Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study

Imani, Farhad; Abolmaesumi, Purang; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gómez, José A.; Romagnoli, Cesare; Leveridge, Michael; Chang, Silvia D.; Fenster, Aaron; Ward, Aaron D.; Mousavi, Parvin

doi:10.1109/tmi.2015.2427739

Cited by 42 publications

(30 citation statements)

References 35 publications

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“…The histopathology information was then overlaid on the ex vivo MR images and subsequently registered to the in vivo ultrasound images. The registration process performed on this data was previously published by Imani et al [21]. …”

Section: Temporal Enhanced Ultrasound Datamentioning

confidence: 99%

“…TeUS Data from the 9 subjects, whose benign and malignant ROIs are clearly labeled, is used in the analysis described here. The ROIs were selected in a way that maximizes the distance between benign and malignant ROIs to avoid mislabeling (see [21] for details regarding the ROI selection process). The number of ROIs per patient, along with GSs of primary and secondary lesions per slice are shown in Table I.…”

Section: Temporal Enhanced Ultrasound Datamentioning

confidence: 99%

“…Previously proposed features include the fractal dimension, wavelet coefficients, frequency amplitudes following Discrete Fourier Transform and mean central frequencies, which were used to train support vector machines for tissue characterization in prostate cancer [19]–21]. More recently, automatic feature extraction using deep-belief networks was proposed for the same purpose [22].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Nahlawi

Goncalves

Imani

et al. 2018

IEEE Trans. Biomed. Eng.

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Objectives: Temporal Enhanced Ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS – temporal order and length – and present a new framework to assess their impact on tissue information. Methods: We utilize a probabilistic modeling approach using Hidden Markov Models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of 9 patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback-Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results: Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal-order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion: The work presented here strongly indicates that temporal order has substantial impact on TeUS performance, thus it plays a significant role in conveying tissue-specific information. Further-more, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance: Under-standing the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.

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Section: Temporal Enhanced Ultrasound Datamentioning

confidence: 99%

Section: Temporal Enhanced Ultrasound Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Nahlawi

Goncalves

Imani

et al. 2018

IEEE Trans. Biomed. Eng.

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“…1 Different feature sets extracted from these signals have been successfully applied to various classification problems for distinguishing cancerous and benign tissue in both ex vivo 1, 2 and in vivo [3][4][5][6] studies. These studies have demonstrated that the tissue classification results with temporal ultrasound outperforms conventional tissue typing approaches, including B-mode texture analysis and spectral methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

Ghavidel

Imani

Khallaghi

et al. 2016

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling

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Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out crossvalidation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

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“…6,7 TeUS is produced by sonicating tissue over a short period of time, without intentionally moving the tissue or the transducer. The result is a sequence (time series) of Radio Frequency (RF) ultrasound frames.…”

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confidence: 99%

Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order

et al. 2017

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Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.

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Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study

Abstract: Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Cited by 42 publications

References 35 publications

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order

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