Exploring a multiparameter MRI-based radiomics approach to predict tumor proliferation status of serous ovarian carcinoma

作者全名："Liu, Li; Zhao, Ling; Jing, Yang; Li, Dan; Linghu, Hua; Wang, Haiyan; Zhou, Linyi; Fang, Yuan; Li, Yongmei"

作者地址："[Liu, Li; Zhao, Ling; Wang, Haiyan; Fang, Yuan] Peoples Hosp Yubei Dist Chongqing City, Dept Radiol, 23 ZhongyangGongyuanBei Rd, Chongqing 401120, Peoples R China; [Liu, Li; Li, Yongmei] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, 1 Youyi Rd, Chongqing 400016, Yuanjiagang, Peoples R China; [Jing, Yang] Huiying Med Technol Co Ltd, Dongsheng Sci & Technol Pk,Room A206,B2, Beijing 100192, Peoples R China; [Li, Dan] Chongqing Med Univ, Dept Pathol, 1 Med Coll Rd, Chongqing 400016, Yuanjiagang, Peoples R China; [Linghu, Hua] Chongqing Med Univ, Dept Obstet & Gynecol, Affiliated Hosp 1, 1 Youyi Rd, Chongqing 400016, Yuanjiagang, Peoples R China; [Zhou, Linyi] Army Med Univ, Daping Hosp, Army Med Ctr, Dept Radiol, 10 Changjiangzhilu, Chongqing 40024, Peoples R China"

通信作者："Fang, Y (通讯作者)，Peoples Hosp Yubei Dist Chongqing City, Dept Radiol, 23 ZhongyangGongyuanBei Rd, Chongqing 401120, Peoples R China.; Li, YM (通讯作者)，Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, 1 Youyi Rd, Chongqing 400016, Yuanjiagang, Peoples R China."

来源：INSIGHTS INTO IMAGING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001186432600001

JCR分区：Q1

影响因子：4.1

年份：2024

卷号：15

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Serous ovarian carcinoma; Tumor proliferation status; Ki-67; Radiomics; MRI

摘要："ObjectivesTo develop a multiparameter magnetic resonance imaging (MRI)-based radiomics approach that can accurately predict the tumor cell proliferation status of serous ovarian carcinoma (SOC).Materials and methodsA total of 134 patients with SOC who met the inclusion and exclusion criteria were retrospectively screened from institution A, spanning from January 2016 to March 2022. Additionally, an external validation set comprising 42 SOC patients from institution B was also included. The region of interest was determined by drawing each ovarian mass boundaries manually slice-by-slice on T2-weighted imaging fat-suppressed fast spin-echo (T2FSE) and T1 with contrast enhancement (T1CE) images using ITK-SNAP software. The handcrafted radiomic features were extracted, and then were selected using variance threshold algorithm, SelectKBest algorithm, and least absolute shrinkage and selection operator. The optimal radiomic scores and the clinical/radiological independent predictors were integrated as a combined model.ResultsCompared with the area under the curve (AUC) values of each radiomic signature of T2FSE and T1CE, respectively, the AUC value of the radiomic signature (T1CE-T2FSE) was the highest in the training set (0.999 vs. 0.965 and 0.860). The homogeneous solid component of the ovarian mass was considered the only independent predictor of tumor cell proliferation status among the clinical/radiological variables. The AUC of the radiomic-radiological model was 0.999.ConclusionsThe radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can accurately predict tumor cell proliferation status of SOC which has high repeatability and may enable more targeted and effective treatment strategies.Critical relevance statementThe proposed radiomic-radiological model combining radiomic scores and the homogeneous solid component of the ovarian mass can predict tumor cell proliferation status of SOC which has high repeatability and may guide individualized treatment programs.Key points center dot The radiomic-radiological nomogram may guide individualized treatment programs of SOC.center dot This radiomic-radiological nomogram showed a favorable prediction ability.center dot Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67.Key points center dot The radiomic-radiological nomogram may guide individualized treatment programs of SOC.center dot This radiomic-radiological nomogram showed a favorable prediction ability.center dot Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67.Key points center dot The radiomic-radiological nomogram may guide individualized treatment programs of SOC.center dot This radiomic-radiological nomogram showed a favorable prediction ability.center dot Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67."

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