The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient’s clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy.

Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions / Gravina, Michela; Spirito, Lorenzo; Celentano, Giuseppe; Capece, Marco; Creta, Massimiliano; Califano, Gianluigi; Collà Ruvolo, Claudia; Morra, Simone; Imbriaco, Massimo; Di Bello, Francesco; Sciuto, Antonio; Cuocolo, Renato; Napolitano, Luigi; La Rocca, Roberto; Mirone, Vincenzo; Sansone, Carlo; Longo, Nicola. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:7(2022), p. 1565. [10.3390/diagnostics12071565]

Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions

Gravina, Michela
Primo
;
Spirito, Lorenzo;Celentano, Giuseppe;Capece, Marco;Creta, Massimiliano;Califano, Gianluigi;Morra, Simone;Imbriaco, Massimo;Di Bello, Francesco;Cuocolo, Renato;Napolitano, Luigi;La Rocca, Roberto
;
Mirone, Vincenzo;Sansone, Carlo;Longo, Nicola
2022

Abstract

The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient’s clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy.
2022
Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions / Gravina, Michela; Spirito, Lorenzo; Celentano, Giuseppe; Capece, Marco; Creta, Massimiliano; Califano, Gianluigi; Collà Ruvolo, Claudia; Morra, Simone; Imbriaco, Massimo; Di Bello, Francesco; Sciuto, Antonio; Cuocolo, Renato; Napolitano, Luigi; La Rocca, Roberto; Mirone, Vincenzo; Sansone, Carlo; Longo, Nicola. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:7(2022), p. 1565. [10.3390/diagnostics12071565]
Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions / Gravina, Michela; Spirito, Lorenzo; Celentano, Giuseppe; Capece, Marco; Creta, Massimiliano; Califano, Gianluigi; Collà Ruvolo, Claudia; Morra, Simone; Imbriaco, Massimo; Di Bello, Francesco; Sciuto, Antonio; Cuocolo, Renato; Napolitano, Luigi; La Rocca, Roberto; Mirone, Vincenzo; Sansone, Carlo; Longo, Nicola. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:7(2022), p. 1565. [10.3390/diagnostics12071565]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/889470
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