Dr. Tara Castellano Profile

Dr. Tara Castellano

at Univ of Oklahoma Health Sciences Ctr

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Publications (2)

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Proceedings Article | 3 March 2020 Paper

Developing a low cost image marker to identify lymph node metastasis for cervical cancer patients: an initial study

Wei Liu, Shiyu Pei, Xuxin Chen, Theresa Thai, Tara Castellano, Camille Gunderson, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

Proceedings Volume 11241, 112410Y (2020) https://doi.org/10.1117/12.2548669

KEYWORDS: Cervical cancer, Computed tomography, Image segmentation

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This study aims to utilize the primary tumor characteristics from CT images to detect lymph node (LN) metastasis for accurately categorizing locally advanced cervical cancer patients (LACC). In clinical practice, LN metastasis is a critical indicator for patients’ prognostic assessment, which is usually investigated by PET/CT (i.e., positron emission tomography/computed tomography) examination. However, the high cost of the PET/CT imaging modality limits its application and also leads to heavy financial burden on patients. Thus it is clinically imperative to develop an economic solution for the LN metastasis identification. For this purpose, a novel image marker was developed, which is based on the primary cervical tumors segmented from CT images. Accordingly, a total of 99 handcrafted features were computed, and an optimal feature set was determined by Laplacian Score (LS) method. Next, a logistic regression model was applied on the optimal feature set to generate a likelihood score for the identification of LN metastasis. Using a retrospective dataset that contains a total of 82 LACC patients, this new model was trained and optimized by leave one out cross validation (LOOCV) strategy. The marker performance was assessed by receiver operator characteristic curve (ROC). The results indicate that the area under the ROC curve (AUC) of this identification model was 0.774±0.050, which demonstrates its strong discriminative power. This study may be able to provide gynecologic oncologists a CT image based low cost clinical marker to identify LN metastasis occurred on LACC patients.

Proceedings Article | 7 March 2019 Paper

Utilizing deep learning technology to develop a novel CT image marker for categorizing cervical cancer patients at early stage

Wei Liu, Abolfazl Zargaria, Theresa Thai, Tara Castellano, Camille Gunderson, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

Proceedings Volume 10879, 108790I (2019) https://doi.org/10.1117/12.2510037

KEYWORDS: Cervical cancer, Computed tomography, Pattern recognition

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The purpose of this investigation is to verify the feasibility of using deep learning technology to generate an image marker for accurate stratification of cervical cancer patients. For this purpose, a pre-trained deep residual neural network (i.e. ResNet-50) is used as a fixed feature extractor, which is applied to the previously identified cervical tumors depicted on CT images. The features at average pooling layer of the ResNet-50 are collected as initial feature pool. Then discriminant neighborhood embedding (DNE) algorithm is employed to reduce the feature dimension and create an optimal feature cluster. Next, a k-nearest neighbors (k-NN) regression model uses this cluster as input to generate an evaluation score for predicting patient’s response to the planned treatment. In order to assess this new model, we retrospectively assembled the pre-treatment CT images from a number of 97 locally advanced cervical cancer (LACC) patients. The leave one out cross validation (LOOCV) strategy is adopted to train and optimize this new scheme and the receiver operator characteristic curve (ROC) is applied for performance evaluation. The result shows that this new model achieves an area under the ROC curve (AUC) of 0.749 ± 0.064, indicating that the deep neural networks enables to identify the most effective tumor characteristics for therapy response prediction. This investigation initially demonstrates the potential of developing a deep learning based image marker to assist oncologists on categorizing cervical cancer patients for precision treatment.

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