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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024:S0030-6665(24)00070-7. [PMID: 38910064 DOI: 10.1016/j.otc.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.
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Affiliation(s)
- Jamie Oliver
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Rahul Alapati
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Jason Lee
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Andrés Bur
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
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Srinivasan Y, Liu A, Rameau A. Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient. Curr Opin Otolaryngol Head Neck Surg 2024; 32:105-112. [PMID: 38116798 DOI: 10.1097/moo.0000000000000948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer. RECENT FINDINGS Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer. SUMMARY Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.
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Affiliation(s)
- Yashes Srinivasan
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
| | - Amy Liu
- University of California, San Diego, School of Medicine, San Diego, California, USA
| | - Anaïs Rameau
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
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Lee TF, Hsieh YW, Yang PY, Tseng CH, Lee SH, Yang J, Chang L, Wu JM, Tseng CD, Chao PJ. Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer. Radiat Oncol 2024; 19:5. [PMID: 38195582 PMCID: PMC10775485 DOI: 10.1186/s13014-023-02381-7] [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: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024] Open
Abstract
PURPOSE The study aims to enhance the efficiency and accuracy of literature reviews on normal tissue complication probability (NTCP) in head and neck cancer patients using radiation therapy. It employs meta-analysis (MA) and natural language processing (NLP). MATERIAL AND METHODS The study consists of two parts. First, it employs MA to assess NTCP models for xerostomia, dysphagia, and mucositis after radiation therapy, using Python 3.10.5 for statistical analysis. Second, it integrates NLP with convolutional neural networks (CNN) to optimize literature search, reducing 3256 articles to 12. CNN settings include a batch size of 50, 50-200 epoch range and a 0.001 learning rate. RESULTS The study's CNN-NLP model achieved a notable accuracy of 0.94 after 200 epochs with Adamax optimization. MA showed an AUC of 0.67 for early-effect xerostomia and 0.74 for late-effect, indicating moderate to high predictive accuracy but with high variability across studies. Initial CNN accuracy of 66.70% improved to 94.87% post-tuning by optimizer and hyperparameters. CONCLUSION The study successfully merges MA and NLP, confirming high predictive accuracy for specific model-feature combinations. It introduces a time-based metric, words per minute (WPM), for efficiency and highlights the utility of MA and NLP in clinical research.
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Affiliation(s)
- Tsair-Fwu Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan, ROC
| | - Yang-Wei Hsieh
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Pei-Ying Yang
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Chi-Hung Tseng
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Shen-Hao Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
- Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Linkou, Taiwan, ROC
| | - Jack Yang
- Medical Physics at Monmouth Medical Center, Barnabas Health Care at Long Branch, Long Branch, NJ, USA
| | - Liyun Chang
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 840, Taiwan, ROC
| | - Jia-Ming Wu
- Heavy Ion Center of Wuwei Cancer Hospital, Gansu Wuwei Academy of Medical Sciences, Gansu Wuwei Tumor Hospital, Wuwei, Gansu Province, China
- Department of Medical Physics, Chengde Medical University, Chengde, Hebei Province, China
| | - Chin-Dar Tseng
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC
| | - Pei-Ju Chao
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, No.415, Jiangong Rd., Sanmin Dist., Kaohsiung, 80778, Taiwan, ROC.
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Chen R, Xie J, Chen J, Li X, Lin Q, Xu Q, Chen Y, Wang L, Zheng R, Xu B. Analysis of the Parotid Glands on an Energy Spectrum CT Iodine Map to Evaluate Irradiation-Induced Acute Xerostomia in Patients With Nasopharyngeal Carcinoma. Technol Cancer Res Treat 2024; 23:15330338241256814. [PMID: 38773777 PMCID: PMC11113032 DOI: 10.1177/15330338241256814] [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: 10/04/2023] [Revised: 04/15/2024] [Accepted: 05/02/2024] [Indexed: 05/24/2024] Open
Abstract
Objective: This prospective study aims to evaluate acute irradiation-induced xerostomia during radiotherapy by utilizing the normalized iodine concentration (NIC) derived from energy spectrum computed tomography (CT) iodine maps. Methods: In this prospective study, we evaluated 28 patients diagnosed with nasopharyngeal carcinoma. At 4 distinct stages of radiotherapy (0, 10, 20, and 30 fractions), each patient underwent CT scans to generate iodine maps. The NIC of both the left and right parotid glands was obtained, with the NIC at the 0-fraction stage serving as the baseline measurement. After statistically comparing the NIC obtained in the arterial phase, early venous phase, late venous phase, and delayed phase, we chose the late venous iodine concentration as the NIC and proceeded to analyze the variations in NIC at each radiotherapy interval. Using the series of NIC values, we conducted hypothesis tests to evaluate the extent of change in NIC within the parotid gland across different stages. Furthermore, we identified the specific time point at which the NIC decay exhibited the most statistically significant results. In addition, we evaluated the xerostomia grades of the patients at these 4 stages, following the radiation therapy oncology group (RTOG) xerostomia evaluation standard, to draw comparisons with the changes observed in NIC. Results: The NIC in the late venous phase exhibited the highest level of statistical significance (P < .001). There was a noticeable attenuation in NIC as the RTOG dry mouth grade increased. Particularly, at the 20 fraction, the NIC experienced the most substantial attenuation (P < .001), a significant negative correlation was observed between the NIC of the left, right, and both parotid glands, and the RTOG evaluation grade of acute irradiation-induced xerostomia (P < .001, r = -0.46; P < .001, r = -0.45; P < .001, r = -0.47). The critical NIC values for the left, right, and both parotid glands when acute xerostomia occurred were 0.175, 0.185, and 0.345 mg/ml, respectively, with AUC = 0.73, AUC = 0.75, and AUC = 0.75. Conclusion: The NIC may be used to evaluate changes in parotid gland function during radiotherapy and acute irradiation-induced xerostomia.
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Affiliation(s)
- Runfan Chen
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Jiangao Xie
- Department of Radiology, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
| | - Jianmin Chen
- Department of Statistics, University of Connecticut, Storrs, USA
| | - Xiaobo Li
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumours (Fujian Medical University), Fuzhou, China
- Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Haematological and Breast Malignancies), Fuzhou, China
| | - Qingliang Lin
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
| | - Qizhen Xu
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
| | - Yanyan Chen
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
| | - Lili Wang
- Department of Radiology, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
| | - Rong Zheng
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumours (Fujian Medical University), Fuzhou, China
- Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Haematological and Breast Malignancies), Fuzhou, China
| | - Benhua Xu
- Department of Radiotherapy, Union Hospital Affiliated to Fujian Medical University, Fuzhou, China
- Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumours (Fujian Medical University), Fuzhou, China
- Clinical Research Center for Radiology and Radiotherapy of Fujian Province (Digestive, Haematological and Breast Malignancies), Fuzhou, China
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Li Z, Raldow AC, Weidhaas JB, Zhou Q, Qi XS. Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT. Cancers (Basel) 2023; 15:5142. [PMID: 37958316 PMCID: PMC10647315 DOI: 10.3390/cancers15215142] [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: 09/18/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.
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Affiliation(s)
- Zirong Li
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - Ann C. Raldow
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Joanne B. Weidhaas
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
| | - Qichao Zhou
- Manteia Medical Technologies Co., Milwaukee, WI 53226, USA;
| | - X. Sharon Qi
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA; (A.C.R.); (J.B.W.)
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Tan D, Mohamad Salleh SA, Manan HA, Yahya N. Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis. J Med Imaging Radiat Oncol 2023; 67:564-579. [PMID: 37309680 DOI: 10.1111/1754-9485.13546] [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: 01/10/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. METHODS A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. RESULTS Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. CONCLUSION Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
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Affiliation(s)
- Daryl Tan
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Abdollahi H, Dehesh T, Abdalvand N, Rahmim A. Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients. Int J Radiat Biol 2023; 99:1669-1683. [PMID: 37171485 DOI: 10.1080/09553002.2023.2214206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND AIM Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features. METHODS Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pretreatment (CT1), mid-treatment (CT2), post-treatment (CT3), and delta features (ΔCT2-1, ΔCT3-1, ΔCT3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained. RESULTS In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT3 model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT2 & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively. CONCLUSION Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies toward improved therapeutic outcomes.
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Affiliation(s)
- Hamid Abdollahi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Tania Dehesh
- Modelling in Health Research Center, Institute for Future Studies in Health, Kerman University ofMedical Sciences, Kerman, Iran
| | - Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
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Jassim H, Nedaei HA, Geraily G, Banaee N, Kazemian A. The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review. BJR Open 2023; 5:20220062. [PMID: 37389008 PMCID: PMC10301728 DOI: 10.1259/bjro.20220062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/24/2023] [Indexed: 07/01/2023] Open
Abstract
Objectives To provide an overview and meta-analysis of different techniques adopted to accomplish kVCBCT for dose calculation and automated segmentation. Methods A systematic review and meta-analysis were performed on eligible studies demonstrating kVCBCT-based dose calculation and automated contouring of different tumor features. Meta-analysis of the performance was accomplished on the reported γ analysis and dice similarity coefficient (DSC) score of both collected results as three subgroups (head and neck, chest, and abdomen). Results After the literature scrutinization (n = 1008), 52 papers were recognized for the systematic review. Nine studies of dosimtric studies and eleven studies of geometric analysis were suitable for inclusion in meta-analysis. Using kVCBCT for treatment replanning depends on a method used. Deformable Image Registration (DIR) methods yielded small dosimetric error (≤2%), γ pass rate (≥90%) and DSC (≥0.8). Hounsfield Unit (HU) override and calibration curve-based methods also achieved satisfactory yielded small dosimetric error (≤2%) and γ pass rate ((≥90%), but they are prone to error due to their sensitivity to a vendor-specific variation in kVCBCT image quality. Conclusions Large cohorts of patients ought to be undertaken to validate methods achieving low levels of dosimetric and geometric errors. Quality guidelines should be established when reporting on kVCBCT, which include agreed metrics for reporting on the quality of corrected kVCBCT and defines protocols of new site-specific standardized imaging used when obtaining kVCBCT images for adaptive radiotherapy. Advances in knowledge This review gives useful knowledge about methods making kVCBCT feasible for kVCBCT-based adaptive radiotherapy, simplifying patient pathway and reducing concomitant imaging dose to the patient.
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Affiliation(s)
| | | | | | - Nooshin Banaee
- Medical Radiation Research Center, Islamic Azad University, Tehran, Iran
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Smith DK, Clark H, Hovan A, Wu J. Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy. Radiat Oncol 2023; 18:77. [PMID: 37158946 PMCID: PMC10165827 DOI: 10.1186/s13014-023-02274-9] [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: 12/13/2022] [Accepted: 04/28/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands. METHODS The pre and post-radiotherapy salivary flow rates of 510 head and neck cancer patients were used to fit three predictive models of salivary hypofunction, (1) the Lyman-Kutcher-Burman (LKB) model, (2) a spline-based model, (3) a neural network. A fourth LKB-type model using literature reported parameter values was included for reference. Predictive performance was evaluated using a cut-off dependent AUC analysis. RESULTS The neural network model dominated the LKB models demonstrating better predictive performance at every cutoff with AUCs ranging from 0.75 to 0.83 depending on the cutoff selected. The spline-based model nearly dominated the LKB models with the fitted LKB model only performing better at the 0.55 cutoff. The AUCs for the spline model ranged from 0.75 to 0.84 depending on the cutoff chosen. The LKB models had the lowest predictive ability with AUCs ranging from 0.70 to 0.80 (fitted) and 0.67 to 0.77 (literature reported). CONCLUSION Our neural network model showed improved performance over the LKB and alternative machine learning approaches and provided clinically useful predictions of salivary hypofunction without relying on summary measures.
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Affiliation(s)
- Derek K Smith
- American Dental Association Science and Research Institute, 211 E Chicago Ave., Chicago, IL, 60611, USA.
| | - Haley Clark
- Medical Physics; BC Cancer, Surrey, BC, Canada
| | | | - Jonn Wu
- Department of Radiation Oncology, BC Cancer, Vancouver, BC, Canada
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Aliotta E, Hu YC, Zhang P, Lichtenwalner P, Caringi A, Allgood N, Tsai CJ, Zakeri K, Lee N, Zhang P, Cerviño L, Aristophanous M. Automated tracking of morphologic changes in weekly magnetic resonance imaging during head and neck radiotherapy. J Appl Clin Med Phys 2023:e13959. [PMID: 37147912 DOI: 10.1002/acm2.13959] [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: 09/28/2022] [Revised: 12/15/2022] [Accepted: 02/20/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND AND PURPOSE Anatomic changes during head and neck radiotherapy can impact dose delivery, necessitate adaptive replanning, and indicate patient-specific response to treatment. We have developed an automated system to track these changes through longitudinal MRI scans to aid identification and clinical intervention. The purpose of this article is to describe this tracking system and present results from an initial cohort of patients. MATERIALS AND METHODS The Automated Watchdog in Adaptive Radiotherapy Environment (AWARE) was developed to process longitudinal MRI data for radiotherapy patients. AWARE automatically identifies and collects weekly scans, propagates radiotherapy planning structures, computes structure changes over time, and reports important trends to the clinical team. AWARE also incorporates manual structure review and revision from clinical experts and dynamically updates tracking statistics when necessary. AWARE was applied to patients receiving weekly T2-weighted MRI scans during head and neck radiotherapy. Changes in nodal gross tumor volume (GTV) and parotid gland delineations were tracked over time to assess changes during treatment and identify early indicators of treatment response. RESULTS N = 91 patients were tracked and analyzed in this study. Nodal GTVs and parotids both shrunk considerably throughout treatment (-9.7 ± 7.7% and -3.7 ± 3.3% per week, respectively). Ipsilateral parotids shrunk significantly faster than contralateral (-4.3 ± 3.1% vs. -2.9 ± 3.3% per week, p = 0.005) and increased in distance from GTVs over time (+2.7 ± 7.2% per week, p < 1 × 10-5 ). Automatic structure propagations agreed well with manual revisions (Dice = 0.88 ± 0.09 for parotids and 0.80 ± 0.15 for GTVs), but for GTVs the agreement degraded 4-5 weeks after the start of treatment. Changes in GTV volume observed by AWARE as early as one week into treatment were predictive of large changes later in the course (AUC = 0.79). CONCLUSION AWARE automatically identified longitudinal changes in GTV and parotid volumes during radiotherapy. Results suggest that this system may be useful for identifying rapidly responding patients as early as one week into treatment.
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Affiliation(s)
- Eric Aliotta
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Peng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Phillip Lichtenwalner
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Amanda Caringi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Natasha Allgood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - C Jillian Tsai
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Kaveh Zakeri
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Michalis Aristophanous
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Araújo ALD, Moraes MC, Pérez-de-Oliveira ME, Silva VMD, Saldivia-Siracusa C, Pedroso CM, Lopes MA, Vargas PA, Kochanny S, Pearson A, Khurram SA, Kowalski LP, Migliorati CA, Santos-Silva AR. Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis. Oral Oncol 2023; 140:106386. [PMID: 37023561 DOI: 10.1016/j.oraloncology.2023.106386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/20/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil; Head and Neck Surgery Department, University of São Paulo Medical School (UFMUSP), São Paulo, São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | | | - Viviane Mariano da Silva
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | - Cristina Saldivia-Siracusa
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Caique Mariano Pedroso
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Marcio Ajudarte Lopes
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Sara Kochanny
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Alexander Pearson
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Syed Ali Khurram
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, S10 2TA Sheffield, United Kingdom
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, Brazil; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, Brazil
| | | | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
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Wiriyakijja P, Niklander S, Santos-Silva AR, Shorrer MK, Simms ML, Villa A, Sankar V, Kerr AR, Riordain RN, Jensen SB, Delli K. World Workshop on Oral Medicine VIII: Development of a Core Outcome Set for Dry Mouth: A Systematic Review of Outcome Domains for Xerostomia. Oral Surg Oral Med Oral Pathol Oral Radiol 2023:S2212-4403(23)00068-8. [PMID: 37198047 DOI: 10.1016/j.oooo.2023.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 01/25/2023] [Indexed: 03/08/2023]
Abstract
OBJECTIVE The purpose of this study was to identify all outcome domains used in clinical studies of xerostomia, that is, subjective sensation of dry mouth. This study is part of the extended project "World Workshop on Oral Medicine Outcomes Initiative for the Direction of Research" to develop a core outcome set for dry mouth. STUDY DESIGN A systematic review was performed on MEDLINE, EMBASE, CINAHL, and Cochrane Central Register of Controlled Trials databases. All clinical and observational studies that assessed xerostomia in human participants from 2001 to 2021 were included. Information on outcome domains was extracted and mapped to the Core Outcome Measures in Effectiveness Trials taxonomy. Corresponding outcome measures were summarized. RESULTS From a total of 34,922 records retrieved, 688 articles involving 122,151 persons with xerostomia were included. There were 16 unique outcome domains and 166 outcome measures extracted. None of these domains or measures were consistently used across all the studies. The severity of xerostomia and physical functioning were the 2 most frequently assessed domains. CONCLUSION There is considerable heterogeneity in outcome domains and measures reported in clinical studies of xerostomia. This highlights the need for harmonization of dry mouth assessment to enhance comparability across studies and facilitate the synthesis of robust evidence for managing patients with xerostomia.
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Berger T, Noble DJ, Yang Z, Shelley LE, McMullan T, Bates A, Thomas S, Carruthers LJ, Beckett G, Duffton A, Paterson C, Jena R, McLaren DB, Burnet NG, Nailon WH. Sub-regional analysis of the parotid glands: model development for predicting late xerostomia with radiomics features in head and neck cancer patients. Acta Oncol 2023; 62:166-173. [PMID: 36802351 DOI: 10.1080/0284186x.2023.2179895] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/08/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND The irradiation of sub-regions of the parotid has been linked to xerostomia development in patients with head and neck cancer (HNC). In this study, we compared the xerostomia classification performance of radiomics features calculated on clinically relevant and de novo sub-regions of the parotid glands of HNC patients. MATERIAL AND METHODS All patients (N = 117) were treated with TomoTherapy in 30-35 fractions of 2-2.167 Gy per fraction with daily mega-voltage-CT (MVCT) acquisition for image-guidance purposes. Radiomics features (N = 123) were extracted from daily MVCTs for the whole parotid gland and nine sub-regions. The changes in feature values after each complete week of treatment were considered as predictors of xerostomia (CTCAEv4.03, grade ≥ 2) at 6 and 12 months. Combinations of predictors were generated following the removal of statistically redundant information and stepwise selection. The classification performance of the logistic regression models was evaluated on train and test sets of patients using the Area Under the Curve (AUC) associated with the different sub-regions at each week of treatment and benchmarked with the performance of models solely using dose and toxicity at baseline. RESULTS In this study, radiomics-based models predicted xerostomia better than standard clinical predictors. Models combining dose to the parotid and xerostomia scores at baseline yielded an AUCtest of 0.63 and 0.61 for xerostomia prediction at 6 and 12 months after radiotherapy while models based on radiomics features extracted from the whole parotid yielded a maximum AUCtest of 0.67 and 0.75, respectively. Overall, across sub-regions, maximum AUCtest was 0.76 and 0.80 for xerostomia prediction at 6 and 12 months. Within the first two weeks of treatment, the cranial part of the parotid systematically yielded the highest AUCtest. CONCLUSION Our results indicate that variations of radiomics features calculated on sub-regions of the parotid glands can lead to earlier and improved prediction of xerostomia in HNC patients.
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Affiliation(s)
- Thomas Berger
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - David J Noble
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Department of Oncology, The University of Cambridge, Cambridge, UK
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Zhuolin Yang
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- School of Engineering, the University of Edinburgh, the King's Buildings, Edinburgh, UK
| | - Leila Ea Shelley
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Thomas McMullan
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Amy Bates
- Department of Oncology, The University of Cambridge, Cambridge, UK
| | - Simon Thomas
- Department of Medical Physics and Clinical Engineering, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Linda J Carruthers
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - George Beckett
- Edinburgh Parallel Computing Centre, Bayes Centre, Edinburgh, UK
| | | | | | - Raj Jena
- Department of Oncology, The University of Cambridge, Cambridge, UK
| | - Duncan B McLaren
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | | | - William H Nailon
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- School of Engineering, the University of Edinburgh, the King's Buildings, Edinburgh, UK
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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Zhou L, Zheng W, Huang S, Yang X. Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients. Discov Oncol 2022; 13:145. [PMID: 36581739 PMCID: PMC9800672 DOI: 10.1007/s12672-022-00606-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Previously, the evaluation of xerostomia depended on subjective grading systems, rather than the accurate saliva amount reduction. Our aim was to quantify acute xerostomia with reduced saliva amount, and apply radiomics, dose-volume histogram (DVH) criteria and clinical features to predict saliva amount reduction by machine learning techniques. MATERIAL AND METHODS Computed tomography (CT) of parotid glands, DVH, and clinical data of 52 patients were collected to extract radiomics, DVH criteria and clinical features, respectively. Firstly, radiomics, DVH criteria and clinical features were divided into 3 groups for feature selection, in order to alleviate the masking effect of the number of features in different groups. Secondly, the top features in the 3 groups composed integrated features, and features selection was performed again for integrated features. In this study, feature selection was used as a combination of eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to alleviate multicollinearity. Finally, 6 machine learning techniques were used for predicting saliva amount reduction. Meanwhile, top radiomics features were modeled using the same machine learning techniques for comparison. RESULT 17 integrated features (10 radiomics, 4 clinical, 3 DVH criteria) were selected to predict saliva amount reduction, with a mean square error (MSE) of 0.6994 and a R2 score of 0.9815. Top 17 and 10 selected radiomics features predicted saliva amount reduction, with MSE of 0.7376, 0.7519, and R2 score of 0.9805, 0.9801, respectively. CONCLUSION With the same number of features, integrated features (radiomics + DVH criteria + clinical) performed better than radiomics features alone. The important DVH criteria and clinical features mainly included, white blood cells (WBC), parotid_glands_Dmax, Age, parotid_glands_V15, hemoglobin (Hb), BMI and parotid_glands_V45.
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Affiliation(s)
- Lang Zhou
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China
- Department of Biomedical Engineering, South China University of Technology, Guangzhou, 510640, Guangdong Province, China
| | - Wanjia Zheng
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China
- Department of Radiation Oncology, Southern Theater Air Force Hospital of the People's Liberation Army, Guangzhou, 510050, Guangdong Province, China
| | - Sijuan Huang
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China.
| | - Xin Yang
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China.
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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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Fanizzi A, Scognamillo G, Nestola A, Bambace S, Bove S, Comes MC, Cristofaro C, Didonna V, Di Rito A, Errico A, Palermo L, Tamborra P, Troiano M, Parisi S, Villani R, Zito A, Lioce M, Massafra R. Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer. Front Med (Lausanne) 2022; 9:993395. [PMID: 36213659 PMCID: PMC9537690 DOI: 10.3389/fmed.2022.993395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/01/2022] [Indexed: 11/23/2022] Open
Abstract
Background and purpose Although the latest breakthroughs in radiotherapy (RT) techniques have led to a decrease in adverse event rates, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers could be useful to predict the toxicity risk related to each individual patient. Our preliminary work aims to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk in 3 months after RT in patients with oropharyngeal cancer (OPC). Materials and methods We performed a multicenter data collection. We enrolled 61 patients referred to three care centers in Apulia, Italy, out of which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and alcohol-smoking habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network (CNN) architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on “fake” parotid contours. Results The best performances were achieved by the model exploiting the radiomic features alone. On the independent test, the model reached median AUC, accuracy, sensitivity, and specificity values of 81.17, 83.33, 71.43, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures. Conclusion Radiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care.
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Affiliation(s)
| | | | | | - Santa Bambace
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | - Samantha Bove
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
- *Correspondence: Samantha Bove,
| | | | | | | | | | - Angelo Errico
- Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
| | | | | | - Michele Troiano
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | - Salvatore Parisi
- IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy
| | | | - Alfredo Zito
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
| | - Marco Lioce
- IRCCS Istituto Tumori “Giovanni Paolo II,”Bari, Italy
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Gros SAA, Santhanam AP, Block AM, Emami B, Lee BH, Joyce C. Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients. Front Oncol 2022; 12:777793. [PMID: 35847951 PMCID: PMC9279735 DOI: 10.3389/fonc.2022.777793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 05/16/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients. Methods We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients’ data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V95<95%) and adaptation (V95<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE10) were set for all Dmax and Dmean DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction Fx when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI95). Results RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid Dmean at EOT. Twelve PTVs had V95<95% (mean coverage decrease of −6.8 ± 2.9%) including six flagged for adaptation at median Fx= 6 (range, 1–16). Seventeen parotids were flagged for exceeding Dmean dose constraints with a median increase of +2.60 Gy (range, 0.99–6.31 Gy) at EOT, including nine with DP>DE10. The differences between predicted and calculated PTV V95 and parotid Dmean was up to 7.6% (mean ± CI95, −2.7 ± 4.1%) and 5 Gy (mean ± CI95, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that Fx ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction. Conclusion Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.
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Affiliation(s)
- Sebastien A. A. Gros
- Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United States
- *Correspondence: Sebastien A. A. Gros,
| | - Anand P. Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Alec M. Block
- Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United States
| | - Bahman Emami
- Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United States
| | - Brian H. Lee
- Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United States
| | - Cara Joyce
- Department of Public Health, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States
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El Naqa I, Pogue BW, Zhang R, Oraiqat I, Parodi K. Image guidance for FLASH radiotherapy. Med Phys 2022; 49:4109-4122. [PMID: 35396707 PMCID: PMC9844128 DOI: 10.1002/mp.15662] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 03/10/2022] [Accepted: 03/30/2022] [Indexed: 01/19/2023] Open
Abstract
FLASH radiotherapy (FLASH-RT) is an emerging ultra-high dose (>40 Gy/s) delivery that promises to improve the therapeutic potential by limiting toxicities compared to conventional RT while maintaining similar tumor eradication efficacy. Image guidance is an essential component of modern RT that should be harnessed to meet the special emerging needs of FLASH-RT and its associated high risks in planning and delivering of such ultra-high doses in short period of times. Hence, this contribution will elaborate on the imaging requirements and possible solutions in the entire chain of FLASH-RT treatment, from the planning, through the setup and delivery with online in vivo imaging and dosimetry, up to the assessment of biological mechanisms and treatment response. In patient setup and delivery, higher temporal sampling than in conventional RT should ensure that the short treatment is delivered precisely to the targeted region. Additionally, conventional imaging tools such as cone-beam computed tomography will continue to play an important role in improving patient setup prior to delivery, while techniques based on magnetic resonance imaging or positron emission tomography may be extremely valuable for either linear accelerator (Linac) or particle FLASH therapy, to monitor and track anatomical changes during delivery. In either planning or assessing outcomes, quantitative functional imaging could supplement conventional imaging for more accurate utilization of the biological window of the FLASH effect, selecting for or verifying things such as tissue oxygen and existing or transient hypoxia on the relevant timescales of FLASH-RT delivery. Perhaps most importantly at this time, these tools might help improve the understanding of the biological mechanisms of FLASH-RT response in tumor and normal tissues. The high dose deposition of FLASH provides an opportunity to utilize pulse-to-pulse imaging tools such as Cherenkov or radiation acoustic emission imaging. These could provide individual pulse mapping or assessing the 3D dose delivery superficially or at tissue depth, respectively. In summary, the most promising components of modern RT should be used for safer application of FLASH-RT, and new promising developments could be advanced to cope with its novel demands but also exploit new opportunities in connection with the unique nature of pulsed delivery at unprecedented dose rates, opening a new era of biological image guidance and ultrafast, pulse-based in vivo dosimetry.
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Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, USA,Corresponding Author:
| | - Brian W. Pogue
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA,Department of Medical Physics, University of Wisconsin-Madison, WI 53705, USA
| | - Rongxiao Zhang
- Giesel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Ibrahim Oraiqat
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, Garching 85748, Germany
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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21
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Magnetic Resonance Guided Radiotherapy for Head and Neck Cancers. J Clin Med 2022; 11:jcm11051388. [PMID: 35268479 PMCID: PMC8911481 DOI: 10.3390/jcm11051388] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/23/2022] [Accepted: 02/28/2022] [Indexed: 11/16/2022] Open
Abstract
Radiotherapy is an integral component of head/neck squamous cell carcinomas (HNSCCs) treatment, and technological developments including advances in image-guided radiotherapy over the past decades have offered improvements in the technical treatment of these cancers. Integration of magnetic resonance imaging (MRI) into image guidance through the development of MR-guided radiotherapy (MRgRT) offers further potential for refinement of the techniques by which HNSCCs are treated. This article provides an overview of the literature supporting the current use of MRgRT for HNSCC, challenges with its use, and developing research areas.
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22
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Wang H, Zhou Y, Wang X, Zhang Y, Ma C, Liu B, Kong Q, Yue N, Xu Z, Nie K. Reproducibility and Repeatability of CBCT-Derived Radiomics Features. Front Oncol 2021; 11:773512. [PMID: 34869015 PMCID: PMC8637922 DOI: 10.3389/fonc.2021.773512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/27/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R2 as correlation parameters. Results The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs ≥ 0.80), while the kurtosis measurements were consistent in H&N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs ≥ 0.80) between packages in both H&N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test–retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&N and pelvic cohorts, showed R2 ≥0.8, meaning a high correlation with volume. Conclusion The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.,Institute of Modern Physics, Fudan University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Chi Ma
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Bo Liu
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ning Yue
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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Pakela JM, Matuszak MM, Ten Haken RK, McShan DL, El Naqa I. Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer. Phys Med Biol 2021; 66. [PMID: 34587597 DOI: 10.1088/1361-6560/ac2b80] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 09/29/2021] [Indexed: 01/01/2023]
Abstract
Objective.Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on pre-learned patterns from a well-defined patient population.Approach.Here, we investigate the feasibility of predicting patient anatomical changes, defined as a joint state of volume and daily setup changes, across a fractionated treatment schedule using two approaches. The first is based on a new joint framework employing quantum mechanics in combination with deep recurrent neural networks, denoted QRNN. The second approach is developed based on a classical framework, which models patient changes as a Markov process, denoted MRNN. We evaluated the performance characteristics of these two approaches on a dataset of 125 head and neck cancer patients, which was supplemented by synthetic data generated using a generative adversarial network. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) scores.Main results.The MRNN framework had slightly better performance than the QRNN framework, with MRNN (QRNN) validation AUC scores of 0.742±0.021 (0.675±0.036), 0.709±0.026 (0.656±0.021), 0.724±0.036 (0.652±0.044), and 0.698±0.016 (0.605±0.035) for system state vector sizes of 4, 6, 8, and 10, respectively. Of these, only the results from the two higher order states had statistically significant differences(p<0.05).A similar trend was also observed when the models were applied to an external testing dataset of 20 patients, yielding MRNN (QRNN) AUC scores of 0.707 (0.623), 0.687 (0.608), 0.723 (0.669), and 0.697 (0.609) for states vectors sizes of 4, 6, 8, and 10, respectively.Significance.These results suggest that both stochastic models have potential value in predicting patient changes during the course of adaptive radiotherapy.
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Affiliation(s)
- Julia M Pakela
- Applied Physics Program, University of Michigan, Ann Arbor, MI, United States of America.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Daniel L McShan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
| | - Issam El Naqa
- Applied Physics Program, University of Michigan, Ann Arbor, MI, United States of America.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States of America
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24
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Adaptive radiation therapy: When, how and what are the benefits that literature provides? Cancer Radiother 2021; 26:622-636. [PMID: 34688548 DOI: 10.1016/j.canrad.2021.08.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with. MATERIALS AND METHODS An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review. RESULTS The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning. CONCLUSION ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.
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25
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Sumner W, Kim SS, Vitzthum L, Moore K, Atwood T, Murphy J, Miyauchi S, Califano JA, Mell LK, Mundt AJ, Sharabi AB. End of treatment cone-beam computed tomography (CBCT) is predictive of radiation response and overall survival in oropharyngeal squamous cell carcinoma. Radiat Oncol 2021; 16:147. [PMID: 34372887 PMCID: PMC8351145 DOI: 10.1186/s13014-021-01871-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Image guidance in radiation oncology has resulted in significant improvements in the accuracy and precision of radiation therapy (RT). Recently, the resolution and quality of cone beam computed tomography (CBCT) for image guidance has increased so that tumor masses and lymph nodes are readily detectable and measurable. During treatment of head and neck squamous cell carcinoma (HNSCC), on-board CBCT setup imaging is routinely obtained; however, this CBCT imaging data is not utilized to predict patient outcomes. Here, we analyzed whether changes in CBCT measurements obtained during a course of radiation therapy correlate with responses on routine 3-month follow-up diagnostic imaging and overall survival (OS). MATERIALS/METHODS Patients with oropharyngeal primary tumors who received radiation therapy between 2015 and 2018 were included. Anatomical measurements were collected of largest nodal conglomerate (LNC) at CT simulation, end of radiation treatment (EOT CBCT), and routine 3-month post-RT imaging. At each timepoint anteroposterior (AP), mediolateral (ML) and craniocaudal (CC) measurements were obtained and used to create a 2-dimensional (2D) maximum. RESULTS CBCT data from 64 node positive patients were analyzed. The largest nodal 2D maximum and CC measurements on EOT CBCT showed a statistically significant correlation with complete response on 3-month post-RT imaging (r = 0.313, p = 0.02 and r = 0.318, p = 0.02, respectively). Furthermore, patients who experienced a 30% or greater reduction in the CC dimension had improved OS (Binary Chi-Square HR 4.85, p = 0.028). CONCLUSION Decreased size of pathologic lymph nodes measured using CBCT setup imaging during a radiation course correlates with long term therapeutic response and overall survival of HNSCC patients. These results indicate that CBCT setup imaging may have utility as an early predictor of treatment response in oropharyngeal HNSCC.
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MESH Headings
- Adult
- Aged
- Aged, 80 and over
- Carcinoma, Squamous Cell/diagnostic imaging
- Carcinoma, Squamous Cell/mortality
- Carcinoma, Squamous Cell/pathology
- Carcinoma, Squamous Cell/radiotherapy
- Cone-Beam Computed Tomography/methods
- Female
- Follow-Up Studies
- Humans
- Image Processing, Computer-Assisted/methods
- Male
- Middle Aged
- Organs at Risk/radiation effects
- Oropharyngeal Neoplasms/diagnostic imaging
- Oropharyngeal Neoplasms/mortality
- Oropharyngeal Neoplasms/pathology
- Oropharyngeal Neoplasms/radiotherapy
- Prognosis
- Radiotherapy Dosage
- Radiotherapy Planning, Computer-Assisted/methods
- Radiotherapy, Image-Guided/methods
- Radiotherapy, Image-Guided/mortality
- Radiotherapy, Intensity-Modulated/methods
- Radiotherapy, Intensity-Modulated/mortality
- Retrospective Studies
- Survival Rate
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Affiliation(s)
- Whitney Sumner
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
| | - Sangwoo S Kim
- School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Lucas Vitzthum
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
| | - Kevin Moore
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
| | - Todd Atwood
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
| | - James Murphy
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Sayuri Miyauchi
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
| | - Joseph A Califano
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
- Department of Surgery, Division of Otolaryngology, University of California, San Diego, La Jolla, CA, USA
| | - Loren K Mell
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Arno J Mundt
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA
| | - Andrew B Sharabi
- Department of Radiation Medicine and Applied Sciences, San Diego Moores Cancer Center, University of California, 3855 Health Sciences Drive, MC 0843, La Jolla, CA, 92093, USA.
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
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Cavallo A, Iacovelli NA, Facchinetti N, Rancati T, Alfieri S, Giandini T, Cicchetti A, Fallai C, Ingargiola R, Licitra L, Locati L, Cavalieri S, Pignoli E, Romanello DA, Valdagni R, Orlandi E. Modelling Radiation-Induced Salivary Dysfunction during IMRT and Chemotherapy for Nasopharyngeal Cancer Patients. Cancers (Basel) 2021; 13:cancers13163983. [PMID: 34439136 PMCID: PMC8392585 DOI: 10.3390/cancers13163983] [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: 05/16/2021] [Revised: 07/24/2021] [Accepted: 07/30/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Radiation-induced xerostomia is one of the most prevalent adverse effects of head and neck cancer treatment, and it could seriously affect patients' qualities of life. It results primarily from damage to the salivary glands, but its onset and severity may also be influenced by other patient-, tumour-, and treatment-related factors. We aimed to build and validate a predictive model for acute salivary dysfunction (aSD) for locally advanced nasopharyngeal carcinoma (NPC) patients by combining clinical and dosimetric factors. METHODS A cohort of consecutive NPC patients treated curatively with IMRT and chemotherapy at 70 Gy (2-2.12 Gy/fraction) were utilised. Parotid glands (cPG, considered as a single organ) and the oral cavity (OC) were selected as organs-at-risk. The aSD was assessed at baseline and weekly during RT, grade ≥ 2 aSD chosen as the endpoint. Dose-volume histograms were reduced to the Equivalent Uniform Dose (EUD). Dosimetric and clinical/treatment features selected via LASSO were inserted into a multivariable logistic model. Model validation was performed on two cohorts of patients with prospective aSD, and scored using the same schedule/scale: a cohort (NPC_V) of NPC patients (as in model training), and a cohort of mixed non-NPC head and neck cancer patients (HNC_V). RESULTS The model training cohort included 132 patients. Grade ≥ 2 aSD was reported in 90 patients (68.2%). Analyses resulted in a 4-variables model, including doses of up to 98% of cPG (cPG_D98%, OR = 1.04), EUD to OC with n = 0.05 (OR = 1.11), age (OR = 1.08, 5-year interval) and smoking history (OR = 1.37, yes vs. no). Calibration was good. The NPC_V cohort included 38 patients, with aSD scored in 34 patients (89.5%); the HNC_V cohort included 93 patients, 77 with aSD (92.8%). As a general observation, the incidence of aSD was significantly different in the training and validation populations (p = 0.01), thus impairing calibration-in-the-large. At the same time, the effect size for the two dosimetric factors was confirmed. Discrimination was also satisfactory in both cohorts: AUC was 0.73, and 0.68 in NPC_V and HNC_V cohorts, respectively. CONCLUSION cPG D98% and the high doses received by small OC volumes were found to have the most impact on grade ≥ 2 acute xerostomia, with age and smoking history acting as a dose-modifying factor. Findings on the development population were confirmed in two prospectively collected validation populations.
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Affiliation(s)
- Anna Cavallo
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Nicola Alessandro Iacovelli
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Nadia Facchinetti
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Clinical Trial Center, 27100 Pavia, Italy
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
- Correspondence:
| | - Salvatore Alfieri
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
- Centro di Riferimento Oncologico di Aviano (PN) CRO IRCCS, Department of Medical Oncology, 33018 Aviano, Italy
| | - Tommaso Giandini
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
| | - Carlo Fallai
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Rossana Ingargiola
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Radiation Oncology Clinical Department, 27100 Pavia, Italy
| | - Lisa Licitra
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
- Department of Oncolgy and Hemato-Oncology, Università Degli Studi di Milano, 20122 Milan, Italy
| | - Laura Locati
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
| | - Stefano Cavalieri
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
| | - Emanuele Pignoli
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Domenico Attilio Romanello
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Riccardo Valdagni
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
- Department of Oncolgy and Hemato-Oncology, Università Degli Studi di Milano, 20122 Milan, Italy
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Ester Orlandi
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Radiation Oncology Clinical Department, 27100 Pavia, Italy
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Tomasik B, Papis-Ubych A, Stawiski K, Fijuth J, Kędzierawski P, Sadowski J, Stando R, Bibik R, Graczyk Ł, Latusek T, Rutkowski T, Fendler W. Serum MicroRNAs as Xerostomia Biomarkers in Patients With Oropharyngeal Cancer Undergoing Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 111:1237-1249. [PMID: 34280472 DOI: 10.1016/j.ijrobp.2021.07.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 07/05/2021] [Accepted: 07/11/2021] [Indexed: 02/09/2023]
Abstract
PURPOSE Severe xerostomia is noted in the majority of patients irradiated for oropharyngeal cancer. Extracellular microRNAs (miRNAs) may serve as effective tools allowing prediction of radiation-related toxicity. The aim of this study was to create an efficient prognostic miRNA-based test for severe, patient-rated xerostomia 3 months after primary treatment. METHODS AND MATERIALS This prospective study enrolled patients with oropharyngeal cancer treated between 2016 and 2018 in 3 centers in Poland. The primary endpoint was severe (grade ≥3) xerostomia as assessed by the European Organisation for Research and Treatment of Cancer H&N-35 questionnaires. Initially, a group of 10 patients with severe xerostomia was randomly selected and matched with a comparative group of 10 patients without severe xerostomia. Samples were collected before radiation therapy, after receiving 20 Gy, and within 24 hours after treatment completion. Quantitative real-time polymerase chain reaction arrays (QIAGEN, Hilden, Germany) were used to quantify expression levels of 752 miRNAs in the serum at all timepoints. The resulting logistic-regression based model was validated in additional 60 patients: 30 with grade >3 xerostomia and 30 without. RESULTS Of 152 eligible patients, we successfully recruited 111 patients. Severe xerostomia 3 months after treatment was reported by 63 patients (56.8%). Mean dose delivered to parotid glands was higher in both the exploratory and validation cohort. The model based on miR-185-5p and miR-425-5p expression levels measured before the start of radiation therapy had an area under the curve of 0.96 (95% confidence interval, 0.88-1.00). The model based on the same miRNAs remained robust when parameters were measured after 20 Gy (area under the curve 0.90; 95% confidence interval, 0.75-1.00). These results were confirmed in the validation group. In the validation group, preradiation therapy model application yielded 73.3% sensitivity and 80.0% specificity. In the samples taken after 20 Gy, the same 2 miRNAs yielded 67.7% sensitivity and 72.4% specificity. The model including pretreatment miR-185-5p and miR-425-5p levels together with mean parotid dose yielded 90.0% sensitivity and 80.0% specificity. In the validation cohort, this model yielded 80.6% sensitivity and 55.2% specificity. The model based on miRNA levels measured after 20 Gy and mean parotid dose had 80.0% sensitivity and 100% specificity in the exploratory group. In the validation cohort its performance fell to 71.0% sensitivity and 58.6% specificity. CONCLUSIONS Serum expression levels of miR-425-5p and miR-185-5p measured before the start of radiation therapy or during therapy (after 20 Gy) had significant prognostic value for the occurrence of severe xerostomia 3 months after treatment completion. The variability explained by miRNAs appears to be, at least partially, independent from that related to the dosimetric data.
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Affiliation(s)
- Bartłomiej Tomasik
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Postgraduate School of Molecular Medicine, Medical University of Warsaw, Poland; Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Anna Papis-Ubych
- Department of Radiotherapy, N. Copernicus Memorial Regional Specialist Hospital, Lodz, Poland
| | - Konrad Stawiski
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
| | - Jacek Fijuth
- Department of Radiotherapy, Medical University of Lodz, Lodz, Poland
| | - Piotr Kędzierawski
- Radiotherapy Department, Holycross Cancer Centre, Kielce, Poland; Jan Kochanowski University, Collegium Medicum, Kielce, Poland
| | - Jacek Sadowski
- Radiotherapy Department, Holycross Cancer Centre, Kielce, Poland
| | - Rafał Stando
- Radiotherapy Department, Holycross Cancer Centre, Kielce, Poland
| | - Robert Bibik
- Department of Radiation Oncology, Oncology Center of Radom, Radom, Poland
| | - Łukasz Graczyk
- Department of Radiation Oncology, Oncology Center of Radom, Radom, Poland
| | - Tomasz Latusek
- Radiotherapy Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice branch, Gliwice, Poland
| | - Tomasz Rutkowski
- I Radiation and Clinical Oncology Department, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice branch, Gliwice, Poland
| | - Wojciech Fendler
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland; Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
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Mohan V, Bruin NM, van de Kamer JB, Sonke JJ, Vogel WV. The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments. Eur J Nucl Med Mol Imaging 2021; 48:3762-3775. [PMID: 33687522 PMCID: PMC8484246 DOI: 10.1007/s00259-021-05284-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/24/2021] [Indexed: 11/26/2022]
Abstract
Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.
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Affiliation(s)
- V Mohan
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - N M Bruin
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J B van de Kamer
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - J-J Sonke
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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Magnetic Resonance and CT Imaging Biomarkers for Prediction of Acute and Chronic Radiation-Induced Xerostomia. MAGNETOCHEMISTRY 2020. [DOI: 10.3390/magnetochemistry7010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Xerostomia is a common adverse effect of radiation therapy at the head and neck area. Radiation-induced xerostomia can be severe and detrimental for the quality of life. Clinicians and radiologists have focused on the prevention of xerostomia as feasible, which has been significantly improved in the recent decades with the use of the contemporary radiation technology. However, radiation-induced xerostomia still remains one of the most devastating side effects of radiation therapy. Clinical risk factors have been identified, but the variation of its incidence and presentation has turned the focus on the investigation of parameters that would be able to predict the onset of acute or chronic xerostomia for each individual patient. Recently, potential imaging parameters and biomarkers are investigated in order for early prediction of the incidence and severity of xerostomia. Here, we compile the resulting imaging biomarkers as have been identified in the recent literature based on MRI and CT performed in correlation with radiation therapy. The identification of such biomarkers is very promising for the prevention and control of xerostomia in the head and neck radiation setting.
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Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020; 10:1708. [PMID: 33117669 PMCID: PMC7574641 DOI: 10.3389/fonc.2020.01708] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 07/30/2020] [Indexed: 12/14/2022] Open
Abstract
Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.
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Affiliation(s)
- Isacco Desideri
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Mauro Loi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Giulio Francolini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Carlotta Becherini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Lorenzo Livi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Pierluigi Bonomo
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
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Weppler S, Schinkel C, Kirkby C, Smith W. Lasso logistic regression to derive workflow-specific algorithm performance requirements as demonstrated for head and neck cancer deformable image registration in adaptive radiation therapy. Phys Med Biol 2020; 65:195013. [PMID: 32580170 DOI: 10.1088/1361-6560/ab9fc8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
As automation in radiation oncology becomes more common, it is important to determine which algorithms are equivalent for a given workflow. Often, algorithm comparisons are performed in isolation; however, clinical context can provide valuable insight into the importance of algorithm features and error magnification in subsequent workflow steps. We propose a strategy for deriving workflow-specific algorithm performance requirements. We considered two independent workflows indicating the need for radiotherapy treatment replanning for 15 head and neck cancer patients (15 planning CTs, 105 on-unit CBCTs). Each workflow was based on a different deformable image registration (DIR) algorithm. Differences in DIR output were assessed using three sets of QA metrics: (1) conventional, (2) workflow-specific, (3) a combination of (1) and (2). For a given set of algorithm metrics, lasso logistic regression modeled the probability of discrepant replan indications. Varying the minimum probability needed to predict a workflow discrepancy produced receiver operating characteristic (ROC) curves. ROC curves were compared using sensitivity, specificity, and the area under the curve (AUC). A heuristic then derived simple algorithm performance requirements. Including workflow-specific QA metrics improved AUC from 0.70 to 0.85, compared to the use of conventional metrics alone. Algorithm performance requirements had high sensitivity of 0.80, beneficial for replan assessments, with specificity of 0.57. This was an improvement over a naïve application of conventional QA criteria, which had sensitivity of 0.57 and specificity of 0.68. In addition, the algorithm performance requirements indicated practical refinements of conventional QA tolerances, indicated where auxiliary workflow processes should be standardized, and may be used to prioritize structures for manual review. Our algorithm performance requirements outperformed current comparison recommendations and provided practical means for ensuring workflow equivalence. This strategy may aid in trial credentialing, algorithm development, and streamlining expert adjustment of workflow output.
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Affiliation(s)
- Sarah Weppler
- Department of Physics and Astronomy, University of Calgary, 2500 University Dr NW, Calgary, Alberta, T2N 1N4, Canada. Department of Medical Physics, Tom Baker Cancer Centre, 1331 29 St NW, Calgary, Alberta, T2N 4N2, Canada. Author to whom any correspondence should be addressed
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Wilkie JR, Mierzwa ML, Casper KA, Mayo CS, Schipper MJ, Eisbruch A, Worden FP, El Naqa I, Viglianti BL, Rosen BS. Predicting late radiation-induced xerostomia with parotid gland PET biomarkers and dose metrics. Radiother Oncol 2020; 148:30-37. [DOI: 10.1016/j.radonc.2020.03.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 01/21/2020] [Accepted: 03/27/2020] [Indexed: 02/06/2023]
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Qin Q, Shi A, Zhang R, Wen Q, Niu T, Chen J, Qiu Q, Wan Y, Sun X, Xing L. Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 2020; 11:964-972. [PMID: 32061061 PMCID: PMC7113065 DOI: 10.1111/1759-7714.13349] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/15/2022] Open
Abstract
Background Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.
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Affiliation(s)
- Qingjin Qin
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Anhui Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.,Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ran Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China
| | - Qiang Wen
- Department of Oncology, Shandong Provincial Hospital, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Tianye Niu
- Nuclear & Radiological Engineering and Medical Physics Programs Woodruff School of Mechanical Engineering Georgia Institute of Technology, Atlanta, Georgia
| | - Jinhu Chen
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang
| | - Xiaorong Sun
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China.,Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Ligang Xing
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
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Stieb S, Kiser K, van Dijk L, Livingstone NR, Elhalawani H, Elgohari B, McDonald B, Ventura J, Mohamed ASR, Fuller CD. Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am 2019; 34:293-306. [PMID: 31739950 DOI: 10.1016/j.hoc.2019.09.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Imaging in radiation oncology is essential for the evaluation of treatment response in tumors and organs at risk. This influences further treatment decisions and could possibly be used to adapt therapy. This review article focuses on the currently used imaging modalities for response assessment in radiation oncology and gives an overview of new and promising techniques within this field.
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Affiliation(s)
- Sonja Stieb
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Kendall Kiser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Lisanne van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Nadia Roxanne Livingstone
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Brigid McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Juan Ventura
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Abdallah Sherif Radwan Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
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Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, McNutt TR, Quon H, Lee J. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol 2019; 14:131. [PMID: 31358029 PMCID: PMC6664784 DOI: 10.1186/s13014-019-1339-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/17/2019] [Indexed: 12/24/2022] Open
Abstract
Purpose To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. Methods A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017–2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). Results Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). Conclusion Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy. Electronic supplementary material The online version of this article (10.1186/s13014-019-1339-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Luke Peng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA.
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Liu Y, Shi H, Huang S, Chen X, Zhou H, Chang H, Xia Y, Wang G, Yang X. Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images. Quant Imaging Med Surg 2019; 9:1288-1302. [PMID: 31448214 DOI: 10.21037/qims.2019.07.08] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Acute xerostomia is the most common side effect of radiation therapy (RT) for head and neck (H&N) malignancies. Investigating radiation-induced changes of computed tomography (CT) radiomics in parotid glands (PGs) and saliva amount (SA) can predict acute xerostomia during the RT for nasopharyngeal cancer (NPC). Methods CT and SA data from 35 patients with stages I-IVB were randomly collected from an NPC clinical trial registered on the clinicaltrials.gov (ID: NCT01762514). All patients received radical treatment based on intensity-modulated RT (IMRT) with a prescription dose of 68.1 Gy in 30 fractions. The patients' ages ranged 24-72 years, and each patient had five CT sets acquired at treatment position: at the 0th, 10th, 20th, 30th fractions during the RT, and at 3-month later after the RT. The PGs for each CT set were delineated by a radiation oncologist and verified independently by another. Patients' saliva was collected every other 10 days during the RT. Acute xerostomia was evaluated based on the RTOG acute toxicity scoring and the SA. In total, 1,703 radiomics features were calculated for PGs from each CT set, including feature value at 0th fraction (FV0F), FV10F, and delta FV (ΔFV10F-0F), respectively. Extensive experiments were conducted to achieve the optimal results. RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while linear regression was used for predicting SA30F. Four more patients were added for independent testing. Results Substantial changes in various radiomics metrics of PGs were observed during the RT. Eight normalized feature value (NFV), selected from NFV0F, predicted SA10F with a mean square error (MSE) of 0.9042 and a R2 score of 0.7406. Fourteen NFV, selected from ΔNFV10F-0F, NFV0F, and NFV10F to predict SA30F, showed the best predictive ability with an MSE of 0.0569. The model predicted the level of acute xerostomia with a precision of 0.9220 and a sensitivity of 100%, compared to the clinical observed SA. For the independent test, the MSE of PSA30F was 0.0233. Conclusions This study demonstrated that radiation-induced acute xerostomia level could be early predicted based on the SA and radiomics changes of the PGs during IMRT delivery. SA, NFV0F, NFV10F, and especially ΔNFV10F-0F provided the best performance on acute xerostomia prediction for individual patient based on RidgeCV_RFE_LinearRegression method of delta radiomics.
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Affiliation(s)
- Yanxia Liu
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Hongyu Shi
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Sijuan Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Xiaochuan Chen
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Huimin Zhou
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.,Department of Oncology, the Seventy-fourth Group Army Hospital of the Chinese People's Liberation Army, Guangzhou 510318, China
| | - Hui Chang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Yunfei Xia
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Guohua Wang
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Xin Yang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
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Men K, Geng H, Zhong H, Fan Y, Lin A, Xiao Y. A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial. Int J Radiat Oncol Biol Phys 2019; 105:440-447. [PMID: 31201897 DOI: 10.1016/j.ijrobp.2019.06.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 05/14/2019] [Accepted: 06/04/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. METHODS AND MATERIALS A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade ≥2 occurring in the 12th month of radiation therapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. RESULTS The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows: accuracy: 0.76, 0.74, 0.73, 0.65, 0.64, and 0.56; sensitivity: 0.76, 0.72, 0.77, 0.59, 0.72, and 0.75; specificity: 0.76, 0.76, 0.71, 0.69, 0.59, and 0.43; F-score: 0.70, 0.68, 0.69, 0.56, 0.60, and 0.57; and area under the receiver operator characteristic curve: 0.84, 0.82, 0.78, 0.70, 0.74, and 0.68, respectively. CONCLUSIONS The proposed model uses 3D rCNN filters to extract low- and high-level spatial features and to achieve promising performance. This is a potentially effective model for predicting objective toxicity for supporting clinical decision making.
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Affiliation(s)
- Kuo Men
- University of Pennsylvania, Philadelphia, Pennsylvania; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Huaizhi Geng
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Haoyu Zhong
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Alexander Lin
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ying Xiao
- University of Pennsylvania, Philadelphia, Pennsylvania
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Matuszak MM, Kashani R, Green M, Lee C, Cao Y, Owen D, Jolly S, Mierzwa M. Functional Adaptation in Radiation Therapy. Semin Radiat Oncol 2019; 29:236-244. [PMID: 31027641 DOI: 10.1016/j.semradonc.2019.02.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The promise of adaptive therapy to improve outcomes in radiation oncology has been an area of interest and research in the community for many years. One of the sources of data that can be used to drive adaptive therapy is functional information about the tumor or normal tissues. This avenue of adaptation includes many potential sources of data including global markers and functional imaging. Global markers can be assessments derived from blood measurements, patient functional testing, and circulating tumor material and functional imaging data comprises spatial physiological information from various imaging studies such as positron emission tomography, magnetic resonance imaging, and single photon emission computed tomography. The goal of functional adaptation is to use these functional data to adapt radiation therapy to improve patient outcomes. While functional adaptation holds a lot of promise, there are challenges such as quantifying and minimizing uncertainties, streamlining clinical implementation, determining the ideal way to incorporate information within treatment plan optimization, and proving the clinical benefit through trials. This paper will discuss the types of functional information currently being used for adaptation, highlight several areas where functional adaptation has been studied, and introduce some of the barriers to more widespread clinical implementation.
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Affiliation(s)
- Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Michael Green
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Dawn Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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Soisson E, Guerrieri P, Balasubramanian S, Ahamad A, Moran JM, Joiner MC, Dominello M, Burmeister J. Three discipline collaborative radiation therapy special debate: All head and neck cancer patients with intact tumors/nodes should have scheduled adaptive replanning performed at least once during the course of radiotherapy. J Appl Clin Med Phys 2019; 20:7-11. [PMID: 30983132 PMCID: PMC6523017 DOI: 10.1002/acm2.12587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 03/28/2019] [Accepted: 03/28/2019] [Indexed: 11/07/2022] Open
Affiliation(s)
- Emilie Soisson
- Department of RadiologyUniversity of VermontBurlingtonVTUSA
- Medical Physics UnitMcGill UniversityMontrealQCCanada
| | | | | | - Anesa Ahamad
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineSylvester Comprehensive Cancer CenterMiamiFLUSA
| | - Jean M. Moran
- Department of Radiation OncologyUniversity of MichiganAnn ArborMIUSA
| | - Michael C. Joiner
- Department of OncologyWayne State University School of MedicineDetroitMIUSA
| | - Michael Dominello
- Department of OncologyWayne State University School of MedicineDetroitMIUSA
| | - Jay Burmeister
- Department of OncologyWayne State University School of MedicineDetroitMIUSA
- Gershenson Radiation Oncology CenterBarbara Ann Karmanos Cancer InstituteDetroitMIUSA
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van Timmeren JE, van Elmpt W, Leijenaar RTH, Reymen B, Monshouwer R, Bussink J, Paelinck L, Bogaert E, De Wagter C, Elhaseen E, Lievens Y, Hansen O, Brink C, Lambin P. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol 2019; 136:78-85. [PMID: 31015133 PMCID: PMC6598851 DOI: 10.1016/j.radonc.2019.03.032] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 03/07/2019] [Accepted: 03/29/2019] [Indexed: 11/23/2022]
Abstract
Longitudinal CBCT radiomics does not show added prognostic information for NSCLC. A CT-radiomics model could be validated in an external validation dataset. Dataset heterogeneity and small cohort sizes could cause poor validation performance.
Background and purpose The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients. Materials and methods This retrospective study includes a training dataset of 141 stage I–IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo)radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan–Meier curves and c-index. Results The radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence. Conclusion In this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets.
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Affiliation(s)
- Janna E van Timmeren
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands
| | - Ralph T H Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Leen Paelinck
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Evelien Bogaert
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | | | - Elamin Elhaseen
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Yolande Lievens
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Olfred Hansen
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Carsten Brink
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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Castelli J, Simon A, Lafond C, Perichon N, Rigaud B, Chajon E, De Bari B, Ozsahin M, Bourhis J, de Crevoisier R. Adaptive radiotherapy for head and neck cancer. Acta Oncol 2018; 57:1284-1292. [PMID: 30289291 DOI: 10.1080/0284186x.2018.1505053] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Large anatomical variations can be observed during the treatment course intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC), leading to potential dose variations. Adaptive radiotherapy (ART) uses one or several replanning sessions to correct these variations and thus optimize the delivered dose distribution to the daily anatomy of the patient. This review, which is focused on ART in the HNC, aims to identify the various strategies of ART and to estimate the dosimetric and clinical benefits of these strategies. MATERIAL AND METHODS We performed an electronic search of articles published in PubMed/MEDLINE and Science Direct from January 2005 to December 2016. Among a total of 134 articles assessed for eligibility, 29 articles were ultimately retained for the review. Eighteen studies evaluated dosimetric variations without ART, and 11 studies reported the benefits of ART. RESULTS Eight in silico studies tested a number of replanning sessions, ranging from 1 to 6, aiming primarily to reduce the dose to the parotid glands. The optimal timing for replanning appears to be early during the first two weeks of treatment. Compared to standard IMRT, ART decreases the mean dose to the parotid gland from 0.6 to 6 Gy and the maximum dose to the spinal cord from 0.1 to 4 Gy while improving target coverage and homogeneity in most studies. Only five studies reported the clinical results of ART, and three of those studies included a non-randomized comparison with standard IMRT. These studies suggest a benefit of ART in regard to decreasing xerostomia, increasing quality of life, and increasing local control. Patients with the largest early anatomical and dose variations are the best candidates for ART. CONCLUSION ART may decrease toxicity and improve local control for locally advanced HNC. However, randomized trials are necessary to demonstrate the benefit of ART before using the technique in routine practice.
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Affiliation(s)
- J. Castelli
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - A. Simon
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - C. Lafond
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - N. Perichon
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - B. Rigaud
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
| | - E. Chajon
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
| | - B. De Bari
- Radiotherapy Department, CHU Jean-Minjoz, Besançon, France
| | - M. Ozsahin
- Radiotherapy Department, Lausanne University Hospital, Lausanne, Switzerland
| | - J. Bourhis
- Radiotherapy Department, Lausanne University Hospital, Lausanne, Switzerland
| | - R. de Crevoisier
- Radiotherapy Department, Centre Eugene Marquis, Rennes, France
- INSERM U1099 LTSI, Rennes, France
- Université de Rennes 1, Rennes, France
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