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Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa

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A Letter to the Editor to this article was published on 03 October 2023

A Letter to the Editor to this article was published on 21 September 2023

Abstract

Purpose

To retrospectively evaluate the performance of different manual segmentation methods of placenta MR images for predicting Placenta Accreta Spectrum (PAS) disorders in patients with placenta previa (PP) using a Machine Learning (ML) Radiomics analysis.

Methods

64 patients (n=41 with PAS and n= 23 without PAS) with PP who underwent MRI examination for suspicion of PAS were retrospectively selected. All MRI examinations were acquired on a 1.5 T using T2-weighted (T2w) sequences on axial, sagittal and coronal planes. Ten different manual segmentation methods were performed on sagittal placental T2-weighted images obtaining five sets of 2D regions of interest (ROIs) and five sets of 3D volumes of interest (VOIs) from each patient. In detail, ROIs and VOIs were positioned on the following areas: placental tissue, retroplacental myometrium, cervix, placenta with underneath myometrium, placenta with underneath myometrium and cervix. For feature stability testing, the same process was repeated on 30 randomly selected placental MRI examinations by two additional radiologists, working independently and blinded to the original segmentation. Radiomic features were extracted from all available ROIs and VOIs. 100 iterations of 5-fold cross-validation with nested feature selection, based on recursive feature elimination, were subsequently run on each ROI/VOI to identify the best-performing method to classify instances correctly.

Results

Among the segmentation methods, the best performance in predicting PAS was obtained by the VOIs covering the retroplacental myometrium (Mean validation score: 0.761, standard deviation: 0.116).

Conclusion

Our preliminary results show that the VOI including the retroplacental myometrium using T2w images seems to be the best method when segmenting images for the development of ML radiomics predictive models to identify PAS in patients with PP.

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Authors and Affiliations

  1. Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy

    Francesco Verde, Arnaldo Stanzione, Valeria Romeo, Martina Di Stasi, Lorenzo Ugga, Maria D’Armiento, Arturo Brunetti & Simone Maurea

  2. Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy

    Renato Cuocolo

  3. Institute of Biostructures and Bioimaging of the National Council of Research (CNR), Naples, Italy

    Pier Paolo Mainenti

  4. Department of Neuroscience, Reproductive and Dentistry Sciences, University of Naples “Federico II”, Naples, Italy

    Laura Sarno & Maurizio Guida

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Verde, F., Stanzione, A., Cuocolo, R. et al. Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa. Abdom Radiol 48, 3207–3215 (2023). https://doi.org/10.1007/s00261-023-03963-5

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