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Positron Emission Tomography-Derived Metrics Predict the Probability of Local Relapse After Oligometastasis-Directed Ablative Radiation Therapy.
Greco, Carlo; Pares, Oriol; Pimentel, Nuno; Louro, Vasco; Morales, Javier; Nunes, Beatriz; Antunes, Inês; Vasconcelos, Ana Luisa; Kociolek, Justyna; Castanheira, Joana; Oliveira, Carla; Silva, Angelo; Vaz, Sofia; Oliveira, Francisco; Carrasquinha, Eunice; Costa, Durval; Fuks, Zvi.
Afiliação
  • Greco C; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Pares O; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Pimentel N; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Louro V; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Morales J; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Nunes B; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Antunes I; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Vasconcelos AL; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Kociolek J; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Castanheira J; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Oliveira C; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Silva A; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Vaz S; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Oliveira F; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Carrasquinha E; Computational Clinical Imaging Group, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Costa D; Department of Nuclear Medicine-Radiopharmacology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
  • Fuks Z; Department of Radiation Oncology, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Adv Radiat Oncol ; 7(2): 100864, 2022.
Article em En | MEDLINE | ID: mdl-35036636
ABSTRACT

PURPOSE:

Early positron emission tomography-derived metrics post-oligometastasis radioablation may predict impending local relapses (LRs), providing a basis for a timely ablation. METHODS AND MATERIALS Positron emission tomography data of 623 lesions treated with either 24 Gy single-dose radiation therapy (SDRT) (n = 475) or 3 ×  9 Gy stereotactic body radiation therapy (SBRT) (n = 148) were analyzed in a training data set (n = 246) to obtain optimal cutoffs for pretreatment maximum standardized uptake value (SUVmax) and its 3-month posttreatment decline (ΔSUVmax) in predicting LR risk, validated in a data set unseen to testing (n = 377).

RESULTS:

At a median of 21.7 months, 91 lesions developed LRs 39 of 475 (8.2%) after SDRT and 52 of 148 (35.1%) after SBRT. The optimal cutoff values were 12 for SUVmax and -75% for ΔSUVmax. Bivariate SUVmax/ΔSUVmax permutations rendered a 3-tiered LR risk stratification of dual-favorable (low risk), 1 adverse (intermediate risk) and dual-adverse (high risk). Actuarial 5-year local relapse-free survival rates were 93.9% versus 89.6% versus 57.1% (P < .0001) and 76.1% versus 48.3% versus 8.2% (P < .0001) for SDRT and SBRT, respectively. The SBRT area under the ROC curve was 0.71 (95% CI, 0.61-0.79) and the high-risk subgroup yielded a 76.5% true positive LR prediction rate.

CONCLUSIONS:

The SBRT dual-adverse SUVmax/ΔSUVmax category LR prediction power provides a basis for prospective studies testing whether a timely ablation of impending LRs affects oligometastasis outcomes.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Adv Radiat Oncol Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Adv Radiat Oncol Ano de publicação: 2022 Tipo de documento: Article