Positron emission tomography scanning poorly predicts response to preoperative chemotherapy in non-small cell lung cancer. Academic Article uri icon

Overview

abstract

  • BACKGROUND: The ability to accurately predict pathologic response to preoperative chemotherapy may have a significant impact on the treatment strategy for non-small cell lung cancer (NSCLC). The purpose of this study was to examine the accuracy of positron emission tomography (PET) scanning in predicting the pathologic response to preoperative chemotherapy in the primary tumor and draining lymph nodes. METHODS: A total of 25 patients were enrolled in two separate phase II trials investigating induction chemotherapy for NSCLC. All patients underwent pre-treatment and post-treatment PET scans followed by surgical resection. A significant PET scan response was defined as a reduction in the standard uptake value by 50% or more. We defined a major pathologic response as either no disease or microscopic disease only in the primary tumor. The percentage change in standard uptake value was then calculated and correlated with pathologic response in the primary tumor. In addition, the presence or absence of nodal metastases as determined by the postchemotherapy PET scan was compared with final pathologic nodal stage. RESULTS: The positive and negative predictive values for PET detection of major pathologic response in the primary tumor were 43% and 100%, respectively. Positron emission tomography did not accurately predict nodal status in 52% of patients. The positive and negative predictive values of PET to detect node-positive disease were 73% and 64%, respectively. For N2 disease the positive predictive value of PET scans was less than 20%. CONCLUSIONS: Positron emission tomography scanning does not reliably predict pathologic response to preoperative chemotherapy in NSCLC in either the primary tumor or the draining lymph nodes.

publication date

  • January 1, 2004

Research

keywords

  • Carcinoma, Non-Small-Cell Lung
  • Lung Neoplasms
  • Tomography, Emission-Computed

Identity

Scopus Document Identifier

  • 1642564210

Digital Object Identifier (DOI)

  • 10.1016/s0003-4975(03)01457-7

PubMed ID

  • 14726072

Additional Document Info

volume

  • 77

issue

  • 1