PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer
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...
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Nature Portfolio
2021
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oai:doaj.org-article:2550acd71c9f4c7d8a3cb1945fb8f1f82021-12-02T17:32:59ZPathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer10.1038/s41598-021-86912-w2045-2322https://doaj.org/article/2550acd71c9f4c7d8a3cb1945fb8f1f82021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86912-whttps://doaj.org/toc/2045-2322Abstract 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 .Farzin NegahbaniRasool SabziBita Pakniyat JahromiDena FirouzabadiFateme MovahediMahsa Kohandel ShiraziShayan MajidiAmirreza DehghanianNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
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Medicine R Science Q Farzin Negahbani Rasool Sabzi Bita Pakniyat Jahromi Dena Firouzabadi Fateme Movahedi Mahsa Kohandel Shirazi Shayan Majidi Amirreza Dehghanian PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| description |
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 . |
| format |
article |
| author |
Farzin Negahbani Rasool Sabzi Bita Pakniyat Jahromi Dena Firouzabadi Fateme Movahedi Mahsa Kohandel Shirazi Shayan Majidi Amirreza Dehghanian |
| author_facet |
Farzin Negahbani Rasool Sabzi Bita Pakniyat Jahromi Dena Firouzabadi Fateme Movahedi Mahsa Kohandel Shirazi Shayan Majidi Amirreza Dehghanian |
| author_sort |
Farzin Negahbani |
| title |
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| title_short |
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| title_full |
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| title_fullStr |
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| title_full_unstemmed |
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| title_sort |
pathonet introduced as a deep neural network backend for evaluation of ki-67 and tumor-infiltrating lymphocytes in breast cancer |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/2550acd71c9f4c7d8a3cb1945fb8f1f8 |
| work_keys_str_mv |
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