Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study

Jung, Min-A; Jin, Min-Sun; Kim, Chungyeul; Lee, Chul; Nikas, Ilias P.; Park, Jeong Hwan; Ryu, Han Suk

doi:10.1038/s41379-022-01077-9

Cited by 12 publications

(13 citation statements)

References 25 publications

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“…Artificial Neural Network (ANN) is a complex network structure formed by interconnecting a series of treating units, which has been proved to be scientific and accurate in disease prediction in recent years 23 – 25 . In this study, we utilized the common regulators selected by RF and SVM as the input layer to construct an ANN model.…”

Section: Methodsmentioning

confidence: 99%

Gene signature of m6A RNA regulators in diagnosis, prognosis, treatment, and immune microenvironment for cervical cancer

Wang

Ding

Wang

et al. 2022

Sci Rep

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Continuing studies imply that m6A RNA modification is involved in the development of cervical cancer (CC), but lack strong support on recurrence and diagnosis prediction. In this research, a comprehensive analysis of 33 m6A regulators was performed to fulfill them. Here, we performed diagnostic and prognosis models and identified key regulators, respectively. Then the CC patients were separated into two clusters in accordance with 33 regulators, and participants in the cluster 1 had a worse prognosis. Subsequently, the m6AScore was calculated to quantify the m6A modification pattern based on regulators and we found that patients in cluster 1 had higher m6AScore. Afterwards, immune microenvironment, cell infiltration, escape analyses and tumor burden mutation analyses were executed, and results showed that m6AScore was correlated with them, but to a limited extent. Interestingly, HLAs and immune checkpoint expression, and immunophenoscore in patients with high-m6AScores were significantly lower than those in the low-m6AScore group. These suggested the m6AScores might be used to predict the feasibility of immunotherapy in patients. Results provided a distinctive perspective on m6A modification and theoretical basis for CC diagnosis, prognosis, clinical treatment strategies, and potential mechanism exploration.

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Section: Methodsmentioning

confidence: 99%

Gene signature of m6A RNA regulators in diagnosis, prognosis, treatment, and immune microenvironment for cervical cancer

Wang

Ding

Wang

et al. 2022

Sci Rep

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“…Such factors include variations in the image quality, staining methods, staining times, types of scanners used for digitization, and file formats of images, which could affect the performance of AI tools. [9][10][11][12][43][44][45][46][47][48][49][50][51][52][53][54] Differences in the performance of AI algorithms in the test sets as compared with training sets in different settings, such as internal versus external sets, are the proofs of such effects. In a recent study by Eloy et al, 36 scanning error was the single most important identified in the retrospective review of discrepant cases of prostate cancer on biopsies.…”

Section: Factors Affecting Performance Of Ai Tools: Quality Of Input ...mentioning

confidence: 99%

“…The model was trained on 689 prostate biopsy cases and was tested on 99 cases. Jung et al did an external validation study for the DeepDx algorithm on 593 biopsies and demonstrated higher concordance with GU pathologists to detect and grade PC 43 …”

Section: Ai For Pc Diagnosismentioning

confidence: 99%

“…In other words, AI performance is susceptible to variations in the input data. Such factors include variations in the image quality, staining methods, staining times, types of scanners used for digitization, and file formats of images, which could affect the performance of AI tools 9–12,43–54 . Differences in the performance of AI algorithms in the test sets as compared with training sets in different settings, such as internal versus external sets, are the proofs of such effects.…”

Section: Current Challenges For Deployment Of Ai Tools For Pc Diagnosismentioning

confidence: 99%

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Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications

Satturwar,

Parwani

2024

Advances in Anatomic Pathology

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In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists’ workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

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“… 18 DeepDx-Prostate Connect Deep Bio ROI detection, Gleason grading, and quantification South Korea Jung et al. 19 , 20 and Ryu et al. 19 , 20 Galen Prostate Ibex Medical Analytics cancer detection and Gleason grading Israel Pantanowitz et al.…”

Section: Introductionmentioning

confidence: 99%

Harnessing artificial intelligence for prostate cancer management

Zhu,

Pan,

Mou

et al. 2024

Cell Reports Medicine

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Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study

Cited by 12 publications

References 25 publications

Gene signature of m6A RNA regulators in diagnosis, prognosis, treatment, and immune microenvironment for cervical cancer

Gene signature of m6A RNA regulators in diagnosis, prognosis, treatment, and immune microenvironment for cervical cancer

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications

Harnessing artificial intelligence for prostate cancer management

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