Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness
| Autor: | Anna Damascelli 1, Francesca Gallivanone 2, Giulia Cristel 1, Claudia Cava 2, Matteo Interlenghi 2, Antonio Esposito 1, 3, Giorgio Brembilla 1, Alberto Briganti 3, 4, Francesco Montorsi 3, Isabella Castiglioni 5, Francesco De Cobelli 1 |
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| Přispěvatelé: | Damascelli, Anna, Gallivanone, Francesca, Cristel, Giulia, Cava, Claudia, Interlenghi, Matteo, Esposito, Antonio, Brembilla, Giorgio, Briganti, Alberto, Montorsi, Francesco, Castiglioni, Isabella, De Cobelli, Francesco |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_treatment
Clinical Biochemistry magnetic resonance imaging prostate cancer prostate cancer aggressiveness radiomics Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Prostate cancer 0302 clinical medicine Radiomics Prostate Medicine Effective diffusion coefficient Multiparametric Magnetic Resonance Imaging lcsh:R5-920 medicine.diagnostic_test business.industry Prostatectomy Magnetic resonance imaging medicine.disease Hierarchical clustering medicine.anatomical_structure 030220 oncology & carcinogenesis lcsh:Medicine (General) business Nuclear medicine |
| Zdroj: | Diagnostics; Volume 11; Issue 4; Pages: 594 Diagnostics Diagnostics, Vol 11, Iss 594, p 594 (2021) Diagnostics (Basel) 11 (2021): 594. doi:10.3390/diagnostics11040594 info:cnr-pdr/source/autori:Anna Damascelli 1, Francesca Gallivanone 2, Giulia Cristel 1, Claudia Cava 2, Matteo Interlenghi 2, Antonio Esposito 1,3, Giorgio Brembilla 1, Alberto Briganti 3,4, Francesco Montorsi 3,4, Isabella Castiglioni 5, Francesco De Cobelli 1,3/titolo:Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness/doi:10.3390%2Fdiagnostics11040594/rivista:Diagnostics (Basel)/anno:2021/pagina_da:594/pagina_a:/intervallo_pagine:594/volume:11 |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics11040594 |
| Popis: | Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample. |
| Databáze: | OpenAIRE |
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