Sebastiano Barbieri^{1} and Harriet C Thoeny^{1}

We prospectively assess the feasibility of using DW-MRI data to train an artificial neural network which distinguishes between prostate cancer lesions with high (≥7) and with low (=6) Gleason scores in 84 patients. The accuracy of the artificial neural network is compared with the accuracy of classification based on apparent diffusion coefficient (ADC) values.

ANNs might be used to classify DW-MRI data (acquired at
several b-values) with high accuracy. Nevertheless, validation on additional data is necessary.

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Figure 1: Boxplot
of average ADC values for lesions with high
(≥7) and with low (=6)
Gleason scores.

Figure 2: Boxplot
of normalized
DW-MRI data for lesions with high
(≥7) and with low (=6)
Gleason scores.

Figure 3: Schematic
representation of the fitted neural network.

Table 1: Parameters of the diffusion-weighted magnetic
resonance imaging sequence. GRAPPA: generalised autocalibrating partially
parallel acquisition.