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

Abstract

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.

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study

“…One hundred and fifty-eight subjects were enrolled in the study where they underwent preoperative mp-MRI examination with three pulse sequences: T2-weighted, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) imaging. Prior to biopsy, suspicious lesions in mp-MRI were identified and scored by two independent radiologists, according to a previously published protocol [15,29]. In this protocol, an overall score in the range from 1 (no cancer) to 5 (aggressive cancer) is assigned to a suspicious area.…”

“…To generate features for each ROI, we take the Fourier transform of the time course signals in the ROI, normalized to the frame rate. For each ROI, we generate 50 positive frequency components by averaging the absolute values of the discrete Fourier transform (DFT) of the zero-mean temporal US data [1,15]. …”

“…Features extracted from the temporal US data have been used in a machine learning framework to predict labels provided by histopathology as the ground truth. This approach has been used successfully for depiction of cancerous and non-cancerous prostate tissue in ex vivo [19,22] and in vivo [12–15,20] studies.…”

“…In previous implementations of temporal RF data technology, features were heuristically determined from spectral analysis of US image sequences, rather than through a systematic approach [1,12,14,15]. Recently [1], we proposed an automatic feature selection framework for analyzing temporal US signals of prostate tissue.…”

“…Recently [1], we proposed an automatic feature selection framework for analyzing temporal US signals of prostate tissue. We addressed the so-called cherry picking of the features [15] by a deep learning-based feature selection framework. This framework exploits deep belief networks (DBN) [2] to automatically learn a highlevel latent feature representation of the temporal US data.…”