Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Diagnostics (Basel), 2022-06, Vol.12 (7), p.1565

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2022

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.