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Individual risk prediction of nodal and distant metastasis for patients with typical bronchial carcinoid tumors

^a Thoracic Surgery Department of Sao Paulo University Medical School, Sao Paulo, SP, Brazil
^b Pathology Department of Sao Paulo University Medical School, Sao Paulo, SP, Brazil
^c Thoracic Surgery Department of Georges Pompidou European Hospital APHP and University Paris 5, Paris, France
^d Laboratory of Histology and Tumor Biology, Hôpital Tenon APHP and EA 3499 University Paris 6, France
^e Thoracic Surgery Department of Argenteuil Hospital, Paris, France

Received 15 November 2007; received in revised form 27 May 2008; accepted 9 June 2008.

^_* Corresponding author. Address: 342 Teodoro Sampaio Street, Brazil. Tel.: +55 11 81747696; fax: +55 11 30697145. (Email: joaocnp{at}hotmail.com).

Objective: Bronchial typical carcinoid tumors are low-grade malignancies. However, metastases are diagnosed in some patients. Predicting the individual risk of these metastases to determine patients eligible for a radical lymphadenectomy and patients to be followed-up because of distant metastasis risk is relevant. Our objective was to screen for predictive criteria of bronchial typical carcinoid tumor aggressiveness based on a logistic regression model using clinical, pathological and biomolecular data. Methods: A multicenter retrospective cohort study, including 330 consecutive patients operated on for bronchial typical carcinoid tumors and followed-up during a period more than 10 years in two university hospitals was performed. Selected data to predict the individual risk for both nodal and distant metastasis were: age, gender, TNM staging, tumor diameter and location (central/peripheral), tumor immunostaining index of p53 and Ki67, Bcl2 and the extracellular density of neoformed microvessels and of collagen/elastic extracellular fibers. Results: Nodal and distant metastasis incidence was 11% and 5%, respectively. Univariate analysis identified all the studied biomarkers as related to nodal metastasis. Multivariate analysis identified a predictive variable for nodal metastasis: neo angiogenesis, quantified by the neoformed pathological microvessels density. Distant metastasis was related to male gender. Discussion: Predictive models based on clinical and biomolecular data could be used to predict individual risk for metastasis. Patients under a high individual risk for lymph node metastasis should be considered as candidates to mediastinal lymphadenectomy. Those under a high risk of distant metastasis should be followed-up as having an aggressive disease. Conclusion: Individual risk prediction of bronchial typical carcinoid tumor metastasis for patients operated on can be calculated in function of biomolecular data. Prediction models can detect high-risk patients and help surgeons to identify patients requiring radical lymphadenectomy and help oncologists to identify those as having an aggressive disease requiring prolonged follow-up.

Key Words: Carcinoid tumors • Neuroendocrine carcinoma • Metastasis • Prediction • Logistic regression • Angiogenesis