Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.
Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.
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DOI：
10.3390/diagnostics13040806
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发表时间：
2023-02-20
期刊：
影响因子：
3.6
通讯作者：
Shahbazi-Gahrouei, Daryoush
中科院分区：
文献类型：
--
作者：
Nematollahi, Hamide;Moslehi, Masoud;Aminolroayaei, Fahimeh;Maleki, Maryam;Shahbazi-Gahrouei, Daryoush
前列腺癌是男性癌症相关死亡的第二大原因。它的早期和正确的诊断对于控制和防止疾病扩散到其他组织特别重要。人工智能和机器学习已经有效地检测到了几种癌症并对其进行了分级，特别是前列腺癌。这篇综述的目的是展示有监督的机器学习算法在使用多参数MRI检测前列腺癌方面的诊断性能(准确性和曲线下面积)。比较了不同有监督机器学习方法的性能。这项综述研究是在截至2023年1月底的科学引文网站上进行的，这些文献来自谷歌学者、PubMed、Scopus和Web of Science等科学引文网站。本综述的结果表明，有监督的机器学习技术在前列腺癌的多参数磁共振成像诊断和预测中具有高精度和曲线下面积的良好性能。在有监督的机器学习方法中，深度学习、随机森林和Logistic回归算法似乎具有最好的性能。
Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis is of particular importance to controlling and preventing the disease from spreading to other tissues. Artificial intelligence and machine learning have effectively detected and graded several cancers, in particular prostate cancer. The purpose of this review is to show the diagnostic performance (accuracy and area under the curve) of supervised machine learning algorithms in detecting prostate cancer using multiparametric MRI. A comparison was made between the performances of different supervised machine-learning methods. This review study was performed on the recent literature sourced from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up to the end of January 2023. The findings of this review reveal that supervised machine learning techniques have good performance with high accuracy and area under the curve for prostate cancer diagnosis and prediction using multiparametric MR imaging. Among supervised machine learning methods, deep learning, random forest, and logistic regression algorithms appear to have the best performance.
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影响因子：
5.2
作者：
通讯作者：
--
前列腺癌的诊断和格里森分级的人工智能：熊猫挑战。
影响因子：
82.9
作者：
Bulten W;Kartasalo K;Chen PC;Ström P;Pinckaers H;Nagpal K;Cai Y;Steiner DF;van Boven H;Vink R;Hulsbergen-van de Kaa C;van der Laak J;Amin MB;Evans AJ;van der Kwast T;Allan R;Humphrey PA;Grönberg H;Samaratunga H;Delahunt B;Tsuzuki T;Häkkinen T;Egevad L;Demkin M;Dane S;Tan F;Valkonen M;Corrado GS;Peng L;Mermel CH;Ruusuvuori P;Litjens G;Eklund M;PANDA challenge consortium
通讯作者：
PANDA challenge consortium
影响因子：
3.1
作者：
Arledge, Chad A.;Sankepalle, Deeksha M.;Crowe, William N.;Liu, Yang;Wang, Lulu;Zhao, Dawen
通讯作者：
Zhao, Dawen
影响因子：
4.6
作者：
Abed, Mustafa;Imteaz, Monzur Alam;Ahmed, Ali Najah;Huang, Yuk Feng
通讯作者：
Huang, Yuk Feng
影响因子：
2.4
作者：
Chatterjee, Aritrick;Gallan, Alexander J.;Oto, Aytekin
通讯作者：
Oto, Aytekin