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


CONCLUSIONS
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.


CONCLUSIONS
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.
Das-Neves-Pereira
Joao-Carlos
JC
Thoracic Surgery Department of Sao Paulo University Medical School, Sao Paulo, SP, Brazil.
Bagan
Patrick
P
Milanez-de-Campos
Jose-Ribas
JR
Capelozzi
Vera-Luiza
VL
Danel
Claire
C
Jatene
Fabio-Biscegli
FB
Bernaudin
Jean-François
JF
Riquet
Marc
M
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism; D001984:Bronchial Neoplasms / Q000098:blood supply / Q000473:pathology*; D002276:Carcinoid Tumor / Q000098:blood supply / Q000473:pathology / Q000556:secondary*; D002648:Child; D002675:Child, Preschool; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D007223:Infant; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D009389:Neovascularization, Pathologic; D011379:Prognosis; D012737:Sex Factors; D055815:Young Adult
D014408:Biomarkers, Tumor