Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using deep learning to detect patients at risk for prostate cancer
  despite benign biopsies   Background: Transrectal ultrasound guided systematic biopsies of the prostate
is a routine procedure to establish a prostate cancer diagnosis. However, the
10-12 prostate core biopsies only sample a relatively small volume of the
prostate, and tumour lesions in regions between biopsy cores can be missed,
leading to a well-known low sensitivity to detect clinically relevant cancer.
As a proof-of-principle, we developed and validated a deep convolutional neural
network model to distinguish between morphological patterns in benign prostate
biopsy whole slide images from men with and without established cancer.
Methods: This study included 14,354 hematoxylin and eosin stained whole slide
images from benign prostate biopsies from 1,508 men in two groups: men without
an established prostate cancer (PCa) diagnosis and men with at least one core
biopsy diagnosed with PCa. 80% of the participants were assigned as training
data and used for model optimization (1,211 men), and the remaining 20% (297
men) as a held-out test set used to evaluate model performance. An ensemble of
10 deep convolutional neural network models was optimized for classification of
biopsies from men with and without established cancer. Hyperparameter
optimization and model selection was performed by cross-validation in the
training data . Results: Area under the receiver operating characteristic curve
(ROC-AUC) was estimated as 0.727 (bootstrap 95% CI: 0.708-0.745) on biopsy
level and 0.738 (bootstrap 95% CI: 0.682 - 0.796) on man level. At a
specificity of 0.9 the model had an estimated sensitivity of 0.348. Conclusion:
The developed model has the ability to detect men with risk of missed PCa due
to under-sampling of the prostate. The proposed model has the potential to
reduce the number of false negative cases in routine systematic prostate
biopsies and to indicate men who could benefit from MRI-guided re-biopsy.

31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat