Prostate Cancer Gleason Score From Biopsy to Radical Surgery: Can Ultrasound Shear Wave Elastography and Multiparametric Magnetic Resonance Imaging Narrow the Gap?

Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

Multimodality Characterization of Cancer-Associated Fibroblasts in Tumor Microenvironment and Its Correlation With Ultrasound Shear Wave-Measured Tissue Stiffness in Localized Prostate Cancer

Introduction

Growing evidence suggests that the tumor microenvironment (TME) represented by cellular and acellular components plays a key role in the multistep process of metastases and response to therapies. However, imaging and molecular characterization of the TME in prostate cancer (PCa) and its role in predicting aggressive tumor behavior and disease progression is largely unexplored. The study explores the PCa TME through the characterization of cancer-associated fibroblasts (CAFs) using both immunohistochemistry (IHC) and genomics approaches. This is then correlated with transrectal ultrasound shear wave elastography (USWE)-measured tissue stiffness.

Patients and Methods

Thirty patients with clinically localized PCa undergoing radical prostatectomy for different risk categories of tumor (low, intermediate, and high) defined by Gleason score (GS) were prospectively recruited into this study. Prostatic tissue stiffness was measured using USWE prior to surgery. The CAFs within the TME were identified by IHC using a panel of six antibodies (FAP, SMAα, FSP1, CD36, PDGFRα, and PDGFRβ) as well as gene expression profiling using TempO-sequence analysis. Whether the pattern and degree of immunohistochemical positivity (measured by Quick score method) and expression of genes characterizing CAFs were correlated with USWE- and GS-measured tissue stiffnesses were tested using Spearman’s rank correlation and Pearson correlation.

Results

There was a statistically significant correlation between GS of cancers, the pattern of staining for CAFs by immunohistochemical staining, and tissue stiffness measured in kPa using USWE (p < 0.001). Significant differences were also observed in immunohistochemical staining patterns between normal prostate and prostatic cancerous tissue. PDGFRβ and SMAα immunostaining scores increased linearly with increasing the USWE stiffness and the GS of PCa. There was a significant positive correlation between increasing tissue stiffness in tumor stroma and SMAα and PDGFRβ gene expression in the fibromuscular stroma (p < 0.001).

Conclusion

USWE-measured tissue stiffness correlates with increased SMAα and PDGFRβ expressing CAFs and PCa GSs. This mechanistic correlation could be used for predicting the upgrading of GS from biopsies to radical surgery and response to novel treatments.