665
Views
0
CrossRef citations to date
0
Altmetric
Review

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

, , , , &
Pages 363-377 | Received 07 Dec 2023, Accepted 19 Apr 2024, Published online: 09 May 2024

References

  • Mobadersany P, Yousefi S, Amgad M, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci, USA. 2018;115(13):E2970–e2979. doi: 10.1073/pnas.1717139115
  • Boiesen P, Bendahl PO, Anagnostaki L, et al. Histologic grading in breast cancer–reproducibility between seven pathologic departments. South Sweden breast cancer group. Acta Oncol. 2000;39(1):41–45. doi: 10.1080/028418600430950
  • Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11) doi: 10.3390/jcm9113697
  • Ibrahim A, Gamble P, Jaroensri R, et al. Artificial intelligence in digital breast pathology: techniques and applications. Breast. 2020;49:267–273. doi: 10.1016/j.breast.2019.12.007
  • Mazo C, Aura C, Rahman A, et al. Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review. J Pers Med. 2022;12(9) doi: 10.3390/jpm12091496
  • Fernandez G, Prastawa M, Madduri AS, et al. Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years. Breast Cancer Res. 2022;24(1):93. doi: 10.1186/s13058-022-01592-2
  • Arnold M, Sierra MS, Laversanne M, et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut. 2017;66(4):683. doi: 10.1136/gutjnl-2015-310912
  • Polyak K. Heterogeneity in breast cancer. J Clin Invest. 2011;121(10):3786–3788. doi: 10.1172/JCI60534
  • Colleoni M, Sun Z, Price KN, et al. Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the International breast cancer study group trials I to V. J Clin Oncol. 2016;34(9):927–935. doi: 10.1200/JCO.2015.62.3504
  • Bhattacharyya GS, Doval DC, Desai CJ, et al. Overview of breast cancer and implications of overtreatment of early-stage breast cancer: an Indian perspective. JCO global oncology. 2020;6(6):789–798. doi: 10.1200/GO.20.00033
  • Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest. 2021;101(4):412–422. doi: 10.1038/s41374-020-00514-0
  • Ahmad Z, Rahim S, Zubair M, et al. Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagn Pathol. 2021;16(1):24. doi: 10.1186/s13000-021-01085-4
  • Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703–715. doi: 10.1038/s41571-019-0252-y
  • Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138–52160. doi: 10.1109/ACCESS.2018.2870052
  • Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021;31(3):685–695. doi: 10.1007/s12525-021-00475-2
  • Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):1.
  • Zhu J, Liu M, Li X. Progress on deep learning in digital pathology of breast cancer: a narrative review. Gland Surg. 2022;11(4):751–766. doi: 10.21037/gs-22-11
  • Baxi V, Edwards R, Montalto M, et al. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2022;35(1):23–32. doi: 10.1038/s41379-021-00919-2
  • Iqbal MJ, Javed Z, Sadia H, et al. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int. 2021;21(1):270. doi: 10.1186/s12935-021-01981-1
  • Betmouni S. Diagnostic digital pathology implementation: learning from the digital health experience. Digit Health. 2021;7:20552076211020240. doi: 10.1177/20552076211020240
  • Rahman A, Jahangir C, Lynch SM, et al. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn. 2020;20(10):1027–1037. doi: 10.1080/14737159.2020.1770599
  • Pantanowitz L, Sinard JH, Henricks WH, et al. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists pathology and Laboratory Quality Center. Arch Pathol Lab Med. 2013;137(12):1710–1722. doi: 10.5858/arpa.2013-0093-CP
  • Maibach F, Sadozai H, Seyed Jafari SM, et al. Tumor-infiltrating lymphocytes and their prognostic value in cutaneous melanoma. Front Immunol. 2020;11:2105. doi: 10.3389/fimmu.2020.02105
  • Barnes TA, Amir E. HYPE or HOPE: the prognostic value of infiltrating immune cells in cancer. Br j cancer. 2017;117(4):451–460. doi: 10.1038/bjc.2017.220
  • Acs B, Ahmed FS, Gupta S, et al. An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma. Nat Commun. 2019;10(1):5440. doi: 10.1038/s41467-019-13043-2
  • Hendry S, Salgado R, Gevaert T, et al. Assessing tumor infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the International Immuno-Oncology Biomarkers Working Group: part 1: assessing the host immune response, TILs in invasive breast carcinoma and ductal carcinoma in situ, metastatic tumor deposits and areas for further research. Adv Anat Pathol. 2017;24(5):235.
  • Hendry S, Salgado R, Gevaert T, et al. Assessing tumor infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the International Immuno-Oncology Biomarkers Working Group: part 2: TILs in melanoma, gastrointestinal tract carcinomas, non-small cell lung carcinoma and mesothelioma, endometrial and ovarian carcinomas, squamous cell carcinoma of the head and neck, genitourinary carcinomas, and primary brain tumors. Adv Anat Pathol. 2017;24(6):311.
  • Swisher SK, Wu Y, Castaneda CA, et al. Interobserver agreement between pathologists assessing tumor-infiltrating lymphocytes (TILs) in breast cancer using methodology proposed by the international TILs working group. Ann Surg Oncol. 2016;23(7):2242–2248. doi: 10.1245/s10434-016-5173-8
  • Klauschen F. Scoring of tumor-infiltrating lymphocytes: from visual estimation to machine learning. Seminars in Cancer Biology; 2018.
  • Page DB, Broeckx G, Jahangir CA, et al. Spatial analyses of immune cell infiltration in cancer: current methods and future directions: a report of the International Immuno-Oncology Biomarker Working Group on breast cancer. J Pathol. 2023;260(5):514–532. doi: 10.1002/path.6165
  • Thagaard J, Broeckx G, Page DB, et al. Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the International Immuno-Oncology Biomarker Working Group on breast cancer. J Pathol. 2023;260(5):498–513. doi: 10.1002/path.6155
  • Bai Y, Cole K, Martinez-Morilla S, et al. An open-source, automated tumor-infiltrating lymphocyte algorithm for prognosis in triple-negative breast cancer. clin cancer res, 2021. Clin Cancer Res. 2021;27(20):5557–5565. doi: 10.1158/1078-0432.CCR-21-0325
  • Heindl A, Sestak I, Naidoo K, et al. Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer. JNCI: Journal Of The National Cancer Institute. 2018;110(2):166–175. doi: 10.1093/jnci/djx137
  • Thagaard J, Stovgaard ES, Vognsen LG, et al. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers (Basel). 2021;13(12):3050. doi: 10.3390/cancers13123050.
  • Makhlouf S, Wahab N, Toss M, et al. Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence. Br J Cancer. 2023;129(11):1747–1758. doi: 10.1038/s41416-023-02451-3.
  • Selli C, Sims AH. Neoadjuvant therapy for breast cancer as a Model for translational research. Breast Cancer. 2019;13:1178223419829072. doi: 10.1177/1178223419829072
  • Esserman LJ, Berry DA, DeMichele A, et al. Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL—CALGB 150007/150012, ACRIN 6657. J Clin Oncol. 2012;30(26):3242–3249. doi: 10.1200/JCO.2011.39.2779
  • Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384(9938):164–172. doi: 10.1016/S0140-6736(13)62422-8
  • Haque W, Verma V, Hatch S, et al. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Res Treat. 2018;170(3):559–567. doi: 10.1007/s10549-018-4801-3
  • Colleoni M, Viale G, Zahrieh D, et al. Chemotherapy is more effective in patients with breast cancer not expressing steroid hormone receptors: a study of preoperative treatment. Clin Cancer Res. 2004;10(19):6622–6628. doi: 10.1158/1078-0432.CCR-04-0380
  • Lee HJ, Cho SY, Cho EY, et al. Artificial intelligence (AI)–powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC). J Clin Oncol. 2022;40(16_suppl):595–595. doi: 10.1200/JCO.2022.40.16_suppl.595
  • Fanucci KA, Bai Y, Pelekanou V, et al. Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial. npj breast cancer. NPJ Breast Cancer. 2023;9(1):38. doi: 10.1038/s41523-023-00535-0
  • Choi S, Cho SI, Jung W, et al. Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer. NPJ Breast Cancer. 2023;9(1):71. doi: 10.1038/s41523-023-00577-4
  • Loi S, Salgado R, Schmid P, et al. Association between biomarkers and clinical outcomes of pembrolizumab monotherapy in patients with metastatic triple-negative breast cancer: KEYNOTE-086 exploratory analysis. JCO Precision Oncol. 2023;7(7):e2200317. doi: 10.1200/PO.22.00317
  • Park S, Ock C-Y, Kim H, et al. Artificial intelligence–powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non–small-Cell lung cancer. J Clin Oncol. 2022;40(17):1916–1928. doi: 10.1200/JCO.21.02010
  • Li S, Zhang N, Zhang H, et al. Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses. J Big Data. 2023;10(1):132. doi: 10.1186/s40537-023-00809-1
  • Tang L, Zhang Z, Fan J, et al. Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning. Front Pharmacol. 2023;14. 10.3389/fphar.2023.1195864
  • Győrffy B, Hatzis C, Sanft T, et al. Multigene prognostic tests in breast cancer: past, present, future. Breast Cancer Res. 2015;17(1):11. doi: 10.1186/s13058-015-0514-2
  • Guillaud M, Ye Q, Leung S, et al. Large-scale DNA organization is a prognostic marker of breast cancer survival. Med Oncol. 2018;35(1):9. doi: 10.1007/s12032-017-1068-1
  • Liu KYP, Zhu SY, Harrison A, et al. Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma. PLOS ONE. 2021;16(11):e0259529. doi: 10.1371/journal.pone.0259529
  • Enfield KSS, Martin SD, Marshall EA, et al. Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence. J Immunother Cancer. 2019;7(1):13. doi: 10.1186/s40425-018-0488-6
  • MacAulay C, Keyes M, Hayes M, et al. Quantification of large scale DNA organization for predicting prostate cancer recurrence. cytometry part a. Cytometry Part A. 2017;91(12):1164–1174. doi: 10.1002/cyto.a.23287
  • Macaulay C, Guillaud M, Enfield K, et al. P3.09-11 genomic organization at large scales (GOALS) within nuclei and cell sociology for predicting lung cancer outcomes. J Thorac Oncol. 2018;13(10):S952. doi: 10.1016/j.jtho.2018.08.1780
  • Rakha EA, Reis-Filho JS, Baehner F, et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 2010;12(4):207. doi: 10.1186/bcr2607
  • Mahmood T, Arsalan M, Owais M, et al. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. J Clin Med. 2020;9(3):9(3. doi: 10.3390/jcm9030749
  • Veta M, Pluim JPW, van Diest PJ, et al. Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng. 2014;61(5):1400–1411. doi: 10.1109/TBME.2014.2303852
  • Pantanowitz L, Hartman D, Qi Y, et al. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses. Diagn Pathol. 2020;15(1):80. doi: 10.1186/s13000-020-00995-z
  • van Bergeijk SA, Stathonikos N, Ter Hoeve ND, et al. Deep learning supported mitoses counting on whole slide images: a pilot study for validating breast cancer grading in the clinical workflow. J Pathol Inform. 2023;14:100316.
  • Ibrahim A, Jahanifar M, Wahab N, et al. Artificial intelligence-based mitosis scoring in breast cancer: clinical application. Mod Pathol. 2024;37(3):100416. doi: 10.1016/j.modpat.2023.100416
  • Jaroensri R, Wulczyn E, Hegde N, et al. Deep learning models for histologic grading of breast cancer and association with disease prognosis. NPJ Breast Cancer. 2022;8(1):113. doi: 10.1038/s41523-022-00478-y
  • Gisselsson D, Björk J, Höglund M, et al. Abnormal nuclear shape in solid tumors reflects mitotic instability. Am J Pathol. 2001;158(1):199–206. doi: 10.1016/S0002-9440(10)63958-2
  • Whitney J, Corredor G, Janowczyk A, et al. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer. 2018;18(1):610. doi: 10.1186/s12885-018-4448-9
  • Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra113. doi: 10.1126/scitranslmed.3002564
  • Mina L, Soule SE, Badve S, et al. Predicting response to primary chemotherapy: gene expression profiling of paraffin-embedded core biopsy tissue. Breast Cancer Res Treat. 2007;103(2):197–208. doi: 10.1007/s10549-006-9366-x
  • Flanagan MB, Dabbs DJ, Brufsky AM, et al. Histopathologic variables predict oncotype DX™ recurrence score. Mod Pathol. 2008;21(10):1255–1261. doi: 10.1038/modpathol.2008.54
  • Romo-Bucheli D, Janowczyk A, Gilmore H, et al. A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry Part A. 2017;91(6):566–573. doi: 10.1002/cyto.a.23065
  • Romo-Bucheli D, Janowczyk A, Gilmore H, et al. Automated tubule nuclei quantification and correlation with Oncotype DX risk categories in ER+ breast cancer whole slide images. Sci Rep. 2016;6(1):32706. doi: 10.1038/srep32706
  • Lu C, Romo-Bucheli D, Wang X, et al. Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Lab Invest. 2018;98(11):1438–1448. doi: 10.1038/s41374-018-0095-7
  • Dodington DW, Lagree A, Tabbarah S, et al. Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res Treat. 2021;186(2):379–389. doi: 10.1007/s10549-020-06093-4
  • Saednia K, Lagree A, Alera MA, et al. Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Sci Rep. 2022;12(1):9690. doi: 10.1038/s41598-022-13917-4
  • Shen B, Saito A, Ueda A, et al. Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues. J Pathol. 2023;9(3):182–194. doi: 10.1002/cjp2.314.
  • Wang M, Zhao J, Zhang L, et al. Role of tumor microenvironment in tumorigenesis. J Cancer. 2017;8(5):761–773. doi: 10.7150/jca.17648
  • Wu J, Liang C, Chen M, et al. Association between tumor-stroma ratio and prognosis in solid tumor patients: a systematic review and meta-analysis. Oncotarget. 2016;7(42):68954–68965. doi: 10.18632/oncotarget.12135
  • Micke P, Strell C, Mattsson J, et al. The prognostic impact of the tumour stroma fraction: a machine learning-based analysis in 16 human solid tumour types. EBioMedicine. 2021;65:103269. doi: 10.1016/j.ebiom.2021.103269.
  • Yan D, Ju X, Luo B, et al. Tumour stroma ratio is a potential predictor for 5-year disease-free survival in breast cancer. BMC Cancer. 2022;22(1):1082. doi: 10.1186/s12885-022-10183-5
  • Millar EK, Browne LH, Beretov J, et al. Tumour stroma ratio assessment using digital image analysis predicts survival in triple negative and luminal breast cancer. Cancers (Basel). 2020;12(12):3749. doi: 10.3390/cancers12123749
  • Abubakar M, Zhang J, Ahearn TU, et al. Tumor-Associated Stromal Cellular Density as a predictor of recurrence and mortality in breast cancer: results from ethnically diverse study populations. Cancer Epidemiol Biomarkers Prev. 2021;30(7):1397–1407. doi: 10.1158/1055-9965.EPI-21-0055
  • Raskov H, Orhan A, Gaggar S, et al. Cancer-associated fibroblasts and tumor-associated macrophages in cancer and cancer immunotherapy. Front Oncol. 2021;11:668731. doi: 10.3389/fonc.2021.668731
  • Ni Y, Zhou X, Yang J, et al. The role of tumor-stroma interactions in drug resistance within tumor microenvironment. Front Cell Dev Biol. 2021;9:637675. doi: 10.3389/fcell.2021.637675
  • Valkenburg KC, de Groot AE, Pienta KJ. Targeting the tumour stroma to improve cancer therapy. Nat Rev Clin Oncol. 2018;15(6):366–381. doi: 10.1038/s41571-018-0007-1
  • Li F, Yang Y, Wei Y, et al. Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer. NPJ Breast Cancer. 2022;8(1):124. doi: 10.1038/s41523-022-00491-1
  • Li F, Yang Y, Wei Y, et al. Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med. 2021;19(1):348. doi: 10.1186/s12967-021-03020-z
  • Li B, Li F, Liu Z, et al. Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer: A multicenter study. Breast. 2022;66:183–190. doi: 10.1016/j.breast.2022.10.004
  • Xu F, Zhu C, Tang W, et al. Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides. Front Oncol. 2021;11:11. doi: 10.3389/fonc.2021.759007
  • Phan NN, Hsu C-Y, Huang C-C, et al. Prediction of breast cancer recurrence using a deep convolutional neural network without region-of-interest labeling. Front Oncol. 2021;11:734015. doi: 10.3389/fonc.2021.734015
  • Yang J, Ju J, Guo L, et al. Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning. Computat Struct Biotechnol j. 2022;20:333–342.
  • Lazard T, Bataillon G, Naylor P, et al. Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. Cell Reports Med. 2022;3(12):100872. doi: 10.1016/j.xcrm.2022.100872
  • Wahab N, Toss M, Miligy IM, et al. AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer. NPJ Precis Oncol. 2023;7(1):122. doi: 10.1038/s41698-023-00472-y
  • Yao Y, Lv Y, Tong L, et al. ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data. Brief Bioinform. 2022;23(6): doi: 10.1093/bib/bbac448
  • Farahmand S, Fernandez AI, Ahmed FS, et al. Deep learning trained on hematoxylin and eosin tumor region of interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod Pathol. 2022;35(1):44–51. doi: 10.1038/s41379-021-00911-w.
  • Hoang D-T, Ben-Zvi D, Hermida LC, et al. Predicting patient response to cancer therapy via histopathology images. J Clin Oncol. 2022;40(16_suppl):e13561–e13561. doi: 10.1200/JCO.2022.40.16_suppl.e13561
  • Huang Z, Shao W, Han Z, et al. Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. NPJ Precis Oncol. 2023;7(1):14. doi: 10.1038/s41698-023-00352-5.
  • Tizhoosh HR, Pantanowitz L. Artificial intelligence and Digital Pathology: challenges and opportunities. J Pathol Inform. 2018;9(1):38–38. doi: 10.4103/jpi.jpi_53_18
  • Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008. doi: 10.1136/bmjopen-2020-048008
  • Clunie D, Hosseinzadeh D, Wintell M, et al. Digital imaging and communications in medicine whole slide imaging connectathon at digital pathology association pathology visions 2017. J Pathol Inform. 2018;9(1):6. United States. doi: 10.4103/jpi.jpi_1_18.
  • Colling R, Pitman H, Oien K, et al. Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice. J Pathol. 2019;249(2):143–150. doi: 10.1002/path.5310
  • Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res. 2020;9(5):2255–2276. doi: 10.21037/tlcr-20-591
  • Drogt J, Milota M, Vos S, et al. Integrating artificial intelligence in pathology: a qualitative interview study of users’ experiences and expectations. Mod Pathol. 2022;35(11):1540–1550. doi: 10.1038/s41379-022-01123-6
  • Flach RN, Fransen NL, Sonnen AFP, et al. Implementation of artificial intelligence in diagnostic practice as a next step after going digital: the UMC utrecht perspective. Diagn (Basel). 2022;12(5):12(5. doi: 10.3390/diagnostics12051042
  • Van Es SL. Digital pathology: semper ad meliora. Pathology. 2019;51(1):1–10. doi: 10.1016/j.pathol.2018.10.011
  • Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut. 2021;70(6):1183. doi: 10.1136/gutjnl-2020-322880
  • Hanna MG, Hanna MH. Current applications and challenges of artificial intelligence in pathology. Human Pathol Rep. 2022;27:300596. doi: 10.1016/j.hpr.2022.300596
  • Coccia M. Deep learning technology for improving cancer care in society: new directions in cancer imaging driven by artificial intelligence. Technol Soc. 2020;60:101198. doi: 10.1016/j.techsoc.2019.101198
  • Viswanathan VS, Toro P, Corredor G, et al. The state of the art for artificial intelligence in lung digital pathology. J Pathol. 2022;257(4):413–429. doi: 10.1002/path.5966
  • Cheng JY, Abel JT, Balis UGJ, et al. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 2021;191(10):1684–1692. doi: 10.1016/j.ajpath.2020.10.018
  • Go H. Digital Pathology and Artificial Intelligence Applications in Pathology. Brain Tumor Res Treat. 2022;10(2):76–82. doi: 10.14791/btrt.2021.0032
  • Stephens K. FDA authorizes prostate AI software. ProQuest: AXIS Imaging News; 2021.
  • Lu MY, Chen TY, Williamson DFK, et al. AI-based pathology predicts origins for cancers of unknown primary. Nature. 2021;594(7861):106–110. doi: 10.1038/s41586-021-03512-4