Publications by authors named "V Di Cataldo"

Purpose: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed.

Methods: A pooled analysis of gynecological oligometastases in terms of efficacy and clinical outcomes as well an exploratory machine learning model to predict the CR to SBRT were carried out.

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Earlier treatment intensification with systemic potent androgen receptor inhibition has been shown to improve clinical outcomes in metastatic hormone sensitive prostate cancer. Nonetheless, oligometastatic patients may benefit from local treatment approaches such as stereotactic body radiotherapy (SBRT). Aiming to explore the benefit of SBRT in this scenario, we designed this trial to specifically test the hypothesis that SBRT will improve clinical outcomes in select population affected by metachronous oligometastatic HSPC treated with androgen deprivation therapy + apalutamide.

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Background And Purpose: We aimed to develop and validate different machine-learning (ML) prediction models for the complete response of oligometastatic gynecological cancer after SBRT.

Material And Methods: One hundred fifty-seven patients with 272 lesions from 14 different institutions and treated with SBRT with radical intent were included. Thirteen datasets including 222 lesions were combined for model training and internal validation purposes, with an 80:20 ratio.

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Radical cystectomy (RC) is considered the standard treatment for muscle invasive bladder cancer (MIBC). However, RC is often burdened by significant impact on quality of life (QoL); Continence preserving methods (e.g.

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