Mr. Siddhartha Nanda Profile

Siddhartha Nanda

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Publications (2)

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 | 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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