TY - JOUR
T1 - Development of a classifier for [18F]fluorodeoxyglucose extravasation severity using semi-quantitative readings from topically applied detectors
AU - Perrin, Steve
AU - Kiser, Jackson W.
AU - Knowland, Josh
AU - Bowen, Spencer L.
N1 - Funding Information:
The authors thank Jonathan Conti, from Carilion Clinic, for discussions on PET/CT acquisition details and retrieving patient data, Ronald Lattanze, from Lucerno Dynamics LLC, for useful discussions and manuscript preparation, and Yin Xi, Ph.D., from the Department of Radiology at UT Southwestern Medical Center, for statistical support.
Funding Information:
This work was funded in part by Lucerno Dynamics, LLC, through a research agreement with the Fralin Biomedical Research Institute at VTC (prior institution of S.L. Bowen).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Background: Radiotracer extravasations, caused largely by faulty tracer injections, can occur in up to 23% of 18F-fluorodeoxyglucose (FDG) PET/CT scans and negatively impact radiological review and tracer quantification. Conventional radiological assessment of extravasation severity on PET has limited performance (e.g., extravasations frequently resolve before scanning) and practical drawbacks. In this study, we develop a new topical detector-based FDG extravasation severity classifier, calibrated from semi-quantitative PET measurements, and assess its performance on human subjects. Methods: A retrospective study examined patients whose FDG injections had been monitored as part of their standard workup for PET/CT imaging. Topical uncollimated gamma ray detectors were applied proximal to the injection site and on the same location on the opposing arm, and readings were acquired continuously during radiotracer uptake. Patients were imaged with their arms in the PET field of view and total extravasation activity quantified from static PET images through a volume of interest approach. The image-derived activities were considered ground truth and used to calibrate and assess quantification of topical detector readings extrapolated to the start of PET imaging. The classifier utilizes the calibrated detector readings to produce four extravasation severity classes: none, minor, moderate, and severe. In a blinded study, a radiologist qualitatively labeled PET images for extravasation severity using the same classifications. The radiologist’s interpretations and topical detector classifications were compared to the ground truth PET results. Results: Linear regression of log-transformed image-derived versus topical detector tracer extravasation activity estimates showed a strong correlation (R2 = 0.75). A total of 24 subject scans were cross-validated with the quantitatively based classifier through a leave-one-out methodology. For binary classification (none vs. extravasated), the topical detector classifier had the highest overall diagnostic performance for identifying extravasations. Specificity, sensitivity, accuracy, and positive predictive value were 100.0%, 80.0%, 95.8%, and 100.0%, respectively, for the topical detector classifier and 31.6%, 100.0%, 45.8%, and 27.8%, respectively, for the radiological analysis. The topical detector classifier, with an optimal detection threshold, produced a significantly higher Matthews correlation coefficient (MCC) than the radiological analysis (0.87 vs. 0.30). Conclusions: The topical detector binary classifier, calibrated using quantitative static PET measurements, significantly improves extravasation detection compared to qualitative image analysis.
AB - Background: Radiotracer extravasations, caused largely by faulty tracer injections, can occur in up to 23% of 18F-fluorodeoxyglucose (FDG) PET/CT scans and negatively impact radiological review and tracer quantification. Conventional radiological assessment of extravasation severity on PET has limited performance (e.g., extravasations frequently resolve before scanning) and practical drawbacks. In this study, we develop a new topical detector-based FDG extravasation severity classifier, calibrated from semi-quantitative PET measurements, and assess its performance on human subjects. Methods: A retrospective study examined patients whose FDG injections had been monitored as part of their standard workup for PET/CT imaging. Topical uncollimated gamma ray detectors were applied proximal to the injection site and on the same location on the opposing arm, and readings were acquired continuously during radiotracer uptake. Patients were imaged with their arms in the PET field of view and total extravasation activity quantified from static PET images through a volume of interest approach. The image-derived activities were considered ground truth and used to calibrate and assess quantification of topical detector readings extrapolated to the start of PET imaging. The classifier utilizes the calibrated detector readings to produce four extravasation severity classes: none, minor, moderate, and severe. In a blinded study, a radiologist qualitatively labeled PET images for extravasation severity using the same classifications. The radiologist’s interpretations and topical detector classifications were compared to the ground truth PET results. Results: Linear regression of log-transformed image-derived versus topical detector tracer extravasation activity estimates showed a strong correlation (R2 = 0.75). A total of 24 subject scans were cross-validated with the quantitatively based classifier through a leave-one-out methodology. For binary classification (none vs. extravasated), the topical detector classifier had the highest overall diagnostic performance for identifying extravasations. Specificity, sensitivity, accuracy, and positive predictive value were 100.0%, 80.0%, 95.8%, and 100.0%, respectively, for the topical detector classifier and 31.6%, 100.0%, 45.8%, and 27.8%, respectively, for the radiological analysis. The topical detector classifier, with an optimal detection threshold, produced a significantly higher Matthews correlation coefficient (MCC) than the radiological analysis (0.87 vs. 0.30). Conclusions: The topical detector binary classifier, calibrated using quantitative static PET measurements, significantly improves extravasation detection compared to qualitative image analysis.
KW - Classification
KW - Extravasation
KW - PET/CT
KW - Quality control
KW - Quantification
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U2 - 10.1186/s40658-022-00488-6
DO - 10.1186/s40658-022-00488-6
M3 - Article
C2 - 36104581
AN - SCOPUS:85139199317
SN - 2197-7364
VL - 9
JO - EJNMMI Physics
JF - EJNMMI Physics
IS - 1
M1 - 61
ER -