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Lai TY, Hu YW, Wang TH, Chen JP, Shiau CY, Huang PI, Lai IC, Liu YM, Huang CC, Tseng LM, Huang N, Liu CJ. Estimating the risk of major adverse cardiac events following radiotherapy for left breast cancer using a modified generalized Lyman normal-tissue complication probability model. Breast 2024; 77:103788. [PMID: 39181040 PMCID: PMC11386497 DOI: 10.1016/j.breast.2024.103788] [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: 06/27/2024] [Revised: 07/31/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND We introduced an adapted Lyman normal-tissue complication probability (NTCP) model, incorporating clinical risk factors and censored time-to-event data, to estimate the risk of major adverse cardiac events (MACE) following left breast cancer radiotherapy (RT). MATERIALS AND METHODS Clinical characteristics and MACE data of 1100 women with left-side breast cancer receiving postoperative RT from 2005 to 2017 were retrospectively collected. A modified generalized Lyman NTCP model based on the individual left ventricle (LV) equivalent uniform dose (EUD), accounting for clinical risk factors and censored data, was developed using maximum likelihood estimation. Subgroup analysis was performed for low-comorbidity and high-comorbidity groups. RESULTS Over a median follow-up 7.8 years, 64 patients experienced MACE, with higher mean LV dose in affected individuals (4.1 Gy vs. 2.9 Gy). The full model accounting for clinical factors identified D50 = 43.3 Gy, m = 0.59, and n = 0.78 as the best-fit parameters. The threshold dose causing a 50 % probability of MACE was lower in the high-comorbidity group (D50 = 30 Gy) compared to the low-comorbidity group (D50 = 45 Gy). Predictions indicated that restricting LV EUD below 5 Gy yielded a 10-year relative MACE risk less than 1.3 and 1.5 for high-comorbidity and low-comorbidity groups, respectively. CONCLUSION Patients with comorbidities are more susceptible to cardiac events following breast RT. The proposed modified generalized Lyman model considers nondosimetric risk factors and addresses incomplete follow-up for late complications, offering comprehensive and individualized MACE risk estimates post-RT.
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Affiliation(s)
- Tzu-Yu Lai
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C; Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - Yu-Wen Hu
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - Ti-Hao Wang
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan, R.O.C; Department of Medicine, China Medical University, Taichung, Taiwan, R.O.C; Everfortune.AI, Taichung, Taiwan, R.O.C
| | - Jui-Pin Chen
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C
| | - Cheng-Ying Shiau
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C
| | - Pin-I Huang
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - I-Chun Lai
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - Yu-Ming Liu
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - Chi-Cheng Huang
- Comprehensive Breast Health Center & Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan, R.O.C
| | - Ling-Ming Tseng
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C; Comprehensive Breast Health Center & Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C
| | - Nicole Huang
- Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C; Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C
| | - Chia-Jen Liu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C; Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C; Division of Transfusion Medicine, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, R.O.C; Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, R.O.C.
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Yang X, Dai Z, Song H, Gong H, Li X. A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients. Sci Rep 2024; 14:18628. [PMID: 39128912 PMCID: PMC11317486 DOI: 10.1038/s41598-024-69293-8] [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: 05/24/2024] [Accepted: 08/02/2024] [Indexed: 08/13/2024] Open
Abstract
Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R2). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.
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Affiliation(s)
- Xiong Yang
- Department of Radiation Oncology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Zeyi Dai
- The Institute for Advanced Studies, Wuhan University, Wuhan, 430072, Hubei, China
| | - Hongbing Song
- Department of Radiation Oncology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China
| | - Hongyun Gong
- Department of Radiation Oncology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
| | - Xiangpan Li
- Department of Radiation Oncology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, Hubei, China.
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Raaijmakers CPJ, Roesink JM, Houweling AC, Braam PM, Dijkema T. In Regard to Dennstädt et al. Int J Radiat Oncol Biol Phys 2023; 115:1004-1005. [PMID: 36822770 DOI: 10.1016/j.ijrobp.2022.11.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/06/2022] [Indexed: 02/24/2023]
Affiliation(s)
| | - Judith M Roesink
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht The Netherlands
| | - Anette C Houweling
- Department of Radiation Oncology University Medical Center Utrecht Utrecht, The Netherlands
| | - Petra M Braam
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tim Dijkema
- Department of Radiation Oncology Radboud University Medical Center, Nijmegen, The Netherlands
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Ong ALK, Knight K, Panettieri V, Dimmock M, Tuan JKL, Tan HQ, Wright C. Predictive modelling for late rectal and urinary toxicities after prostate radiotherapy using planned and delivered dose. Front Oncol 2022; 12:1084311. [PMID: 36591496 PMCID: PMC9800591 DOI: 10.3389/fonc.2022.1084311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Background and purpose Normal tissue complication probability (NTCP) parameters derived from traditional 3D plans may not be ideal in defining toxicity outcomes for modern radiotherapy techniques. This study aimed to derive parameters of the Lyman-Kutcher-Burman (LKB) NTCP model using prospectively scored clinical data for late gastrointestinal (GI) and genitourinary (GU) toxicities for high-risk prostate cancer patients treated using volumetric-modulated-arc-therapy (VMAT). Dose-volume-histograms (DVH) extracted from planned (DP) and accumulated dose (DA) were used. Material and methods DP and DA obtained from the DVH of 150 prostate cancer patients with pelvic-lymph-nodes irradiation treated using VMAT were used to generate LKB-NTCP parameters using maximum likelihood estimations. Defined GI and GU toxicities were recorded up to 3-years post RT follow-up. Model performance was measured using Hosmer-Lemeshow goodness of fit test and the mean area under the receiver operating characteristics curve (AUC). Bootstrapping method was used for internal validation. Results For mild-severe (Grade ≥1) GI toxicity, the model generated similar parameters based on DA and DP DVH data (DA-D50:71.6 Gy vs DP-D50:73.4; DA-m:0.17 vs DP-m:0.19 and DA/P-n 0.04). The 95% CI for DA-D50 was narrower and achieved an AUC of >0.6. For moderate-severe (Grade ≥2) GI toxicity, DA-D50 parameter was higher and had a narrower 95% CI (DA-D50:77.9 Gy, 95% CI:76.4-79.6 Gy vs DP-D50:74.6, 95% CI:69.1-85.4 Gy) with good model performance (AUC>0.7). For Grade ≥1 late GU toxicity, D50 and n parameters for DA and DP were similar (DA-D50: 58.8 Gy vs DP-D50: 59.5 Gy; DA-n: 0.21 vs DP-n: 0.19) with a low AUC of<0.6. For Grade ≥2 late GU toxicity, similar NTCP parameters were attained from DA and DP DVH data (DA-D50:81.7 Gy vs DP-D50:81.9 Gy; DA-n:0.12 vs DP-n:0.14) with an acceptable AUCs of >0.6. Conclusions The achieved NTCP parameters using modern RT techniques and accounting for organ motion differs from QUANTEC reported parameters. DA-D50 of 77.9 Gy for GI and DA/DP-D50 of 81.7-81.9 Gy for GU demonstrated good predictability in determining the risk of Grade ≥2 toxicities especially for GI derived D50 and are recommended to incorporate as part of the DV planning constraints to guide dose escalation strategies while minimising the risk of toxicity.
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Affiliation(s)
- Ashley Li Kuan Ong
- Division of Radiation Oncology, National Cancer Centre, Singapore, Singapore,Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,*Correspondence: Ashley Li Kuan Ong,
| | - Kellie Knight
- Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
| | - Vanessa Panettieri
- Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,Alfred Health Radiation Oncology, Alfred Hospital, Melbourne, VIC, Australia
| | - Mathew Dimmock
- Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,School of Allied Health Professions, Keele University, Staffordshire, United Kingdom
| | | | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre, Singapore, Singapore
| | - Caroline Wright
- Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
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