Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

Journal: European radiology

PMID: 38507053

Abstract

OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.

Authors

Giulia Marvaso

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Lars Johannes Isaksson

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Mattia Zaffaroni

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy. mattia.zaffaroni@ieo.it.
Maria Giulia Vincini

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy. mariagiulia.vincini@ieo.it.
Paul Eugene Summers

Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Matteo Pepa

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Giulia Corrao

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Giovanni Carlo Mazzola

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Marco Rotondi

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Federico Mastroleo

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Sara Raimondi

Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Sarah Alessi

Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Paola Pricolo

Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy.
Stefano Luzzago

Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.
Francesco Alessandro Mistretta

Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.
Matteo Ferro

Department of Urology, European Institute of Oncology (IEO) IRCCS, Milan, Italy.
Federica Cattani

Medical Physics Unit, IEO European Institute of Oncology IRCCS, Milan, Italy.
Francesco Ceci

Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Gennaro Musi

Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy.
Ottavio De Cobelli

Department of Urology, European Institute of Oncology (IEO), Via Giuseppe Ripamonti, 435, 20141, Milan, Italy.
Marta Cremonesi

Radiation Research Unit, IEO European Institute of Oncology IRCCS, Milan, Italy.
Sara Gandini

Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
Davide La Torre

Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Roberto Orecchia

Scientific Directorate, IEO European Institute of Oncology IRCCS, Milan, Italy.
Giuseppe Petralia

Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Barbara Alicja Jereczek-Fossa

Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.

Keywords

Aged Decision Trees Humans Machine Learning Male Middle Aged Multiparametric Magnetic Resonance Imaging Predictive Value of Tests Prostate Prostatectomy Prostatic Neoplasms Radiomics Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (38507053)

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

Abstract

Authors

Keywords

External Resources

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