|Table of Contents|

The predictive value of CT-based radiomics model for overall survival of radiotherapy in esophageal cancer

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 23
Page:
4388-4393
Research Field:
Publishing date:

Info

Title:
The predictive value of CT-based radiomics model for overall survival of radiotherapy in esophageal cancer
Author(s):
JI Ying1; BO Huiming1; YANG Yu1; HUANG Liyou2
1.Department of Radiotherapy,Yancheng Second People's Hospital,Jiangsu Yancheng 224002,China；2.Department of Oncology,Suqian Hospital of Nanjing Drum Tower Hospital Group,Jiangsu Suqian 223800,China.
Keywords:
esophageal cancer; radiotherapy; radiomics; romography; X-ray compute; overall survival
PACS:
R735.1
DOI:
10.3969/j.issn.1672-4992.2023.23.017
Abstract:
Objective:To investigate the value of CT radiomics features in predicting the overall survival of esophageal cancer patients treated with radical radiotherapy.Methods:A retrospective study was conducted on the clinical,pathological,and imaging data of 133 esophageal cancer patients who received radical radiotherapy.A total of 395 radiomics features were extracted from patient's CT images.The optimal radiomics features were selected using the least absolute shrinkage and selection operator method,and a radiomics score(Radscore) was calculated.Univariate and multivariate analyses were performed to identify independent prognostic factors for esophageal cancer patients undergoing radical radiotherapy,and a Cox regression model was established.The performance of different prediction models was assessed using the C-index,decision curve analysis,and integrated discrimination improvement index.Results:Six optimal radiomics features were selected.Univariate and multivariate analyses revealed that Radscore,chemotherapy,and short-term radiotherapy efficacy were independent prognostic factors.The C-index of the radiomics model in the training and validation sets was 0.746 and 0.721,respectively,higher than that of the clinical model(0.651 and 0.643).Decision curve analysis showed that,within a threshold range of 0.1~0.7,the net clinical benefit of the radiomics model was higher than that of the clinical model.The integrated discrimination improvement index results indicated that the radiomics model outperformed the clinical model in terms of overall discriminative performance for 1-year,3-year,and 5-year overall survival by 11.3%,22.2%,and 45.6%,respectively.Conclusion:The CT radiomics model shows promise in predicting the overall survival of esophageal cancer patients undergoing radical radiotherapy.

References:

[1] BRAY F,FERLAY J,SOERJOMATARAM I,et al.Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］.CA Cancer J Clin,2018,68(6):394-424.
[2] SMYTH EC,LAGERGREN J,FITZGERALD RC,et al.Oesophageal cancer［J］.Nat Rev Dis Primers,2017,3:17048.
[3] XIE CY,HU YH,HO JW,et al.Using genomics feature selection method in radiomics pipeline improves prognostication performance in locally advanced esophageal squamous cell carcinoma-a pilot study［J］.Cancers(Basel),2021,13(9):2145-2160.
[4] LAGARDE SM,TEN KATE FJ,REITSMA JB,et al.Prognostic factors in adenocarcinoma of the esophagus or gastroesophageal junction［J］.J Clin Oncol,2006,24(26):4347-4355.
[5] FRANZESE C,COZZI L,BADALAMENTI M,et al.Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy［J］.Strahlenther Onkol,2022,198(8):710-718.
[6] KADOYA N,TANAKA S,KAJIKAWA T,et al.Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics［J］.Med Phys,2020,47(5):2197-2205.
[7] TAGLIAFICO AS,PIANA M,SCHENONE D,et al.Overview of radiomics in breast cancer diagnosis and prognostication［J］.Breast,2020,49:74-80.
[8] FAN J,LIU Z,MAO X,et al.Global trends in the incidence and mortality of esophageal cancer from 1990 to 2017［J］.Cancer Med,2020,9(18):6875-6887.
[9] 孙骏,沈力,傅剑雄,等.基于增强CT灰度共生矩阵的纹理分析鉴别胃间质瘤和胃神经鞘瘤［J］.实用放射学杂志,2021,37(3):416-419. SUN J,SHEN L,FU JX,et al.Differential diagnosis between the gastric stromal tumor and gastric schwannoma by using texture analysis based on enhanced CT gray-level co-occurrence matrix ［J］.J Pract Radiol,2021,37(3):416-419.
[10] 陈旭婷,麦慧,郭美芬,等.基于灰度共生矩阵的MRI纹理分析预测乳腺癌腋窝淋巴结转移［J］.实用放射学杂志,2021,37(9):1459-1463. CHEN XT,MAI H,GUO MF,et al.The value of MRI texture analysis based on gray level co-occurrence matrix to predict axillary lymph node metastasis of breast carcinoma ［J］.J Pract Radiol,2021,37(9):1459-1463.
[11] 姚易明，叶靖，高慧,等.基于矢状位T2WI灰度共生矩阵对I期子宫内膜癌肌层浸润深度的评估价值［J］.现代肿瘤医学,2021,29(08)：1404－1409. YAO YM,YE J,GAO H,et al.Evaluation of the depth of myometrial invasion in stage I endometrial cancer based on gray level co-occurrence matrix of sagittal T2-weighted MRI［J］.Modern Oncology,2021,29(08)：1404－1409.
[12] SAIHOOD A,KARSHENAS H,NILCHI A.Deep fusion of gray level co-occurrence matrices for lung nodule classification［J］.PLoS One,2022,17(9):e0274516.
[13] 孔洁,祝淑钗,刘志坤,等.基于影像组学的食管鳞状细胞癌放疗后生存预测模型的建立与验证［J］.中国肿瘤临床,2022,49(19):973-981. KONG J,ZHU SC,LIU ZK,et al.Construction and validation of a radiomic-based survival prediction model for patients with esophageal squamous cell carcinoma after radiotherapy［J］.Chin J Clin Oncol,2022,49(19):973-981.
[14] LUO HS,HUANG SF,XU HY,et al.A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer［J］.Radiat Oncol,2020,15(1):249-260.
[15] VICKERS AJ,WOO S.Decision curve analysis in the evaluation of radiology research［J］.Eur Radiol,2022,32(9):5787-5789.
[16] LAI L.A Nomogram to predict patients with obstructive coronary artery disease:Development and validation［J］.Cardiovascular Innovations and Applications,2021,4(2):245-255.

Memo

Memo:
-
Last Update: 2023-10-31