Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Abstract

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.

Keywords: artificial intelligence; deep learning; machine learning; neural network; prostate carcinoma; prostate mpMRI.

Figure 1

Relationship between artificial intelligence, machine learning, and deep learning. Artificial intelligence is an umbrella term that includes machine learning and deep learning. Deep learning is a hyponym of machine learning.

Figure 2

Machine learning versus deep learning used for multiparametric magnetic resonance imaging (mpMRI) sequence identification. In machine learning, the computer receives inputs of mpMRI images and goes through feature extraction specific to the different sequences of T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE). Then, the computer is trained on additional images and is able to identify the correct sequence as an output. Deep learning differs from machine learning in that feature extraction and training can be done simultaneously to produce the output.

Figure 3

Prostate organ segmentation performed by machine learning methods. The computer takes multiparametric magnetic resonance imaging images as inputs and applies the developed machine learning algorithm to correctly identify the borders of the prostate.

Figure 4

Prostate lesion detection using machine learning methods. The computer takes multiparametric magnetic resonance imaging images of the prostate as inputs and applies the developed machine learning algorithm to correctly localize lesions in the prostate.

Figure 5

Prostate lesion segmentation using machine learning techniques. The computer takes multiparametric magnetic resonance imaging images of the prostate as inputs and applies the developed machine learning algorithm to correctly identify the borders of the lesion.

Figure 6

Prostate lesion characterization using machine-learning techniques. The computer receives multiparametric magnetic imaging images of prostate lesions and applies the developed machine learning algorithm to categorize the lesion as clinically significant prostate cancer or non-significant prostate cancer.