Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer.

Autor: Gumus KZ; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Kazim.Gumus@jax.ufl.edu., Menendez M; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Manuel.Menendez@jax.ufl.edu., Baerga CG; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Carlos.GonzalezBaerga@jax.ufl.edu., Harmon I; Center for Data Solutions, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Ira.Harmon@jax.ufl.edu8., Kumar S; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Sindhu.Kumar@jax.ufl.edu., Mete M; Department of Information Science, University of North Texas, Denton, TX, USA. Electronic address: Mutlu.Mete@unt.edu., Hernandez M; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: Mauricio.Hernandez@jax.ufl.edu., Ozdemir S; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: savas.ozdemir@jax.ufl.edu., Yuruk N; Department of Computer Science, Southern Methodist University, Dallas, TX, USA. Electronic address: nyuruk@smu.edu., Balaji KC; Department of Urology, University of Florida College of Medicine Jacksonville, FL, USA. Electronic address: Kethandapatti.Balaji@jax.ufl.edu., Gopireddy DR; Department of Radiology, University of Florida, College of Medicine Jacksonville, FL, USA. Electronic address: DheerajReddy.Gopireddy@jax.ufl.edu.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2025 Feb; Vol. 259, pp. 108528. Date of Electronic Publication: 2024 Nov 23.
DOI: 10.1016/j.cmpb.2024.108528
Abstrakt: Background and Objective: Detection of extraprostatic extension (EPE) preoperatively is of critical importance in the context of prostate cancer (PCa) management and outcomes. This study aimed to characterize the radiomic features of malignant prostate lesions based on multi-paramagnetic magnetic resonance imaging (mpMRI).
Methods: We analyzed 20 patients who underwent mpMRI followed by radical prostatectomy. Two experienced radiologists manually segmented the 3D lesions using the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) imaging sequences. A total of 210 radiomic features were extracted from each lesion. We used the Recursive Feature Elimination with Cross-Validation to select key features. Using the selected radiomic features, we developed a Multilayer Perceptron (MLP) neural network to classify the EPE and non-EPE lesions. The pathology results were accepted as gold standard for EPE. We measured the performance of the classifier, calculating the area-under-curve (AUC), sensitivity, and specificity.
Results: A total of 25 lesions were segmented, including 12 lesions with EPE and 13 lesions without EPE, based on the pathology reports. We selected 18 radiomic features (18/210). The MLP classifier using these features provided a good sensitivity (0.75), specificity (0.79), and AUC of 0.82, 95 % CL [0.59 - 0.96] in identifying the EPE lesions.
Conclusions: This pilot study presents 18 radiomic features derived from T2-weighted and ADC images and demonstrates their potential in the preoperative prediction of EPE in PCa using an MLP model.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)
Databáze: MEDLINE
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