Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review

Shah, Uzair; Biswas, Md. Rafiul; Alzubaidi, Mahmood; Ali, Hazrat; Alam, Tanvir; Househ, Mowafa; Shah, Zubair

doi:10.3233/shti210911

Cited by 7 publications

(11 citation statements)

References 8 publications

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“…In addition, the use of AI has already shown promise in supporting pathologists and in improving diagnostic efficiency, which could address the variability in diagnostic capabilities 48 .…”

Section: Discussionmentioning

confidence: 99%

Declining incidence and improving survival of ocular and orbital lymphomas in the US between 1995 and 2018

Alfaar,

Yousef,

W. Wilson

et al. 2024

Sci Rep

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This epidemiological study examined ocular and orbital lymphomas in the United States from 1995 to 2018, using data from the North American Association of Central Cancer Registries database of 87,543 patients with ocular and adnexal malignancies. We identified 17,878 patients (20.4%) with ocular and orbital lymphomas, with an age-standardized incidence rate (ASIR) of 2.6 persons per million (ppm). The incidence was the highest in the orbit (ASIR = 1.24), followed by the conjunctiva (ASIR = 0.57). Non-Hodgkin B-cell lymphoma was the most prevalent subtype (85.4%), particularly marginal-zone lymphoma (45.7%). Racial disparities were noted, with Asia–Pacific Islanders showing the highest incidence (orbit, 1.3 ppm). The incidence increased significantly from 1995 to 2003 (Average Percent Change, APC = 2.1%) but declined thereafter until 2018 (APC = − 0.7%). 5-year relative survival (RS) rates varied, with the highest rate for conjunctival lymphoma (100%) and the lowest for intraocular lymphoma (70.6%). Survival rates have generally improved, with an annual increase in the 5-year RS of 0.45%. This study highlights the changing epidemiological landscape, pointing to initial increases and subsequent decreases in incidence until 2003, with survival improvements likely due to advancements in treatment. These findings underscore the need for further research to investigate the root causes of these shifts and the declining incidence of ocular lymphoma.

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“…In addition, the use of AI has already shown promise in supporting pathologists and in improving diagnostic efficiency, which could address the variability in diagnostic capabilities 48 .…”

Section: Discussionmentioning

confidence: 99%

Declining incidence and improving survival of ocular and orbital lymphomas in the US between 1995 and 2018

Alfaar,

Yousef,

W. Wilson

et al. 2024

Sci Rep

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“…Tools based on A.I. can help physicians give more accurate prostate cancer diagnostic strategies [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Prostate Cancer Detection and Analysis using Advanced Machine Learning

Alzboon,

Al-Batah

2023

IJACSA

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Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning model for detecting and diagnosing prostate cancer using clinical and radiological data. The dataset consists of 200 patients with prostate cancer and 200 healthy controls and extracted features from their clinical and radiological data. Then, the data trained and evaluated using several machines learning models, including logistic Regression, decision tree, random forest, support vector machine, and neural network models, using 10-fold crossvalidation. Our results show that the random forest model achieved the highest accuracy of 0.92, with a sensitivity of 0.95 and a specificity of 0.89. The decision tree model achieved a nearly similar accuracy of 0.91, while the logistic regression, support vector machine, and neural network models achieved lower accuracies of 0.86, 0.87, and 0.88, respectively. Our findings suggest that machine learning models can effectively detect and diagnose prostate cancer using clinical and radiological data. The random forest model may be the most suitable model for this task.

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“… 44 Lately, most of the AI methods used are convolutional neural networks(CNNs), because they have the potential to extract complex variables. 173 Other researches focused on ML methods, like SVM, DT for classification, and LASSO and Ridge regression methods, for features extraction, are heavily implemented in recent studies.…”

Section: Notes On the Advantages And Limitations Of Classifications A...mentioning

confidence: 99%

Radiomics in prostate cancer: an up-to-date review

Ferro

Cobelli

Musi

et al. 2022

Therapeutic Advances in Urology

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Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

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Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review

Cited by 7 publications

References 8 publications

Declining incidence and improving survival of ocular and orbital lymphomas in the US between 1995 and 2018

Declining incidence and improving survival of ocular and orbital lymphomas in the US between 1995 and 2018

Prostate Cancer Detection and Analysis using Advanced Machine Learning

Radiomics in prostate cancer: an up-to-date review

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