PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer.

Journal: Scientific reports
Published Date:

Abstract

The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression and probable response to chemotherapy. The value of Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC) that is the most common cancer in women worldwide, has been highlighted in literature. Considering that estimation of both factors are dependent on professional pathologists' observation and inter-individual variations may also exist, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 cell detection and further annotated classification of cells, In this study we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and backend, for estimation of Ki-67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. Further, we show that despite the challenges that our proposed model has encountered, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date with regard to harmonic mean measure acquired. Dataset is publicly available in http://shiraz-hidc.com and all experiment codes are published in https://github.com/SHIDCenter/PathoNet .

Authors

  • Farzin Negahbani
    Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran.
  • Rasool Sabzi
    Department of Computer Science and Engineering, Shiraz University, Shiraz, Iran.
  • Bita Pakniyat Jahromi
    Department of Pathology, Shiraz University of Medical science, Shiraz, Iran.
  • Dena Firouzabadi
    Department of Clinical Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Fateme Movahedi
    Student Research Committee, Shiraz University of Medical Science, Shiraz, Iran.
  • Mahsa Kohandel Shirazi
    Department of Pathology, Shiraz University of Medical science, Shiraz, Iran.
  • Shayan Majidi
    Student Research Committee, Shiraz University of Medical Science, Shiraz, Iran.
  • Amirreza Dehghanian
    Molecular Pathology and Cytogenetics Division, Department of Pathology, Shiraz University of Medical Sciences, Shiraz, Iran. adehghan@sums.ac.ir.