Lung cancer: computerized quantification of tumor response--initial results.
OBJECTIVE
To prospectively quantify tumor response or progression in patients with lung cancer by using thin-section computed tomography (CT) and a semiautomated algorithm to calculate tumor volume and other parameter values.


METHODS
This HIPAA-compliant study was institutional review board approved; informed patient consent was waived. CT scans of 15 measurable non-small cell lung cancers (in five men and 10 women; mean age, 64 years; range, 38-78 years) before and after gefitinib treatment were analyzed. A semiautomated three-dimensional lung cancer segmentation algorithm was developed and applied to each tumor at baseline and follow-up. The computer calculated the greatest diameter (unidimensional measurement), the product of the greatest diameter and the greatest perpendicular diameter (bidimensional measurement), and the volume of each tumor. Exact McNemar tests were used to analyze differences in the percentage change calculated with different measurement techniques.


RESULTS
The computer accurately segmented 14 of the 15 tumors. One paramediastinal tumor required manual separation from the mediastinum. Eleven (73%) of the 15 patients had an absolute change in tumor volume of at least 20%, compared with one (7%) and four (27%) patients who had similar changes in unscaled unidimensional (P < .01) and bidimensional (P = .04) tumor measurements, respectively. Seven (47%) patients had an absolute change in tumor volume of at least 30%. In contrast, at unscaled analysis, no patients at unidimensional measurement (P = .02) and two (13%) patients at bidimensional measurement (P = .06) had a change of at least 30%.


CONCLUSIONS
Compared with the unidimensional and bidimensional techniques, semiautomated tumor segmentation enabled the identification of a larger number of patients with absolute changes in tumor volume of at least 20% and 30%.
Zhao
Binsheng
B
Department of Medical Physics and Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10021, USA. zhaob@mskcc.org
Schwartz
Lawrence H
LH
Moskowitz
Chaya S
CS
Ginsberg
Michelle S
MS
Rizvi
Naiyer A
NA
Kris
Mark G
MG
D000328:Adult; D000368:Aged; D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000473:pathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted; D018709:Statistics, Nonparametric; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden