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Tzikas A, Lavdas E, Kehagias D, Amdur R, Mendenhall W, Sheets N, Green R, Chera B, Mavroidis P. NTCP modelling of xerostomia after radiotherapy for oropharyngeal cancer using the PRO-CTCAE and CTCAE scoring systems at different time-points post-RT. Phys Med 2023; 116:103169. [PMID: 37989042 DOI: 10.1016/j.ejmp.2023.103169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 09/30/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023] Open
Abstract
PURPOSE This study aims at determining the parameter values of three normal tissue complication probability (NTCP) models for the contralateral parotid gland, contralateral submandibular gland (SMG) and contralateral salivary glands regarding the endpoint of xerostomia 6-24 months after radiotherapy for oropharynx cancer. METHODS The treatment and outcome data of 231 patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma are analyzed. 60 Gy intensity modulated radiotherapy was delivered to all the patients. The presence and severity of xerostomia was recorded (pre- and post- radiotherapy) by the PRO-CTCAE and the CTCAE scoring systems. In both scoring systems, patients with a change in symptom severity (from baseline) of ≥ 2 were considered responders. RESULTS Xerostomia was observed in 61.3 %, 39.2 %, 28.6 % and 27.0 % of the patients based on the PRO-CTCAE scoring system at 6-, 12-, 18- and 24-months post-RT, respectively. The AUCs of the contralateral salivary glands ranged between 0.58-0.64 in the LKB model with the gEUD ranging between 20.3 Gy and 24.7 Gy. CONCLUSIONS Based on the PRO-CTCAE scores, mean dose < 22 Gy, V50 < 10 % for the contralateral salivary glands and mean dose < 18 Gy, V45 < 10 % for the contralateral parotid were found to significantly reduce by a factor of 2-3 the risk for radiation induced xerostomia that is observed at 6-24 months post-RT, respectively. Also, gEUD < 22 Gy to the contralateral salivary glands and < 18 Gy to the contralateral parotid was found to significantly reduce the risk for radiation induced xerostomia that is observed at 6-24 months post-RT by 2.0-2.3 times.
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Affiliation(s)
- Athanasios Tzikas
- University of West Attica, Department of Biomedical Sciences, Athens, Greece
| | - Eleftherios Lavdas
- University of West Attica, Department of Biomedical Sciences, Athens, Greece
| | - Dimitrios Kehagias
- University of West Attica, Department of Biomedical Sciences, Athens, Greece
| | - Robert Amdur
- Department of Radiation Oncology, University of Florida Hospitals, Gainesville, FL, United States
| | - William Mendenhall
- Department of Radiation Oncology, University of Florida Hospitals, Gainesville, FL, United States
| | - Nathan Sheets
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC, United States
| | - Rebecca Green
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC, United States
| | - Bhishamjit Chera
- Department of Radiation Oncology, MUSC Hollings Cancer Center, Charleston, SC, United States
| | - Panayiotis Mavroidis
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC, United States.
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Tajiki S, Joya M, Gharekhani V, Richeson D, Gholami S. A systematic review of the normal tissue complication probability models and parameters: Head and neck cancers treated with conformal radiotherapy. Head Neck 2023; 45:3146-3156. [PMID: 37767820 DOI: 10.1002/hed.27469] [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: 03/24/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 09/29/2023] Open
Abstract
This systematic review study aims to provide comprehensive data on different radiobiological models, parameters, and endpoints used for calculating the normal tissue complication probability (NTCP) based on clinical data from head and neck cancer patients treated with conformal radiotherapy. A systematic literature search was carried out according to the PRISMA guideline for the identification of relevant publications in six electronic databases of Embase, PubMed, Scopus, and Google Scholar to July 2022 using specific keywords in the paper's title and abstract. The initial search resulted in 1368 articles for all organs for the review article about the NTCP parameters. One hundred and seventy-eight articles were accepted for all organs with complete parameters for the mentioned models and finally, 20 head and neck cancer articles were accepted for review. Analysis of the studies shows that the Lyman-Kutcher-Burman (LKB) model properly links the NTCP curve parameters to the postradiotherapy endpoints. In the LKB model for esophagus, the minimum, and maximum corresponding parameters were reported as TD50 = 2.61 Gy with grade ≥3 radiation-induced esophagitis endpoints as the minimum TD50 and TD50 = 68 Gy as the maximum ones. nmin = 0.06, nmax = 1.04, mmin = 0.1, and mmax = 0.65, respectively. Unfortunately, there was not a wide range of published articles on other organs at risk like ear or cauda equina except Burman et al. (Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys Ther. 1991;21:123-135). Findings suggest that the validation of different radiobiological models and their corresponding parameters need to be investigated in vivo and in vitro for developing a more accurate NTCP model to be used for radiotherapy treatment planning optimization.
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Affiliation(s)
- Sareh Tajiki
- Radiotherapy Oncology Research Centre, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Musa Joya
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahideh Gharekhani
- Department of Radiobiology, Faculty of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
| | - Dylan Richeson
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Somayeh Gholami
- Department of Radiation Oncology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
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van Rijn-Dekker MI, van Luijk P, Schuit E, van der Schaaf A, Langendijk JA, Steenbakkers RJHM. Prediction of Radiation-Induced Parotid Gland-Related Xerostomia in Patients With Head and Neck Cancer: Regeneration-Weighted Dose. Int J Radiat Oncol Biol Phys 2023; 117:750-762. [PMID: 37150262 DOI: 10.1016/j.ijrobp.2023.04.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 05/09/2023]
Abstract
PURPOSE Despite improvements to treatment, patients with head and neck cancer (HNC) still experience radiation-induced xerostomia due to salivary gland damage. The stem cells of the parotid gland (PG), concentrated in the gland's main ducts (stem cell rich [SCR] region), play a critical role in the PG's response to radiation. Treatment optimization requires a dose metric that properly accounts for the relative contributions of dose to this SCR region and the PG's remainder (non-SCR region) to the risk of xerostomia in normal tissue complication probability (NTCP) models for xerostomia. MATERIALS AND METHODS Treatment and toxicity data of 1013 prospectively followed patients with HNC treated with definitive radiation therapy (RT) were used. The regeneration-weighted dose, enabling accounting for the hypothesized different effects of dose to the SCR and non-SCR region on the risk of xerostomia, was defined as Dreg PG = Dmean SCR region + r × Dmean non-SCR region, where Dreg is the regeneration-weighted dose, Dmean is the mean dose, and r is the weighting factor. Considering the different volumes of these regions, r > 3.6 in Dreg PG demonstrates an enhanced effect of the SCR region. The most predictive value of r was estimated in 102 patients of a previously published trial testing stem cell sparing RT. For each endpoint, Dreg PG, dose to other organs, and clinical factors were used to develop NTCP models using multivariable logistic regression analysis in 663 patients. The models were validated in 350 patients. RESULTS Dose to the contralateral PG was associated with daytime, eating-related, and physician-rated grade ≥2 xerostomia. Consequently, r was estimated and found to be smaller than 3.6 for most PG function-related endpoints. Therefore, the contribution of Dmean SCR region to the risk of xerostomia was larger than predicted by Dmean PG. Other frequently selected predictors were pretreatment xerostomia and Dmean oral cavity. The validation showed good discrimination and calibration. CONCLUSIONS Tools for clinical implementation of stem cell sparing RT were developed: regeneration-weighted dose to the parotid gland that accounted for regional differences in radiosensitivity within the gland and NTCP models that included this new dose metric and other prognostic factors.
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Affiliation(s)
- Maria I van Rijn-Dekker
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van Luijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Roel J H M Steenbakkers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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Zhao DW, Teng F, Meng LL, Fan WJ, Luo YR, Jiang HY, Chen NX, Zhang XX, Yu W, Cai BN, Zhao LJ, Wang PG, Ma L. Development and validation of a nomogram for prediction of recovery from moderate-severe xerostomia post-radiotherapy in nasopharyngeal carcinoma patients. Radiother Oncol 2023; 184:109683. [PMID: 37120102 DOI: 10.1016/j.radonc.2023.109683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/28/2023] [Accepted: 04/21/2023] [Indexed: 05/01/2023]
Abstract
PURPOSE Aim to create and validate a comprehensive nomogram capable of accurately predicting the transition from moderate-severe to normal-mild xerostomia post-radiotherapy (postRT) in patients with nasopharyngeal carcinoma (NPC). Materials and methods We constructed and internally verified a prediction model using a primary cohort comprising 223 patients who were pathologically diagnosed with NPC from February 2016 to December 2019. LASSO regression model was used to identify the clinical factors and relevant variables (the pre-radiotherapy (XQ-preRT) and immediate post-radiotherapy (XQ-postRT) xerostomia questionnaire scores, as well as the mean dose (Dmean) delivered to the parotid gland (PG), submandibular gland (SMG), sublingual gland (SLG), tubarial gland (TG), and oral cavity). Cox proportional hazards regression analysis was performed to develop the prediction model, which was presented as a nomogram. The models' performance with regard to calibration, discrimination, and clinical usefulness was evaluated. The external validation cohort comprised 78 patients. Results Due to better discrimination and calibration in the training cohort, age, gender, XQ-postRT, and Dmean of PG, SMG, and TG were included in the individualized prediction model (C-index of 0.741 (95% CI:0.717 to 0.765). Verification of the nomogram's performance in internal and external validation cohorts revealed good discrimination (C-index of 0.729 (0.692 to 0.766) and 0.736 (0.702 to 0.770), respectively) and calibration. Decision curve analysis revealed that the nomogram was clinically useful. The 12-month and 24-month moderate-severe xerostomia rate was statistically lower in the SMG-spared arm (28.4% (0.230 to 35.2) and 5.2% (0.029 to 0.093), respectively) than that in SMG-unspared arm (56.8% (0.474 to 0.672) and 12.5% (0.070 to 0.223), respectively), with an HR of 1.84 (95%CI: 1.412 to 2.397, p= 0.000). The difference in restricted mean survival time for remaining moderate-severe xerostomia between the two arms at 24 months was 5.757 months (95% CI, 3.863 to 7.651; p=0.000). Conclusion The developed nomogram, incorporating age, gender, XQ-postRT, and Dmean to PG, SMG, and TG, can be used for predicting recovery from moderate-severe xerostomia post-radiotherapy in NPC patients. Sparing SMG is highly important for the patient's recovery.
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Affiliation(s)
- Da-Wei Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China; Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Feng Teng
- Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing, China
| | - Ling-Ling Meng
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen-Jun Fan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China; Department of Radiation Oncology, Armed Police Forces Corps Hospital of Henan Province, Zhengzhou, 450052, China
| | - Yan-Rong Luo
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Hua-Yong Jiang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Nan-Xiang Chen
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin-Xin Zhang
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wei Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bo-Ning Cai
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lu-Jun Zhao
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Pei-Guo Wang
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Lin Ma
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
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Yahya N, Manan HA. Quality of Life and Patient-Reported Outcomes Following Proton Therapy for Oropharyngeal Carcinoma: A Systematic Review. Cancers (Basel) 2023; 15:cancers15082252. [PMID: 37190180 DOI: 10.3390/cancers15082252] [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: 12/08/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Complex anatomy surrounding the oropharynx makes proton therapy (PT), especially intensity-modulated PT (IMPT), a potentially attractive option due to its ability to reduce the volume of irradiated healthy tissues. Dosimetric improvement may not translate to clinically relevant benefits. As outcome data are emerging, we aimed to evaluate the evidence of the quality of life (QOL) and patient-reported outcomes (PROs) following PT for oropharyngeal carcinoma (OC). MATERIALS AND METHODS We searched PubMed and Scopus electronic databases (date: 15 February 2023) to identify original studies on QOL and PROs following PT for OC. We employed a fluid strategy in the search strategy by tracking citations of the initially selected studies. Reports were extracted for information on demographics, main results, and clinical and dose factor correlates. Quality assessment was performed using the NIH's Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. The PRISMA guidelines were followed in the preparation of this report. RESULTS Seven reports were selected, including one from a recently published paper captured from citation tracking. Five compared PT and photon-based therapy, although none were randomized controlled trials. Most endpoints with significant differences favored PT, including xerostomia, cough, need for nutritional supplements, dysgeusia, food taste, appetite, and general symptoms. However, some endpoints favored photon-based therapy (sexual symptoms) or showed no significant difference (e.g., fatigue, pain, sleep, mouth sores). The PROs and QOL improve following PT but do not appear to return to baseline. CONCLUSION Evidence suggests that PT causes less QOL and PRO deterioration than photon-based therapy. Biases due to the non-randomized study design remain obstacles to a firm conclusion. Whether or not PT is cost-effective should be the subject of further investigation.
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Affiliation(s)
- Noorazrul Yahya
- Diagnostic Imaging and Radiotherapy, Center for Diagnostic, Therapeutic and Investigative Studies (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia
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Samant P, Ruysscher DD, Hoebers F, Canters R, Hall E, Nutting C, Maughan T, Van den Heuvel F. Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation. Clin Transl Radiat Oncol 2023; 39:100595. [PMID: 36880063 PMCID: PMC9984444 DOI: 10.1016/j.ctro.2023.100595] [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: 01/31/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Background and purpose A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML. Materials and methods Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set. Results We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC. Conclusion We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.
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Key Words
- AB, AdaBooost (aka Adaptive Boosting)
- Clinical radiobiology
- DA, Dual Annealing
- DE, Differential Evolution
- DT, Decision Tree
- DVH, Dose Volume Histogram
- GB, Gradient Boost
- GD, Gradient Descent
- GMD, Generalized Mean Dose
- Head and Neck Cancer
- LKB, Lyman Kutcher Burman
- LR, Logistic Regression
- ML, Machine Learning
- Machine Learning
- NTCP, Normal Tissue Complication Probability
- Normal Tissue Complication Probability
- OAR, Organ(s) at Risk
- RT, Radiotherapy
- Radiotherapy
- Treatment Planning
- Xerostomia
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Affiliation(s)
- Pratik Samant
- Oxford University Hospitals NHS Foundation Trust, Radiotherapy Physics, Oxford, United Kingdom
- University of Oxford, Department of Oncology, Oxford, United Kingdom
| | - Dirk de Ruysscher
- Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands
| | - Frank Hoebers
- Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands
| | - Richard Canters
- Maastricht University Medical Centre, Department of Radiation Oncology (Maastro), Maastricht, The Netherlands
| | - Emma Hall
- Institute of Cancer Research, Division of Clinical Studies, Sutton, United Kingdom
| | - Chris Nutting
- Institute of Cancer Research, Division of Radiotherapy and Imaging, Sutton, United Kingdom
| | - Tim Maughan
- University of Oxford, Department of Oncology, Oxford, United Kingdom
| | - Frank Van den Heuvel
- University of Oxford, Department of Oncology, Oxford, United Kingdom
- Zuidwest Radiotherapeutisch Instituut, Physics, Vlissingen (Flushing), The Netherlands
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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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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: 0] [Impact Index Per Article: 0] [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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Cai XL, Hu J, Shi JT, Chen JS, Bai SM, Liu YM, Yu XL. Contouring the accessory parotid gland and major parotid glands as a single organ at risk during nasopharyngeal carcinoma radiotherapy. Front Oncol 2022; 12:958961. [DOI: 10.3389/fonc.2022.958961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposeNo research currently exists on the role of the accessory parotid gland (APG) in nasopharyngeal carcinoma (NPC). We thereby aimed to assess the effects of APG on the dosimetry of the parotid glands (PGs) during NPC radiotherapy and evaluate its predictive value for late xerostomia.Material and methodsThe clinical data of 32 NPC patients with radiological evidence of the APG treated at Sun Yat-sen Memorial Hospital between November 2020 and February 2021 were retrospectively reviewed. Clinically approved treatment plans consisted of only the PGs as an organ at risk (OAR) (Plan1), while Plan2 was designed by considering the APG as a single organ at risk (OAR). The APG on Plan1 was delineated, and dose–volume parameters of the PGs alone (PG-only) and of the combined structure (PG+APG) were analyzed in both plans. The association of such dosimetric parameters in Plan1 with xerostomia at 6–9 months post-radiotherapy was further explored.ResultsFifty APGs were found, with a mean volume of 3.3 ± 0.2 ml. Significant differences were found in all dosimetric parameters between Plan1 and Plan2. The mean dose and percentage of OAR volumes receiving more than 30 Gy significantly reduced in Plan1 itself (PG-only vs. PG+APG, 39.55 ± 0.83 Gy vs. 37.71 ± 0.75 Gy, and 62.00 ± 2.00% vs. 57.41 ± 1.56%, respectively; p < 001) and reduced further in Plan2 (PG+APG, 36.40 ± 0.74 Gy, and 55.54 ± 1.61%, respectively; p < 0.001). Three additional patients met the dose constraint in Plan1, which increased to seven in Plan2. With APG included, the predictive power of the dosimetric parameters for xerostomia tended to improve, although no significant differences were observed.ConclusionAPG is anatomically similar to the PGs. Our findings suggest the potential benefits of treating the APG and PGs as a single OAR during radiotherapy (RT) of NPC by improving PG sparing.
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Li M, Zhang J, Zha Y, Li Y, Hu B, Zheng S, Zhou J. A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy. BMC Oral Health 2022; 22:239. [PMID: 35715856 PMCID: PMC9206362 DOI: 10.1186/s12903-022-02269-0] [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: 07/19/2021] [Accepted: 06/02/2022] [Indexed: 12/24/2022] Open
Abstract
Background This study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors. Methods Totally, 365 patients with locoregionally advanced NPC who underwent radical radiotherapy were randomly divided into the training set (n = 255) and the testing set (n = 110) at a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC) and accuracy were analyzed to evaluate the predictive performance of the models. Results In the RF model for predicting xerostomia, the sensitivity was 1.000 (95%CI 1.000–1.000), the PPV was 0.990 (95%CI 0.975–1.000), the NPV was 1.000 (95%CI 1.000–1.000), the AUC was 0.999 (95%CI 0.997–1.000) and the accuracy was 0.992 (95%CI 0.981–1.000) in the training set. The sensitivity was 0.933 (95%CI 0.880–0.985), the PPV was 0.933 (95%CI 0.880–0.985), and the AUC was 0.915 (95%CI 0.860–0.970) in the testing set. Hypertension, age, total radiotherapy dose, dose at 50% of the left parotid volume, mean dose to right parotid gland, mean dose to oral cavity, and course of induction chemotherapy were important variables associated with the risk of xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The AUC of DTC model for predicting xerostomia was 0.769 (95%CI 0.666–0.872) in the testing set. The AUC of the XGB model for predicting xerostomia was 0.834 (0.753–0.916) in the testing set. The RF model showed the good predictive ability with the AUC of 0.986 (95%CI 0.972–1.000) in the training set, and 0.766 (95%CI 0.626–0.905) in the testing set for identifying patients who at high risk of Grade 3 xerostomia in those with high risk of xerostomia. Conclusions An RF model for predicting xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy and an RF model for predicting Grade 3 xerostomia in those with high risk of xerostomia showed good predictive ability.
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Affiliation(s)
- Minying Li
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China.
| | - Jingjing Zhang
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
| | - Yawen Zha
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
| | - Yani Li
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
| | - Bingshuang Hu
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
| | - Siming Zheng
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
| | - Jiaxiong Zhou
- Department of Oncology Radiotherapy, Zhongshan City People's Hospital, No.2 Sunwen Middle Road, Shiqi District, Zhongshan City, 528403, Guangdong, China
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11
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Chao M, El Naqa I, Bakst RL, Lo YC, Peñagarícano JA. Cluster model incorporating heterogeneous dose distribution of partial parotid irradiation for radiotherapy induced xerostomia prediction with machine learning methods. Acta Oncol 2022; 61:842-848. [PMID: 35527717 DOI: 10.1080/0284186x.2022.2073187] [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: 11/01/2022]
Abstract
PURPOSE A cluster model incorporating heterogeneous dose distribution within the parotid gland was developed and validated retrospectively for radiotherapy (RT) induced xerostomia prediction with machine learning (ML) techniques. METHODS Sixty clusters were obtained at 1 Gy step size with threshold doses ranging from 1 to 60 Gy, for each of the enrolled 155 patients with HNC from three institutions. Feature clusters were selected with the neighborhood component analysis (NCA) and subsequently fed into four supervised ML models for xerostomia prediction comparison: support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), and random forest (RF). The predictive performance of each model was evaluated using cross validation resampling with the area-under-the-curves (AUC) of the receiver-operating-characteristic (ROC). The xerostomia predicting capacity using testing data was assessed with accuracy, sensitivity, and specificity for these models and three cluster connectivity choices. Mean dose based logistic regression served as the benchmark for evaluation. RESULTS Feature clusters identified by NCA fell in three threshold dose ranges: 5-15Gy, 25-35Gy, and 45-50Gy. Mean dose predictive power was 15% lower than that of the cluster model using the logistic regression classifier. Model validation demonstrated that kNN model outperformed slightly other three models but no substantial difference was observed. Applying the fine-tuned models to testing data yielded that the mean accuracy from SVM, kNN and NB models were between 0.68 and 0.7 while that of RF was ∼0.6. SVM model yielded the best sensitivity (0.76) and kNN model delivered consistent sensitivity and specificity. This is consistent with cross validation. Clusters calculated with three connectivity choices exhibited minimally different predictions. CONCLUSION Compared to mean dose, the proposed cluster model has shown its improvement as the xerostomia predictor. When combining with ML techniques, it could provide a clinically useful tool for xerostomia prediction and facilitate decision making during radiotherapy planning for patients with HNC.
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Affiliation(s)
- Ming Chao
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Richard L. Bakst
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - Yeh-Chi Lo
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, USA
| | - José A. Peñagarícano
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
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12
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Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
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Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
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13
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Lim SB, Lee N, Zakeri K, Greer P, Fuangrod T, Coffman F, Cerviño L, Lovelock DM. Can the Risk of Dysphagia in Head and Neck Radiation Therapy Be Predicted by an Automated Transit Fluence Monitoring Process During Treatment? A First Comparative Study of Patient Reported Quality of Life and the Fluence-Based Decision Support Metric. Technol Cancer Res Treat 2021; 20:15330338211027906. [PMID: 34190006 PMCID: PMC8252347 DOI: 10.1177/15330338211027906] [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] [Indexed: 11/15/2022] Open
Abstract
PURPOSE/OBJECTIVE(S) The additional personnel and imaging procedures required for Adaptive Radiation Therapy (ART) pose a challenge for a broad implementation. We hypothesize that a change in transit fluence during the treatment course is correlated with the change of quality of life and thus can be used as a replanning trigger. MATERIALS/METHODS Twenty-one head and neck cancer (HNC) patients filled out an MD Anderson Dysphagia Inventory (MDADI) questionnaire, before-and-after the radiotherapy treatment course. The transit fluence was measured by the Watchdog (WD) in-vivo portal dosimetry system. The patients were monitored with daily WD and weekly CBCTs. The region of interest (ROI) of each patient was defined as the outer contour of the patient between approximate spine levels C1 to C4, essentially the neck and mandible inside the beam's eye view. The nth day integrated transit fluence change, Δϕn, and the volume change, ΔVROI, of the ROI of each patient was calculated from the corresponding WD and CBCT measurements. The correlation between MDADI scores and age, gender, planning mean dose to salivary glands <Dsg>, weight change ΔW, ΔVROI, and Δϕn, were analyzed using the ranked-Pearson correlation. RESULTS No statistically significant correlation was found for age, gender and ΔW. <Dsg> was found to have clinically important correlation with functional MDADI (ρ = -0.39, P = 0.081). ΔVROI was found to have statistically significant correlation of 0.44, 0.47 and 0.44 with global, physical and functional MDADI (P-value < 0.05). Δϕn was found to have statistically significant ranked-correlation (-0.46, -0.46 and -0.45) with physical, functional and total MDADI (P-value < 0.05). CONCLUSION A transit fluence based decision support metric (DSM) is statistically correlated with the dysphagia risk. It can not only be used as an early signal in assisting clinicians in the ART patient selection for replanning, but also lowers the resource barrier of ART implementation.
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Affiliation(s)
- Seng Boh Lim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kaveh Zakeri
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Greer
- Calvary Mater Newcastle Hospital, New South Wales, Australia.,University of Newcastle, New South Wales, Australia
| | - Todsaporn Fuangrod
- HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | | | - Laura Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - D Michael Lovelock
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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14
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Teng F, Fan W, Luo Y, Xu S, Gong H, Ge R, Zhang X, Wang X, Ma L. A Risk Prediction Model by LASSO for Radiation-Induced Xerostomia in Patients With Nasopharyngeal Carcinoma Treated With Comprehensive Salivary Gland-Sparing Helical Tomotherapy Technique. Front Oncol 2021; 11:633556. [PMID: 33718219 PMCID: PMC7953987 DOI: 10.3389/fonc.2021.633556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop a least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) model to predict radiation-induced xerostomia in patients with nasopharyngeal carcinoma (NPC) treated with comprehensive salivary gland–sparing helical tomotherapy technique. Methods and Materials LASSO with the extended bootstrapping technique was used to build multivariable NTCP models to predict factors of patient-reported xerostomia relieved by 50% and 80% compared with the level at the end of radiation therapy within 1 year and 2 years, R50-1year and R80-2years, in 203 patients with NPC. The model assessment was based on 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC). Results The prediction model by LASSO with 10-fold cross-validation showed that radiation-induced xerostomia recovery could be predicted by prognostic factors of R50-1year (age, gender, T stage, UICC/AJCC stage, parotid Dmean, oral cavity Dmean, and treatment options) and R80-2years (age, gender, T stage, UICC/AJCC stage, oral cavity Dmean, N stage, and treatment options). These prediction models also demonstrated a good performance by the AUC. Conclusion The prediction models of R50-1year and R80-2years by LASSO with 10-fold cross-validation were recommended to validate the NTCP model before comprehensive salivary gland–sparing radiation therapy in patients with NPC.
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Affiliation(s)
- Feng Teng
- Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing, China.,Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China
| | - Wenjun Fan
- Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University, Foshan, China.,Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Radiation Oncology, Armed Police Corps Hospital of Henan Province, Zhengzhou, China
| | - Yanrong Luo
- Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China.,Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hanshun Gong
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ruigang Ge
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xinxin Zhang
- Departmant of Otorhinolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoning Wang
- School of Data Science and Media Intelligence, Communication University of China, Beijing, China.,State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, Medical School of the Chinese People's Liberation Army (PLA), Beijing, China.,Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China
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15
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Generalizability assessment of head and neck cancer NTCP models based on the TRIPOD criteria. Radiother Oncol 2020; 146:143-150. [DOI: 10.1016/j.radonc.2020.02.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/06/2020] [Accepted: 02/17/2020] [Indexed: 12/23/2022]
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16
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Bruno JS, Miranda‐Silva W, Guedes VDS, Parahyba CJ, Moraes FYD, Fregnani ER. Digital Workflow for Producing Oral Positioning Radiotherapy Stents for Head and Neck Cancer. J Prosthodont 2020; 29:448-452. [DOI: 10.1111/jopr.13155] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/29/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Julia Stephanie Bruno
- Oral Medicine ServiceHospital Sírio‐Libanês Rua Adma Jafet, 91 São Paulo SP 01308‐050 Brazil
| | - Wanessa Miranda‐Silva
- Oral Medicine ServiceHospital Sírio‐Libanês Rua Adma Jafet, 91 São Paulo SP 01308‐050 Brazil
| | | | | | - Fabio Ynoe de Moraes
- Division of Radiation Oncology, Department of OncologyQueen's University, Kingston Health Science Centre 15 Arch Street Kingston, ON Kingston Canada, K7L 3N6 Canada
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17
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Radaideh KM. Dosimetric impact of weight loss and anatomical changes at organs at risk during intensity-modulated radiotherapy for head-and-neck cancer. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2020. [DOI: 10.1080/16878507.2020.1731125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Chao M, Wei J, Lo YC, Peñagarícano JA. Dose cluster model parameterization of the parotid gland in irradiation of head and neck cancer. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 43:10.1007/s13246-019-00829-3. [PMID: 31792724 DOI: 10.1007/s13246-019-00829-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 11/25/2019] [Indexed: 12/19/2022]
Abstract
To explore the parotid normal tissue complication probability (NTCP) modeling with percolation-based dose clusters for head-and-neck patients receiving concomitant chemotherapy and radiation therapy. Cluster models incorporating the spatial dose distribution in the parotid gland were developed to evaluate the radiation induced complication. Cluster metrics including the mean cluster size (NMCS) and the largest cluster size both normalized by the gland volume (NSLC) were evaluated and scrutinized against the benchmark NTCP. Two fitting strategies to the Lyman-Kutcher-Burman (LKB) model using the maximum likelihood method were devised: the volume parameter n fixed at 1.0 (mean dose model) and unrestricted (full LKB model). The fitted parameters TD50 and m were assessed with the LKB NTCP models with the available xerostomia data. NSLC was a better metric than NMCS with reference to the LKB model and strong correlation (r ~ 0.95) was observed between NTCP and NSLC. The mean dose model returned the parameter TD50 (39.9 Gy) and m (0.4) from the NSLC of threshold dose at around 40 Gy. Drastically different TD50 and m values were obtained from the fittings via the full LKB model, where the threshold dose would be near 27 Gy. Bootstrapping analyses further confirmed the fitting outcomes. Strong correlation with the traditional NTCP models revealed that the cluster model could achieve what NTCP models attain and may offer additional information. Parameterization of the model indicated that the model could have different predictions from current clinical recommendations. Further investigation using toxicity data is under way to validate the cluster model.
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Affiliation(s)
- Ming Chao
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, 10029, USA.
| | - Jie Wei
- Department of Computer Science, City College of New York, New York, NY, 10031, USA
| | - Yeh-Chi Lo
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY, 10029, USA
| | - José A Peñagarícano
- Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA
- Department of Radiation Oncology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
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19
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Astaburuaga R, Gabryś HS, Sánchez-Nieto B, Floca RO, Klüter S, Schubert K, Hauswald H, Bangert M. Incorporation of Dosimetric Gradients and Parotid Gland Migration Into Xerostomia Prediction. Front Oncol 2019; 9:697. [PMID: 31417872 PMCID: PMC6684756 DOI: 10.3389/fonc.2019.00697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/15/2019] [Indexed: 12/23/2022] Open
Abstract
Purpose: Due to the sharp gradients of intensity-modulated radiotherapy (IMRT) dose distributions, treatment uncertainties may induce substantial deviations from the planned dose during irradiation. Here, we investigate if the planned mean dose to parotid glands in combination with the dose gradient and information about anatomical changes during the treatment improves xerostomia prediction in head and neck cancer patients. Materials and methods: Eighty eight patients were retrospectively analyzed. Three features of the contralateral parotid gland were studied in terms of their association with the outcome, i.e., grade ≥ 2 (G2) xerostomia between 6 months and 2 years after radiotherapy (RT): planned mean dose (MD), average lateral dose gradient (GRADX), and parotid gland migration toward medial (PGM). PGM was estimated using daily megavoltage computed tomography (MVCT) images. Three logistic regression models where analyzed: based on (1) MD only, (2) MD and GRADX, and (3) MD, GRADX, and PGM. Additionally, the cohort was stratified based on the median value of GRADX, and a univariate analysis was performed to study the association of the MD with the outcome for patients in low- and high-GRADX domains. Results: The planned MD failed to recognize G2 xerostomia patients (AUC = 0.57). By adding the information of GRADX (second model), the model performance increased to AUC = 0.72. The addition of PGM (third model) led to further improvement in the recognition of the outcome (AUC = 0.79). Remarkably, xerostomia patients in the low-GRADX domain were successfully identified (AUC = 0.88) by the MD alone. Conclusions: Our results indicate that GRADX and PGM, which together serve as a proxy of dosimetric changes, provide valuable information for xerostomia prediction.
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Affiliation(s)
- Rosario Astaburuaga
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Medical Faculty of Heidelberg, Universität Heidelberg, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Institute of Physics, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Hubert S Gabryś
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Medical Faculty of Heidelberg, Universität Heidelberg, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
| | | | - Ralf O Floca
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Medical Image Computing, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Sebastian Klüter
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Kai Schubert
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Henrik Hauswald
- Heidelberg Institute for Radiation Oncology, Heidelberg, Germany.,Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany.,Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - Mark Bangert
- Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
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Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician. Semin Radiat Oncol 2019; 29:258-273. [PMID: 31027643 DOI: 10.1016/j.semradonc.2019.02.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
For nearly 2 decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye toward improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing the writer Elbert Hubbard, "Art is not a thing, it is a way."
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Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1319-1329. [PMID: 30003997 DOI: 10.1016/j.ijrobp.2018.06.048] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 05/29/2018] [Accepted: 06/27/2018] [Indexed: 12/23/2022]
Abstract
PURPOSE To determine whether serial cone beam computed tomography (CBCT) images taken during head and neck radiation therapy (HNR) can improve chronic xerostomia prediction. METHODS AND MATERIALS In a retrospective analysis, parotid glands (PGs) were delineated on daily kV CBCT images using deformable image registration for 119 HNR patients (60 or 70 Gy in 2 Gy fractions over 6 or 7 weeks). Deformable image registration accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient and mean distance to agreement in comparison with manually drawn contours. Average weekly changes in CBCT-measured mean Hounsfield unit intensity and volume were calculated for each PG relative to week 1. Dose-volume histogram statistics were extracted from each plan, and interactions among dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at 1 year after HNR. Models including CBCT delta imaging features were compared with clinical and dose-volume histogram-only models using area under the receiver operating characteristic curve (AUC) for grade ≥1 and grade ≥2 xerostomia prediction. RESULTS All patients experienced end-treatment PG volume reduction with mean (range) ipsilateral and contralateral PG shrinkage of 19.6% (0.9%-58.4%) and 17.7% (4.4%-56.3%), respectively. Midtreatment volume change was highly correlated with mean PG dose (r = -0.318, P < 1e-6). Incidence of grade ≥1 and grade ≥2 xerostomia was 65% and 16%, respectively. For grade ≥1 xerostomia prediction, the delta-imaging model had an AUC of 0.719 (95% confidence interval [CI], 0.603-0.830), compared with 0.709 (95% CI, 0.603-0.815) for the dose/clinical model. For grade ≥2 xerostomia prediction, the dose/clinical model had an AUC of 0.692 (95% CI, 0.615-0.770), and the addition of contralateral PG changes modestly improved predictive performance, with an AUC of 0.776 (0.643-0.912). CONCLUSIONS The rate of CBCT-measured PG image feature changes improves prediction over dose alone for chronic xerostomia prediction. Analysis of CBCT images acquired for treatment positioning may provide an inexpensive monitoring system to support toxicity-reducing adaptive radiation therapy.
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Gabryś HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. Front Oncol 2018; 8:35. [PMID: 29556480 PMCID: PMC5844945 DOI: 10.3389/fonc.2018.00035] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 02/01/2018] [Indexed: 01/13/2023] Open
Abstract
Purpose The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. Material and methods A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0–6 months (early), 6–15 months (late), 15–24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis. Results NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60). The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior–posterior (AUC = 0.72) and the right–left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right–left (AUCs > 0.78), and the anterior–posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose–volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods. Conclusion We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.
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Affiliation(s)
- Hubert S Gabryś
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Medical Faculty of Heidelberg, Heidelberg University, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - Florian Buettner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Florian Sterzing
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.,Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Henrik Hauswald
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.,Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mark Bangert
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
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