Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation
Talat Zehra,1 Mahin Shams,2 Rabia Ali,3 Asad Jafri,3 Amna Khurshid,3 Humaira Erum,3 Hanna Naqvi,3 Jamshid Abdul-Ghafar4 1Pathology Department, Jinnah Sindh Medical University, Karachi, Pakistan; 2Pathology Department, United Medical and Dental College, Karachi, Pakistan; 3Histopathology Department,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2023-12-01
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| Series: | International Journal of General Medicine |
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| Online Access: | https://www.dovepress.com/use-of-novel-open-source-deep-learning-platform-for-quantification-of--peer-reviewed-fulltext-article-IJGM |
| _version_ | 1797403896542396416 |
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| author | Zehra T Shams M Ali R Jafri A Khurshid A Erum H Naqvi H Abdul-Ghafar J |
| author_facet | Zehra T Shams M Ali R Jafri A Khurshid A Erum H Naqvi H Abdul-Ghafar J |
| author_sort | Zehra T |
| collection | DOAJ |
| description | Talat Zehra,1 Mahin Shams,2 Rabia Ali,3 Asad Jafri,3 Amna Khurshid,3 Humaira Erum,3 Hanna Naqvi,3 Jamshid Abdul-Ghafar4 1Pathology Department, Jinnah Sindh Medical University, Karachi, Pakistan; 2Pathology Department, United Medical and Dental College, Karachi, Pakistan; 3Histopathology Department, Liaquat National Hospital, Karachi, Pakistan; 4Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, AfghanistanCorrespondence: Jamshid Abdul-Ghafar, Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, Afghanistan, Tel+93792827287, Email jamshid.jalal@fmic.org.afBackground: Neuroendocrine tumors (NETs) represent a diverse group of neoplasms that arise from neuroendocrine cells, with Ki-67 immunostaining serving as a crucial biomarker for assessing tumor proliferation and prognosis. Accurate and reliable quantification of Ki-67 labeling index is essential for effective clinical management.Methods: We aimed to evaluate the performance of open-source/open-access deep learning cloud-native platform, DeepLIIF (https://deepliif.org), for the quantification of Ki-67 expression in gastrointestinal neuroendocrine tumors and compare it with the manual quantification method.Results: Our results demonstrate that the DeepLIIF quantification of Ki-67 in NETs achieves a high degree of accuracy with an intraclass correlation coefficient (ICC) = 0.885 with 95% CI (0.848– 0.916) which indicates good reliability when compared to manual assessments by experienced pathologists. DeepLIIF exhibits excellent intra- and inter-observer agreement and ensures consistency in Ki-67 scoring. Additionally, DeepLIIF significantly reduces analysis time, making it a valuable tool for high-throughput clinical settings.Conclusion: This study showcases the potential of open-source/open-access user-friendly deep learning platforms, such as DeepLIIF, for the quantification of Ki-67 in neuroendocrine tumors. The analytical validation presented here establishes the reliability and robustness of this innovative method, paving the way for its integration into routine clinical practice. Accurate and efficient Ki-67 assessment is paramount for risk stratification and treatment decisions in NETs and AI offers a promising solution for enhancing diagnostic accuracy and patient care in the field of neuroendocrine oncology.Keywords: digital image analysis, histopathology, Ki-67 proliferation index, neuroendocrine tumors, machine learning |
| first_indexed | 2024-03-09T02:45:04Z |
| format | Article |
| id | doaj.art-3c856e1adb2946dfa0dbbaeb6cedf4f3 |
| institution | Directory Open Access Journal |
| issn | 1178-7074 |
| language | English |
| last_indexed | 2024-03-09T02:45:04Z |
| publishDate | 2023-12-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | International Journal of General Medicine |
| spelling | doaj.art-3c856e1adb2946dfa0dbbaeb6cedf4f32023-12-05T17:06:55ZengDove Medical PressInternational Journal of General Medicine1178-70742023-12-01Volume 165665567388702Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical ValidationZehra TShams MAli RJafri AKhurshid AErum HNaqvi HAbdul-Ghafar JTalat Zehra,1 Mahin Shams,2 Rabia Ali,3 Asad Jafri,3 Amna Khurshid,3 Humaira Erum,3 Hanna Naqvi,3 Jamshid Abdul-Ghafar4 1Pathology Department, Jinnah Sindh Medical University, Karachi, Pakistan; 2Pathology Department, United Medical and Dental College, Karachi, Pakistan; 3Histopathology Department, Liaquat National Hospital, Karachi, Pakistan; 4Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, AfghanistanCorrespondence: Jamshid Abdul-Ghafar, Department of Pathology and Clinical Laboratory, French Medical Institute for Mothers and Children (FMIC), Kabul, Afghanistan, Tel+93792827287, Email jamshid.jalal@fmic.org.afBackground: Neuroendocrine tumors (NETs) represent a diverse group of neoplasms that arise from neuroendocrine cells, with Ki-67 immunostaining serving as a crucial biomarker for assessing tumor proliferation and prognosis. Accurate and reliable quantification of Ki-67 labeling index is essential for effective clinical management.Methods: We aimed to evaluate the performance of open-source/open-access deep learning cloud-native platform, DeepLIIF (https://deepliif.org), for the quantification of Ki-67 expression in gastrointestinal neuroendocrine tumors and compare it with the manual quantification method.Results: Our results demonstrate that the DeepLIIF quantification of Ki-67 in NETs achieves a high degree of accuracy with an intraclass correlation coefficient (ICC) = 0.885 with 95% CI (0.848– 0.916) which indicates good reliability when compared to manual assessments by experienced pathologists. DeepLIIF exhibits excellent intra- and inter-observer agreement and ensures consistency in Ki-67 scoring. Additionally, DeepLIIF significantly reduces analysis time, making it a valuable tool for high-throughput clinical settings.Conclusion: This study showcases the potential of open-source/open-access user-friendly deep learning platforms, such as DeepLIIF, for the quantification of Ki-67 in neuroendocrine tumors. The analytical validation presented here establishes the reliability and robustness of this innovative method, paving the way for its integration into routine clinical practice. Accurate and efficient Ki-67 assessment is paramount for risk stratification and treatment decisions in NETs and AI offers a promising solution for enhancing diagnostic accuracy and patient care in the field of neuroendocrine oncology.Keywords: digital image analysis, histopathology, Ki-67 proliferation index, neuroendocrine tumors, machine learninghttps://www.dovepress.com/use-of-novel-open-source-deep-learning-platform-for-quantification-of--peer-reviewed-fulltext-article-IJGMdigital image analysishistopathologyki-67 proliferation indexneuroendocrine tumorsmachine learning |
| spellingShingle | Zehra T Shams M Ali R Jafri A Khurshid A Erum H Naqvi H Abdul-Ghafar J Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation International Journal of General Medicine digital image analysis histopathology ki-67 proliferation index neuroendocrine tumors machine learning |
| title | Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation |
| title_full | Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation |
| title_fullStr | Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation |
| title_full_unstemmed | Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation |
| title_short | Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation |
| title_sort | use of novel open source deep learning platform for quantification of ki 67 in neuroendocrine tumors ndash analytical validation |
| topic | digital image analysis histopathology ki-67 proliferation index neuroendocrine tumors machine learning |
| url | https://www.dovepress.com/use-of-novel-open-source-deep-learning-platform-for-quantification-of--peer-reviewed-fulltext-article-IJGM |
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