An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

Wu, Shaoxu; Xiong, Chen; Pan, Jiexin; Dong, Wen; Diao, Xiayao; Zhang, Ruiyun; Zhang, Yonghai; Zhang, Yuanfeng; Qian, Guangren; Chen, Hao; Lin, Haotian; Xu, Shizhong; Chen, Zhiwen; Zhou, Xiaozhou; Mei, Hongbing; Wu, Chenglong; Lv, Qiang; Yuan, Baorui; Chen, Zeshi; Liao, Wenjian; Yang, Xuefan; Chen, Haige; Huang, Jian; Lin, Tianxin

doi:10.1093/jnci/djab179

Cited by 31 publications

(23 citation statements)

References 23 publications

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“…Wu et al found that their AI Algorithm was superior to expert urologists in the detection of complex lesions such as CIS and very small lesions. 68 Further prospective studies are needed to determine incorporation of AI in clinical practice and its use in different patient populations.…”

Section: Artificial Intelligencementioning

confidence: 99%

The Development of Non-Invasive Diagnostic Tools in Bladder Cancer

Schulz¹,

Loloi²,

Martina³

et al. 2022

OTT

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Bladder cancer is a common urinary tract cancer with a difficult clinical course. With frequent recurrence, patients with a history of bladder cancer often undergo surveillance that involves invasive cystoscopies and biopsies. Not only is this financially burdensome for patients but it is also mentally and physically intensive. Given this predicament, the field has shifted towards the use of non-invasive urinary tests to detect bladder cancer earlier in the disease course and to avoid unnecessary procedures. The first noninvasive test developed was urine cytology; however, that was found to have a low sensitivity, especially for low-grade lesions. There are many tests that are available that utilize common protein biomarkers to enhance the sensitivity of detection. However, many of these tests lack the specificity seen with cytology. With recent technological and research advancements, there are newer detection systems such as RNA sequencing and microfluidics along with novel bladder cancer biomarkers including mRNAs, methylation patterns and exosomes, which have potential to be used in clinical practice. The aim of this review is to highlight established non-invasive bladder cancer diagnostic tests as well as innovative methodologies that are on the horizon for use in bladder cancer detection.

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Section: Artificial Intelligencementioning

confidence: 99%

The Development of Non-Invasive Diagnostic Tools in Bladder Cancer

Schulz¹,

Loloi²,

Martina³

et al. 2022

OTT

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“…A recent study by Wu et al conducted an extensive study incorporating the cystoscopic images of 10 729 patients from 6 different centres. 85 They proposed an algorithm based on a pyramid pooling module, allowing the utilization of information gathered from both the whole image and small areas within the image. Moreover, they trained their algorithm with the help of the large dataset of cystoscopy images and Im-ageNet, and the model was able to achieve accuracies ranging from 97.8 to 99.1% on different external validation sets.…”

Section: Cystoscopy and Aimentioning

confidence: 99%

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging

et al. 2022

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Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.

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“…Summarizing its global research trends and research hotspots are of great significance to the next research. Our research team also conducted some AI research in the field of urogenital neoplasms (26)(27)(28). However, there is no research on bibliometric analysis to summarize.…”

Section: Introductionmentioning

confidence: 99%

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

Shen

Chen

et al. 2022

Front. Oncol.

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BackgroundWith the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.ObjectiveThis study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.MethodsThe articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.ResultsA total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.ConclusionsArtificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

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An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

Cited by 31 publications

References 23 publications

The Development of Non-Invasive Diagnostic Tools in Bladder Cancer

The Development of Non-Invasive Diagnostic Tools in Bladder Cancer

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

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