Giuseppe Pizzi
Research Subjects
Giuseppe Pizzi has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- Andrea Russo
Department of Pathology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
1 shared publication - Anna Ianiro
Laboratory of Medical Physics and Expert Systems, IRCCS Regina Elena National Cancer Institute, Rome, Italy
1 shared publication - Antonella Soriani
Laboratory of Medical Physics and Expert Systems, IRCCS Regina Elena National Cancer Institute, Rome, Italy
1 shared publication - Antonello Vidiri
Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
1 shared publication - Davide Vitale
Department of Radiology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy
1 shared publication - Emilio Mezzenga
Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy
1 shared publication
Latest Publications
- Open Access
- 6 March 2026
- Published by MDPI AG in Current Oncology
- Vol. 33 (3) , 151
Artificial Intelligence in Prostate MRI: Comparison of an AI-Based Software and an Experienced Radiologist for Detecting Clinically Significant Prostate Cancer
Background: Multiparametric MRI is central to detecting clinically significant prostate cancer (csPCa), but diagnostic accuracy depends on reader experience. Artificial intelligence (AI) tools may support prostate MRI interpretation and reduce inter-reader variability. This study compared the detection rate of a trial, non-commercial version an AI-based software (PAROS) with that of an experienced radiologist. Methods: This retrospective single-center study included 150 patients who underwent prostate MRI followed by combined systematic and MRI-targeted transperineal biopsy. MRI examinations were interpreted by an experienced radiologist according to PI-RADS v2.1 and independently analyzed using a precommercial trial version of PAROS operating on biparametric MRI. Histopathology served as the reference standard. Detection rate was evaluated using sensitivity, specificity, and positive and negative likelihood ratios (PLR and NLR) at PI-RADS thresholds ≥3 and ≥4. Results: CsPCa was present in 63.3% of patients. At both PI-RADS thresholds, PAROS and the radiologist showed comparable sensitivity and specificity, wuth extremely low NLRs, indicating excellent rule-out capability. PLRs were modest and similar at PI-RADS ≥ 3 (1.26 vs. 1.42) and 1.88 for both at PI-RADS ≥ 4. PAROS detected more lesions, particularly in the transition zone. Conclusions: PAROS achieved csPCa detection comparable to an experienced radiologist, supporting its role as a decision-support tool in prostate MRI interpretation.
- 6 March 2026
- Published by MDPI AG in Current Oncology
- Vol. 33 (3) , 151
Open AccessArtificial Intelligence in Prostate MRI: Comparison of an AI-Based Software and an Experienced Radiologist for Detecting Clinically Significant Prostate Cancer
Background: Multiparametric MRI is central to detecting clinically significant prostate cancer (csPCa), but diagnostic accuracy depends on reader experience. Artificial intelligence (AI) tools may support prostate MRI interpretation and reduce inter-reader variability. This study compared the detection rate of a trial, non-commercial version an AI-based software (PAROS) with that of an experienced radiologist. Methods: This retrospective single-center study included 150 patients who underwent prostate MRI followed by combined systematic and MRI-targeted transperineal biopsy. MRI examinations were interpreted by an experienced radiologist according to PI-RADS v2.1 and independently analyzed using a precommercial trial version of PAROS operating on biparametric MRI. Histopathology served as the reference standard. Detection rate was evaluated using sensitivity, specificity, and positive and negative likelihood ratios (PLR and NLR) at PI-RADS thresholds ≥3 and ≥4. Results: CsPCa was present in 63.3% of patients. At both PI-RADS thresholds, PAROS and the radiologist showed comparable sensitivity and specificity, wuth extremely low NLRs, indicating excellent rule-out capability. PLRs were modest and similar at PI-RADS ≥ 3 (1.26 vs. 1.42) and 1.88 for both at PI-RADS ≥ 4. PAROS detected more lesions, particularly in the transition zone. Conclusions: PAROS achieved csPCa detection comparable to an experienced radiologist, supporting its role as a decision-support tool in prostate MRI interpretation.
- Open Access
- 19 October 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (20) , 10541
A Workflow for Dosimetry of 90Y Radioembolization Based on Quantitative 99mTc-MAA SPECT/CT Imaging and a 3D-Printed Phantom
At a time of increasing evidence for dose-effect relationships in radioembolization (RE) with 90Y-microspheres, the general consensus is that there is an urgent need for accurate treatment planning and dose assessment in patients undergoing RE treatment. This work aimed at assessing the usefulness of 99mTc macroaggregated albumin (MAA) SPECT/CT imaging for personalized provisional RE dosimetry considering a 3D-printed patientlike phantom (AdboMan phantom). A homemade tool was developed in MATLAB for image analysis and absorbed dose calculation. Two dose calculaton methods were implemented and used to calculate dose volume histograms: (I) dose kernel method and (II) local energy deposition method. The accuracy of the two different dosimetric methods was evaluated by means of 3D -index (1%–1 mm and 2%–2 mm) implemented in the tool. Differences between the two dose calculation methods using the 3D -index are within 1%–1 mm and 2%–2 mm for all AbdoMan inserts, with a passing rate of 99.9% and 100%, respectively, proving a good agreement between the two calculation methods. The present study supports the use of 99mTc-MAA SPECT acquisition for provisional dosimetry along with the local energy deposition method to convert reconstructed SPECT data into absorbed dose maps. As long as 99mTc-MAA SPECT acquisitions are performed on liver lesions larger than 40 mm, the absorbed dose computed by means of the local energy deposition method can lead to results in line with those obtained by Monte Carlo calculations.- 19 October 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (20) , 10541
Open AccessA Workflow for Dosimetry of 90Y Radioembolization Based on Quantitative 99mTc-MAA SPECT/CT Imaging and a 3D-Printed Phantom
At a time of increasing evidence for dose-effect relationships in radioembolization (RE) with 90Y-microspheres, the general consensus is that there is an urgent need for accurate treatment planning and dose assessment in patients undergoing RE treatment. This work aimed at assessing the usefulness of 99mTc macroaggregated albumin (MAA) SPECT/CT imaging for personalized provisional RE dosimetry considering a 3D-printed patientlike phantom (AdboMan phantom). A homemade tool was developed in MATLAB for image analysis and absorbed dose calculation. Two dose calculaton methods were implemented and used to calculate dose volume histograms: (I) dose kernel method and (II) local energy deposition method. The accuracy of the two different dosimetric methods was evaluated by means of 3D -index (1%–1 mm and 2%–2 mm) implemented in the tool. Differences between the two dose calculation methods using the 3D -index are within 1%–1 mm and 2%–2 mm for all AbdoMan inserts, with a passing rate of 99.9% and 100%, respectively, proving a good agreement between the two calculation methods. The present study supports the use of 99mTc-MAA SPECT acquisition for provisional dosimetry along with the local energy deposition method to convert reconstructed SPECT data into absorbed dose maps. As long as 99mTc-MAA SPECT acquisitions are performed on liver lesions larger than 40 mm, the absorbed dose computed by means of the local energy deposition method can lead to results in line with those obtained by Monte Carlo calculations.