{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:19:37Z","timestamp":1768277977287,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Land Schleswig-Holstein","award":["FR1410.000.156"],"award-info":[{"award-number":["FR1410.000.156"]}]},{"name":"Kantonsspital Aarau","award":["FR1410.000.156"],"award-info":[{"award-number":["FR1410.000.156"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In this work, several machine learning (ML) algorithms, both classical ML and modern deep learning, were investigated for their ability to improve the performance of a pipeline for the segmentation and classification of prostate lesions using MRI data. The algorithms were used to perform a binary classification of benign and malignant tissue visible in MRI sequences. The model choices include support vector machines (SVMs), random decision forests (RDFs), and multi-layer perceptrons (MLPs), along with radiomic features that are reduced by applying PCA or mRMR feature selection. Modern CNN-based architectures, such as ConvNeXt, ConvNet, and ResNet, were also evaluated in various setups, including transfer learning. To optimize the performance, different approaches were compared and applied to whole images, as well as gland, peripheral zone (PZ), and lesion segmentations. The contribution of this study is an investigation of several ML approaches regarding their performance in prostate cancer (PCa) diagnosis algorithms. This work delivers insights into the applicability of different approaches for this context based on an exhaustive examination. The outcome is a recommendation or preference for which machine learning model or family of models is best suited to optimize an existing pipeline when the model is applied as an upstream filter.<\/jats:p>","DOI":"10.3390\/diagnostics14151677","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T09:34:17Z","timestamp":1722591257000},"page":"1677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Malte","family":"Rippa","sequence":"first","affiliation":[{"name":"Institute for Medical Informatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"},{"name":"Fuse-AI GmbH, 20457 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruben","family":"Schulze","sequence":"additional","affiliation":[{"name":"Fuse-AI GmbH, 20457 Hamburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8894-9920","authenticated-orcid":false,"given":"Georgia","family":"Kenyon","sequence":"additional","affiliation":[{"name":"Australian Institute of Machine Learning, University of Adelaide, Adelaide, SA 5005, Australia"},{"name":"Precision Imaging Beacon, University of Nottingham, Nottingham NG7 2RD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marian","family":"Himstedt","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-4554","authenticated-orcid":false,"given":"Maciej","family":"Kwiatkowski","sequence":"additional","affiliation":[{"name":"Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"},{"name":"Medical Faculty, University Hospital Basel, 4056 Basel, Switzerland"},{"name":"Department of Urology, Academic Hospital Braunschweig, 38126 Brunswick, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1901-7967","authenticated-orcid":false,"given":"Rainer","family":"Grobholz","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"},{"name":"Medical Faculty, University of Zurich, 8032 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Wyler","sequence":"additional","affiliation":[{"name":"Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"},{"name":"Medical Faculty, University Hospital Basel, 4056 Basel, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Cornelius","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Schindera","sequence":"additional","affiliation":[{"name":"Medical Faculty, University Hospital Basel, 4056 Basel, Switzerland"},{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4362-162X","authenticated-orcid":false,"given":"Felice","family":"Burn","sequence":"additional","affiliation":[{"name":"Institute of Radiology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"},{"name":"AI & Data Science CoE, Cantonal Hospital Aarau, 5001 Aarau, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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