Artificial intelligence and frozen section histopathology: A systematic review.
artificial intelligence
cancer
computer vision
frozen section
Journal
Journal of cutaneous pathology
ISSN: 1600-0560
Titre abrégé: J Cutan Pathol
Pays: United States
ID NLM: 0425124
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
revised:
14
05
2023
received:
26
08
2022
accepted:
29
05
2023
medline:
14
8
2023
pubmed:
3
7
2023
entrez:
3
7
2023
Statut:
ppublish
Résumé
Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin-fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology.
Types de publication
Systematic Review
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
852-859Informations de copyright
© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-1930. doi:10.1161/CIRCULATIONAHA.115.001593
Chen Y, Anderson KR, Xu J, Goldsmith JD, Heher YK. Frozen-section checklist implementation improves quality and patient safety. Am J Clin Pathol. 2019;151(6):607-612. doi:10.1093/ajcp/aqz009
Davis DA, Pellowski DM, Hanke CW. Preparation of frozen sections. Dermatol Surg. 2004;30(12p1):1479-1485. doi:10.1111/j.1524-4725.2004.30506.x
Desciak EB, Maloney ME. Artifacts in frozen section preparation. Dermatol Surg. 2000;26(5):500-504. doi:10.1046/j.1524-4725.2000.99246.x
van Zon MCM, van der Waa JD, Veta M, Krekels GAM. Whole-slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma. Exp Dermatol. 2021;30(5):733-738. doi:10.1111/exd.14306
Murphree DH, Puri P, Shamim H, et al. Deep learning for dermatologists: Part I. Fundamental concepts. J Am Acad Dermatol. 2020;87:1343-1351. doi:10.1016/j.jaad.2020.05.056
Krafft C, Popp J. The many facets of Raman spectroscopy for biomedical analysis. Anal Bioanal Chem. 2015;407(3):699-717. doi:10.1007/s00216-014-8311-9
Sohn GK, Sohn JH, Yeh J, Chen Y, Brian Jiang SI. A deep learning algorithm to detect the presence of basal cell carcinoma on Mohs micrographic surgery frozen sections. J Am Acad Dermatol. 2021;84(5):1437-1438. doi:10.1016/j.jaad.2020.06.080
Campanella G, Nehal KS, Lee EH, et al. A deep learning algorithm with high sensitivity for the detection of basal cell carcinoma in Mohs surgery frozen sections. J Am Acad Dermatol. 2021;85:1285-1286. doi:10.1016/j.jaad.2020.09.012
Valkonen M, Hognas G, Bova GS, Ruusuvuori P. Generalized fixation invariant nuclei detection through domain adaptation based deep learning. IEEE J Biomed Health Inform. 2021;25(5):1747-1757. doi:10.1109/JBHI.2020.3039414
Durkee MS, Abraham R, Ai J, Veselits M, Clark MR, Giger ML. Quantifying the effects of biopsy fixation and staining panel design on automatic instance segmentation of immune cells in human lupus nephritis. J Biomed Opt. 2021;26(2):022910. doi:10.1117/1.JBO.26.2.022910
Pérez-Sanz F, Riquelme-Pérez M, Martínez-Barba E, et al. Efficiency of machine learning algorithms for the determination of macrovesicular steatosis in frozen sections stained with sudan to evaluate the quality of the graft in liver transplantation. Sensors. 2021;21(6):1993. doi:10.3390/s21061993
Inglis A, Cruz L, Roe DL, Stanley HE, Rosene DL, Urbanc B. Automated identification of neurons and their locations. J Microsc. 2008;230(Pt 3):339-352. doi:10.1111/j.1365-2818.2008.01992.x
Sun L, Marsh JN, Matlock MK, et al. Deep learning quantification of percent steatosis in donor liver biopsy frozen sections. EBioMedicine. 2020;60:103029. doi:10.1016/j.ebiom.2020.103029
Marsh JN, Matlock MK, Kudose S, et al. Deep learning global glomerulosclerosis in transplant kidney frozen sections. IEEE Trans Med Imaging. 2018;37(12):2718-2728. doi:10.1109/TMI.2018.2851150
Marsh JN, Liu TC, Wilson PC, Swamidass SJ, Gaut JP. Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy specimens. JAMA Netw Open. 2021;4(1):e2030939. doi:10.1001/jamanetworkopen.2020.30939
Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559-1567. doi:10.1038/s41591-018-0177-5
Kalra S, Tizhoosh HR, Shah S, et al. Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence. NPJ Digit Med. 2020;3(1):1-15. doi:10.1038/s41746-020-0238-2
Kim YG, Kim S, Cho CE, et al. Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections. Sci Rep. 2020;10(1):21899. doi:10.1038/s41598-020-78129-0
Kim YG, Song IH, Lee H, et al. Challenge for diagnostic assessment of deep learning algorithm for metastases classification in sentinel lymph nodes on frozen tissue section digital slides in women with breast cancer. Cancer Res Treat. 2020;52(4):1103-1111. doi:10.4143/crt.2020.337
Mi W, Li J, Guo Y, et al. Deep learning-based multi-class classification of breast digital pathology images. Cancer Manag Res. 2021;13:4605-4617. doi:10.2147/CMAR.S312608
Li Y, Chen P, Li Z, Su H, Yang L, Zhong D. Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning. Artif Intell Med. 2020;108:101918. doi:10.1016/j.artmed.2020.101918
Chen P, Liang Y, Shi X, Yang L, Gader P. Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion. Neurocomputing. 2021;453:312-325. doi:10.1016/j.neucom.2020.04.153
Sitnik D, Aralica G, Hadžija M, et al. A dataset and a methodology for intraoperative computer-aided diagnosis of a metastatic colon cancer in a liver. Biomed Signal Process Control. 2021;66:102402. doi:10.1016/j.bspc.2020.102402
ImageNet. Accessed August 24, 2022. https://www.image-net.org/
CIFAR. Accessed January 3, 2023. https://cifar.ca/
CAMELYON16 - Grand Challenge. Accessed August 24, 2022. https://camelyon16.grand-challenge.org/Data
Wu CJ, Brooks D, Chen K, et al. Machine learning at Facebook: understanding inference at the edge. In: 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA). 2019:331-344. doi:10.1109/HPCA.2019.00048
Sarmadi S, Izadi-Mood N, Sanii S, Motevalli D. Inter-observer variability in the histologic criteria of diagnosis of hydatidiform moles. Malays J Pathol. 2019;41(1):15-24.
Larghi A, Fornelli A, Lega S, et al. Concordance, intra- and inter-observer agreements between light microscopy and whole slide imaging for samples acquired by EUS in pancreatic solid lesions. Dig Liver Dis. 2019;51(11):1574-1579. doi:10.1016/j.dld.2019.04.019
Rojo MG, García GB, Mateos CP, García JG, Vicente MC. Critical comparison of 31 commercially available digital slide systems in pathology. Int J Surg Pathol. 2006;14(4):285-305. doi:10.1177/1066896906292274
Wang F, Oh TW, Vergara-Niedermayr C, Kurc T, Saltz J. Managing and querying whole slide images. Proc SPIE. 2012;8319:83190J.
Mak AS, Poon AM, Leung CY, Kwan KH, Wong TT, Tung MK. Audit of basal cell carcinoma in Princess Margaret Hospital, Hong Kong: usefulness of frozen section examination in surgical treatment. Scand J Plast Reconstr Surg Hand Surg. 1995;29(2):149-152. doi:10.3109/02844319509034331