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

Abstract

Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

“…The best result to date involving TeUS B-mode data (AUC = 0.7) is based on spectral analysis and deep belief network (DBN) as the underlying machine learning framework [5,7]. Comparing our LSTM-RNN approach with that method as the most related work, a two-way paired t -test shows statistically significant improvement in AUC ( p < 0.05).…”

“…1) is based on a machine learning framework and involves the analysis of a time series of US frames, captured following insonification of tissue over a short period of time, to extract tissue-specific information. Over the last decade, TeUS has been used for characterization of PCa in ex vivo [23,24] and in vivo studies [5,6,16,17,25], with reported areas under the receiver operating characteristic curve (AUC) of 0.76–0.93 [16,17]. Despite significant success of TeUS in research studies, its dissemination to clinical end-users demands multicenter studies to assess robustness and versatility.…”

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

“…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]. In an earlier retrospective feasibility trial, we introduced a stochastic tissue characterization framework using Hidden Markov Models (HMMs) to explicitly incorporate and model temporal relations that were not taken into account in previous models of TeUS.…”

Untitled

“…Machine learning is a method established from artificial intelligence, where the computer captures patterns in data sets and uses these patterns extensively in decision making. Machine learning offers different abilities with regards to medical imaging [4,5]. The main purpose of the different types of algorithms is to elevate the diagnostic accuracy and the consistency of image interpretation.…”

“…"Machine Learning" ist eine Methode aus dem Bereich der künstlichen Intelligenz, bei der der Computer Muster in Datensätzen erfasst und diese Muster zur Entscheidungsfindung verwendet. "Machine Learning" bietet verschiedene Vorteile in Bezug auf medizinische Bildgebung [4,5]. Der Hauptzweck der verschiedenen Algorithmen besteht darin, die diagnostische Genauigkeit und die Konsistenz der Bildinterpretation zu erhöhen.…”