A Systematic Review of Artificial Intelligence in Prostate Cancer

Booven, Derek J. Van; Kuchakulla, Manish; Pai, Raghav; Frech, Fabio Stefano; Ramasahayam, Reshna; Reddy, Pritika; Parmar, Madhumita; Ramasamy, Ranjith; Arora, Himanshu

doi:10.2147/rru.s268596

Cited by 41 publications

(42 citation statements)

References 38 publications

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“…The use of AI is constantly gaining more interest in healthcare, motivating numerous applications being proposed for several diseases and pathologies [10][11][12]. The ANNA approach is one of the AI approaches available, de ned as a form of machine learning, composed of adaptative computational statistical models (designed to mimic a biological nervous system) which is able to recognize complex patterns in data and can predict outcomes after being trained with datasets including input and known output (or outcome) data [10,11]. These methods have shown promising results in improving and automating the ability to diagnose, characterize, and assess the severity of PCa by interpolating clinical or image-based information.…”

Section: Discussionmentioning

confidence: 99%

External validation of the computerized analysis of TRUS of the prostate with the system ANNA/C-TRUS: a potential role of Artificial Intelligence for improving prostate cancer detection

Lorusso

Kabre

Pignot

et al. 2022

Preprint

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PURPOSE:Prostate cancer (PCa) imaging has been revolutionized by the introduction of multi-parametric Magnetic Resonance Imaging (mpMRI). Transrectal ultrasound (TRUS) has always been considered a low-performance modality. To overcome this, a computerized artificial neural network analysis (ANNA/C–TRUS) of the TRUS based on an artificial intelligence (AI) analysis has been proposed. Our aim was to evaluate the diagnostic performance of the ANNA/C-TRUS system and its ability to improve conventional TRUS in PCa diagnosis.METHODSWe retrospectively analyzed data from 64 patients with PCa and scheduled for radical prostatectomy who underwent TRUS followed by ANNA/C-TRUS analysis before the procedure. The results of ANNA/C-TRUS analysis with whole mount sections from final pathology.RESULTS On a per-sectors analysis, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy were 62%, 81%, 80%, 64% and 78% respectively. The values for the detection of clinically significant prostate cancer were 69%, 77%, 88%, 50% and 75%. The diagnostic values for high grade tumours were 70%, 74%, 91%, 41% and 74%, respectively. Cancer volume (<0.5 or greater) did not influence the diagnostic performance of the ANNA/C-TRUS system.CONCLUSIONSANNA/C-TRUS represents a promising diagnostic tool and application of AI for PCa diagnosis. It improves the ability of conventional TRUS to diagnose prostate cancer, preserving its simplicity and availability. Since it is an AI system, it does not hold the inter-observer variability nor a learning curve. Multicenter biopsy-based studies with the inclusion of an adequate number of patients are needed to confirm these results.

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Section: Discussionmentioning

confidence: 99%

External validation of the computerized analysis of TRUS of the prostate with the system ANNA/C-TRUS: a potential role of Artificial Intelligence for improving prostate cancer detection

Lorusso

Kabre

Pignot

et al. 2022

Preprint

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“…Meanwhile, the FDA granted a 510(k) clearance to the first DP-based AI tool, Paige Prostate, for detecting prostate cancer on whole slide images (WSIs) [32]. While many applications of AI in pathology have focused on breast [33][34][35][36], prostate [37][38][39][40], and head and neck cancers [41][42][43][44], there has been a steady interest in applying these tools in the context of lung diseases. The introduction of AI-based tools, with their power to unlock pathological diagnostic, prognostic, and predictive features, could assist pathologists, pulmonologists, and thoracic oncologists to guide patient management [45] (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

The state of the art for artificial intelligence in lung digital pathology

Viswanathan

Toro

Corredor

et al. 2022

The Journal of Pathology

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Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)‐based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand‐crafted and deep learning‐based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID‐19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD‐L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP‐based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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“…AI model development and services are being utilized for diagnosis, treatment, and outcome prediction in urologic diseases. 1 , 2 , 3 , 4 …”

Section: Introductionmentioning

confidence: 99%

Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists

Rho

Park

Moon

et al. 2022

Prostate International

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A Systematic Review of Artificial Intelligence in Prostate Cancer

Cited by 41 publications

References 38 publications

External validation of the computerized analysis of TRUS of the prostate with the system ANNA/C-TRUS: a potential role of Artificial Intelligence for improving prostate cancer detection

External validation of the computerized analysis of TRUS of the prostate with the system ANNA/C-TRUS: a potential role of Artificial Intelligence for improving prostate cancer detection

The state of the art for artificial intelligence in lung digital pathology

Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists

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