Background: The diagnosis of prostate cancer can only be obtained following the analysis of the tissue taken by means of a biopsy. Given the position of the organ, the biopsy is typically assisted by ultrasound images and the procedure consists of taking different portions of tissue from different areas, according to a map well-defined by international standards. Given the invasiveness of the procedure, the objective set within the Synergy-Net project is to analyze biomedical images in order to guide the operator on identifying the most suspicious tissues. Results: The dataset acquired by the Synergy-Net Platform at the "Vanvitelli" Urology Operating Unit is made up of a total of 350 outpatient services from which the diagnosis emerged on ultrasound, elastosonography, RNM, and biopsy of 50 prostate carcinomas which were then operated on. In the context of the Synergy-Net project, a new convolutional architecture was therefore created based on the U-Net paradigm, designed to perform a slice-by-slice segmentation in DCE-MRI of the prostate. The data processing with CNNs was carried out on a dataset of 37 patients, selected from the initial 50 for completeness and uniformity of the data, all affected by k-prostatic disease, using a tenfold cross-validation in order to obtain a statistically more significant estimate of the goodness of the results obtained. The performance metric used was the DICE coefficient. Conclusion: The results present a low intra-subject variability and a high inter-subject variability, with DICE values ranging between a minimum of 5.8% and a maximum of 60.3%. On average, a value of 35% is reported, considering the arithmetic mean of the dice achieved on all folds (macro-average).
Artificial Intelligence in Prostate Cancer Diagnosis: “Synergy-Net” in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project
Parmeggiani D.;De Sio M.;Allaria A.;Sciarra A.;Arcaniolo D.;Colapietra F.;Di Domenico M.;Docimo L.;Agresti M.
2024
Abstract
Background: The diagnosis of prostate cancer can only be obtained following the analysis of the tissue taken by means of a biopsy. Given the position of the organ, the biopsy is typically assisted by ultrasound images and the procedure consists of taking different portions of tissue from different areas, according to a map well-defined by international standards. Given the invasiveness of the procedure, the objective set within the Synergy-Net project is to analyze biomedical images in order to guide the operator on identifying the most suspicious tissues. Results: The dataset acquired by the Synergy-Net Platform at the "Vanvitelli" Urology Operating Unit is made up of a total of 350 outpatient services from which the diagnosis emerged on ultrasound, elastosonography, RNM, and biopsy of 50 prostate carcinomas which were then operated on. In the context of the Synergy-Net project, a new convolutional architecture was therefore created based on the U-Net paradigm, designed to perform a slice-by-slice segmentation in DCE-MRI of the prostate. The data processing with CNNs was carried out on a dataset of 37 patients, selected from the initial 50 for completeness and uniformity of the data, all affected by k-prostatic disease, using a tenfold cross-validation in order to obtain a statistically more significant estimate of the goodness of the results obtained. The performance metric used was the DICE coefficient. Conclusion: The results present a low intra-subject variability and a high inter-subject variability, with DICE values ranging between a minimum of 5.8% and a maximum of 60.3%. On average, a value of 35% is reported, considering the arithmetic mean of the dice achieved on all folds (macro-average).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
