基于增强CT影像组学预测卵巢癌患者铂类药物敏感性

doi:10.6040/j.issn.1671-7554.0.2023.0738

摘要/Abstract

摘要： 目的构建并比较临床模型、影像组学模型及联合模型在预测卵巢癌(OC)患者铂类药物敏感性中的优劣。方法回顾性分析94例病理证实为OC的患者临床及影像资料。在静脉期图像上勾画2D-ROI、3D-ROI,分别提取影像组学特征并进行降维筛选,计算影像组学评分,根据评分构建2D、3D影像组学模型,并对比两者预测效能。保留有统计学意义的临床特征构建临床模型,加入影像组学评分构建联合模型。利用受试者工作特征(ROC)曲线、校准曲线及决策曲线分析(DCA)评估临床模型、影像组学模型及联合模型的预测能力。结果 3D较2D模型显示出较高的诊断效能,ROC曲线下面积(AUC)分别为0.766,0.677。相较于临床和影像组学模型,联合模型显示出最优的预测效能,ROC曲线下面积(AUC)分别为0.708、0.766、0.827,其中临床模型与联合模型差异有统计学意义(P=0.010)。结论基于增强CT影像组学及临床特征建模可预测OC患者对铂类药物化疗的敏感性,且联合模型具有较高的诊断效能。

关键词: 卵巢癌, 影像组学, 增强CT, 铂类药物, 铂敏感性

Abstract: Objective To construct and compare the performance of clinical model, radiomics model and combined model in predicting the patients response to platinum-based chemotherapy for ovarian cancer(OC). Methods The complete data of 94 OC patients confirmed by pathology were retrospectively analyzed. The 2D-ROI and 3D-ROI of tumors were sketched along the tumor contour on the venous phase images, and then radiomics features were extracted and dimensionality reduction filtering was performed. After the radscore was calculated, 2D and 3D radiomics models were constructed, and their prediction efficiency was compared. Meaningful clinical features were retained to build a clinical model, and then radscore was added to construct a combined model. The predictive efficacy of clinical, radiomics and combined models was evaluated with the receiver operating characteristic(ROC)curve, calibration curve, and decision analysis curve(DCA). Results The 3D model showed higher predictive efficiency than 2D model, with the area under ROC curve(AUC)being 0.766 and 0.677, respectively. Compared with the clinical model(age, RT)and radiomics model, the combined model showed the best predictive efficacy, with the AUC being 0.708, 0.766, and 0.827, respectively(P=0.010). Conclusion Modeling based on enhanced CT radiomics and clinical features can predict OC patients response to platinum-based chemotherapy, and the combined model has high predictive efficacy.

Key words: Ovarian cancer, Radiomics, Enhanced CT, Platinum-based chemotherapy, Response to platinum-based chemotherapy

中图分类号:

R445.3

焦光丽,石子馨,陈蓉,宋亚博,杨飞,崔书君. 基于增强CT影像组学预测卵巢癌患者铂类药物敏感性[J]. 山东大学学报 (医学版), 2023, 61(12): 62-69.

JIAO Guangli, SHI Zixin, CHEN Rong, SONG Yabo, YANG Fei, CUI Shujun. Radiomics of enhanced CT in predicting the response to platinum-based chemotherapy for ovarian cancer[J]. Journal of Shandong University (Health Sciences), 2023, 61(12): 62-69.

参考文献

[1] An Y, Yang Q. Tumor-associated macrophage-targeted therapeutics in ovarian cancer [J]. Int J Cancer, 2021, 149(1): 21-30. doi: 10.1002/ijc.33408.
[2] Havasi A, Cainap SS, Havasi AT, et al. Ovarian cancer-insights into platinum resistance and overcoming it [J]. Medicina(Kaunas). 2023, 59(3): 544. doi: 10.3390/medicina59030544..
[3] Konstantinopoulos PA, Matulonis UA. Clinical and translational advances in ovarian cancer therapy [J]. Nat Cancer. 2023, 4(9): 1239-1257. doi: 10.1038/s43018-023-00617-9.
[4] 王芳, 夏雨薇, 柴象飞, 等. 影像组学分析流程及临床应用的研究进展[J]. 中华解剖与临床杂志, 2021, 26(2): 236-241. WANG Fang, XIA Yuwei, CHAI Xiangfei, et al. Analysis process and clinical application of radiomics [J]. Chinese Journal of Anatomy and Clinics, 2021, 26(2): 236-241.
[5] Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology [J]. Cancer Cell. 2021, 39(7): 916-927.
[6] 魏明翔, 柏根基, 郭莉莉. 影像组学在卵巢肿瘤中的研究进展[J]. 磁共振成像, 2020, 11(5): 386-389. WEI Mingxiang, BO Genji, GUO Lili. The research progress of radiomics in ovarian tumors [J]. Chinese Journal of Magnetic Resonance Imaging, 2020, 11(5): 386-389.
[7] 毛咪咪, 李海明, 石健, 等. 基于多序列MRI影像组学列线图预测上皮性卵巢癌患者对铂类药物化疗的敏感性[J]. 中华医学杂志, 2022, 102(3): 201-208. MAO Mimi, LI Haiming, ShI Jian, et al. Prediction of platinum based chemotherapy sensitivity for epithelial ovarian cancer by multi sequence MRI based radiomic nomogram [J]. National Medical Journal of China, 2022, 102(3): 201-208.
[8] Lu J, Li HM, Cai SQ, et al. Prediction of platinum-based chemotherapy response in advanced high-grade serous ovarian cancer: ADC histogram analysis of primary tumors [J]. Acad Radiol, 2021, 28(3): e77-e85.
[9] Veeraraghavan H, Vargas HA, Jimenez-Sanchez A, et al. Integrated multi-Tumor radio-genomic marker of outcomes in patients with high serous ovarian carcinoma [J]. Cancers(Basel), 2020, 12(11): 3403. doi: 10.3390/cancers12113403.
[10] Wilson MK, Pujade-Lauraine E, Aoki D, et al. Fifth ovarian cancer consensus conference of the gynecologic cancer interGroup: recurrent disease [J]. Ann Oncol, 2017, 28(4): 727-732.
[11] 王雪, 张广美, 何征秦. 复发性卵巢癌的药物治疗及进展[J]. 中国现代医学杂志, 2021, 31(1): 38-44. WANG Xue, ZhANG Guangmei, HE Zhengqin. Drug treatment and research progress of recurrent ovarian cancer [J]. China Journal of Modern Medicine, 2021, 31(1): 38-44.
[12] Li SY, Liu JQ, Xiong YH, et al. A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography [J]. Sci Rep, 2021, 11(1): 8730. doi: 10.1038/s41598-021-87775-x.
[13] Zhu H, Ai Y, Zhang J, et al. Preoperative nomogram for differentiation of histological subtypes in ovarian cancer based on computer tomography radiomics [J]. Front Oncol, 2021, 11: 642892. doi: 10.3389/fonc.2021.642892.
[14] anala G, Thai T, Gunderson CC, et al. Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy [J]. Acad Radiol, 2017, 24(10): 1233-1239.
[15] Jian J, Li Y, Pickhardt PJ, et al. MR image-based radiomics to differentiate type Iota and type iotaIota epithelial ovarian cancers [J]. Eur Radiol, 2021, 31(1): 403-410.
[16] Li C, Wang H, Chen Y, et al. Nomograms of combining MRI multisequences radiomics and clinical factors for differentiating high-grade from low-grade serous ovarian carcinoma [J]. Front Oncol, 2022, 12: 816982. doi: 10.3389/fonc.2022.816982.
[17] Yi X, Liu Y, Zhou B, et al. Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment [J]. Biomed Pharmacother, 2021, 133: 111013. doi: 10.1016/j.biopha.2020.111013.
[18] Kazerooni AF, Malek M, Haghighatkhah H, et al. Semiquantitative dynamic contrast-enhanced MRI for accurate classification of complex adnexal masses [J]. J Magn Reson Imaging, 2017, 45(2): 418-427.
[19] Rizzo S, Botta F, Raimondi S, et al. Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months [J]. Eur Radiol, 2018, 28(11): 4849-4859.
[20] Qiu Y, Tan M, McMeekin S, et al. Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis [J]. Acta Radiol, 2016, 57(9): 1149-1155.
[21] Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics [J]. J Nucl Med, 2020, 61(4): 488-495.
[22] 刘鹏, 王丽嘉, 马超. 影像组学及分析工具浅谈[J]. 生物医学工程与临床, 2022, 26(4): 511-518. LIU Peng, WANG Lijia, MA Chao. Introduction of radiomics and analytical tools [J]. Biomedical Engineering and Clinical Medicine, 2022, 26(4): 511-518.
[23] 潘淑淑, 沈起钧, 陈文辉, 等. 增强CT影像组学鉴别卵巢良性与交界性浆液性肿瘤[J].中国介入影像与治疗学, 2020, 17(6): 355-359. Pan Shushu, Shen Qijun, Chen Wenhui, et al. Radiomic of enhanced CT for identification of benign and borderline serous tumors of ovary [J]. Chinese Journal of Interventional Imaging and Therapy, 2020, 17(6): 355-359.
[24] Vargas HA, Veeraraghavan H, Micco M, et al. A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome [J]. Eur Radiol, 2017, 27(9): 3991-4001.
[25] Mingzhu L, Yaqiong G, Mengru L, et al. Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images [J]. BMC Med Imaging, 2021, 21(1): 180. doi: 10.1186/s12880-021-00711-3.
[26] Li S, Liu J, Xiong Y, et al. Application values of 2D and 3D radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors [J]. Biomed Res Int, 2022, 2022: 5952296. doi: 10.1155/2022/5952296.
[27] Winarno GNA, Pasaribu M, Susanto H, et al. The platelet to lymphocyte and neutrophil to lymphocyte ratios in predicting response to platinum-based chemotherapy for epithelial ovarian cancer [J]. Asian Pac J Cancer Prev, 2021, 22(5): 1561-1566.
[28] Karam A, Ledermann JA, Kim JW, et al. Fifth ovarian cancer consensus conference of the gynecologic cancer Intergroup: first-line interventions [J]. Ann Oncol, 2017, 28(4): 711-717.
[29] Braman N, Prasanna P, Whitney J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2(ERBB2)-positive breast cancer [J]. JAMA Netw Open, 2019, 2(4): e192561. doi: 10.1001/jamanetworkopen.2019.2561.
[30] Hu Y, Xie C, Yang H, et al. Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma [J]. JAMA Netw Open, 2020, 3(9): e2015927. doi: 10.1001/jamanetworkopen.2020.15927.
[31] 苏亚英, 石子馨, 张苗,等. 基于DCE-MRI影像组学定量预测进展期宫颈鳞癌同步放化疗反应的价值[J].河北北方学院学报(自然科学版),2023,39(2):11-17. SU Yaying, ShI Zixin,ZhANG Miao,et al. Value of Quantitative Prediction of Response to Concurrent Chemotherapy and Radiation Therapy in Advanced Cervical Squamous Cell Carcinoma Based on DCE-MRI Radiomics [J]. Journal of Hebei North University(Natural Sciences Edition), 2023, 39(2): 11-17.
[32] Chen M, Cao J, Hu J, et al. Clinical-radiomic analysis for pretreatment prediction of objective response to first transarterial chemoembolization in hepatocellular carcinoma [J]. Liver Cancer, 2021, 10(1): 38-51.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed