ArticleJapanese journal of radiology2025
Usefulness of decision tree analysis of MRI features for diagnosis of placenta accreta spectrum in cases with placenta previa.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Advances in Prenatal Diagnosis of Placenta Accreta Spectrum.Medicina (Kaunas, Lithuania) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePlacenta previa complicated by placenta accrete spectrum (PAS) is a life-threatening obstetrical condition; therefore, preoperative diagnosis of PAS is important to determine adequate management. Although several MRI features that suggest PAS has been reported, the diagnostic importance, as well as optimal use of each feature has not been fully evaluated. MATERIALS AND
methodsThe occurrence of 11 PAS-related MRI features was investigated in MR images of 145 patients with placenta previa. The correlation between each MRI feature and pathological diagnosis of PAS was evaluated using univariate analysis. A decision tree model was constructed according to a random forest machine learning model of variable selection.
resultsEight MRI features showed a significant correlation with PAS in univariate analysis. Among these features, placental/uterine bulge and myometrial thinning showed high odds ratios: 138.2 (95% CI: 12.7-1425.6) and 66.0 (95% CI: 18.01-237.1), respectively. A decision tree was constructed based on five selected MRI features: myometrial thinning, placental bulge, serosal hypervascularity, placental ischemic infarction/recess, and intraplacental T2 dark bands. The decision tree predicted the presence of PAS in the randomly assigned validation cohort with significance (p < 0.001). The sensitivity and the specificity of the decision tree for detecting PAS were 90.0% (95%CI: 53.2-98.9) and 95.5% (95%CI: 89.9-96.8), respectively.
conclusionAmong PAS-related MRI features, placental/uterine bulge and myometrial thinning showed high diagnostic values. In addition, the present decision tree model was shown to be effective in predicting the presence of PAS in cases with placenta previa.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.