Advances in Quantitative Imaging, AI, and Novel Imaging Techniques for Precision Radiology in Prostate Cancer

Dear Colleagues,

Worldwide, approximately 1,000,000 men are diagnosed and 300,000 men die from prostate cancer (PCa) each year. PCa, therefore, poses a significant economic and societal burden. Proper patient care and management of PCa relies on the accurate detection and prognostication of malignant lesions, assessment for potential metastases, and evaluation of the possibility for further growth. Non-invasive prostate serum antigen (PSA) pre-screens and preliminary measurements assess the possible need for medical intervention. The widely implemented PSA indicator has significantly reduced PCa mortality, although its low specificity can lead to under- and overtreatment. When PSA values are above threshold levels, PCa is standardly diagnosed and risk stratified through 6–12 core transrectal ultrasound-based needle biopsy, supplemented with magnetic resonance imaging (MRI). However, invasive biopsies present risks of pain, hemorrhage, and infection for patients. In addition, misplacement of the needle can underestimate the tumor Gleason score and inaccurately determine the patient’s status and optimal treatment approach.

To improve PCa diagnosis and grading, and to alleviate patient suffering, non-invasive strategies have been developed, such as imaging patients with suspected disease. The entire prostate gland can be non-invasively viewed, minimizing the likelihood of missing sampling from the most malignant part of a tumor. Multiparametric magnetic resonance imaging (mpMRI) fused with ultrasound (US), and positron emission tomography combined with computed tomography (PET/CT), are playing an increasingly important role in the early diagnosis of PCa. New biomarkers that target the membrane antigens, such as the Prostate-Specific Membrane Antigen (PSMA) for PET, have been developed, greatly bolstering tumor and metastases detection. Prostate Imaging Reporting and Data System (PI-RADS) is a semi-quantitative protocol for radiologists to visually assess multiple MRI sequences and combine them to predict the potential aggressiveness of a prostate tumor. Building on this, more quantitative approaches, including the application of artificial intelligence (AI) to imaging data and radiology reports, are showing promising results. AI harnesses the vast amount of imaging data and increasing computational power to deliver accurate, efficient, and reproducible analyses. Other quantitative approaches evaluate spatially registered multi-parametric MRI (mpMRI) and bi-parametric MRI (bpMRI), the latter of which excludes contrast-enhanced sequences. By avoiding contrast injection, bpMRI simplifies the imaging process and reduces patient burden while maintaining diagnostic value.

This Special Issue of Cancers, entitled “Advances in Quantitative Imaging, AI, and Novel Imaging Techniques for Precision Radiology in Prostate Cancer”, compiles articles on a number of research areas such as, but not restricted to, the following:

1. Scanning patients with suspected PCa with a number of imaging modalities, such as multi-parametric MRI fused with ultrasound, and positron emission tomography combined with computed tomography (PET/CT), to detect prostate cancer and localize the lesion.
2. Enhancements to AI applied to mpMRI through refinements of deep learning algorithms and texture generation.
3. Foundation models—particularly vision-language models—enabling the integration of imaging data and corresponding textual reports, allowing for cross-modal understanding and completing tasks such as report generation, image-text retrieval, and multimodal representation learning.
4. Combining patient data with imaging to predict clinically significant prostate cancer (csPCa).
5. Applying supervised and unsupervised target detection algorithms to spatially registered multi-parametric MRI to assess prostate cancer.
6. Spatial registration techniques.
7. Incorporating and combining novel biomarkers with imaging to predict clinically significant prostate cancer.
8. Comparison of results from different clinics and/or clinical situations (i.e., different magnetic fields).
9. Bi-parametric MRI assessments of prostate cancer.
10. Biomarker development and testing of PSMA-PET for prostate cancer and metastases detection

Dr. Rulon R. Mayer
Dr. Charles Simone
Dr. Yuan Yuan
Guest Editors

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• prostate cancer
• multi-parametric MRI (mpMRI)
• bi-parametric MRI
• positron emission tomography (PET)
• computed tomography (CT)
• ultrasound (US)
• artificial intelligence (AI)
• convolutional neural network
• visual language model
• computer-aided diagnosis
• spatially registered multi-parametric MRI
• prostate specific membrane antigen in PET/CT