25 × 2.5 mm3 resolution, yielding a field of view of 200 × 200 × 125 mm3 . These MRI bowel motion sequences were acquired in 22 gynecological patients. During a 10-min scan, 160 dynamics were acquired. Subsequent dynamics were deformably registered using a B-spline transformation model, resulting in 159 3D deformation vector fields (DVFs) per MRI set. From the 159 DVFs, the average vector length was calculated per voxel to generate bowel motion maps. Quality assurance was performed on all 159 DVFs per MRI, using the Jacobian Determinant and the Harmonic Energy as deformable image registration error metrics. In order to quantify bowel motion, we introduced the concept of cumulative motion-volume histogram (MVH) of the bowel bag volume. Finally, interpatient variation of bowel motion was analyzed using the MVH parameters M10%, M50%, and M90%. The M10%/M50%/M90% represents the minimum bowel motion per frame of 10%/50%/90% of the bowel bag volume. RESULTS The motion maps resulted in a visualization of areas with small and large movements within the bowel bag. After applying quality assurance, the M10%, M50%, and M90% were 4.4 (range 2.2-7.6) mm, 2.2 (range 0.9-4.1) mm, and 0.5 (range 0.2-1.4) mm per frame, on average over all patients, respectively. CONCLUSION We have developed a method to visualize and quantify 3D bowel motion with the use of bowel motion specific MRI sequences in 22 gynecological cancer patients. This 3D cine-MRI-based quantification tool and the concept of MVHs can be used in further studies to determine the effect of radiotherapy on bowel motion and to find the relation with dose effects to the small bowel. In addition, the developed technique can be a very interesting application for bowel motility assessment in diagnostic radiology.
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Affiliation(s)
- Danique L J Barten
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Janna J Laan
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Koen J Nelissen
- Department of Radiation Oncology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Jorrit Visser
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Henrike Westerveld
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Arjan Bel
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Catharina S de Jonge
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
| | - Zdenko van Kesteren
- Department of Radiation Oncology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands
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Alam SR, Zhang P, Zhang SY, Chen I, Rimner A, Tyagi N, Hu YC, Lu W, Yorke ED, Deasy JO, Thor M. Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:883-892. [PMID: 33453309 DOI: 10.1016/j.ijrobp.2021.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. METHODS AND MATERIALS Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). RESULTS Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). CONCLUSIONS A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.
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Affiliation(s)
- Sadegh R Alam
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Pengpeng Zhang
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Si-Yuan Zhang
- Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ishita Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Neelam Tyagi
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yu-Chi Hu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wei Lu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ellen D Yorke
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Thor
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Lee D, Zhang P, Nadeem S, Alam S, Jiang J, Caringi A, Allgood N, Aristophanous M, Mechalakos J, Hu YC. Predictive dose accumulation for HN adaptive radiotherapy. Phys Med Biol 2020; 65:235011. [PMID: 33007769 DOI: 10.1088/1361-6560/abbdb8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1-3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 [Formula: see text] 0.07/0.81 [Formula: see text] 0.04 (week 4), 0.79 [Formula: see text] 0.06/0.81 [Formula: see text] 0.05 (week 5) and 0.78 [Formula: see text] 0.06/0.82 [Formula: see text] (week 6). The DICE with the Demons model were 0.78 [Formula: see text] 0.08/0.82 [Formula: see text] 0.03 (week 4), 0.77 [Formula: see text] 0.07/0.82 [Formula: see text] 0.04 (week 5) and 0.75 [Formula: see text] 0.07/0.82 [Formula: see text] 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center New York, NY, United States of America
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Duffy IR, Boyle AJ, Vasdev N. Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology. Mol Imaging 2020; 18:1536012119869070. [PMID: 31429375 PMCID: PMC6702769 DOI: 10.1177/1536012119869070] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.
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Affiliation(s)
- Ian R Duffy
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Amanda J Boyle
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neil Vasdev
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,2 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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18
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Deng X, Ali-Adeeb R, Andrews JL, Shreeves P, Lum JJ, Brolo A, Jirasek A. Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares. APPLIED SPECTROSCOPY 2020; 74:701-711. [PMID: 32098482 DOI: 10.1177/0003702820906221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Radiation therapy (RT) is one of the most commonly prescribed cancer treatments. New tools that can accurately monitor and evaluate individual patient responses would be a major advantage and lend to the implementation of personalized treatment plans. In this study, Raman spectroscopy (RS) was applied to examine radiation-induced cellular responses in H460, MCF7, and LNCaP cancer cell lines across different dose levels and times post-irradiation. Previous Raman data analysis was conducted using principal component analysis (PCA), which showed the ability to extract biological information of glycogen. In the current studies, the use of non-negative matrix factorization (NMF) allowed for the discovery of multiplexed biological information, specifically uncovering glycogen-like and lipid-like component bases. The corresponding scores of glycogen and previously unidentified lipids revealed the content variations of these two chemicals in the cellular data. The NMF decomposed glycogen and lipid-like bases were able to separate the cancer cell lines into radiosensitive and radioresistant groups. A further lipid phenotype investigation was also attempted by applying non-negative least squares (NNLS) to the lipid-like bases decomposed individually from three cell lines. Qualitative differences found in lipid weights for each lipid-like basis suggest the lipid phenotype differences in the three tested cancer cell lines. Collectively, this study demonstrates that the application of NMF and NNLS on RS data analysis to monitor ionizing radiation-induced cellular responses can yield multiplexed biological information on bio-response to RT not revealed by conventional chemometric approaches.
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Affiliation(s)
- Xinchen Deng
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Jeffrey L Andrews
- Department of Statistics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Phillip Shreeves
- Department of Statistics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
| | - Julian J Lum
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, Canada
| | - Alexandre Brolo
- Department of Chemistry, University of Victoria, Victoria, Canada
| | - Andrew Jirasek
- Department of Physics, I.K. Barber School of Arts and Sciences, The University of British Columbia, Kelowna, Canada
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Yang W, Xia F, Wang J, Zhou M, Li G, Shen L, Zhang H, Wang Y, Zhang J, Zhang Z. Quantifying skeletal muscle wasting during chemoradiotherapy with Jacobian calculations for the prediction of survival and toxicity in patients with gastric cancer. Eur J Surg Oncol 2020; 46:1254-1261. [PMID: 32307187 DOI: 10.1016/j.ejso.2020.03.223] [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: 02/26/2020] [Accepted: 03/28/2020] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE This study aims to investigate changes in body composition during adjuvant chemoradiotherapy (CRT) quantitatively and visually in patients with gastric cancer and evaluate the correlation of the changes with survival and toxicity. MATERIALS AND METHODS A total of 182 gastric cancer patients who underwent surgery and adjuvant CRT were included. CT images at the level of the third lumbar vertebra (L3) were analyzed to assess the areas of muscles. We further proposed a framework to quantify muscle changes using Jacobian calculations. Jacobian value (Jac) was calculated for each voxel of muscle by measuring the ratio of muscle changes, where Jac <1 indicated shrinkage and Jac >1 indicated expansion. Variances between the pre- and post-CRT body composition were quantitatively computed, and their impact on toxicity and long-term outcomes were studied. RESULTS The skeletal muscle index decreased from 44.4 ± 7.4 cm2/m2 to 43.6 ± 7.0 cm2/m2 after CRT. Skeletal muscle wasting and Jac 0.35 ≥ 5% were negatively associated with overall survival (p = 0.036 and p = 0.008, respectively). A new prediction model combining clinical variables and Jacobian features was constructed, and yielded a c statistic of 0.78 (95% CI 0.68-0.87). Jac 0.75 to Jac 1.15 (-25%<muscle changes<15%) were illustrated to be predictive for toxicity of concurrent chemotherapy, with the most significant predictor being Jac 1 (p = 0.003). CONCLUSION The Jacobian calculations are novel tools for quantifying morphometric changes and the distribution of body composition. The new predictive model combining clinical variables and Jacobian features achieved high accuracy for predicting overall survival.
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Affiliation(s)
- Wang Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Fan Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Menglong Zhou
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Guichao Li
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Lijun Shen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Hui Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Yaqi Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Jing Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, PR China.
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Alam S, Thor M, Rimner A, Tyagi N, Zhang SY, Kuo LC, Nadeem S, Lu W, Hu YC, Yorke E, Zhang P. Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2020; 13:36-43. [PMID: 32411833 PMCID: PMC7224352 DOI: 10.1016/j.phro.2020.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
MRI is suited for tracking volumetric changes/accumulating doses in the esophagus. Introduced medial axis of esophagus to calculate inter-fraction positional uncertainty. Planned and accumulated esophagus dose-volume parameter differences are significant. Longitudinal expansion of esophagus may link to acute esophagitis.
Background and purpose Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT). Material and methods Eleven patients treated via intensity-modulated RT to 60–70 Gy in 2–3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs. Eight patients developed AE grade 2 (AE2), 3–6 weeks after RT started. First, weekly MRI esophagus contours were rigidly propagated to planning CT and the distances between the medial esophageal axes were calculated as positional uncertainties. Then, the weekly MRI were deformably registered to the planning CT and the total dose delivered to esophagus was accumulated. Weekly Maximum Esophagus Expansion (MEex) was calculated using the Jacobian map. Eventually, esophageal dose parameters (Mean Esophagus Dose (MED), V90% and D5cc) between the planned and accumulated dose were compared. Results Positional esophagus uncertainties were 6.8 ± 1.8 mm across patients. For the entire cohort at the end of RT: the median accumulated MED was significantly higher than the planned dose (24 Gy vs. 21 Gy p = 0.006). The median V90% and D5cc were 12.5 cm3 vs. 11.5 cm3 (p = 0.05) and 61 Gy vs. 60 Gy (p = 0.01), for accumulated and planned dose, respectively. The median MEex was 24% and was significantly associated with AE2 (p = 0.008). Conclusions MRI is well suited for tracking esophagus volumetric changes and accumulating doses. Longitudinal esophagus expansion could reflect radiation-induced inflammation that may link to AE.
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Affiliation(s)
- Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Si-Yuan Zhang
- Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Li Cheng Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Ellen Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
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21
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Murali N, Kucukkaya A, Petukhova A, Onofrey J, Chapiro J. Supervised Machine Learning in Oncology: A Clinician's Guide. ACTA ACUST UNITED AC 2020; 4:73-81. [PMID: 32869010 DOI: 10.1055/s-0040-1705097] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.
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Affiliation(s)
- Nikitha Murali
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Ahmet Kucukkaya
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - Alexandra Petukhova
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
| | - John Onofrey
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut.,Department of Urology, Yale University School of Medicine, New Haven, Connecticut
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
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22
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Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 2019; 46:2656-2672. [PMID: 31214791 PMCID: PMC6879445 DOI: 10.1007/s00259-019-04372-x] [Citation(s) in RCA: 147] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 05/23/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process. METHODS Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies. RESULTS Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials. CONCLUSIONS The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Lidija Antunovic
- Nuclear Medicine, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
- Nuclear Medicine, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Margarita Kirienko
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy.
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23
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Kuznetsova S, Grendarova P, Roy S, Sinha R, Thind K, Ploquin N. Structure guided deformable image registration for treatment planning CT and post stereotactic body radiation therapy (SBRT) Primovist ® (Gd-EOB-DTPA) enhanced MRI. J Appl Clin Med Phys 2019; 20:109-118. [PMID: 31755658 PMCID: PMC6909124 DOI: 10.1002/acm2.12773] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 07/12/2019] [Accepted: 10/23/2019] [Indexed: 01/01/2023] Open
Abstract
The purpose of this study was to assess the performance of structure‐guided deformable image registration (SG‐DIR) relative to rigid registration and DIR using TG‐132 recommendations. This assessment was performed for image registration of treatment planning computed tomography (CT) and magnetic resonance imaging (MRI) scans with Primovist® contrast agent acquired post stereotactic body radiation therapy (SBRT). SBRT treatment planning CT scans and posttreatment Primovist® MRI scans were obtained for 14 patients. The liver was delineated on both sets of images and matching anatomical landmarks were chosen by a radiation oncologist. Rigid registration, DIR, and two types of SG‐DIR (using liver contours only; and using liver structures along with anatomical landmarks) were performed for each set of scans. TG‐132 recommended metrics were estimated which included Dice Similarity Coefficient (DSC), Mean Distance to Agreement (MDA), Target Registration Error (TRE), and Jacobian determinant. Statistical analysis was performed using Wilcoxon Signed Rank test. The median (range) DSC for rigid registration was 0.88 (0.77–0.89), 0.89 (0.81–0.93) for DIR, and 0.90 (0.86–0.94) for both types of SG‐DIR tested in this study. The median MDA was 4.8 mm (3.7–6.8 mm) for rigid registration, 3.4 mm (2.4–8.7 mm) for DIR, 3.2 mm (2.0–5.2 mm) for SG‐DIR where liver structures were used to guide the registration, and 2.8 mm (2.1–4.2 mm) for the SG‐DIR where liver structures and anatomical landmarks were used to guide the registration. The median TRE for rigid registration was 7.2 mm (0.5–23 mm), 6.8 mm (0.7–30.7 mm) for DIR, 6.1 mm (1.1–20.5 mm) for the SG‐DIR guided by only the liver structures, and 4.1 mm (0.8–19.7 mm) for SG‐DIR guided by liver contours and anatomical landmarks. The SG‐DIR shows higher liver conformality as per TG‐132 metrics and lowest TRE compared to rigid registration and DIR in Velocity AI software for the purpose of registering treatment planning CT and post‐SBRT MRI for the liver region. It was found that TRE decreases when liver contours and corresponding anatomical landmarks guide SG‐DIR.
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Affiliation(s)
- Svetlana Kuznetsova
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada
| | - Petra Grendarova
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Soumyajit Roy
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada.,Department of Radiation Oncology, The Ottawa Hospital Cancer Program, University of Ottawa, Ottawa, Ontario, Canada
| | - Rishi Sinha
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Kundan Thind
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada.,Department of Oncology, University of Calgary, Calgary, Alberta, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada
| | - Nicolas Ploquin
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada.,Department of Oncology, University of Calgary, Calgary, Alberta, Canada.,Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada
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Riyahi S, Choi W, Liu CJ, Nadeem S, Tan S, Zhong H, Chen W, Wu AJ, Mechalakos JG, Deasy JO, Lu W. Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response. DATA DRIVEN TREATMENT RESPONSE ASSESSMENT AND PRETERM, PERINATAL, AND PAEDIATRIC IMAGE ANALYSIS 2018. [DOI: 10.1007/978-3-030-00807-9_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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