176
Views
0
CrossRef citations to date
0
Altmetric
General Medicine

Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors – Analytical Validation

, , , , , , & show all
Pages 5665-5673 | Received 18 Oct 2023, Accepted 22 Nov 2023, Published online: 04 Dec 2023

References

  • Vesterinen T, Säilä J, Blom S, Pennanen M, Leijon H, Arola J. Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning. APMIS. 2022;130(1):11–20. PMID: 34741788; PMCID: PMC9299468. doi:10.1111/apm.13190
  • Wang HY, Li ZW, Sun W, et al.. Automated quantification of Ki-67 index associates with pathologic grade of pulmonary neuroendocrine tumors. Chin Med J. 2019;132(5):551–561. PMID: 30807354; PMCID: PMC6416093. doi:10.1097/CM9.0000000000000109
  • Satturwar SP, Pantanowitz JL, Manko CD, et al. Ki-67 proliferation index in neuroendocrine tumors: can augmented reality microscopy with image analysis improve scoring?. Cancer Cytopathol. 2020;128(8):535–544. doi:10.1002/cncy.22272
  • Liu SZ, Staats PN, Goicochea L, et al. Automated quantification of Ki-67 proliferative index of excised neuroendocrine tumors of the lung. Diagn Pathol. 2014;9(1):174. doi:10.1186/s13000-014-0174-z
  • Cui M, Zhang DY. Artificial intelligence and computational pathology. Laboratory Investigation. 2021;101(4):412–422. doi:10.1038/s41374-020-00514-0
  • Basile ML, Kuga FS, Del Carlo Bernardi F. Comparation of the quantification of the proliferative index KI-67 between eyeball and semi- automated digital analysis in gastro-intestinal neuroendocrine tumors. Surg Exp Pathol. 2019;2(21). doi:10.1186/s42047-019-
  • Cives M, Strosberg JR. Gastroenteropancreatic neuroendocrine tumors. Ca a Cancer J Clinicians. 2018;68(6):471–487. doi:10.3322/caac.21493
  • Uxa S, Castillo-Binder P, Kohler R, Stangner K, Müller GA, England K. Ki-67 gene expression. Cell Death Differ. 2021;28(12):3357–3370. doi:10.1038/s41418-021-00823-x
  • Beck A, Glass B, Elliott H, et al. An empirical framework for validating artificial intelligence–derived PD-L1 positivity predictions applied to urothelial carcinoma. J Immunother Cancer. 2019;7(1):730.
  • Shaikh A, Jamal N, Shabbir A, Arif B, Ferozuddin N. Use of artificial intelligence in health diagnostics-a validation study on chorionic villi. Pk J Pathol. 2021;32(4):147–151.
  • Guilmette JM, Nose V. Neoplasms of the neuroendocrine pancreas: an update in the classification, definition and molecular genetic advances. Adv Anat Pathol. 2019;26(1):13–30. doi:10.1097/PAP.0000000000000201
  • Grosse C, Noack P, Silye R. Accuracy of grading pancreatic neuroendocrine neoplasms with Ki-67 index in fine-needle aspiration cellblock material. Cytopathology. 2019;30(2):187–193. doi:10.1111/cyt.12643
  • Abi-Raad R, Lavik JP, Barbieri AL, Zhang X, Adeniran AJ, Cai G. Grading pancreatic neuroendocrine tumors by Ki-67 index evaluated on fine-needle aspiration cell block material. Am J Clin Pathol. 2020;153(1):74–81. doi:10.1093/ajcp/aqz110
  • Volynskaya Z, Mete O, Pakbaz S, Al-Ghamdi D, Asa SL. Ki-67 quantitative interpretation: insights using image analysis. J Pathol Inform. 2019;10(1):8. doi:10.4103/jpi.jpi_76_18
  • Chen PC, Gadepalli K, Macdonald R. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med. 2019;25(9):1453–1457. doi:10.1038/s41591-019-0539-7
  • Hacking SM, Sajjan S, Lee L, et al. Potential pitfalls in diagnostic digital image analysis: experience with Ki-67 and PHH3 in gastrointestinal neuroendocrine tumors. Pathol Res Pract. 2020;216(3):152753. doi:10.1016/j.prp.2019.152753
  • Dogukan FM, Yilmaz Ozguven B, Dogukan R, Kabukcuoglu F. Comparison of monitor-image and printout-image methods in Ki-67 scoring of gastroenteropancreatic neuroendocrine tumors. Endocr Pathol. 2019;30(1):17–23. doi:10.1007/s12022-018-9554-3
  • Tschuchnig ME, Oostingh GJ, Gadermayr M. Generative adversarial networks in digital pathology: a survey on trends and future potential. Patterns. 2020;1(6):100089. doi:10.1016/j.patter.2020.100089
  • Tang LH, Gonen M, Hedvat C, Modlin IM, Klimstra DS. Objective quantification of the Ki-67 proliferative index in neuroendocrine tumors of the gastroenteropancreatic system: a comparison of digital image analysis with manual methods. Am J Surg Pathol. 2012;36(12):1761–1770. PMID: 23026928. doi:10.1097/PAS.0b013e318263207c
  • Lea D, Gudlaugsson EG, Skaland I, Lillesand M, Søreide K, Søreide JA. Digital image analysis of the proliferation markers Ki-67 and Phosphohistone H3 in gastroenteropancreatic neuroendocrine neoplasms: accuracy of grading compared with routine manual hot spot evaluation of the Ki-67 index. Appl Immunohistochem Mol Morphol. 2021;29(7):499–505. PMID: 33758143; PMCID: PMC8354564. doi:10.1097/PAI.0000000000000934
  • Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11):3697. doi:10.3390/jcm9113697
  • Fulawka L, Blaszczyk J, Tabakov M, Halon A. Assessment of ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ). Sci Rep. 2022;12(1):3166. doi:10.1038/s41598-022-06555-3
  • Feng M, Deng Y, Yang L, et al. Automated quantitative analysis of ki-67 staining and he images recognition and registration based on whole tissue sections in breast carcinoma. Diagn Pathol. 2020;15(1):65. doi:10.1186/s13000-020-00957-5
  • Niazi MK, Tavolara TE, Arole V, Hartman DJ, Pantanowitz L, Gurcan MN. Identifying tumor in pancreatic neuroendocrine neoplasms from Ki-67 images using transfer learning. PloS one. 2018;13(4):e0195621. doi:10.1371/journal.pone.0195621
  • Boukhar SA, Gosse MD, Bellizzi AM, Rajan KDA. Ki-67 proliferation index assessment in gastroenteropancreatic neuroendocrine tumors by digital image analysis with stringent case and hotspot level concordance requirements. Am J Clin Pathol. 2021;156(4):607–619. doi:10.1093/AJCP/AQAA275
  • Owens R, Gilmore E, Bingham V, et al. Comparison of different anti-Ki-67 antibody clones and hotspot sizes for assessing proliferative index and grading in pancreatic neuroendocrine tumours using manual and image analysis. Histopathology. 2020;77(4):646–658. doi:10.1111/his.14200
  • Trikalinos NA, Chatterjee D, Lee J, et al. Accuracy of grading in pancreatic neuroendocrine neoplasms and effect on survival estimates: an institutional experience. Ann Surg Oncol. 2020;27(9):3542–3550. doi:10.1245/s10434-020-08377-x
  • Zehra T, Anjum S, Mahmood T, et al.. A novel deep learning-based mitosis recognition approach and dataset for uterine leiomyosarcoma histopathology. Cancers. 2022;14(15):3785. doi:10.3390/cancers14153785
  • Zehra T, Parwani A, Abdul-Ghafar J, Ahmad Z. A suggested way forward for adoption of AI-Enabled digital pathology in low resource organizations in the developing world. Diagn Pathol. 2023;18(1):1–6. doi:10.1186/s13000-023-01352-6
  • Zehra T, Shams M, Ahmad Z, Chundriger Q, Ahmed A, Jaffar N. Ki-67 quantification in breast cancer by digital imaging ai software and its concordance with manual method. JCPSP. 2023;33(5):544–547.
  • Zehra T, Jaffar N, Shams M, et al.. Use of a novel deep learning open-source model for quantification of Ki-67 in breast cancer patients in Pakistan: a comparative study between the manual and automated methods. Diagnostics. 2023;13(19):3105. doi:10.3390/diagnostics13193105
  • Ghahremani P, Marino J, Dodds R, Nadeem S. Deepliif: An Online Platform for Quantification of Clinical Pathology Slides. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2022:21399–21405.
  • Ghahremani P, Marino J, Hernandez-Prera J, et al.. An AI-ready multiplex staining dataset for reproducible and accurate characterization of tumor immune microenvironment. ArXiv Preprint arXiv. 2023;2305:16465v1. PMID: 37292462; PMCID: PMC10246071.