Background: Distant metastases in adenoid cystic carcinoma (ACC) are common. There is no consensus on the management of metastatic disease because no therapeutic approach has demonstrated improvement in overall survival (OS) and because of prolonged life expectancy. The aim of this study is to build and validate a prognostic nomogram for metastatic ACC patients. Methods: The study end-point was OS, measured from the date of first metastatic presentation to death/last follow-up. A retrospective analysis including metastatic ACC patients was performed to build the prognostic nomogram at the INT (Milan, Italy). The model was validated on an independent cohort of patients with similar characteristics treated at Leuven (Belgium). Outcome data and covariates were modelled by resorting to a random forest method. This machine-learning approach was used to guide and benchmark the subsequent use of more conventional modelling methods. Cox model performance was assessed in terms of discrimination (Harrell's c-index). Results: Two hundred ninety-eight patients with metastatic ACC (testing set 259 INT, validation set 39 Leuven) were studied. Akaike Information Criterion–based backward selection yielded a 5-factor model showing a bias-corrected c-index of 0.730. Five independent prognostic factors were found: gender, disease-free interval and presence of lung, liver or bone metastases. Nomogram discrimination in the validation series was c = 0.701. Conclusion: This retrospective analysis allowed the building of an externally validated prognostic nomogram. This tool might help clinicians to discriminate patients requiring prompt management from who can benefit from a ‘watchful waiting’. In addition, the nomogram might be useful to stratify patients in clinical trials.
Prognostic nomogram in patients with metastatic adenoid cystic carcinoma of the salivary glands
Mariani L.;Bossi P.;
2020-01-01
Abstract
Background: Distant metastases in adenoid cystic carcinoma (ACC) are common. There is no consensus on the management of metastatic disease because no therapeutic approach has demonstrated improvement in overall survival (OS) and because of prolonged life expectancy. The aim of this study is to build and validate a prognostic nomogram for metastatic ACC patients. Methods: The study end-point was OS, measured from the date of first metastatic presentation to death/last follow-up. A retrospective analysis including metastatic ACC patients was performed to build the prognostic nomogram at the INT (Milan, Italy). The model was validated on an independent cohort of patients with similar characteristics treated at Leuven (Belgium). Outcome data and covariates were modelled by resorting to a random forest method. This machine-learning approach was used to guide and benchmark the subsequent use of more conventional modelling methods. Cox model performance was assessed in terms of discrimination (Harrell's c-index). Results: Two hundred ninety-eight patients with metastatic ACC (testing set 259 INT, validation set 39 Leuven) were studied. Akaike Information Criterion–based backward selection yielded a 5-factor model showing a bias-corrected c-index of 0.730. Five independent prognostic factors were found: gender, disease-free interval and presence of lung, liver or bone metastases. Nomogram discrimination in the validation series was c = 0.701. Conclusion: This retrospective analysis allowed the building of an externally validated prognostic nomogram. This tool might help clinicians to discriminate patients requiring prompt management from who can benefit from a ‘watchful waiting’. In addition, the nomogram might be useful to stratify patients in clinical trials.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
