Ammoren E Dohm1, Tanner M Nickles2, Caroline E Miller2, Haley J Bowers2, Michael I Miga3, Albert Attia4,5, Michael D Chan1,6, Jared A Weis2,6. 1. Department of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC, USA. 2. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA. 3. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. 4. Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN, USA. 5. Bon Secours Mercy Health St. Francis Cancer Center, Greenville, SC, USA. 6. Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA.
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.
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.
Authors: Deborah R Smith; Yandong Bian; Cheng-Chia Wu; Anurag Saraf; Cheng-Hung Tai; Tavish Nanda; Andrew Yaeh; Matthew E Lapa; Jacquelyn I S Andrews; Simon K Cheng; Guy M McKhann; Michael B Sisti; Jeffrey N Bruce; Tony J C Wang Journal: J Neurooncol Date: 2019-03-14 Impact factor: 4.130
Authors: Jared A Weis; Michael I Miga; Lori R Arlinghaus; Xia Li; Vandana Abramson; A Bapsi Chakravarthy; Praveen Pendyala; Thomas E Yankeelov Journal: Cancer Res Date: 2015-09-02 Impact factor: 12.701
Authors: Ammoren Dohm; Emory R McTyre; Catherine Okoukoni; Adrianna Henson; Christina K Cramer; Michael C LeCompte; Jimmy Ruiz; Michael T Munley; Shadi Qasem; Hui-Wen Lo; Fei Xing; Kounosuke Watabe; Adrian W Laxton; Stephen B Tatter; Michael D Chan Journal: Neurosurgery Date: 2018-07-01 Impact factor: 4.654