Ultrasound-Based Predication of Prostate Cancer in MRI-guided Biopsy

Uniyal, Nishant; Imani, Farhad; Tahmasebi, Amir M.; Agarwal, Harsh; Bharat, Shyam; Yan, Pingkun; Kruecker, Jochen; Kwak, Jin Tae; Xu, Sheng; Wood, Bradford J.; Pinto, Peter A.; Türkbey, Barış; Choyke, Peter L.; Abolmaesumi, Purang; Mousavi, Parvin; Moradi, Mehdi

doi:10.1007/978-3-319-13909-8_18

Cited by 5 publications

(9 citation statements)

References 14 publications

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“…Through a comprehensive search in the parameter space, we determined those parameters that provided us with the best classification results in our training data set. In our previous study, we have shown that classification results achieved using random forest is similar to those obtained with SVM [27]. …”

Section: Discussionmentioning

confidence: 57%

“…This method uses a supervised machine learning framework to determine the correlation between features extracted from the temporal ultrasound data with the tissue label provided by histopathology. In addition to several ex vivo experiments [14, 15], we have demonstrated, in a series of in vivo experiments[7,16,27], that the technique can distinguish benign and cancerous tissue.…”

Section: Introductionmentioning

confidence: 99%

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Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

et al. 2015

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Purpose In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS) guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has shown to be highly sensitive to detect higher grade prostate cancer, but suffers from a high rate of false positives for lower grade cancer, leading to unnecessary biopsies. This paper utilizes a machine-learning framework to further improve the sensitivity of targeted biopsy through analyzing temporal ultrasound data backscattered from the prostate tissue. Methods Temporal ultrasound data was acquired during targeted fusion prostate biopsy from suspicious cancer foci identified in mp-MRI. Several spectral features, representing the signature of backscattered signal from the tissue, were extracted from the temporal ultrasound data. A supervised support vector machine classification model was trained to relate the features to the result of histopathology analysis of biopsy cores obtained from cancer foci. The model was used to predict the label of biopsy cores for mp-MRI identified targets in an independent group of subjects. Results Training of the classier was performed on data obtained from 35 biopsy cores. A five-fold cross-validation strategy was utilized to examine the consistency of the selected features from temporal ultrasound data, where we achieved the classification accuracy and area under receiver operating characteristic curve of 94% and 0.98, respectively. Subsequently, an independent group of 25 biopsy cores was used for validation of the model, in which mp-MRI had identified suspicious cancer foci. Using the trained model, we predicted the tissue pathology using temporal ultrasound data: 16 out of 17 benign cores, as well as all three higher grade cancer cores, were correctly identified. Conclusion The results show that temporal analysis of ultrasound data is potentially an effective approach to complement mp-MRI-TRUS-guided prostate cancer biopsy, specially to reduce the number of unnecessary biopsies and to reliably identify higher grade cancers.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

et al. 2015

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“…The parameters of SVM were determined using a grid-search algorithm which uses leave-one-core-out crossvalidation on latent features obtained from D 1 . In addition to the binary output of the classifier, we estimate the likelihood of the ROIs to be cancerous by using the approach presented in [12]. Finally, the classification model developed using data set D 1 , was validated with the data set D 2 in the testing step (See Fig.…”

Section: Classificationmentioning

confidence: 99%

“…In addition to challenges associated with defining features that are correlated with the underlying tissue properties, it is also difficult to determine the best combination of those features for effective tissue typing as the number of features increases. The lack of a systematic approach for feature selection can lead to a so-called "cherry picking" of the features [4,5,12].…”

Section: Introductionmentioning

confidence: 99%

“…Another hypothesis indicates that micro-vibration of acoustic scatterers, contribute to tissue typing in this approach. Previous works [4,5,12] utilize a machine learning framework to determine the correlation between features extracted from the temporal ultrasound data and cancer presence or grade provided by histopathology. In these methods, features were heuristically determined from the spectral/wavelet analysis of the ultrasound data.…”

Section: Introductionmentioning

confidence: 99%

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Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks

Azizi

Imani

Zhuang

et al. 2015

Lecture Notes in Computer Science

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Abstract.We propose an automatic feature selection framework for analyzing temporal ultrasound signals of prostate tissue. The framework consists of: 1) an unsupervised feature reduction step that uses Deep Belief Network (DBN) on spectral components of the temporal ultrasound data; 2) a supervised fine-tuning step that uses the histopathology of the tissue samples to further optimize the DBN; 3) a Support Vector Machine (SVM) classifier that uses the activation of the DBN as input and outputs a likelihood for the cancer. In leave-one-core-out cross-validation experiments using 35 biopsy cores, an area under the curve of 0.91 is obtained for cancer prediction. Subsequently, an independent group of 36 biopsy cores was used for validation of the model. The results show that the framework can predict 22 out of 23 benign, and all of cancerous cores correctly. We conclude that temporal analysis of ultrasound data can potentially complement multi-parametric Magnetic Resonance Imaging (mp-MRI) by improving the differentiation of benign and cancerous prostate tissue.

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Cross‐Layer Connection SegFormer Attention U‐Net for Efficient TRUS Image Segmentation

Shi,

Du,

Gao

et al. 2024

Int J Imaging Syst Tech

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Accurately and rapidly segmenting the prostate in transrectal ultrasound (TRUS) images remains challenging due to the complex semantic information in ultrasound images. The paper discusses a cross‐layer connection with SegFormer attention U‐Net for efficient TRUS image segmentation. The SegFormer framework is enhanced by reducing model parameters and complexity without sacrificing accuracy. We introduce layer‐skipping connections for precise positioning and combine local context with global dependency for superior feature recognition. The decoder is improved with Multi‐layer Perceptual Convolutional Block Attention Module (MCBAM) for better upsampling and reduced information loss, leading to increased accuracy. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the dice similarity coefficient (DSC) of 97.55% and the intersection over union (IoU) of 95.23%. This approach balances encoder efficiency, multi‐layer information flow, and parameter reduction.

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Ultrasound-Based Predication of Prostate Cancer in MRI-guided Biopsy

Cited by 5 publications

References 14 publications

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

Augmenting MRI–transrectal ultrasound-guided prostate biopsy with temporal ultrasound data: a clinical feasibility study

Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks

Cross‐Layer Connection SegFormer Attention U‐Net for Efficient TRUS Image Segmentation

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