A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT

被引:0
|
作者
Luo, Wei [1 ]
Xiu, Zijian [1 ]
Wang, Xiaoqin [2 ]
Mcgarry, Ronald [1 ]
Allen, Joshua [3 ]
机构
[1] Univ Kentucky, Dept Radiat Med, 800 Rose St, Lexington, KY 40536 USA
[2] Univ Kentucky, Dept Radiol, 800 Rose St, Lexington, KY 40536 USA
[3] AdventHlth, 2501 N Orange Ave, Orlando, FL 32804 USA
关键词
tumor response assessment; stereotactic radiation therapy (SBRT); cone-beam computerized tomography (CBCT); tumor area; tumor linear attenuation coefficient (mu); tumor contrast-to-noise ratio (CNR); RADIATION-THERAPY; CANCER; RADIOTHERAPY; REGRESSION;
D O I
10.3390/cancers16010020
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: We aimed to develop a new tumor response assessment method for lung SBRT. Methods: In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (mu) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and mu (R-A, R-CNR, R-mu) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images. Results: R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (R-C) was determined as 1.1. The results based on R-C achieved a 94% accuracy, 94% specificity, and 90% sensitivity. Conclusion: The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] A novel CBCT-based method for derivation of CTV-PTV margins for prostate and pelvic lymph nodes treated with stereotactic ablative radiotherapy
    Lyons, Ciara A.
    King, Raymond B.
    Osman, Sarah O. S.
    McMahon, Stephen J.
    O'Sullivan, Joe M.
    Hounsell, Alan R.
    Jain, Suneil
    McGarry, Conor K.
    RADIATION ONCOLOGY, 2017, 12
  • [32] A novel mTOR-associated gene signature for predicting prognosis and evaluating tumor immune microenvironment in lung adenocarcinoma
    Zheng, Zhi
    Li, Yanqi
    Lu, Xiao
    Zhang, Jiao
    Liu, Quanxing
    Zhou, Dong
    Deng, Xufeng
    Qiu, Yuan
    Chen, Qian
    Zheng, Hong
    Dai, Jigang
    COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 145
  • [33] An Automatic Segmentation Method for Lung Tumor Based on Improved Region Growing Algorithm
    Wang, Monan
    Li, Donghui
    DIAGNOSTICS, 2022, 12 (12)
  • [34] CT images segmentation method of rectal tumor based on modified U-net
    Zheng, Biao
    Cai, Chenxiao
    Ma, Lei
    16TH IEEE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2020), 2020, : 672 - 677
  • [35] The dose-response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
    Begosh-Mayne, Dustin
    Kumar, Shruti Siva
    Toffel, Steven
    Okunieff, Paul
    O'Dell, Walter
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [36] A Simple Correction-Based Method for Independent Monitor Unit (MU) Verification in Monte Carlo (MC) Lung SBRT Plans
    Pokhrel, D.
    Badkul, R.
    Jiang, H.
    Estes, C.
    Kumar, P.
    Wang, F.
    MEDICAL PHYSICS, 2014, 41 (06) : 227 - +
  • [37] Utilization of a hybrid finite-element based registration method to quantify heterogeneous tumor response for adaptive treatment for lung cancer patients
    Sharifi, Hoda
    Zhang, Hong
    Bagher-Ebadian, Hassan
    Lu, Wei
    Ajlouni, Munther I.
    Jin, Jian-Yue
    Kong, Feng-Ming
    Chetty, Indrin J.
    Zhong, Hualiang
    PHYSICS IN MEDICINE AND BIOLOGY, 2018, 63 (06)
  • [38] A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning
    Deng, Yongjin
    Qiu, Minmin
    Wu, Shuyu
    Zhong, Jiajian
    Huang, Jiexing
    Luo, Ning
    Lu, Yao
    Bao, Yong
    JOURNAL OF MEDICAL IMAGING AND RADIATION SCIENCES, 2024, 55 (04)
  • [39] A novel marker-less lung tumor localization strategy on low-rank fluoroscopic images with similarity learning
    Huang, Wei
    Li, Jing
    Zhang, Peng
    Wan, Min
    Fang, Can
    Shen, Minmin
    MULTIMEDIA TOOLS AND APPLICATIONS, 2015, 74 (23) : 10535 - 10558
  • [40] Novel method for rapid identification of micropapillary or solid components in early-stage lung adenocarcinoma
    Zhao, Ze-Rui
    Lau, Rainbow W. H.
    Long, Hao
    Mok, Tony S. K.
    Chen, George G.
    Underwood, Malcolm J.
    Ng, Calvin S. H.
    JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY, 2018, 156 (06) : 2310 - +