MRI-Based Radiomics Analysis for Intraoperative Risk Assessment in Gravid Patients at High Risk with Placenta Accreta Spectrum
Open accessAbstract
Background: Gravid patients at high risk with placenta accreta spectrum (PAS) face life-threatening risk at delivery. Intraoperative risk assessment for patients is currently insufficient. We aimed to develop an assessment system of intraoperative risks through MRI-based radiomics. Methods: A total of 131 patients enrolled were randomly grouped according to a ratio of 7:3. Clinical data were analyzed retrospectively. Radiomic features were extracted from sagittal Fast Imaging Employing State-sate Acquisition images. Univariate and multivariate regression analyses were performed to build models using R software. A receiver operating characteristic curve and decision curve analysis (DCA) were performed to determine the predictive performance of models. Results: Six radiomic features and two clinical variables were used to construct the combined model for selection of removal protocols of the placenta, with an area under the curve (AUC) of 0.90 and 0.91 in the training and test cohorts, respectively. Nine radiomic features and two clinical variables were obtained to establish the combined model for prediction of intraoperative blood loss, with an AUC of 0.90 and 0.88 in the both cohorts, respectively. The DCA confirmed the clinical utility of the combined model. Conclusion: The analysis of combined MRI-based radiomics with clinics could be clinically beneficial for patients.
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Previous radiomic studies in PAS found that radiomic features from image filters were more predictive and had higher diagnostic accuracy than those from the original image.
“Our results support the finding of previous radiomic studies in PAS, where radiomic features from image filters were more predictive and had higher diagnostic accuracy than those from the original image [45, 46].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [45]
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References 28 · Cited by 12
- Authors
- Chu, C., Liu, M., Zhang, Y., Zhao, S., Ge, Y., Li, W., & Gao, C.
- Affiliations
- XinHua Hospital (CN)
- GE Healthcare, Pudong New Town, No. 1, Huatuo Road, Shanghai 201203, China
- Corresponding author
- Li, Wenhua
- Gao, Chengjin
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-02-14
- Volume
- 12
- Issue
- 2
- Pages
- 485
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8870740
- OpenAlex
- W4213164213
- Cited by
- 12
- References
- 28
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 5 · 2022: 1
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81873947, 82172138)
- Topics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Maternal and fetal healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Obstetrics and Gynecology › Uterine Myomas and Treatments
- Keywords
- placenta accreta spectrum, MRI radiomics, high-risk pregnancy, decision curve analysis
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