Comparisons among radiologist, MR findings and radiomics-clinical models in predicting placenta accreta spectrum disorders: a multicenter study
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Abstract
OBJECTIVE: To assess and compare the diagnostic accuracy of radiologist, MR findings, and radiomics-clinical models in the diagnosis of placental implantation disorders. METHODS: Retrospective collection of MR images from patients suspected of having placenta accreta spectrum (PAS) was conducted across three institutions: Institution I (n = 505), Institution II (n = 67), and Institution III (n = 58). Data from Institution I were utilized to form a training set, while data from Institutions II and III served as an external test set. Radiologist diagnosis was performed by radiologists of varying levels of experience. The interpretation of MR findings was conducted by two radiologists with 10-15 years of experience in pelvic MR diagnosis, following the guidelines for diagnosis. Radiomics analysis extracted features from sagittal T2-weighted images and combined them with prenatal clinical features to construct predictive models. These models were then evaluated for discrimination and calibration to assess their performance. RESULTS: As measured by the area under the receiver operating characteristic curve (AUC), the diagnostic efficacy was 0.587 (0.542-0.630) for junior radiologists from Institution I, 0.568 (0.441-0.689) from Institution II, and 0.507 (0.373-0.641) from Institution III. The AUC was 0.623 (0.580-0.666) for senior radiologists from Institution I, 0.635 (0.508-0.749) from Institution II, and 0.632 (0.495-0.755) from Institution III. The diagnostic efficacy of MR findings was 0.648 (0.601-0.695) for Institution I, 0.569 (0.429-0.709) for Institution II, and 0.588 (0.442-0.735) for Institution III. The diagnostic efficacy of the radiomics-clinical model was significantly higher, with an AUC of 0.794 (0.754-0.833) for Institution I, 0.783 (0.664-0.903) for Institution II, and 0.816 (0.704-0.927) for Institution III. The diagnostic efficacy of the Fusion model was significantly higher, with an AUC of 0.867 (0.836-0.899) for Institution I, 0.849 (0.753-0.944) for Institution II, and 0.823 (0.708-0.939) for Institution III. CONCLUSION: The fusion models demonstrated superior diagnostic efficacy compared to radiologists, MR findings, and the radiomics-clinical models. Furthermore, the diagnostic accuracy of PAS was notably higher when utilizing the radiomics-clinical models than when relying solely on radiologist diagnosis or MR findings. ADVANCES IN KNOWLEDGE: Radiomics analysis substantially augments the diagnostic precision in PAS, providing a significant enhancement over conventional radiologist and MRI findings. The diagnostic efficacy of the fusion model is notably superior to that of individual diagnostic modalities.
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Over the last five years, the application of ML in diagnosing PAS has undergone significant evolution, with researchers exploring more complex neural networks, improved feature extraction techniques, and larger, multi-institutional datasets.
“Over the last five years, the application of ML in diagnosing PAS has undergone significant evolution, with researchers exploring more complex neural networks, improved feature extraction techniques, and larger, multi-institutional datasets to enhance diagnostic performance [33-35].”
From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [33]
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Cites
Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777 · see citing works
<scp>FIGO</scp> classification for the clinical diagnosis of placenta accreta spectrum disorders,
Jauniaux, E., Ayres‐de‐Campos, D., Langhoff‐Roos, J., Fox, K. A., Collins, S., & Panel, F. P. A. D. A. M. E. C. (2019) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.12761 · Cited by 668 · see citing works
<scp>FIGO</scp> consensus guidelines on placenta accreta spectrum disorders: Epidemiology,
Jauniaux, E., Chantraine, F., Silver, R. M., Langhoff‐Roos, J., & Panel, F. T. F. P. A. D. A. M. E. C. (2018) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.12407 · Cited by 498 · see citing works
A review in radiomics: Making personalized medicine a reality via routine imaging
Guiot, J., Vaidyanathan, A., Deprez, L., Zerka, F., Danthine, D., Frix, A.-N., Lambin, P., Bottari, F., Tsoutzidis, N., Miraglio, B., et al. (2021) · Medicinal Research Reviews · doi:10.1002/med.21846 · Cited by 344 · see citing works
Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274 · see citing works
Self-citationCriteria for the translation of radiomics into clinically useful tests
Huang, E. P., O’Connor, J. P. B., McShane, L. M., Giger, M. L., Lambin, P., Kinahan, P. E., Siegel, E. L., & Shankar, L. K. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00707-0 · Cited by 244 · see citing works
Cui, Y., Zhang, J., Li, Z., Wei, K., Ye, L., Ren, J., Wu, L., Shi, Z., Meng, X., Yang, X., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101348 · Cited by 184 · see citing works
Jha, P., Pōder, L., Bourgioti, C., Bharwani, N., Lewis, S., Kamath, A., Nougaret, S., Soyer, P., Weston, M., Castillo, R. P., et al. (2020) · European Radiology · doi:10.1007/s00330-019-06617-7 · Cited by 173 · see citing works
As artificial intelligence goes multimodal, medical applications multiply
Topol, E. J. (2023) · Science · doi:10.1126/science.adk6139 · Cited by 132 · see citing works
New evidence-based diagnostic and management strategies for placenta accreta spectrum disorders
Jauniaux, E. R. M., Hussein, A. M., Fox, K. A., & Collins, S. (2019) · Best Practice & Research Clinical Obstetrics & Gynaecology · doi:10.1016/j.bpobgyn.2019.04.006 · Cited by 118 · see citing works
Radiomics analysis based on lumbar spine CT to detect osteoporosis
Jiang, Y.-W., Xu, X., Wang, R., & Chen, C. (2022) · European Radiology · doi:10.1007/s00330-022-08805-4 · Cited by 96 · see citing works
Kapoor, H., Hanaoka, M. M., Dawkins, A. A., & Khurana, A. (2020) · Placenta · doi:10.1016/j.placenta.2020.11.004 · Cited by 87 · see citing works
Patel-Lippmann, K. K., Planz, V. B., Phillips, C. H., Ohlendorf, J. M., Zuckerwise, L. C., & Moshiri, M. (2023) · Radiographics · doi:10.1148/rg.220090 · Cited by 79 · see citing works
Radiomics applications in cardiac imaging: a comprehensive review
Polidori, T., Santis, D. D., Rucci, C., Tremamunno, G., Piccinni, G., Pugliese, L., Zerunian, M., Guido, G., Pucciarelli, F., Bracci, B., et al. (2023) · La radiologia medica · doi:10.1007/s11547-023-01658-x · Cited by 77 · see citing works
Zhou, S., Sun, D., Mao, W., Liu, Y., Cen, W., Ye, L., Liang, F., Xu, J., Shi, H., Ji, Y., et al. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102271 · Cited by 56 · see citing works
Gasmi, I., Calinghen, A., Parienti, J., Belloy, F., Fohlen, A., & Pelage, J. (2023) · Pediatric Radiology · doi:10.1007/s00247-023-05621-w · Cited by 45 · see citing works
Radiomics and radiogenomics in pediatric neuro-oncology: A review
Madhogarhia, R., Haldar, D., Bagheri, S., Familiar, A., Anderson, H., Arif, S., Vossough, A., Storm, P. B., Resnick, A. C., Davatzikos, C. A., et al. (2022) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdac083 · Cited by 39 · see citing works
Xu, D. M., Sui, L., Zhang, C., Xiong, J., Wang, V. Y., Zhou, Y., Zhu, X., Chen, C., Zhao, Y., Xie, Y., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03510-z · Cited by 31 · see citing works
Placenta accreta spectrum disorders: Prenatal diagnosis still lacks clinical correlation
D’Antonio, F., Palacios-Jaraquemada, J. M., Timor‐Trisch, I., & Calì, G. (2018) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13374 · Cited by 29 · see citing works
Zhang, H., Fan, X., Zhang, J., Wei, Z., Feng, W., Hu, Y., Ni, J., Yao, F., Zhou, G., Wan, C., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1143688 · Cited by 29 · see citing works
Self-citationLin, R., Lin, J., Xu, Y., Yu, Y., Foster, E., Lin, M., Xu, H., Li, X., Ye, J., Yao, F., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09429-y · Cited by 25 · see citing works
Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal
Stanzione, A., Verde, F., Cuocolo, R., Romeo, V., Paolo Mainenti, P., Brunetti, A., & Maurea, S. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110497 · Cited by 22 · see citing works
Radiomics in Cardiac Computed Tomography
Ayx, I., Froelich, M. F., Baumann, S., Papavassiliu, T., & Schoenberg, S. O. (2023) · Diagnostics · doi:10.3390/diagnostics13020307 · Cited by 22 · see citing works
Stüber, A. T., Coors, S., Schachtner, B. M., Weber, T., Rügamer, D., Bender, A., Mittermeier, A., Öcal, O., Seidensticker, M., Ricke, J., et al. (2023) · Investigative Radiology · doi:10.1097/rli.0000000000001009 · Cited by 20 · see citing works
Verde, F., Stanzione, A., Cuocolo, R., Romeo, V., Stasi, M. D., Ugga, L., Mainenti, P. P., D'Armiento, M. R., Sarno, L., Guida, M., et al. (2023) · Abdominal Radiology · doi:10.1007/s00261-023-03963-5 · Cited by 19 · see citing works
Liu, J., Tang, J., Xia, B., Gu, Z., Yin, H., Zhang, H., Yang, H., & Song, B. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.06.022 · Cited by 17 · see citing works
Cavalli, C., Maggi, C., Gambarini, S., Fichera, A., Santoro, A., Grazioli, L., Prefumo, F., Odicino, F. E., & Fratelli, N. (2021) · Journal of Perinatal Medicine · doi:10.1515/jpm-2021-0334 · Cited by 16 · see citing works
Zhong, J., Shi, L., Liu, J., Cao, F., Ma, Y., & Zhang, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-31869-1 · Cited by 14 · see citing works
Matsubara, S., Takahashi, H., & Takei, Y. (2018) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2018.04.058 · Cited by 12 · see citing works
Buckley, B. W., Delaney, F. T., Welaratne, I., Windrim, C., Geoghegan, T., & Cronin, C. G. (2022) · Emergency Radiology · doi:10.1007/s10140-022-02102-9 · Cited by 10 · see citing works
A novel MRI-based diagnostic model for predicting placenta accreta spectrum
Xia, J., Hu, Y. S., Huang, Z., Song, C., Huang, L., Ruan, Q., Zhao, C., Deng, S., Wang, M., & Zhang, Y. (2024) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.02.014 · Cited by 10 · see citing works
Radiomics and Breast Cancer Management
Dean, P. B. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.09.012 · Cited by 9 · see citing works
Placenta accreta spectrum disorders in the first trimester: a systematic review
López, J. A. G., Gutiérrez‐Sánchez, L. Á., Pinilla-Monsalve, G. D., & Timor‐Tritsch, I. E. (2022) · Journal of Obstetrics and Gynaecology · doi:10.1080/01443615.2022.2071151 · Cited by 8 · see citing works
Jariyawattanarat, W., Thiravit, S., Suvannarerg, V., Srisajjakul, S., & Sutchritpongsa, P. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110695 · Cited by 6 · see citing works
Si, L., Liu, X., Li, X., Yang, K., & Wang, L. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110633 · Cited by 4 · see citing works
Vimercati, A., Galante, A., Cirignaco, F., Nicolì, P., Malvası, A., Cicinelli, E., & Cerbone, M. (2024) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-024-07575-2 · Cited by 3 · see citing works
Radiomics-based T-staging of hollow organ cancers
Huang, D. J., Xu, X., Du, P., Feng, Y., Zhang, X., Lu, H., & Liu, Y. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1191519 · Cited by 2 · see citing works
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Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum
Waszczuk, D., Manikandan, V., Wong, B. L., Martin, F., Bhargava, N., Mondal, A., Loy, M., Strnad, D., Panchatcharam, M., Sadanala, G., et al. (2026) · Cureus · doi:10.7759/cureus.106592
“Over the last five years, the application of ML in diagnosing PAS has undergone significant evolution, with researchers exploring more complex neural networks, improved feature extraction techniques, and larger, multi-institutional datasets to enhance diagnostic performance [33-35].”
Zhou, Z., Ge, H., Zou, P., Ye, T., Zou, T., Fang, Q., Zeng, Y., Wang, Y. H., Zhang, X., Xu, Z., et al. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02348-6
Self-citationWaseem, S., Zeng, S., Xiao, X., Han, L., Zhao, H., Zhan, J., & Bai, P. (2026) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-026-09531-1
Lu, X., Lei, X., Guo, C., Tang, Q., 张小梅, Mai, W., Mo, M., Yuan, M., Wang, W., & Fan, D. (2026) · Journal of Global Health · doi:10.7189/jogh.16.04353
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- Authors
- Guo, C., Guo, S., He, C., Zhang, X., Han, D., Tan, H., Huang, X., & Li, Y.
- Affiliations
- Lanzhou University (CN)
- First Hospital of Lanzhou University (CN)
- Affiliated Hospital of Shaanxi University of Chinese Medicine (CN)
- Shaanxi University of Chinese Medicine (CN)
- Yanan University Affiliated Hospital (CN)
- Shangqiu First People's Hospital (CN)
- Corresponding author
- Guo, Shunlin
- Venue
- Archives of Gynecology and Obstetrics
- Published
- 2025-01-30
- Volume
- 311
- Issue
- 6
- Pages
- 1751-1764
- ISSN
- 0003-9128 (ISSN-L)
- 1432-0711
- PMC
- PMC12055865
- OpenAlex
- W4407003350
- Cited by
- 4
- References
- 37
- Citations by year
- 2026: 4
- FWCI
- 6.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Archives of Gynecology and Obstetrics (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Hospital Intramural Fund: Molecular Imaging Clinical Medicine Clinical Application Innovation Team (2020XKTD-C02)
- The application of deep learning based on MR in predicting the risk of placenta accreta spectrum disorders (L2024-QCY-ZYYJJQ-268)
- 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 › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- placenta accreta spectrum, placental implantation disorders, radiomics, clinical prediction model, magnetic resonance imaging, diagnostic accuracy, radiologist experience, prenatal diagnosis, external validation, pregnancy complications, placental abruption, placental insufficiency, placental calcification
- MeSH terms
- Radiologists, Radiomics, Adult, Clinical Competence, Female, Humans, Magnetic Resonance Imaging, Placenta Accreta, Predictive Value of Tests, Pregnancy, Retrospective Studies, ROC Curve, Area Under Curve
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