Literature DB >> 33672608

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.

Octavian Sabin Tătaru1, Mihai Dorin Vartolomei2,3, Jens J Rassweiler4, Oșan Virgil1, Giuseppe Lucarelli5, Francesco Porpiglia6, Daniele Amparore6, Matteo Manfredi6, Giuseppe Carrieri7, Ugo Falagario7, Daniela Terracciano8, Ottavio de Cobelli9,10, Gian Maria Busetto11, Francesco Del Giudice12, Matteo Ferro9.   

Abstract

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.

Entities:  

Keywords:  artificial intelligence; artificial neural network; biomarker; genomics; prostate cancer

Year:  2021        PMID: 33672608     DOI: 10.3390/diagnostics11020354

Source DB:  PubMed          Journal:  Diagnostics (Basel)        ISSN: 2075-4418


  20 in total

1.  The Application of Biopsy Density in Transperineal Templated-Guided Biopsy Patients With PI-RADS<3.

Authors:  Hai Zhu; Xue-Fei Ding; Sheng-Ming Lu; Ning Ding; Shi-Yi Pi; Zhen Liu; Qin Xiao; Liang-Yong Zhu; Yang Luan; Yue-Xing Han; Hao-Peng Chen; Zhong Liu
Journal:  Front Oncol       Date:  2022-06-08       Impact factor: 5.738

2.  Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion.

Authors:  Ling Yang; Zhengyan Li; Xu Liang; Jingxu Xu; Yusen Cai; Chencui Huang; Mengni Zhang; Jin Yao; Bin Song
Journal:  Front Oncol       Date:  2022-06-28       Impact factor: 5.738

3.  Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements.

Authors:  Ciprian Cosmin Secasan; Darian Onchis; Razvan Bardan; Alin Cumpanas; Dorin Novacescu; Corina Botoca; Alis Dema; Ioan Sporea
Journal:  Curr Oncol       Date:  2022-06-10       Impact factor: 3.109

Review 4.  The state of the art for artificial intelligence in lung digital pathology.

Authors:  Vidya Sankar Viswanathan; Paula Toro; Germán Corredor; Sanjay Mukhopadhyay; Anant Madabhushi
Journal:  J Pathol       Date:  2022-06-20       Impact factor: 9.883

5.  A Genetic Folding Strategy Based Support Vector Machine to Optimize Lung Cancer Classification.

Authors:  Mohammad A Mezher; Almothana Altamimi; Ruhaifa Altamimi
Journal:  Front Artif Intell       Date:  2022-06-30

6.  Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI.

Authors:  Christian Mata; Paul Walker; Arnau Oliver; Joan Martí; Alain Lalande
Journal:  Clin Pract       Date:  2022-05-20

7.  Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients.

Authors:  Mladen Marinkovic; Marina Popovic; Suzana Stojanovic-Rundic; Milos Nikolic; Milena Cavic; Dusica Gavrilovic; Dusan Teodorovic; Nenad Mitrovic; Ljiljana Mijatovic Teodorovic
Journal:  Biomed Res Int       Date:  2022-02-07       Impact factor: 3.411

8.  CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

Authors:  Luis Martí Bonmatí; Ana Miguel; Amelia Suárez; Mario Aznar; Jean Paul Beregi; Laure Fournier; Emanuele Neri; Andrea Laghi; Manuela França; Francesco Sardanelli; Tobias Penzkofer; Phillipe Lambin; Ignacio Blanquer; Marion I Menzel; Karine Seymour; Sergio Figueiras; Katharina Krischak; Ricard Martínez; Yisroel Mirsky; Guang Yang; Ángel Alberich-Bayarri
Journal:  Front Oncol       Date:  2022-02-24       Impact factor: 6.244

9.  Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia.

Authors:  Shaogao Gui; Min Lan; Chaoxiong Wang; Si Nie; Bing Fan
Journal:  Front Oncol       Date:  2022-04-14       Impact factor: 5.738

10.  Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis.

Authors:  Yo-Liang Lai; Chia-Hsin Liu; Shu-Chi Wang; Shu-Pin Huang; Yi-Chun Cho; Bo-Ying Bao; Chia-Cheng Su; Hsin-Chih Yeh; Cheng-Hsueh Lee; Pai-Chi Teng; Chih-Pin Chuu; Deng-Neng Chen; Chia-Yang Li; Wei-Chung Cheng
Journal:  Cancers (Basel)       Date:  2022-03-19       Impact factor: 6.639

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