TY - JOUR
T1 - Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression
AU - Muller, M.
AU - Winz, O.
AU - Gutsche, R.
AU - Leijenaar, R.T.H.
AU - Kocher, M.
AU - Lerche, C.
AU - Filss, C.P.
AU - Stoffels, G.
AU - Steidl, E.
AU - Hattingen, E.
AU - Steinbach, J.P.
AU - Maurer, G.D.
AU - Heinzel, A.
AU - Galldiks, N.
AU - Mottaghy, F.M.
AU - Langen, K.J.
AU - Lohmann, P.
N1 - Funding Information:
Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 491111487 and 428090865/SPP 2177 (RG, NG, and PL).
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/9
Y1 - 2022/9
N2 - Purpose To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[F-18]fluoroethyl)-l-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. Patients and Methods One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[F-18]fluoroethyl)-l-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBRmean, TBRmax, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. Results Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBRmean and TBRmax resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). Conclusion The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.
AB - Purpose To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[F-18]fluoroethyl)-l-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. Patients and Methods One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[F-18]fluoroethyl)-l-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBRmean, TBRmax, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. Results Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBRmean and TBRmax resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). Conclusion The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.
KW - Amino acid PET
KW - Brain tumors
KW - Artificial intelligence (AI)
KW - Machine learning
KW - POSITRON-EMISSION-TOMOGRAPHY
KW - F-18-FET PET
KW - DIAGNOSIS
KW - PSEUDOPROGRESSION
KW - RECURRENCE
KW - ACCURACY
U2 - 10.1007/s11060-022-04089-2
DO - 10.1007/s11060-022-04089-2
M3 - Article
C2 - 35852737
SN - 0167-594X
VL - 159
SP - 519
EP - 529
JO - Journal of Neuro-Oncology
JF - Journal of Neuro-Oncology
IS - 3
ER -