Abstract
Introduction
Different subtypes of non-small cell lung cancer (NSCLC) are associated with different
patterns of metastatic spread. Anatomic location of lesions in the chest may influence
patterns of cancer growth and the shrinkage to therapy. Consequently, lesion location
could affect apparent response rates per RECIST. We sought to explore this and develop,
as needed, treatment response assessments less affected by the location.
Methods
Cases of advanced oncogene-addicted NSCLC (EGFR, ALK, and ROS1) with pre- and on-therapy
imaging during initial targeted therapy were identified. Lesions located in the lung
parenchyma, pleural space or intra-thoracic lymph nodes were identified and analyzed
separately from each other by RECIST 1.1 (unidimensional measurements) and by a novel
MAX methodology (bidimensional measurements) which takes the axis with the greatest
absolute percentage change on therapy in each location as the representative measurement.
Results
Three hundred three patients with 446 unidimensional measured lesions were included
for RECIST analysis. Two hundred forty nine patients with 386 bidimensional measured
lesions were included for MAX analysis, as well as the analysis comparing RECIST and
MAX. Intrathoracic location significantly impacted percentage shrinkage and the response
rate per RECIST. The response rates for pleural, intra-parenchymal and nodal lesions
were 34.1%, 49.6%, and 68.3%, respectively (P = .0002). The MAX methodology both increased the apparent treatment effect and made
it consistent between intrathoracic locations. For pleural, parenchymal and nodal
lesions, the MAX calculated response rate were 83.7%, 72.2%, and 75.4%, respectively
(P-value = .24).
Conclusion
Intrathoracic lesion location affects RECIST-based treatment effectiveness estimations.
The MAX methodology neutralizes location effect when examining impact of treatment
and should be explored further.
Keywords
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Article info
Publication history
Published online: August 06, 2022
Accepted:
August 3,
2022
Received in revised form:
July 25,
2022
Received:
April 16,
2022
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
