The research, conducted by radiologist Yongjun Ni and neonatologist Shuhui Chen at Jiaxing Maternity and Child Health Care Hospital in Zhejiang Province, China, focused on a technique called intravoxel incoherent motion imaging, or IVIM. Unlike conventional diffusion-weighted MRI, which treats all movement of water molecules in tissue as a single phenomenon, IVIM separates two distinct processes. The first is true molecular diffusion, the random Brownian motion of water within cells and tissue spaces, quantified by a parameter known as D. The second is pseudo-diffusion, the incoherent motion of water driven by blood flowing through the microscopic network of capillaries, captured by the perfusion fraction f and the pseudo-diffusion coefficient D*. By fitting MRI signals acquired at multiple diffusion weightings, IVIM can effectively probe both the tissue architecture and the microcirculation of the placenta in a single examination.

This distinction matters because invasive placentas are not simply thicker or darker on a scan; they are biologically different. Abnormal vascular remodeling, disrupted tissue boundaries, and altered cellularity change both how water diffuses and how blood perfuses the placental tissue. The researchers reasoned that these microscopic changes should leave measurable fingerprints in the IVIM parameters, fingerprints that conventional MRI visual assessment alone might miss.

To test that idea, the team retrospectively analyzed 110 patients with placenta accreta spectrum who had undergone MRI at their institution. The cohort was divided into 47 women with invasive PAS, where the placenta penetrated deeply into or through the uterine wall, and 63 women with non-invasive disease. For each patient, the investigators compiled clinical data, reviewed conventional MRI findings such as morphological features and signal characteristics, and extracted the three IVIM parameters from regions of interest placed within the placenta. Measurement reliability was assessed using intraclass correlation coefficients, and the team checked that predictor variables were not redundantly entangled by examining variance inflation factors before modeling.

The statistical core of the study was multivariate logistic regression, a method that weighs multiple candidate predictors simultaneously to determine which ones independently distinguish invasive from non-invasive disease. Out of this process emerged six independent predictors, a combination of conventional MRI features and IVIM-derived parameters that together formed a prediction model. The model’s discrimination, its ability to separate invasive from non-invasive cases, was quantified with the area under the receiver operating characteristic curve, a standard metric in diagnostic research. On the original dataset, the model achieved an AUC of 0.926, with a 95 percent confidence interval of 0.889 to 0.953, a figure that places it in the range of excellent diagnostic performance.

Impressive as that number is, diagnostic models built and tested on the same data almost always look better than they truly are, a statistical phenomenon known as optimism. To address this, the researchers performed internal validation using bootstrap resampling, a technique that repeatedly draws random samples with replacement from the original dataset, refits the model on each resample, and measures how much its apparent performance overstates its true accuracy. After 1,000 bootstrap iterations, the optimism-corrected AUC settled at 0.887, with a confidence interval of 0.841 to 0.933. That the model retained strong discrimination after this correction is a meaningful signal of robustness, though the authors are explicit that external validation in independent cohorts is required before the model can be implemented clinically.

The study went beyond diagnosis. Using ROC analysis, the researchers evaluated whether the IVIM parameters could also predict adverse clinical outcomes, the cascade of complications, including severe hemorrhage, disseminated intravascular coagulation, intensive care admission, and neonatal harm, that follows in the wake of invasive placentation. The combined IVIM parameters achieved an AUC of 0.866 for predicting these adverse outcomes, indicating that the microstructural and microvascular information captured by IVIM carries prognostic weight, not merely diagnostic value. In other words, the same numbers that help identify an invasive placenta may also foreshadow how stormy the clinical course will be.

The outcome analysis also delivered a sobering finding about newborns. Invasive PAS was significantly associated with adverse neonatal outcomes, with a relative risk of 5.203 and a 95 percent confidence interval of 1.646 to 16.446, meaning that babies born to mothers with invasive disease faced roughly five times the risk of complications compared with the non-invasive group. This statistic underscores why preoperative identification of invasive PAS is so consequential: knowing in advance allows delivery to be planned in a center with the surgical, blood banking, and neonatal intensive care capacity that these cases demand.

One association the data could not confirm involved fetal congenital anomalies. Although the point estimate suggested an elevated risk, with a relative risk of 6.787, the 95 percent confidence interval of 0.939 to 49.039 crossed unity, and none of the individual malformation categories reached statistical significance. Critically, these estimates rested on only eight events in total, a sample so small that the analysis was severely underpowered. The authors are careful to state that no established association between invasive PAS and congenital anomalies can be inferred from this dataset, a caveat that guards against overinterpretation of an intriguing but unproven signal.

The work was approved by the Ethics Committee of Jiaxing Maternity and Child Health Care Hospital, conducted in accordance with the Declaration of Helsinki, and supported by the Jiaxing Public Welfare Research Program. Its practical promise lies in a workflow that obstetric units could realistically adopt: when ultrasound or clinical risk factors raise suspicion of PAS, an IVIM-enabled MRI protocol could quantify diffusion and perfusion parameters alongside conventional imaging signs, feeding a validated statistical model that flags invasive disease and predicts the likelihood of a complicated course. With cesarean rates rising globally and PAS incidence climbing in parallel, a noninvasive tool that turns uncertainty into quantified risk could spare mothers from unprepared emergencies and give surgical teams the one resource they value most before a dangerous delivery: time to plan.

Subject of Research: Using intravoxel incoherent motion MRI parameters combined with conventional imaging to predict invasive placenta accreta spectrum and adverse clinical outcomes

Article Title: Predicting invasive placenta accreta spectrum and adverse clinical outcomes using magnetic resonance imaging combined with intravoxel incoherent motion parameters

Article References: Ni, Y., & Chen, S. (2026). Predicting invasive placenta accreta spectrum and adverse clinical outcomes using magnetic resonance imaging combined with intravoxel incoherent motion parameters. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02735-z

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02735-z

Keywords: placenta accreta spectrum, magnetic resonance imaging, intravoxel incoherent motion, diffusion imaging, obstetrics, predictive model, logistic regression, neonatal outcomes, radiology, bootstrap validation, adverse clinical outcomes, Predicting