Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness.

Anna Damascelli,Francesco De Cobelli,Matteo Interlenghi,Alberto Briganti,Francesco Montorsi,Francesca Gallivanone,Giulia Cristel,Claudia Cava,Antonio Esposito,Giorgio Brembilla,Isabella Castiglioni

doi:10.3390/diagnostics11040594

Abstract

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.

Highlights

Prostate cancer (PCa) is a heterogeneous disease characterized by a wide spectrum of clinical presentations and possible outcomes [1]
Several studies explored a radiomic approach applied to multiparametric magnetic resonance imaging for detection and characterization of PCa [8,9]
There is a growing attention in finding reliable tools that could provide a non-invasive assessment of PCa aggressiveness, allowing for a patient-tailored management, ranging from radical-prostatectomy with nodes dissection to active surveillance

Summary

Introduction

Prostate cancer (PCa) is a heterogeneous disease characterized by a wide spectrum of clinical presentations and possible outcomes [1]. The role of radiomic in oncological setting is to identify reliable biomarkers of cancer aggressiveness [7,8]. Despite the recommendations of international working groups to develop a standardized pipeline to extract image biomarkers, they are characterized by a great variability in methodological approach and results. Most of these studies focused the analysis on the prediction of GGG solely, while the association of radiomic features with other important prognostic parameters, such as extracapsular extension (ECE) and nodal involvement, remains quite unexplored

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Methods

Conclusion