Dr. Justin T. Brady Profile

Dr. Justin T. Brady

at Univ Hospitals Cleveland Medical Ctr

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Publications (4)

Proceedings Article | 16 March 2020 Paper

Texture kinetic features from pre-treatment DCE MRI for predicting pathologic tumor stage regression after neoadjuvant chemoradiation in rectal cancers

Siddhartha Nanda, Jacob Antunes, Kaustav Bera, Justin Brady, Kenneth Friedman, Joseph Willis, Raj Paspulati, Satish Viswanath

Proceedings Volume 11315, 1131530 (2020) https://doi.org/10.1117/12.2552175

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Feature extraction, Surgery, Medical research, In vivo imaging, Diffusion, Data analysis, Oncology, Machine learning, Pattern recognition, Image analysis

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Dynamic contrast-enhanced (DCE) MRI is increasingly used to stage and evaluate rectal cancer extent in vivo in order to plan and target interventions for locally advanced tumors. The major clinical challenge faced with rectal cancers today is to personalize interventions through early identification of patients will benefit from neoadjuvant chemoradiation (nCRT) alone and who will benefit from aggressive surgery (with adjuvant radiation) instead; via baseline imaging. In this study, we evaluated texture kinetic features of rectal tumors using baseline DCE MRI scans, in order to predict pathologic tumor stage regression in response to nCRT. Our texture kinetics approach utilized a combination of texture features (from multiple DCE uptake phases) and polynomial curve fitting to uniquely quantify spatiotemporal patterns of lesion texture during contrast uptake and diffusion that were different between responders and non-responders to nCRT. We utilized a cohort of 48 rectal cancer patients for whom pre-nCRT 3 T DCE MRI was available, including pre-, early-, and delayed-enhancement phases. All DCE MRI phases were processed for motion and spatial alignment artifacts via rigid co-registration, and the tumor ROI on all 3 contrast phases was normalized with respect to non-enhancing muscle. 191 texture features were extracted from each of 3 contrast phases separately, following which each feature was plotted with respect to time to yield a feature enhancement curve. Polynomial fitting was applied to each feature enhancement curve to result in a vector of coefficients which was considered the texture kinetic representation of that feature. All 191 features were evaluated in terms of their texture kinetic representation as well as the raw feature enhancement, for predicting pathologically regressed tumor stages (ypT0-2) from non-regressed tumors (ypT3-4) via a cross-validated QDA classifier. Texture kinetics of gradient XY enhancement yielded the best overall AUC=0:762±0:053, which was significantly higher than any feature enhancement representation (best AUC=0:696±0:050). Texture kinetic representations also outperformed their corresponding raw feature enhancement representations in 54.5% of the features compared, and performed significantly worse in only 13% of the comparisons. Non-invasive guidance of interventions in rectal cancers could therefore be enhanced through the use of texture kinetic features from DCE MRI, which may better characterize spatiotemporal differences between responders and non-responders on baseline imaging.

Proceedings Article | 16 March 2020 Presentation + Paper

Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers

Amrish Selvam, Jacob Antunes, Kaustav Bera, Asya Ofshteyn, Justin Brady, Katherine Bingmer, Kenneth Friedman, Sharon Stein, Rajmohan Paspulati, Andrei Purysko, Matthew Kalady, Anant Madabhushi, Satish Viswanath

Proceedings Volume 11314, 113140V (2020) https://doi.org/10.1117/12.2549085

KEYWORDS: Cancer, Tumors, Magnetic resonance imaging, Rectum, Feature selection, Feature extraction, Visualization, Oncology, CRTs, Tissues

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Tumor downstaging after neoadjuvant chemoradiation (CRT) in rectal cancer patients is typically assessed via Magnetic Resonance Imaging (MRI) in order to determine follow-up surgical interventions, but is associated with marked inter-reader variability and limited performance. While radiomic features have shown promise for evaluating chemoradiation response and tumor stage in rectal cancers, there is a need to determine how reproducible these features are across different MRI scanners and acquisitions. In this study, we evaluated radiomic feature reproducibility in terms of feature instability within a uniquely curated true healthy" rectum cohort in order to construct a stability-informed radiomic classifier for differentiating poorly from markedly down-staged rectal tumors after chemoradiation in a multi-site setting. We utilized a cohort of 156 patients, with (a) 74 MRIs visualizing the healthy rectum, (b) 52 post-CRT MRI scans in the discovery cohort, and (c) 30 post-CRT MRI scans in a second-site validation cohort; the latter 2 being from rectal cancer patients. 764 radiomic features were extracted from within the entire rectal wall on each MRI scan. Feature instability was used to quantify how reproducible each radiomic feature was between the discovery cohort and the healthy rectum cohort, using locations along the rectum that were spatially distinct from the treated tumor region. From the resulting stability-informed" feature set, the most relevant features were identified to distinguish pathologic tumor stage groups in the discovery cohort via a QDA classifier with cross-validation to ensure robustness. The top 4 radiomic features were then evaluated in hold-out fashion on scans from the validation cohort. We found that utilizing a stability-informed radiomic model (which comprised features that were reproducible in 100% of all comparisons) was significantly more accurate in identifying pathological tumor stage regression in both discovery (AUC=0:66 ± 0:09) and validation (AUC=0.73) cohorts, compared to a basic radiomic model that used all extracted features (AUC=0:60 ± 0:07 in discovery, AUC=0.62 in validation). Evaluating feature instability with respect to healthy rectal tissue may thus enhance the performance of radiomic models in characterizing pathologic downstaging in rectal cancers, via MRI.

Proceedings Article | 8 March 2019 Paper

Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation

Michael Yim, Zhouping Wei, Jacob Antunes, Neil K. Sehgal, Kaustav Bera, Justin Brady, Kenneth Friedman, Joseph Willis, Andrei Purysko, Raj Paspulati, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10951, 109512A (2019) https://doi.org/10.1117/12.2512612

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Lymphatic system, Medical research, Feature extraction, Feature selection, Data analysis, Oncology

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Proceedings Article | 8 March 2019 Paper

Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation

Siddhartha Nanda, Jacob Antunes, Amrish Selvam, Kaustav Bera, Justin Brady, Jayakrishna Gollamudi, Kenneth Friedman, Joseph Willis, Conor Delaney, Raj Paspulati, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10951, 109512R (2019) https://doi.org/10.1117/12.2513945

KEYWORDS: Magnetic resonance imaging, Tumors, Cancer, CRTs, Feature extraction, In vivo imaging, Medical research, Feature selection, Tissues, Lung cancer

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