Rationale and objectives: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopathological analysis after radical prostatectomy. Texture analysis (TA) is a quantitative postprocessing method for data extraction, while machine learning (ML) employs artificial intelligence algorithms for data classification. Purpose of this study was to assess whether ML algorithms could predict histopathological EPE using TA features extracted from unenhanced MR images. Materials and methods: Index lesions from biparametric MRI examinations of 39 patients with prostate cancer who underwent radical prostatectomy were manually segmented on both T2-weighted images and ADC maps for TA data extraction. Combinations of different feature selection methods and ML classifiers were tested, and their performance was compared to a baseline accuracy reference. Results: The classifier showing the best performance was the Bayesian Network, using the dataset obtained by the Subset Evaluator feature selection method. It showed a percentage of correctly classified instances of 82%, an area under the curve of 0.88, a weighted true positive rate of 0.82 and a weighted true negative rate of 0.80. Conclusion: A combined ML and TA approach appears as a feasible tool to predict histopathological EPE on biparametric MR images.

Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results

Stanzione, Arnaldo;Cuocolo, Renato
;
Cocozza, Sirio;Romeo, Valeria;Persico, Francesco;Fusco, Ferdinando;Longo, Nicola;Brunetti, Arturo;Imbriaco, Massimo
2019

Abstract

Rationale and objectives: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopathological analysis after radical prostatectomy. Texture analysis (TA) is a quantitative postprocessing method for data extraction, while machine learning (ML) employs artificial intelligence algorithms for data classification. Purpose of this study was to assess whether ML algorithms could predict histopathological EPE using TA features extracted from unenhanced MR images. Materials and methods: Index lesions from biparametric MRI examinations of 39 patients with prostate cancer who underwent radical prostatectomy were manually segmented on both T2-weighted images and ADC maps for TA data extraction. Combinations of different feature selection methods and ML classifiers were tested, and their performance was compared to a baseline accuracy reference. Results: The classifier showing the best performance was the Bayesian Network, using the dataset obtained by the Subset Evaluator feature selection method. It showed a percentage of correctly classified instances of 82%, an area under the curve of 0.88, a weighted true positive rate of 0.82 and a weighted true negative rate of 0.80. Conclusion: A combined ML and TA approach appears as a feasible tool to predict histopathological EPE on biparametric MR images.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/733525
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