Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice

Leeor Langer, Yoav Binenbaum, Leonid Gugel, Moran Amit, Ziv Gil, Shai Dekel

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Purpose:    Digital pathology diagnostics are often based on subjective qualitative measures. A murine model of early-phase pancreatic ductal adenocarcinoma provides a controlled environment with a priori knowledge of the genetic mutation and stage of the disease. Use of this model enables the application of supervised learning methods to digital pathology. A computerized diagnostics system for histological detection of pancreatic adenocarcinoma was developed and tested. Methods : Pathological H&E-stained specimens with early pancreatic lesions were identified and evaluated with a system that models cancer detection using a top-down object learning paradigm, mimicking the way a pathologist learns. First, the dominant primitives were identified and segmented in the images, i.e., the ducts, nuclei and tumor stroma. A boost-based machine learning technique was used for duct segmentation, classification and outlier pruning. Second, a set of morphological features traditionally used for cancer diagnosis which provides quantitative image features was employed to quantify subtle findings such as duct deformation and nuclei malformations. Finally, a visually interpretable predictive model was trained to distinguish between normal tissue and premalignant cancer lesions, given ground truth samples. Results : A predictive success rate of 92 % was achieved using tenfold cross-validation and 93 % on an independent test set. Comparison was made with state-of-the-art classification algorithms that are not interpretable as visible features yielded the contribution of individual primitive features to the prediction outcome. Conclusions : Quantitative image analysis and classification were successful in preclinical histology diagnosis for early-stage pancreatic adenocarcinoma. The usage of annotated contours coupled with interpretable supervised learning methods and outlier pruning can be adapted to other medical imaging tasks. The usage of interpretable supervised learning techniques may improve the success of CAD in histopathological diagnosis.

Original languageEnglish (US)
Pages (from-to)1043-1054
Number of pages12
JournalInternational Journal of Computer Assisted Radiology and Surgery
Issue number7
StatePublished - Oct 30 2015


  • Boosting
  • CAD
  • Cancer prediction
  • Segmentation

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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