Digital diagnostics and artificial intelligence in prostate cancer treatment in 5 years from now

The uropathologist of the future: getting ready with intelligence for the prostate cancer tsunami

According to the recently published paper by the Lancet Commission on prostate cancer (PCa) 1, the projections of new cases of PCa will rise from 1.4 million in 2020 to 2.9 million by 2040. Such a rise cannot be prevented by public health interventions and lifestyle changes. Late diagnosis of PCa is "widespread worldwide but especially in low-income and middle-income countries" 1. The best way to cope with the harm due to the increase in case numbers is to develop systems for earlier diagnosis. Early diagnosis systems will have to integrate the growing power of artificial intelligence (AI), including digital pathology (DP) diagnostics, to aid the interpretation of prostate tissue specimens 1. This contribution aims to point out how DP and AI can help pathologists for the prostate cancer "tsunami" about to come.

Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology

CONTEXT

Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading.

OBJECTIVE

To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed.

DATA SOURCES

The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities.

CONCLUSIONS

It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.

Histopathology of Prostate Cancer and its Precursors

Starting in the mid-1970s, we formed a group of pathologists with a major interest in uropathology. Originally, it included 2 (R.M. and M.S.). In the years the followed, the group was enlarged to include 4 more people, 2 in the mid- and late-1980s (A.L.B. and L.C.) and another in the mid-1990s (R.Ma.); a sixth (A.C.) joined the group ∼5 years ago. Two have reached the retirement age (R.M. and M.S.), while others are in the process of joining the group to replace them. A fruitful collaboration spanned for ∼45 years. This contribution is based on a series of personal recollections of the successive changes in the interpretation of prostate cancer and its precursors, starting in the mid-1970s. Here we have retraced our involvement steps, sharing issues related to them with a junior uropathologist (A.C.).

Artificial intelligence in prostate cancer: Definitions, current research, and future directions

Multiple novel modalities tasking artificial intelligence based computational pathology applications and integrating other variables, such as risk factors, tumor microenvironment, genomic testing data, laboratory findings, clinical history, and radiology findings, will improve diagnostic consistency and generate a synergistic diagnostic workflow. In this article, we present the concise and contemporary review on the utilization of artificial intelligence in prostate cancer and identify areas for possible future applications.

MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer

Prostate cancer (PCa) is one of the most frequent causes of cancer death worldwide. Historically, diagnosis was based on physical examination, transrectal (TRUS) images, and TRUS biopsy resulting in overdiagnosis and overtreatment. Recently magnetic resonance imaging (MRI) has been identified as an evolving tool in terms of diagnosis, staging, treatment decision, and follow-up. In this review we provide the key studies and concepts of MRI as a promising tool in the diagnosis and management of prostate cancer in the general population and in challenging scenarios, such as anteriorly located lesions, enlarged prostates determining extracapsular extension and seminal vesicle invasion, and prior negative biopsy and the future role of MRI in association with artificial intelligence (AI).

Prostate Cancer in 2021: Novelties in Prognostic and Therapeutic Biomarker Evaluation

Simple Summary

In 2021, the identification of effective biomarkers became a major focus of prostate cancer (PCa) in order to improve outcomes and select potentially responsive patients. The aim of this contribution is to review the main 2021 novelties in prognostic and therapeutic markers in PCa, with special reference to PCa grading, aggressive variant PCa and molecular markers predicting significant disease or response to therapy.

Abstract

The 2021 novelties in prognostic and therapeutic tissue markers in patients with prostate cancer (PCa) can be subdivided into two major groups. The first group is related to prognostic markers based on morphological and immunohistochemical evaluations. The novelties in this group can then be subdivided into two subgroups, one involving morphologic evaluation only, i.e., PCa grading, and the other involving both morphologic and immunohistochemical evaluations, i.e., aggressive variant PCa (AVPCa). Grading concerns androgen-dependent PCa, while AVPCa represents a late phase in its natural history, when it becomes androgen-independent. The novelties of the other major group are related to molecular markers predicting significant disease or response to therapy. This group mainly includes novelties in the molecular evaluation of PCa in tissue material and liquid biopsies.