<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_raon-2019-0058_s_005_w2aab3b8c18b1b7b1aab1c16b1Aa"><h3>Background</h3><p>Aim of our study was to assess the load of bone disease at starting and during Ra-223 treatment as an overall survival (OS) predictor in metastatic castration-resistant prostate cancer (mCRPC) patients. Bone scan index (BSI) is defined as the percentage of total amount of bone metastasis on whole-body scintigraphic images. We present a specific software (DASciS) developed by an engineering team of “Sapienza” University of Rome for BSI calculation.</p></sec><sec id="j_raon-2019-0058_s_006_w2aab3b8c18b1b7b1aab1c16b2Aa"><h3>Patients and methods</h3><p>127 mCRPC patients bone scan images were processed with DASciS software, and BSI was tested as OS predictor.</p></sec><sec id="j_raon-2019-0058_s_007_w2aab3b8c18b1b7b1aab1c16b3Aa"><h3>Results</h3><p>546 bone scans were analyzed revealing that the extension of disease is a predictor of OS (0–3% = 28 months of median survival (MoMS]; 3%–5% = 11 MoMS, > 5% = 5 MoMS). BSI has been analyzed as a single parameter for OS, determining an 88% AUC. Moreover, the composition between the BSI and the 3-PS (3-variable prognostic score) determines a remarkable improvement of the AUC (91%), defining these two parameters as the best OS predictors.</p></sec><sec id="j_raon-2019-0058_s_008_w2aab3b8c18b1b7b1aab1c16b4Aa"><h3>Conclusions</h3><p>This study suggests that OS is inversely correlated with the load of bone disease in mCRPC Ra-223-treated subjects. DASciS software appears a promising tool in identifying mCRPC patients that more likely take advantage from Ra-223 treatment. BSI is proposed as a predictive variable for OS and included to a multidimensional clinical evaluation permits to approach the patients’ enrollment in a rational way, allowing to enhance the treatment effectiveness together with cost optimization.</p></sec></abstract>

BackgroundAim of our study was to assess the load of bone disease at starting and during Ra-223 treatment as an overall survival (OS) predictor in metastatic castration-resistant prostate cancer (mCRPC) patients. Bone scan index (BSI) is defined as the percentage of total amount of bone metastasis on whole-body scintigraphic images. We present a specific software (DASciS) developed by an engineering team of “Sapienza” University of Rome for BSI calculation.Patients and methods127 mCRPC patients bone scan images were processed with DASciS software, and BSI was tested as OS predictor.Results546 bone scans were analyzed revealing that the extension of disease is a predictor of OS (0–3% = 28 months of median survival (MoMS]; 3%–5% = 11 MoMS, > 5% = 5 MoMS). BSI has been analyzed as a single parameter for OS, determining an 88% AUC. Moreover, the composition between the BSI and the 3-PS (3-variable prognostic score) determines a remarkable improvement of the AUC (91%), defining these two parameters as the best OS predictors.ConclusionsThis study suggests that OS is inversely correlated with the load of bone disease in mCRPC Ra-223-treated subjects. DASciS software appears a promising tool in identifying mCRPC patients that more likely take advantage from Ra-223 treatment. BSI is proposed as a predictive variable for OS and included to a multidimensional clinical evaluation permits to approach the patients’ enrollment in a rational way, allowing to enhance the treatment effectiveness together with cost optimization.

Scintigraphic load of bone disease evaluated by DASciS software as a survival predictor in metastatic castration-resistant prostate cancer patients candidates to 223RaCl treatment

Radiology and Oncology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

BackgroundAim of our study was to assess the load of bone disease at starting and during Ra-223 treatment as an overall survival (OS) predictor in metastatic...

Scintigraphic load of bone disease evaluated by DASciS software as a survival predictor in metastatic castration-resistant prostate cancer patients candidates to 223RaCl treatment

Article Category: Research Article

Publicado en línea: 19 dic 2019

Páginas: 40 - 47

Recibido: 18 jun 2019

Aceptado: 22 oct 2019

DOI: https://doi.org/10.2478/raon-2019-0058

© 2020 Viviana Frantellizzi, Arianna Pani, Maria Dea Ippoliti, Alessio Farcomeni, Irvin Aloise, Mirco Colosi, Claudia Polito, Roberto Pani, Giuseppe De Vincentis, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.