Improved Visualization of Prostate Cancer Using Multichannel Computed Diffusion Images: Combining ADC and DWI.
Matthias HammonMarc SaakeFrederik B LaunRafael HeißNicola SeussRolf JankaAlexander CavallaroMichael UderHannes SeussPublished in: Diagnostics (Basel, Switzerland) (2022)
(1) Background: For the peripheral zone of the prostate, diffusion weighted imaging (DWI) is the most important MRI technique; however, a high b-value image (hbDWI) must always be evaluated in conjunction with an apparent diffusion coefficient (ADC) map. We aimed to unify the important contrast features of both a hbDWI and ADC in one single image, termed multichannel computed diffusion images (mcDI), and evaluate the values of these images in a retrospective clinical study; (2) Methods: Based on the 2D histograms of hbDWI and ADC images of 70 patients with histologically proven prostate cancer (PCa) in the peripheral zone, an algorithm was designed to generate the mcDI. Then, three radiologists evaluated the data of 56 other patients twice in three settings (T2w images +): (1) hbDWI and ADC; (2) mcDI; and (3) mcDI, hbDWI, and ADC. The sensitivity, specificity, and inter-reader variability were evaluated; (3) Results: The overall sensitivity/specificity were 0.91/0.78 (hbDWI + ADC), 0.85/0.88 (mcDI), and 0.97/0.88 (mcDI + hbDWI + ADC). The kappa-values for the inter-reader variability were 0.732 (hbDWI + ADC), 0.800 (mcDI), and 0.853 (mcDI + hbDWI + ADC). (4) Conclusions: By using mcDI, the specificity of the MRI detection of PCa was increased at the expense of the sensitivity. By combining the conventional diffusion data with the mcDI data, both the sensitivity and specificity were improved.
Keyphrases
- diffusion weighted imaging
- contrast enhanced
- deep learning
- prostate cancer
- magnetic resonance imaging
- convolutional neural network
- diffusion weighted
- optical coherence tomography
- magnetic resonance
- electronic health record
- radical prostatectomy
- artificial intelligence
- computed tomography
- big data
- end stage renal disease
- machine learning
- ejection fraction
- clinical trial
- prognostic factors
- peritoneal dialysis
- structural basis
- benign prostatic hyperplasia
- loop mediated isothermal amplification