A clinical model to estimate recurrence risk in resected stage I non-small cell lung cancer
There are no reliable markers to predict recurrence in resected Stage I non-small cell lung cancer (NSCLC). A validated clinical model to estimate the risk of recurrence would help select patients for adjuvant therapy. We reviewed the medical records of 715 patients who had a potentially curative re...
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Veröffentlicht in: | American journal of clinical oncology 2008-02, Vol.31 (1), p.22-28 |
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Sprache: | eng |
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Zusammenfassung: | There are no reliable markers to predict recurrence in resected Stage I non-small cell lung cancer (NSCLC). A validated clinical model to estimate the risk of recurrence would help select patients for adjuvant therapy.
We reviewed the medical records of 715 patients who had a potentially curative resection for Stage I NSCLC at our institution from 1990 to 2000. Recurrence rates were estimated by the Kaplan-Meier method. A model to estimate risk of recurrence was developed by combining independent risk factors.
With a median follow-up of 4.7 years, the 5-year survival rates for Stages IA and IB were 66% and 55% respectively, and 5-year recurrence rates were 19% and 30%, respectively. Four factors were independently associated with tumor recurrence: tumor size >3 cm (hazard ratio [HR] = 2.4), surgery other than lobectomy (HR = 2.0), nonsquamous histology (HR = 1.4), and high-grade cellular differentiation (HR = 1.4). A scoring system for recurrence was developed by assigning 2 points for each major risk factor (tumor size and surgery) and 1 point for each minor risk factor (histologic subtype and cellular grade). Scores were grouped as low (0-1), intermediate (2-3), and high (>3), yielding 5-year estimates of risk of recurrence of 14%, 27%, and 43%, respectively.
This model, based upon readily available clinicopathologic characteristics, can estimate the risk of recurrence in Stage I NSCLC, independent of T classification. This model could be used to select patients for adjuvant therapy if validated in independent data sets. |
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ISSN: | 0277-3732 1537-453X |
DOI: | 10.1097/COC.0b013e3180ca77d1 |