Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models
- 20.03.2024
- Urogenital
Erschienen in:
- Verfasst von
- Giulia Marvaso
- Lars Johannes Isaksson
- Mattia Zaffaroni
- Maria Giulia Vincini
- Paul Eugene Summers
- Matteo Pepa
- Giulia Corrao
- Giovanni Carlo Mazzola
- Marco Rotondi
- Federico Mastroleo
- Sara Raimondi
- Sarah Alessi
- Paola Pricolo
- Stefano Luzzago
- Francesco Alessandro Mistretta
- Matteo Ferro
- Federica Cattani
- Francesco Ceci
- Gennaro Musi
- Ottavio De Cobelli
- Marta Cremonesi
- Sara Gandini
- Davide La Torre
- Roberto Orecchia
- Giuseppe Petralia
- Barbara Alicja Jereczek-Fossa
- Erschienen in
- European Radiology | Ausgabe 10/2024
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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- Titel
- Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models
- Verfasst von
-
Giulia Marvaso
Lars Johannes Isaksson
Mattia Zaffaroni
Maria Giulia Vincini
Paul Eugene Summers
Matteo Pepa
Giulia Corrao
Giovanni Carlo Mazzola
Marco Rotondi
Federico Mastroleo
Sara Raimondi
Sarah Alessi
Paola Pricolo
Stefano Luzzago
Francesco Alessandro Mistretta
Matteo Ferro
Federica Cattani
Francesco Ceci
Gennaro Musi
Ottavio De Cobelli
Marta Cremonesi
Sara Gandini
Davide La Torre
Roberto Orecchia
Giuseppe Petralia
Barbara Alicja Jereczek-Fossa
- Publikationsdatum
- 20.03.2024
- Verlag
- Springer Berlin Heidelberg
- Erschienen in
-
European Radiology / Ausgabe 10/2024
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084 - DOI
- https://doi.org/10.1007/s00330-024-10699-3
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