Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review (2303.18005v2)

Published 31 Mar 2023 in eess.IV, cs.AI, and cs.LG

Abstract: Purpose - To characterise and assess the quality of published research evaluating AI methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer. The risk of bias was assessed using PROBAST. Information about each model of interest was tabulated and summary statistics were reported. PRISMA 2020 reporting guidelines were followed. Results - 1573 records were identified, of which 45 were eligible for inclusion. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. Models were developed using 1-1375 slides from 1-776 ovarian cancer patients. Model outcomes included treatment response (11/80), malignancy status (10/80), stain quantity (9/80), and histological subtype (7/80). All models were found to be at high or unclear risk of bias overall, with most research having a high risk of bias in the analysis and a lack of clarity regarding participants and predictors in the study. Research frequently suffered from insufficient reporting and limited validation using small sample sizes. Conclusion - Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the associated models have been demonstrated to be ready for real-world implementation. Key aspects to help ensure clinical translation include more transparent and comprehensive reporting of data provenance and modelling approaches, as well as improved quantitative performance evaluation using cross-validation and external validations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (72)
  1. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71, 2021.
  2. Ovarian cancer population screening and mortality after long-term follow-up in the uk collaborative trial of ovarian cancer screening (ukctocs): a randomised controlled trial. The Lancet, 397, 2021.
  3. A systematic review of symptoms for the diagnosis of ovarian cancer. American Journal of Preventive Medicine, 50, 2016.
  4. Cancer of the ovary, fallopian tube, and peritoneum: 2021 update. International Journal of Gynecology and Obstetrics, 155, 2021.
  5. Ovarian carcinoma subtypes are different diseases: Implications for biomarker studies. PLoS Medicine, 5, 2008.
  6. Jaime Prat. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. International Journal of Gynecology and Obstetrics, 124:1–5, 2014.
  7. Agreement for tumor grade of ovarian carcinoma: Analysis of archival tissues from the surveillance, epidemiology, and end results residual tissue repository. Cancer Causes and Control, 24, 2013.
  8. Ovarian carcinoma histotype determination is highly reproducible, and is improved through the use of immunohistochemistry. Histopathology, 64, 2014.
  9. Inter-pathologist and pathology report agreement for ovarian tumor characteristics in the nurses’ health studies. Gynecologic Oncology, 150, 2018.
  10. Access to pathology and laboratory medicine services: a crucial gap. The Lancet, 391, 2018.
  11. Royal College of Pathologists. Meeting pathology demand: Histopathology workforce census. 2018.
  12. Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics. Histopathology, 73, 2018.
  13. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Seminars in Cancer Biology, 84, 2022.
  14. Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Archives of Pathology & Laboratory Medicine, 2022.
  15. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Medical Research Methodology, 22:101, 12 2022.
  16. Probast: A tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170, 2019.
  17. Automatic segmentation for ovarian cancer immunohistochemical image based on chroma criterion. In 2010 2nd International Conference on Advanced Computer Control, volume 2, pages 147–150. IEEE, 2010.
  18. Automatic segmentation for ovarian cancer immunohistochemical image based on yuv color space. In 2010 International Conference on Biomedical Engineering and Computer Science, pages 1–4. IEEE, 2010.
  19. Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides. Signal Processing, 90, 2010.
  20. Local morphologic scale: application to segmenting tumor infiltrating lymphocytes in ovarian cancer tmas. In Medical Imaging 2011: Image Processing, volume 7962, pages 827–840. SPIE, 2011.
  21. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. IEEE Transactions on Biomedical Engineering, 59, 2012.
  22. Biological interpretation of morphological patterns in histopathological whole-slide images. In Proceedings of the ACM conference on bioinformatics, computational biology and biomedicine, pages 218–225, 2012.
  23. Exploration of genomic, proteomic, and histopathological image data integration methods for clinical prediction. In 2013 IEEE China Summit and International Conference on Signal and Information Processing, pages 259–263. IEEE, 2013.
  24. Automatic diagnosis of ovarian carcinomas via sparse multiresolution tissue representation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18, pages 629–636. Springer, 2015.
  25. Clinically-inspired automatic classification of ovarian carcinoma subtypes. Journal of pathology informatics, 7(1):28, 2016.
  26. A structured latent model for ovarian carcinoma subtyping from histopathology slides. Medical image analysis, 39:194–205, 2017.
  27. Automated wholeslide analysis of multiplex-brightfield ihc images for cancer cells and carcinoma-associated fibroblasts. In Medical Imaging 2017: Digital Pathology, volume 10140, pages 41–46. SPIE, 2017.
  28. Classification of tumor epithelium and stroma by exploiting image features learned by deep convolutional neural networks. Annals of Biomedical Engineering, 46, 2018.
  29. Microenvironmental niche divergence shapes brca1-dysregulated ovarian cancer morphological plasticity. Nature Communications, 9, 2018.
  30. Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence. npj Digital Medicine, 3, 2020.
  31. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks. Journal of Pathology, 252, 2020.
  32. Cross-domain knowledge transfer for prediction of chemosensitivity in ovarian cancer patients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 928–929, 2020.
  33. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Medicine, 18, 2020.
  34. Integration of computer-aided automated analysis algorithms in the development and validation of immunohistochemistry biomarkers in ovarian cancer. Journal of Clinical Pathology, 74, 2021.
  35. Multi-modal evolutionary deep learning model for ovarian cancer diagnosis. Symmetry, 13, 2021.
  36. Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer. Journal of Pathology Informatics, 12, 2021.
  37. Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone. Scientific Reports, 11, 2021.
  38. Prognostic image-based quantification of cd8cd103 t cell subsets in high-grade serous ovarian cancer patients. OncoImmunology, 10, 2021.
  39. Style transfer strategy for developing a generalizable deep learning application in digital pathology. Computer Methods and Programs in Biomedicine, 198:105815, 2021.
  40. Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecologic Oncology, 163, 2021.
  41. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nature Cancer, 3:723–733, 6 2022.
  42. The utility of color normalization for ai-based diagnosis of hematoxylin and eosin-stained pathology images. Journal of Pathology, 256, 2022.
  43. Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology. Frontiers in Oncology, 12, 10 2022.
  44. Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images. Modern Pathology, 35:1983–1990, 12 2022.
  45. Predicting molecular traits from tissue morphology through self-interactive multi-instance learning. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II, pages 130–139. Springer, 2022.
  46. Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma. Frontiers in Medicine, 9, 9 2022.
  47. Prediction and classification of ovarian cancer using enhanced deep convolutional neural network. International Journal of Engineering Trends and Technology, 70:310–318, 3 2022.
  48. Convolutional neural networks in the ovarian cancer detection. In Computational Intelligence and Mathematics for Tackling Complex Problems 2, pages 55–64. Springer, 2022.
  49. Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. Cell Reports Medicine, 3(12):100872, 2022.
  50. Eocsa: Predicting prognosis of epithelial ovarian cancer with whole slide histopathological images. Expert Systems with Applications, 206, 11 2022.
  51. How to learn with intentional mistakes: Noisyensembles to overcome poor tissue quality for deep learning in computational pathology. Frontiers in Medicine, 9, 2022.
  52. Deep-learning to predict brca mutation and survival from digital h&e slides of epithelial ovarian cancer. International Journal of Molecular Sciences, 23, 10 2022.
  53. Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach. In Medical Imaging 2022: Digital and Computational Pathology, volume 12039, pages 20–24. SPIE, 2022.
  54. A weakly supervised deep learning method for guiding ovarian cancer treatment and identifying an effective biomarker. Cancers, 14(7):1651, 2022(a).
  55. Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. Computerized Medical Imaging and Graphics, 99, 7 2022(b).
  56. O3c glass-class: A machine-learning framework for prognostic prediction of ovarian clear-cell carcinoma. Bioinformatics and Biology Insights, 16:11779322221134312, 2022.
  57. Deep interactive learning-based ovarian cancer segmentation of h&e-stained whole slide images to study morphological patterns of brca mutation. Journal of Pathology Informatics, 14:100160, 2023.
  58. A deep learning-based system trained for gastrointestinal stromal tumor screening can identify multiple types of soft tissue tumors. The American Journal of Pathology, 2023.
  59. A hybridized channel selection approach with deep convolutional neural network for effective ovarian cancer prediction in periodic acid-schiff-stained images. Concurrency and Computation: Practice and Experience, 35(5):e7568, 2023.
  60. Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations. Computerized Medical Imaging and Graphics, 107:102233, 2023.
  61. Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network. Frontiers in Genetics, 13, 2023.
  62. The cancer genome atlas ovarian cancer collection (tcga-ov) (version 4) [data set]. The Cancer Imaging Archive, 2016.
  63. Diagnosis of ovarian carcinoma cell type is highly reproducible: A transcanadian study. American Journal of Surgical Pathology, 34, 2010.
  64. Attention-based deep multiple instance learning. In International conference on machine learning, pages 2127–2136. PMLR, 2018.
  65. Data-efficient and weakly supervised computational pathology on whole-slide images. Nature Biomedical Engineering, 5, 2021.
  66. Transformers in medical image analysis: A review. Intelligent Medicine, 2022.
  67. A systematic review on the use of artificial intelligence in gynecologic imaging–background, state of the art, and future directions. Gynecologic Oncology, 2022.
  68. Application of artificial intelligence in the diagnosis and prognostic prediction of ovarian cancer. Computers in Biology and Medicine, page 105608, 2022.
  69. Machine learning applications in gynecological cancer: a critical review. Critical Reviews in Oncology/Hematology, page 103808, 2022.
  70. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine, 53:101662, 2022.
  71. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): the tripod statement. Annals of internal medicine, 162(1):55–63, 2015.
  72. Biased data, biased ai: deep networks predict the acquisition site of tcga images. Diagnostic Pathology, 18(1):1–12, 2023.
Citations (20)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now