Clinical assessment of a biophysical model for distinguishing tumor progression from radiation necrosis. Issue 7 (16th June 2021)

• Record Type: Journal Article
• Authors: Dohm, Ammoren E.; Nickles, Tanner M.; Miller, Caroline E.; Bowers, Haley J.; Miga, Michael I.; Attia, Albert; Chan, Michael D.; Weis, Jared A.
• Is Part Of: Medical physics. Volume 48:Issue 7(2021)
• Page Start: 3852
• Page End: 3859
• Publication Date: 2021-06-16
• Subjects: brain metastasis -- computational model -- radiation necrosis -- stereotactic radiosurgery -- tumor progression; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14999
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The efficacy of an imaging‐driven mechanistic biophysical model of tumor growth for distinguishing radiation necrosis from tumor progression in patients with enhancing lesions following stereotactic radiosurgery (SRS) for brain metastasis is validated. Methods: We retrospectively assessed the model using 73 patients with 78 lesions and histologically confirmed radiation necrosis or tumor progression. Postcontrast T1‐weighted MRI images were used to extract parameters for a mechanistic reaction‐diffusion logistic growth model mechanically coupled to the surrounding tissue. The resulting model was then used to estimate mechanical stress fields, which were then compared with edema visualized on FLAIR imaging using DICE similarity coefficients. DICE, model, and standard radiographic morphometric analysis parameters were evaluated using a receiver operating characteristic (ROC) curve for prediction of radiation necrosis or tumor progression. Multivariate logistic regression models were then constructed using mechanistic model parameters or advanced radiomic features. An independent validation was performed to evaluate predictive performance. Results: Tumor cell proliferation rate resulted in ROC AUC = 0.86, 95% CI: 0.76–0.95, P < 0.0001, 74% sensitivity and 95% specificity) and DICE similarity coefficient associated with high stresses demonstrated an ROC AUC = 0.93, 95% CI: 0.86–0.99, P < 0.0001, 81% sensitivity and 95% specificity. In a multivariate logistic regression model using an independent validation dataset, mechanistic modeling parameters had an ROC AUC of 0.95, with 94% sensitivity and 96% specificity. Conclusions: Imaging‐driven biophysical modeling of tumor growth represents a novel method for accurately predicting clinically significant tumor behavior.