Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer.

Autor: Macchia G; Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Molise, Italy. Electronic address: gabriella.macchia@responsible.hospital., Cilla S; Medical Physics Unit, Responsible Research Hospital, Campobasso, Molise, Italy., Pezzulla D; Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Molise, Italy., Campitelli M; UOC di Radioterapia, Dipartimento di Scienze Radiologiche, Radioterapiche ed Ematologiche, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy., Laliscia C; Department of Translational Medicine, Division of Radiation Oncology, University of Pisa, Italy., Lazzari R; Department of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy., Draghini L; Radiation Oncology Center, S Maria Hospital, Terni, Italy., Fodor A; Department of Radiation Oncology, IRCCS San Raffaele Scientific Institute, Milan, Italy., D'Agostino GR; Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Center-IRCCS, via Manzoni 56, 20089, Rozzano, Mi, Italy., Russo D; Radiotherapy Unit, Ospedale 'Vito Fazzi', Lecce, Italy., Balcet V; UOC Radioterapia, Nuovo Ospedale degli Infermi, Biella, Italy., Ferioli M; Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, 40138, Italy., Vicenzi L; Radiation Oncology Unit, Azienda Ospedaliera Universitaria Ospedali Riuniti, Ancona, Italy., Raguso A; UOC Radioterapia, Fondazione 'Casa Sollievo della Sofferenza', IRCCS, S. Giovanni Rotondo, Foggia, Italy., Di Cataldo V; Radiation Oncology Unit, Oncology Department, University of Florence, Firenze, Italy., Perrucci E; Radiation Oncology Section, Perugia General Hospital, Perugia, Italy., Borghesi S; Radiation Oncology Unit of Arezzo-Valdarno, Azienda USL Toscana sud est, Arezzo, Toscana, Italy., Ippolito E; Department of Radiation Oncology, Campus Bio-Medico University, Roma, Italy., Gentile P; Radiation Oncology Unit, UPMC Hillman Cancer Center San Pietro FBF, Roma, Italy., De Sanctis V; Radiotherapy Oncology, Department of Medicine and Surgery and Translational Medicine, Sapienza University of Rome, S. Andrea Hospital, Roma, Italy., Titone F; Department of Radiation Oncology, University Hospital Udine, Italy., Delle Curti CT; Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133 Milan, Italy., Huscher A; Fondazione Poliambulanza, U.O. di Radioterapia Oncologica 'Guido Berlucchi', Brescia, Italy., Gambacorta MA; UOC di Radioterapia, Dipartimento di Scienze Radiologiche, Radioterapiche ed Ematologiche, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy; Istituto di Radiologia, Università Cattolica del Sacro Cuore Roma, Italy., Ferrandina G; UOC Ginecologia Oncologica, Dipartimento Scienze della Salute della Donna e del Bambino, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy., Morganti AG; Radiation Oncology, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, 40138, Italy; Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy., Deodato F; Radiation Oncology Unit, Responsible Research Hospital, Campobasso, Molise, Italy; Istituto di Radiologia, Università Cattolica del Sacro Cuore Roma, Italy.
Jazyk: angličtina
Zdroj: Gynecologic oncology [Gynecol Oncol] 2024 May; Vol. 184, pp. 16-23. Date of Electronic Publication: 2024 Jan 24.
DOI: 10.1016/j.ygyno.2024.01.023
Abstrakt: 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. The CR rate following radiotherapy (RT) was the study main endpoint. The secondary endpoints included the 2-year actuarial LC, DMFS, PFS, and OS.
Results: 501 patients from 21 radiation oncology institutions with 846 gynecological metastases were analyzed, mainly ovarian (53.1%) and uterine metastases(32.1%).Multiple fraction radiotherapy was used in 762 metastases(90.1%).The most frequent schedule was 24 Gy in 3 fractions(13.4%). CR was observed in 538(63.7%) lesions. The Machine learning analysis showed a poor ability to find covariates strong enough to predict CR in the whole series. Analyzing them separately, in uterine cancer, if RT dose≥78.3Gy, the CR probability was 75.4%; if volume was <13.7 cc, the CR probability became 85.1%. In ovarian cancer, if the lesion was a lymph node, the CR probability was 71.4%; if volume was <17 cc, the CR probability rose to 78.4%. No covariate predicted the CR for cervical lesions. The overall 2-year actuarial LC was 79.2%, however it was 91.5% for CR and 52.5% for not CR lesions(p < 0.001). The overall 2-year DMFS, PFS and OS rate were 27.3%, 24.8% and 71.0%, with significant differences between CR and not CR.
Conclusions: CR was substantially associated to patient outcomes in our series of gynecological cancer oligometastatic lesions. The ability to predict a CR through artificial intelligence could also drive treatment choices in the context of personalized oncology.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: