Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences
Authors: Mylona Eugenia , Zaridis Dimitrios I. , Kalantzopoulos Charalampos Ν. , Tachos Nikolaos S. , Regge Daniele , Papanikolaou Nikolaos , Tsiknakis Manolis , Marias Kostas ,   ProCAncer-I Consortium, Mylona Eugenia , Zaridis Dimitris , Kalantzopoulos Charalampos , Tachos Nikolaos S. , Regge Daniele , Papanikolaou Nikolaos , Tsiknakis Manolis , Marias Kostas , Fotiadis Dimitris , Sfakianakis Stelios , Kalokyri Varvara , Trivizakis Eleftherios , Kalliatakis Grigorios , Dimitriadis Avtantil , de Almeida José Guilherme , Verde Ana Castro , Rodrigues Ana Carolina , Rodrigues Nuno , Chambel Miguel , Huisman Henkjan , de Rooij Maarten , Saha Anindo , Twilt Jasper J. , Futterer Jurgen , Martí-Bonmatí Luis , Cerdá-Alberich Leonor , Ribas Gloria , Navarro Silvia , Marfil Manuel , Neri Emanuele , Aringhieri Giacomo , Tumminello Lorenzo , Mendola Vincenzo , nan  , Akata Deniz , Özmen Mustafa , Karaosmanoglu Ali Devrim , Atak Firat , Karcaaltincaba Musturay , Vilanova Joan C. , Usinskiene Jurgita , Briediene Ruta , Untanas Audrius , Slidevska Kristina , Vasilis Katsaros , Georgios Georgiou , Koh Dow-Mu , Emsley Robby , Vit Sharon , Ribeiro Ana , Doran Simon , Jacobs Tiaan , García-Martí Gracián , Giannini Valentina , Mazzetti Simone , Cappello Giovanni , Maimone Giovanni , Napolitano Valentina , Colantonio Sara , Pascali Maria Antonietta , Pachetti Eva , del Corso Giulio , Germanese Danila , Berti Andrea , Carloni Gianluca , Kalpathy-Cramer Jayashree , Bridge Christopher , Correia Joao , Hernandez Walter , Giavri Zoi , Pollalis Christos , Agraniotis Dimitrios , Pastor Ana Jiménez , Mora Jose Munuera , Saillant Clara , Henne Theresa , Marquez Rodessa , Fotiadis Dimitrios I. 
Published: November 2024

Abstract
Objectives
Radiomics-based analyses encompass multiple steps, leading to ambiguity regarding the optimal approaches for enhancing model performance. This study compares the effect of several feature selection methods, machine learning (ML) classifiers, and sources of radiomic features, on models’ performance for the diagnosis of clinically significant prostate cancer (csPCa) from bi-parametric MRI.

Methods
Two multi-centric datasets, with 465 and 204 patients each, were used to extract 1246 radiomic features per patient and MRI sequence. Ten feature selection methods, such as Boruta, mRMRe, ReliefF, recursive feature elimination (RFE), random forest (RF) variable importance, L1-lasso, etc., four ML classifiers, namely SVM, RF, LASSO, and boosted generalized linear model (GLM), and three sets of radiomics features, derived from T2w images, ADC maps, and their combination, were used to develop predictive models of csPCa. Their performance was evaluated in a nested cross-validation and externally, using seven performance metrics.

Results
In total, 480 models were developed. In nested cross-validation, the best model combined Boruta with Boosted GLM (AUC = 0.71, F1 = 0.76). In external validation, the best model combined L1-lasso with boosted GLM (AUC = 0.71, F1 = 0.47). Overall, Boruta, RFE, L1-lasso, and RF variable importance were the top-performing feature selection methods, while the choice of ML classifier didn’t significantly affect the results. The ADC-derived features showed the highest discriminatory power with T2w-derived features being less informative, while their combination did not lead to improved performance.

Conclusion
The choice of feature selection method and the source of radiomic features have a profound effect on the models’ performance for csPCa diagnosis.

Critical relevance statement
This work may guide future radiomic research, paving the way for the development of more effective and reliable radiomic models; not only for advancing prostate cancer diagnostic strategies, but also for informing broader applications of radiomics in different medical contexts.

Key Points

Radiomics is a growing field that can still be optimized.

Feature selection method impacts radiomics models’ performance more than ML algorithms.

Best feature selection methods: RFE, LASSO, RF, and Boruta.

ADC-derived radiomic features yield more robust models compared to T2w-derived radiomic features.

Graphical Abstract