Computer-assisted quantitative evaluation of therapeutic responses for lymphoma using serial PET/CT imaging.
OBJECTIVE
Molecular imaging modalities such as positron emission tomography (PET)/computed tomography (CT) have emerged as an essential diagnostic tool for monitoring treatment response in lymphoma patients. However, quantitative assessment of treatment outcomes from serial scans is often difficult, laborious, and time consuming. Automatic quantization of longitudinal PET/CT scans provides more efficient and comprehensive quantitative evaluation of cancer therapeutic responses. This study develops and validates a Longitudinal Image Navigation and Analysis (LINA) system for this quantitative imaging application.


METHODS
LINA is designed to automatically construct longitudinal correspondence along serial images of individual patients for changes in tumor volume and metabolic activity via regions of interest (ROI) segmented from a given time point image and propagated into the space of all follow-up PET/CT images. We applied LINA retrospectively to nine lymphoma patients enrolled in an immunotherapy clinical trial conducted at the Center for Cell and Gene Therapy, Baylor College of Medicine. This methodology was compared to the readout by a diagnostic radiologist, who manually measured the ROI metabolic activity as defined by the maximal standardized uptake value (SUVmax).


RESULTS
Quantitative results showed that the measured SUVs obtained from automatic mapping are as accurate as semiautomatic segmentation and consistent with clinical examination findings. The average of relative squared differences of SUVmax between automatic and semiautomatic segmentation was found to be 0.02.


CONCLUSIONS
These data support a role for LINA in facilitating quantitative analysis of serial PET/CT images to efficiently assess cancer treatment responses in a comprehensive and intuitive software platform.
Gao
Xin
X
The Center for Bioengineering and Informatics, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Medical College of Cornell University, Houston, TX 77030, USA.
Xue
Zhong
Z
Xing
Jiong
J
Lee
Daniel Y
DY
Gottschalk
Stephen M
SM
Heslop
Helen E
HE
Bollard
Catherine M
CM
Wong
Stephen T C
ST
D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008223:Lymphoma / Q000175:diagnosis* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome