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

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...

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Bibliographic Details
Published in:Scientific reports 2021-04, Vol.11 (1), p.8489-8489, Article 8489
Main Authors: Negahbani, Farzin, Sabzi, Rasool, Pakniyat Jahromi, Bita, Firouzabadi, Dena, Movahedi, Fateme, Kohandel Shirazi, Mahsa, Majidi, Shayan, Dehghanian, Amirreza
Format: Article
Language:English
Subjects:
631/67
639/705
692/4017
692/4028
Automation
Benchmarks
Breast cancer
Chemotherapy
Deep learning
Humanities and Social Sciences
Learning algorithms
Lymphocytes
Machine learning
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Tumor-infiltrating lymphocytes
Tumors
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Summary: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 .
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-86912-w