PMID- 38499907
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240321
IS  - 1869-4101 (Print)
IS  - 1869-4101 (Electronic)
IS  - 1869-4101 (Linking)
VI  - 15
IP  - 1
DP  - 2024 Mar 18
TI  - Exploring a multiparameter MRI-based radiomics approach to predict tumor 
      proliferation status of serous ovarian carcinoma.
PG  - 74
LID - 10.1186/s13244-024-01634-7 [doi]
LID - 74
AB  - OBJECTIVES: To 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 METHODS: A 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. RESULTS: Compared 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. CONCLUSIONS: The 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 
      STATEMENT: The 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: * The radiomic-radiological 
      nomogram may guide individualized treatment programs of SOC. * This 
      radiomic-radiological nomogram showed a favorable prediction ability. * 
      Homogeneous slightly higher signal intensity on T2FSE is vital for Ki-67.
CI  - (c) 2024. The Author(s).
FAU - Liu, Li
AU  - Liu L
AD  - Department of Radiology, The People's Hospital of Yubei District of Chongqing 
      City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China.
AD  - Department of Radiology, The First Affiliated Hospital of Chongqing Medical 
      University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, 
      China.
FAU - Zhao, Ling
AU  - Zhao L
AD  - Department of Radiology, The People's Hospital of Yubei District of Chongqing 
      City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China.
FAU - Jing, Yang
AU  - Jing Y
AD  - Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Room 
      A206, B2Haidian District, Beijing, 100192, China.
FAU - Li, Dan
AU  - Li D
AD  - Department of Pathology, Chongqing Medical University, No.1 Medical College Road, 
      Yuzhong District, Chongqing, 400016, China.
FAU - Linghu, Hua
AU  - Linghu H
AD  - Department of Obstetrics and Gynecology, The First Affiliated Hospital of 
      Chongqing Medical University, No. 1 Youyi Road Yuzhong District, Chongqing, 
      400016, Yuanjiagang, China.
FAU - Wang, Haiyan
AU  - Wang H
AD  - Department of Radiology, The People's Hospital of Yubei District of Chongqing 
      City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China.
FAU - Zhou, Linyi
AU  - Zhou L
AD  - Department of Radiology, Army Medical Center, Daping Hospital, Army Medical 
      University, 10# Changjiangzhilu, Chongqing, 40024, China.
FAU - Fang, Yuan
AU  - Fang Y
AD  - Department of Radiology, The People's Hospital of Yubei District of Chongqing 
      City, No. 23 ZhongyangGongyuanBei Road, Yubei District, Chongqing, 401120, China. 
      nmjyfy@sohu.com.
FAU - Li, Yongmei
AU  - Li Y
AUID- ORCID: 0000-0003-2829-6416
AD  - Department of Radiology, The First Affiliated Hospital of Chongqing Medical 
      University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, Yuanjiagang, 
      China. lymzhang70@163.com.
LA  - eng
PT  - Journal Article
DEP - 20240318
PL  - Germany
TA  - Insights Imaging
JT  - Insights into imaging
JID - 101532453
PMC - PMC10948697
OTO - NOTNLM
OT  - Ki-67
OT  - MRI
OT  - Radiomics
OT  - Serous ovarian carcinoma
OT  - Tumor proliferation status
COIS- YJ is an employee of Huiying Medical Technology Co., Ltd. The remaining authors 
      declare that they have no competing interests.
EDAT- 2024/03/19 06:43
MHDA- 2024/03/19 06:44
PMCR- 2024/03/18
CRDT- 2024/03/19 00:38
PHST- 2023/06/23 00:00 [received]
PHST- 2024/01/27 00:00 [accepted]
PHST- 2024/03/19 06:44 [medline]
PHST- 2024/03/19 06:43 [pubmed]
PHST- 2024/03/19 00:38 [entrez]
PHST- 2024/03/18 00:00 [pmc-release]
AID - 10.1186/s13244-024-01634-7 [pii]
AID - 1634 [pii]
AID - 10.1186/s13244-024-01634-7 [doi]
PST - epublish
SO  - Insights Imaging. 2024 Mar 18;15(1):74. doi: 10.1186/s13244-024-01634-7.