[an error occurred while processing this directive] | [an error occurred while processing this directive]
Early assessment of treatment response during radiotherapy for esophageal cancer based on CT radiomics analysis
Li Dingjie, Li, Wu Hui, Liu Ru, Zhang Yougai, Guo Wei, Lou Zhaoyang, Ge Hong
Department of Radiation Oncology,Henan Cancer Hospital,Department of Radiation Oncology,Affiliated Cancer Hospital of Zhengzhou University,Zhengzhou 450008,China
AbstractObjective To investigate the feasibility of assessing the treatment response using diagnostic-quality CT imaging features during radiotherapy for esophageal cancer. Methods Thirty-three patients with stage Ⅰ to IV esophageal cancer undergoing intensity-modulated radiotherapy were recruited in this study. CT images were acquired using a CT-on-rail imaging system. Imaging data of CT images including gross tumor volume (GTV), the volume of spinal cord and non-irradiated tissue (NIT), CT mean (MCTN),standard deviation ,and skewness were collected and analyzed by using MIM image processing system. Patients were divided into the effective group (complete remission and partial remission,n=24) and ineffective group (no change and progression, n=9) based on the outcomes of 3-month follow-up. The imaging data were statistically compared between two groups using the self-designed Matlab software. Results The tumor volume and MCTN of 33 patients were gradually decreased with the increase of radiotherapy dose. The tumor volume and MCTN were decreased by 42.46% and 5.76HU in the effective group, more significant compared with 21.76% and 3.66HU in the ineffective group (both P<0.005). The skewness in the ineffective group was decreased by 0.503 with the increasing radiation dose, whereas that in the effective group was increased by -0.450(P=0.034). Spinal cord and NIT did not significantly change with the increasing radiation dose. Conclusion Analysis of the characteristic data of CT images of patients with esophageal cancer during radiotherapy may early predict the clinical efficacy of radiotherapy.
Corresponding Authors:
Ge Hong,Email:gehong666@126.com
Cite this article:
. Early assessment of treatment response during radiotherapy for esophageal cancer based on CT radiomics analysis[J]. Chinese Journal of Radiation Oncology, 2019, 28(10): 731-734.
. Early assessment of treatment response during radiotherapy for esophageal cancer based on CT radiomics analysis[J]. Chinese Journal of Radiation Oncology, 2019, 28(10): 731-734.
[1] Lambin P,Rios-Velazquez E,Leijenaar R,et al. Radiomics:Extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer,2012,48(4):441-446. DOI:10.1016/j.ejca.2011.11.036. [2] Chen X,Oshima K,Schott D,et al. Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs:An exploratory study[J/OL]. PLoS One,2017,12(6):e0178961. DOI:10.1371/journal.pone.0178961. [3] Nowosielski M,Recheis W,Goebel G,et al. ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma[J]. Neuroradiology,2011,53(4):291-302. DOI:10.1007/s00234-010-0808-0. [4] Yip SS,Coroller TP,Sanford NN,Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction[J]. Phys Med Biol,2016,61(2):906-922. DOI:10.1088/0031-9155/61/2/906. [5] 夏凡,胡盼盼,王佳舟,等. 影像组学应用于肝脏特征分析及其预测治疗反应的初步研究[J]. 中国肺癌杂志,2016,26(6):521-526. DOI:10.19401/j.cnki.1007-3639.2016.06.007. Xia F,Hu PP,Wang JZ,et al. Preliminary study on the application of imaging in liver characteristic analysis and predictive therapeutic response[J]. Chin J Lung Cancer,2016,26(6):521-526. DOI:10.19401/j.cnki.1007-3639.2016.06.007. [6] 何兰,黄燕琪,马泽兰,等. CT影像组学在非小细胞肺癌临床分期中的价值[J]. 中华放射学杂志,2017,51(12):906-910. DOI:10.3760/cma.j.issn.1005-1201.2017.12.004. He L,Huang YQ,Ma ZL,et al. Value of CT imaging in clinical staging of non-small cell lung cancer[J]. Chin J Radiol,2017,51:906-910. DOI:10.3760/cma.j.issn.1005-1201.2017.12.004. [7] Umeoka S,Koyama T,Togashi K,et al. Esophageal cancer:evaluation with triple-phase dynamic CT—initial experience[J]. Radiology,2006,239(3):777-783. DOI:10.1148/radiol.2393050222. [8] Diot Q,Kavanagh B,Schefter T,et al. Regional normal lung tissue density changes in patients treated with stereotactic body radiationtherapy for lung tumors[J]. Int J Radiat Oncol Biol Phys,2012,84(4):1024-1030. DOI:10.1016/j.ijrobp.2011.11.080. [9] Howells CC,Stinauer MA,Diot Q,et al. Normal liver tissue density dose response in patients treated with stereotactic body radiationtherapy for liver metastases[J]. Int J Radiat Oncol Biol Phys,2012,84(3):e441-446. DOI:10.1109/ICMLA.2011.121. [10] Mayer R,Stanton K,Kleinberg L,et al. CT number distribution and its association with local control and as a marker of lung tumor response to radiation[J]. Radiat Oncol Investig,1998,6(6):281-288. DOI:10.1002/(SICI)1520-6823(1998)6:6<281::AID-ROI6>3.0. CO;2-H. [11] Herfarth KK,Hof H,Bahner ML,et al. Assessment of focal liver reaction by multiphasic CT after stereotactic single-dose radiotherapy of liver tumors[J]. Int J Radiat Oncol Biol Phys,2003,57(2):444-445. DOI:10.1016/s0360-3016(03)00586-8. [12] Feng M,Yang C,Chen X,et al. Computed tomography number changes observed during computed tomography-guided radiation therapy for head and neck cancer[J]. Int J Radiat Oncol Biol Phys,2015,91(5):1041-1047. DOI:10.1016/j.ijrobp.2014.12.057. [13] Yu H,Scalera J,Khalid M,et al. Texture analysis as a radiomic marker for differentiating renal tumors[J]. Abdom Radiol (NY),2017,42(10):2470-2478. DOI:10.1007/s00261-017-1144-1. [14] Ma J,Zhao J. Automatic lung nodule classification with radiomics approach[J]. Spie Med Imag,2016,9789:978906. DOI:10.1117/12.2220768. [15] Suo S,Cheng J,Cao M,et al.Assessment of heterogeneity difference between edge and core by using texture analysis:differentiation of malignant from inflammatory pulmonary nodules and masses[J]. Acad Radiol,2016,23(9):1115-1122. DOI:10.1016/j.acra.2016.04.009. [16] De Cecco CN,Ganeshan B,Ciolina M,et al,Texture Analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance[J]. Invest Radiol,2015,50(4):239-245. DOI:10.1097/RLI.0000000000000116. [17] Fike JR,Le Couteur RA,Cann CE,et al. Computerized tomography of brain tumors of the rostral and middle fossas in the dog[J]. Am J Veter Res,1980,42(2):275-281. DOI:10.2754/avb198150030237.
2019, Vol. 28 
