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For: T JMC, Arif M, Niessen WJ, Schoots IG, Veenland JF. Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications. Cancers (Basel) 2020;12:E1606. [PMID: 32560558 DOI: 10.3390/cancers12061606] [Cited by in Crossref: 31] [Cited by in F6Publishing: 34] [Article Influence: 10.3] [Reference Citation Analysis]
Number Citing Articles
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16 Gravina M, Spirito L, Celentano G, Capece M, Creta M, Califano G, Collà Ruvolo C, Morra S, Imbriaco M, Di Bello F, Sciuto A, Cuocolo R, Napolitano L, La Rocca R, Mirone V, Sansone C, Longo N. Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions. Diagnostics 2022;12:1565. [DOI: 10.3390/diagnostics12071565] [Reference Citation Analysis]
17 Liu Y, Shu X, Qiao X, Ai G, Liu L, Liao J, Qian S, He X. Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer. Front Oncol 2022;12:911426. [DOI: 10.3389/fonc.2022.911426] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Wu S, Wang J, Guo Q, Lan H, Zhang J, Wang L, Janne E, Luo X, Wang Q, Song Y, Mathew JL, Xun Y, Yang N, Lee MS, Chen Y. Application of artificial intelligence in clinical diagnosis and treatment: an overview of systematic reviews. Intelligent Medicine 2022;2:88-96. [DOI: 10.1016/j.imed.2021.12.001] [Reference Citation Analysis]
19 Zhang KS, Schelb P, Netzer N, Tavakoli AA, Keymling M, Wehrse E, Hog R, Rotkopf LT, Wennmann M, Glemser PA, Thierjung H, von Knebel Doeberitz N, Kleesiek J, Görtz M, Schütz V, Hielscher T, Stenzinger A, Hohenfellner M, Schlemmer HP, Maier-Hein K, Bonekamp D. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022. [PMID: 35467572 DOI: 10.1097/RLI.0000000000000878] [Reference Citation Analysis]
20 Sushentsev N, Moreira Da Silva N, Yeung M, Barrett T, Sala E, Roberts M, Rundo L. Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review. Insights Imaging 2022;13. [DOI: 10.1186/s13244-022-01199-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
21 Bertelli E, Mercatelli L, Marzi C, Pachetti E, Baccini M, Barucci A, Colantonio S, Gherardini L, Lattavo L, Pascali MA, Agostini S, Miele V. Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI. Front Oncol 2021;11:802964. [PMID: 35096605 DOI: 10.3389/fonc.2021.802964] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
22 Li D, Han X, Gao J, Zhang Q, Yang H, Liao S, Guo H, Zhang B. Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations. Front Med (Lausanne) 2021;8:810995. [PMID: 35096899 DOI: 10.3389/fmed.2021.810995] [Reference Citation Analysis]
23 Bleker J, Kwee TC, Rouw D, Roest C, Borstlap J, de Jong IJ, Dierckx RAJO, Huisman H, Yakar D. A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics. Eur Radiol 2022;32:6526-35. [PMID: 35420303 DOI: 10.1007/s00330-022-08712-8] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Duran A, Dussert G, Rouviére O, Jaouen T, Jodoin P, Lartizien C. ProstAttention-Net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI scans. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2021.102347] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
25 Srivatsan S, Bamrah SK, Gayathri KS. Determining the Severity of Dementia Using Ensemble Learning. Big Data Analytics 2022. [DOI: 10.1007/978-3-031-24094-2_8] [Reference Citation Analysis]
26 Li B, Oka R, Xuan P, Yoshimura Y, Nakaguchi T. Robust multi-modal prostate cancer classification via feature autoencoder and dual attention. Informatics in Medicine Unlocked 2022;30:100923. [DOI: 10.1016/j.imu.2022.100923] [Reference Citation Analysis]
27 Castillo T JM, Arif M, Starmans MPA, Niessen WJ, Bangma CH, Schoots IG, Veenland JF. Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers (Basel) 2021;14:12. [PMID: 35008177 DOI: 10.3390/cancers14010012] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
28 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
29 Bleker J, Yakar D, van Noort B, Rouw D, de Jong IJ, Dierckx RAJO, Kwee TC, Huisman H. Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer. Insights Imaging 2021;12:150. [PMID: 34674058 DOI: 10.1186/s13244-021-01099-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
30 Corradini D, Brizi L, Gaudiano C, Bianchi L, Marcelli E, Golfieri R, Schiavina R, Testa C, Remondini D. Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data. Cancers (Basel) 2021;13:3944. [PMID: 34439099 DOI: 10.3390/cancers13163944] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
31 Palombo M, Valindria V, Singh S, Chiou E, Giganti F, Pye H, Whitaker HC, Atkinson D, Punwani S, Alexander DC, Panagiotaki E. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer grading with relaxation-VERDICT MRI.. [DOI: 10.1101/2021.06.24.21259440] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021;11:959. [PMID: 34073627 DOI: 10.3390/diagnostics11060959] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 9.5] [Reference Citation Analysis]
33 Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 2021;94:20201272. [PMID: 33882244 DOI: 10.1259/bjr.20201272] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
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35 Castillo T JM, Starmans MPA, Arif M, Niessen WJ, Klein S, Bangma CH, Schoots IG, Veenland JF. A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade. Diagnostics (Basel) 2021;11:369. [PMID: 33671533 DOI: 10.3390/diagnostics11020369] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
36 Chaddad A, Kucharczyk MJ, Cheddad A, Clarke SE, Hassan L, Ding S, Rathore S, Zhang M, Katib Y, Bahoric B, Abikhzer G, Probst S, Niazi T. Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers (Basel) 2021;13:552. [PMID: 33535569 DOI: 10.3390/cancers13030552] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
37 Santhirasekaram A, Pinto K, Winkler M, Aboagye E, Glocker B, Rockall A. Multi-scale Hybrid Transformer Networks: Application to Prostate Disease Classification. Multimodal Learning for Clinical Decision Support 2021. [DOI: 10.1007/978-3-030-89847-2_2] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
38 Suvarna K, Biswas D, Pai MGJ, Acharjee A, Bankar R, Palanivel V, Salkar A, Verma A, Mukherjee A, Choudhury M, Ghantasala S, Ghosh S, Singh A, Banerjee A, Badaya A, Bihani S, Loya G, Mantri K, Burli A, Roy J, Srivastava A, Agrawal S, Shrivastav O, Shastri J, Srivastava S. Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential. Front Physiol 2021;12:652799. [PMID: 33995121 DOI: 10.3389/fphys.2021.652799] [Cited by in Crossref: 30] [Cited by in F6Publishing: 20] [Article Influence: 15.0] [Reference Citation Analysis]
39 Tian Y, Fu S. A descriptive framework for the field of deep learning applications in medical images. Knowledge-Based Systems 2020;210:106445. [DOI: 10.1016/j.knosys.2020.106445] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]