Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study(12 views visite) Stanzione A, Cuocolo R, Del Grosso R, Nardiello A, Romeo V, Travaglino A, Raffone A, Bifulco G, Zullo F, Insabato L, Maurea S, Mainenti PP
Keywords Parole chiave: Deep Myometrial Invasion, Endometrial Cancer, Machine Learning, Radiomics
Affiliations Affiliazioni: *** IBB - CNR ***
Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples "Federico II", Italy.
Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
References Riferimenti: Not available. Non disponibili.
Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study
RATIONALE AND OBJECTIVES: To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability. MATERIALS AND METHODS: Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings. RESULTS: Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48). CONCLUSION: We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.
Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study
No results. Nessun risultato.
Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study
9 Records (8 escludendo Abstract e Conferenze). Impact factor totale: 19.156 (14.339 escludendo Abstract e Conferenze). Impact factor a 5 anni totale: 12.519 (7.73 escludendo Abstract e Conferenze).
Last modified by Ultima modifica di Gennaro Angrisano on in data Thursday 14 January 2021, 18:15:09 12 views visite. Last view on Ultima visita in data Wednesday 03 February 2021, 23:05:45