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

  • Urogenital
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European Radiology Aims and scope Submit manuscript

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.

Methods

Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.

Results

The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.

Conclusions

Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.

Clinical relevance statement

The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.

Key Points

• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.

• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.

•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.

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Abbreviations

ADC:

Apparent diffusion coefficient

AI:

Artificial intelligence

AUC:

Area under the curve

cN:

Clinical N stage

cT:

Clinical T stage

EPE:

Extraprostatic extension

GBDT:

Gradient-boosted decision tree

GS:

Gleason Score

ISUP :

International Society of Urological Pathology

MAE:

Mean absolute error

MCC:

Matthews correlation coefficient

ML:

Machine learning

mp-MRI:

Multiparametric magnetic resonance imaging

PCa:

Prostate cancer

PI-RADS:

Prostate Imaging Reporting and Data System

pN:

Pathological N stage

Post-op:

Post-operative

PSA:

Prostate-specific antigen

pT:

Pathological T stage

RP:

Radical prostatectomy

RT:

Radiation therapy

SHAP:

SHapley Additive exPlanations

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Acknowledgements

JLI is a Ph.D. student within the European School of Molecular Medicine (SEMM) in Milan, Italy. MGV was supported by a research fellowship from the Associazione Italiana per la Ricerca sul Cancro (AIRC) entitled “Radioablation ± hormonotherapy for prostate cancer oligorecurrences (RADIOSA trial): potential of imaging and biology” registered at ClinicalTrials.gov NCT03940235, approved by the Ethics Committee of IRCCS Istituto Europeo di Oncologia and Centro Cardiologico Monzino (IEO-997). GC was partially supported by a research fellowship from Accuray Inc. IEO, the European Institute of Oncology, is partially supported by the Italian Ministry of Health (with “Ricerca Corrente” and “5x1000” funds).

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Matteo Pepa & Marco Rotondi

    Present address: Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy

Authors and Affiliations

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

    Giulia Marvaso, Lars Johannes Isaksson, Mattia Zaffaroni, Maria Giulia Vincini, Giulia Corrao, Giovanni Carlo Mazzola, Federico Mastroleo & Barbara Alicja Jereczek-Fossa

  2. Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

    Giulia Marvaso, Giulia Corrao, Stefano Luzzago, Francesco Alessandro Mistretta, Francesco Ceci, Gennaro Musi, Ottavio De Cobelli, Davide La Torre, Giuseppe Petralia & Barbara Alicja Jereczek-Fossa

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

    Paul Eugene Summers, Sarah Alessi, Paola Pricolo & Giuseppe Petralia

  4. University of Piemonte Orientale, Novara, Italy

    Federico Mastroleo

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

    Sara Raimondi & Sara Gandini

  6. Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy

    Stefano Luzzago, Francesco Alessandro Mistretta, Matteo Ferro, Gennaro Musi & Ottavio De Cobelli

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

    Federica Cattani

  8. Division of Nuclear Medicine, IEO European Institute of Oncology, IRCCS, Milan, Italy

    Francesco Ceci

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

    Marta Cremonesi

  10. SKEMA Business School, Université Côte d’Azur, Sophia Antipolis, France

    Davide La Torre

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

    Roberto Orecchia

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Corresponding authors

Correspondence to Mattia Zaffaroni or Maria Giulia Vincini.

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Guarantor

The scientific guarantor of this publication is Dr. Giulia Marvaso.

Conflict of interest

Division of Radiotherapy IEO received research funding from AIRC (Italian Association for Cancer Research), Fondazione IEO-CCM (Istituto Europeo di Oncologia-Centro Cardiologico Monzino) all outside the current project. BAJF received speakers fees from Roche, Bayer, Janssen, Carl Zeiss, Ipsen, Accuray, Astellas, Elekta, and IBA Astra Zeneca (all outside the current project). The remaining authors declare no conflicts of interest that are relevant to the content of this article.

Statistics and biometry

One of the authors (Sara Gandini) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

The study complies with the principles of the Declaration of Helsinki and was approved by the Ethical Committee of IEO-European Institute of Oncology IRCSS and Cardiologic Centre Monzino IRCCS (Notification No. UID 2438).

Study subjects or cohorts overlap

None.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher's Note

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Giulia Marvaso and Lars Johannes Isaksson are co-first authors.

Giuseppe Petralia and Barbara Alicja Jereczek-Fossa are co-last authors.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 67 KB)

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Cite this article

Marvaso, G., Isaksson, L.J., Zaffaroni, M. et al. Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10699-3

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  • DOI: https://doi.org/10.1007/s00330-024-10699-3

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