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. 2017 Jun 19;1(1):22.
doi: 10.1038/s41698-017-0022-1. eCollection 2017.

Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care

Ugljesa Djuric  1   2 , Gelareh Zadeh  1 , Kenneth Aldape  1   2   3 , Phedias Diamandis  1   2   3
Affiliations
Free PMC article

Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care

Ugljesa Djuric et al. NPJ Precis Oncol. .
Free PMC article
No abstract available

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Diagnostic decision-making algorithms in pathology. a Digital image of a H&E stained slide showing classic histologic features of an anaplastic oligodendroglioma, WHO grade III. b Unaided human-based classification algorithms utilize simple and highly reproducible decision tree-based approaches. Even with new more objective molecular tools, the need for highly uniform and reproducible diagnostic reporting limits the number of alterations that can be extracted from data-rich molecular data sets. c, d Machine learning-based approaches may allow for multi-parametric feature extraction and aggregation that permit even subtle but reproducible groups of features to be integrated into classification schemes. DNNs may thus tolerate reliable extraction of a larger number of unappreciated features that can be correlated with specific entities, outcomes, and molecular changes. IDH isocitrate dehydrogenase, IDH-mut IDH-mutated, IDH-wt IDH-wildtype, CIC capicua, Oligo oligodendroglioma, Astro astrocytoma, Oligoastro oligoastrocytoma, WHO World Health Organization malignancy grading scheme
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