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1.
NMR Biomed ; 36(1): e4824, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36057449

RESUMO

The purpose of this study was to evaluate the value of amide proton transfer-weighted (APTw) MRI radiomic features for the differentiation of tumor recurrence from treatment effect in malignant gliomas. Eighty-six patients who had suspected tumor recurrence after completion of chemoradiation or radiotherapy, and who had APTw-MRI data acquired at 3 T, were retrospectively analyzed. Using a fluid-attenuated inversion recovery (FLAIR) image-based mask, radiomics analysis was applied to the processed APTw and structural MR images. A chi-square automatic interaction detector decision tree was used for classification analysis. Models with and without APTw features were built using the same strategy. Tenfold cross-validation was applied to obtain the overall classification performance of each model. Sixty patients were confirmed as having tumor recurrence, and the remainder were confirmed as having treatment effect, at median time points of 190 and 171 days after therapy, respectively. There were 525 radiomic features extracted from each of the processed APTw and structural MR images. Based on these, the APTw-based model yielded the highest accuracy (86.0%) for the differentiation of tumor recurrence from treatment effect, compared with 74.4%, 76.7%, 83.7%, and 76.7% for T1 w, T2 w, FLAIR, and Gd-T1 w, respectively. Model classification accuracy was 82.6% when using the combined structural MR images (T1 w, T2 w, FLAIR, Gd-T1 w), and increased to 89.5% when using these structural plus APTw images. The corresponding sensitivity and specificity were 85.0% and 76.9% for the combination of structural MR images, and 85.0% and 100% after adding APTw image features. Adding APTw-based radiomic features increased MRI accuracy in the assessment of the treatment response in post-treatment malignant gliomas.


Assuntos
Glioma , Prótons , Humanos , Amidas , Recidiva Local de Neoplasia/diagnóstico por imagem , Estudos Retrospectivos , Glioma/diagnóstico por imagem , Glioma/terapia
2.
Neuro Oncol ; 16(6): 856-67, 2014 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-24366911

RESUMO

BACKGROUND: The inability of structural MRI to accurately measure tumor response to therapy complicates care management for patients with gliomas. The purpose of this study was to assess the potential of several noninvasive functional and molecular MRI biomarkers for the assessment of glioma response to radiotherapy. METHODS: Fourteen U87 tumor-bearing rats were irradiated using a small-animal radiation research platform (40 or 20 Gy), and 6 rats were used as controls. MRI was performed on a 4.7 T animal scanner, preradiation treatment, as well as at 3, 6, 9, and 14 days postradiation. Image features of the tumors, as well as tumor volumes and animal survival, were quantitatively compared. RESULTS: Structural MRI showed that all irradiated tumors still grew in size during the initial days postradiation. The apparent diffusion coefficient (ADC) values of tumors increased significantly postradiation (40 and 20 Gy), except at day 3 postradiation, compared with preradiation. The tumor blood flow decreased significantly postradiation (40 and 20 Gy), but the relative blood flow (tumor vs contralateral) did not show a significant change at most time points postradiation. The amide proton transfer weighted (APTw) signals of the tumor decreased significantly at all time points postradiation (40 Gy), and also at day 9 postradiation (20 Gy). The blood flow and APTw maps demonstrated tumor features that were similar to those seen on gadolinium-enhanced T1-weighted images. CONCLUSIONS: Tumor ADC, blood flow, and APTw were all useful imaging biomarkers by which to predict glioma response to radiotherapy. The APTw signal was most promising for early response assessment in this model.


Assuntos
Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/radioterapia , Glioma/patologia , Glioma/radioterapia , Imageamento por Ressonância Magnética/métodos , Animais , Biomarcadores , Modelos Animais de Doenças , Processamento de Imagem Assistida por Computador , Estimativa de Kaplan-Meier , Masculino , Prótons , Ratos , Ratos Nus
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