Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI

Jingjing Lu; Songqi Cai; Fang Wang; Pu-Yeh Wu; Xianpan Pan; Jinwei Qiang; Haiming Li; Mengsu Zeng

doi:10.1016/j.acra.2022.12.019

Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI

Acad Radiol. 2023 Sep;30(9):1823-1831. doi: 10.1016/j.acra.2022.12.019. Epub 2022 Dec 31.

Authors

Jingjing Lu¹, Songqi Cai¹, Fang Wang², Pu-Yeh Wu³, Xianpan Pan², Jinwei Qiang⁴, Haiming Li⁵, Mengsu Zeng¹

Affiliations

¹ Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai 200032, China; Shanghai Institute of Medical Imaging.
² Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd. Shanghai, 200232, China.
³ Department of Research and Development, GE Healthcare, Beijing 100176, China.
⁴ Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai 201508, China.
⁵ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University. Electronic address: lihaiming0109@163.com.

PMID: 36587996
DOI: 10.1016/j.acra.2022.12.019

Abstract

Rationale and objectives: To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram.

Methods: A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts.

Results: MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram.

Conclusion: The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.

Keywords: High-grade serous ovarian carcinoma; nomogram; radiomics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Abdomen / pathology
Female
Humans
Multiparametric Magnetic Resonance Imaging* / methods
Nomograms
Ovarian Neoplasms* / diagnostic imaging
Ovarian Neoplasms* / surgery
Pelvis / pathology
Positron Emission Tomography Computed Tomography