A review of artificial intelligence in prostate cancer detection on imaging

Abstract

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

Keywords: artificial intelligence; histopathology images; magnetic resonance imaging; prostate cancer diagnosis; registration; ultrasound images.

Figure 1.

Potential of AI to assist prostate cancer diagnosis on imaging. AI models can help in detecting and characterizing cancer aggressiveness on non-invasive radiology images (MRI and ultrasound), as well as on histopathology images acquired through prostate biopsy. Aggressive cancer is shown in yellow, and indolent cancer in green in the ‘AI for cancer diagnosis’ panel. AI models can also help in supporting tasks for cancer detection, namely prostate gland segmentation, MRI-ultrasound registration, and MRI-histopathology registration.

Figure 2.

AI models for prostate cancer detection on MRI can be subdivided into two major tasks: lesion classification and lesion detection. Lesion classification involves classifying a radiologist-outlined lesion (region of interest) into categories (cancer vs benign, clinically significant cancer vs benign or indolent, or Gleason grade groups). Lesion detection involves detecting and characterizing cancer aggressiveness on the entire prostate MRI.

Figure 3.

The AI-predicted automated aggressive (Gleason pattern 4, green) and indolent (Gleason Pattern 3, blue) cancers visually match the manual cancer annotations by an expert pathologist (black, yellow, orange, red). (a) Whole mount histopathology image with (b–d) Close-up into the two cancer lesions. (c) Cancer labels manually outlined by an expert pathologist (black outline) shows high agreement with overall cancer (combined blue and green) predicted by the AI model. (b, d) It is impractically time-consuming for a human pathologist to manually assign pixel-level Gleason patterns (yellow, orange, red) to each gland in detail as done by the AI model (blue, green).

Figure 4.

AI can help in supporting tasks for cancer detection like prostate gland segmentation on MRI and ultrasound (left), and MRI-ultrasound registration (right). The AI-predicted prostate segmentations on MRI and ultrasound can help in automated MRI-ultrasound registration which aligns the two modalities, mapping lesions from MRI onto ultrasound. MRI-ultrasound registration helps guide systematic and targeted fusion biopsy procedures.