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Article

Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine

  • Authors:
    • Boan Lai
    • Jianjiang Fu
    • Qingxin Zhang
    • Nan Deng
    • Qingping Jiang
    • Juan Peng
  • View Affiliations / Copyright

    Affiliations: Department of Pathology, Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China, Department of Urology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510150, P.R. China
  • Article Number: 107
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    Published online on: August 1, 2023
       https://doi.org/10.3892/ijo.2023.5555
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Abstract

Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combination of cancer pathology and artificial intelligence (AI) can meet this requirement. In the present review, the past, present and emerging integrations of AI into cancer pathology were comprehensively reviewed, which were divided into four main groups to highlight the roles of AI‑integrated cancer pathology in precision medicine. Furthermore, the unsolved problems and future challenges in AI‑integrated cancer pathology were also discussed. It was found that, although AI‑integrated cancer pathology could enable the amalgamation of complex morphological phenotypes with the multi‑omics datasets that drove precision medicine, synergies of cancer pathology with other medical tools could be more promising for the clinic when making an accurate and rapid decision in personalized treatments for patients. It was hypothesized by the authors that exploring the potential advantages of the multimodal integration of cancer pathology, imaging‑omics, protein‑omics and other‑omics, as well as clinical data to decide upon appropriate management and improve patient outcomes may be the most challenging issue of cancer precision medicine in the future.
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1 

LeCun Y, Bengio Y and Hinton G: Deep learning. Nature. 521:436–444. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Kriegeskorte N and Golan T: Neural network models and deep learning. Curr Biol. 29:R231–R236. 2019. View Article : Google Scholar : PubMed/NCBI

3 

Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A and Balis UGJ: Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol. 16:669–685. 2020. View Article : Google Scholar : PubMed/NCBI

4 

Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y: Generative Adversarial Networks. 10–Jun;2014.doi: 10.48550/arXiv.1406.2661.

5 

Fanous MJ and Popescu G: GANscan: Continuous scanning microscopy using deep learning deblurring. Light Sci Appl. 11:2652022. View Article : Google Scholar : PubMed/NCBI

6 

Barker J, Hoogi A, Depeursinge A and Rubin DL: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med Image Anal. 30:60–71. 2016. View Article : Google Scholar : PubMed/NCBI

7 

Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS and Fuchs TJ: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 25:1301–1309. 2019. View Article : Google Scholar : PubMed/NCBI

8 

Ciresan D, Giusti A, Gambardella L and Schmidhuber J: Deep neural net-works segment neuronal membranes in electron microscopy images. NIPS. 2852–2860. 2012.

9 

Shelhamer E, Long J and Darrell T: Fully Convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 39:640–651. 2017. View Article : Google Scholar : PubMed/NCBI

10 

Signaevsky M, Prastawa M, Farrell K, Tabish N, Baldwin E, Han N, Iida MA, Koll J, Bryce C, Purohit D, et al: Artificial intelligence in neuropathology: Deep learning-based assessment of tauopathy. Lab Invest. 99:1019–1029. 2019. View Article : Google Scholar : PubMed/NCBI

11 

Yi F, Yang L, Wang S, Guo L, Huang C, Xie Y and Xiao G: Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics. 19:642018. View Article : Google Scholar : PubMed/NCBI

12 

Ronneberger O, Fischer P and Brox T: U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer Assisted Intervention (MICCAI). Springer, LNCS; pp. 9351pp. 234–241. 2015

13 

Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Böhm A, Deubner J, Jäckel Z, Seiwald K, et al: U-Net: Deep learning for cell counting, detection, and morphometry. Nat Methods. 16:67–70. 2019. View Article : Google Scholar : PubMed/NCBI

14 

Lee S, Zhao Y, Masoud M and Belkasim S: Quantitative spatial analysis on whole slide images using U-net. Computational Biol Bioinform. 8:90–96. 2020. View Article : Google Scholar

15 

Oskal KRJ, Risdal M, Janssen EAM, Undersrud ES and Gulsrud TO: A U-net based approach to epidermal tissue segmentation in whole slide histopathological images. SN Applied Sciences. 1:6722019. View Article : Google Scholar

16 

Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT and Kather JN: Deep learning in cancer pathology: A new generation of clinical biomarkers. Br J Cancer. 124:686–696. 2021. View Article : Google Scholar : PubMed/NCBI

17 

Woerl AC, Eckstein M, Geiger J, Wagner DC, Daher T, Stenzel P, Fernandez A, Hartmann A, Wand M, Roth W, et al: Deep learning predicts molecular subtype of muscle-invasive bladder cancer from conventional Histopathological Slides. Eur Urol. 78:256–264. 2020. View Article : Google Scholar : PubMed/NCBI

18 

Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, et al: Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol. 256:50–60. 2022. View Article : Google Scholar : PubMed/NCBI

19 

Choi H and Na KJ: A Risk stratification model for lung cancer based on gene coexpression network and deep learning. Biomed Res Int. 16:29142802018.PubMed/NCBI

20 

Choi Y, Qu J, Wu S, Hao Y, Zhang J, Ning J, Yang X, Lofaro L, Pankratz DG, Babiarz J, et al: Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics. 13:1512020. View Article : Google Scholar : PubMed/NCBI

21 

Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, et al: Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 23:181–193. 2018. View Article : Google Scholar : PubMed/NCBI

22 

Rakaee M, Adib E, Ricciuti B, Sholl LM, Shi W, Alessi JV, Cortellini A, Fulgenzi CAM, Viola P, Pinato DJ, et al: Association of machine learning-based assessment of tumor-infiltrating lymphocytes on standard histologic images with outcomes of immunotherapy in patients with NSCLC. JAMA Oncol. 9:51–60. 2023. View Article : Google Scholar : PubMed/NCBI

23 

Bao X, Shi R, Zhao T, Wang Y, Anastasov N, Rosemann M and Fang W: Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC. Cancer Immunol Immunother. 70:189–202. 2021. View Article : Google Scholar : PubMed/NCBI

24 

Cancian P, Cortese N, Donadon M, Di Maio M, Soldani C, Marchesi F, Savevski V, Santambrogio MD, Cerina L, Laino ME, et al: Development of a deep-learning pipeline to recognize and characterize macrophages in colo-rectal liver metastasis. Cancers (Basel). 13:33132021. View Article : Google Scholar : PubMed/NCBI

25 

Bian C, Wang Y, Lu Z, An Y, Wang H, Kong L, Du Y and Tian J: ImmunoAIzer: A deep learning-based computational framework to characterize cell distribution and gene mutation in tumor microenvironment. Cancers (Basel). 13:16592021. View Article : Google Scholar : PubMed/NCBI

26 

Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F and Cereda M: Artificial intelligence in bulk and single-cell RNA-sequencing data to foster precision oncology. Int J Mol Sci. 22:45632021. View Article : Google Scholar : PubMed/NCBI

27 

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, et al: mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 6:377–382. 2009. View Article : Google Scholar : PubMed/NCBI

28 

Wu Y, Yang S, Ma J, Chen Z, Song G, Rao D, Cheng Y, Huang S, Liu Y, Jiang S, et al: Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov. 12:134–153. 2022. View Article : Google Scholar : PubMed/NCBI

29 

Geistlinger L, Oh S, Ramos M, Schiffer L, LaRue RS, Henzler CM, Munro SA, Daughters C, Nelson AC, Winterhoff BJ, et al: Multiomic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma. Cancer Res. 80:4335–4345. 2020. View Article : Google Scholar : PubMed/NCBI

30 

Hao Q, Li J, Zhang Q, Xu F, Xie B, Lu H, Wu X and Zhou X: Single-cell transcriptomes reveal heterogeneity of high-grade serous ovarian carcinoma. Clin Transl Med. 11:e5002021. View Article : Google Scholar : PubMed/NCBI

31 

Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, Rodman C, Luo CL, Mroz EA, Emerick KS, et al: Single-Cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell. 171:1611–1624. 2017. View Article : Google Scholar : PubMed/NCBI

32 

Zhang Q, He Y, Luo N, Patel SJ, Han Y, Gao R, Modak M, Carotta S, Haslinger C, Kind D, et al: Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell. 179:829–845.e20. 2019. View Article : Google Scholar : PubMed/NCBI

33 

Cascianelli S, Molineris I, Isella C, Masseroli M and Medico E: Machine learning for RNA sequencing-based intrinsic subtyping of breast cancer. Sci Rep. 10:140712020. View Article : Google Scholar : PubMed/NCBI

34 

Yu Z, Wang Z, Yu X and Zhang Z: RNA-Seq-Based breast cancer subtypes classification using machine learning approaches. Comput Intell Neurosci. 2020:47379692020. View Article : Google Scholar : PubMed/NCBI

35 

Valle F, Osella M and Caselle M: A Topic modeling analysis of TCGA breast and lung cancer transcriptomic data. Cancers (Basel). 12:37992020. View Article : Google Scholar : PubMed/NCBI

36 

Gao F, Wang W, Tan M, Zhu L, Zhang Y, Fessler E, Vermeulen L and Wang X: DeepCC: A novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis. 8:442019. View Article : Google Scholar : PubMed/NCBI

37 

Chen YP, Yin JH, Li WF, Li HJ, Chen DP, Zhang CJ, Lv JW, Wang YQ, Li XM, Li JY, et al: Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 30:1024–1042. 2020. View Article : Google Scholar : PubMed/NCBI

38 

Zhou Y, Yang D, Yang Q, Lv X, Huang W, Zhou Z, Wang Y, Zhang Z, Yuan T, Ding X, et al: Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma. Nat Commun. 11:63222020. View Article : Google Scholar : PubMed/NCBI

39 

Sorin M, Rezanejad M, Karimi E, Fiset B, Desharnais L, Perus LJM, Milette S, Yu MW, Maritan SM, Doré S, et al: Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature. 614:548–554. 2023. View Article : Google Scholar : PubMed/NCBI

40 

Kong J, Lee H, Kim D, Han SK, Ha D, Shin K and Kim S: Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat Commun. 11:54852020. View Article : Google Scholar : PubMed/NCBI

41 

Ching T, Zhu X and Garmire LX: Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput Biol. 14:e10060762018. View Article : Google Scholar : PubMed/NCBI

42 

Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T and Kluger Y: DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 18:242018. View Article : Google Scholar : PubMed/NCBI

43 

Huang Z, Johnson TS, Han Z, Helm B, Cao S, Zhang C, Salama P, Rizkalla M, Yu CY, Cheng J, et al: Deep learning-based cancer survival prognosis from RNA-seq data: Approaches and evaluations. BMC Med Genomics. 13:412020. View Article : Google Scholar : PubMed/NCBI

44 

van IJzendoorn DGP, Szuhai K, Briaire-de Bruijn IH, Kostine M, Kuijjer ML and Bovée JVMG: Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Comput Biol. 15:e10068262019. View Article : Google Scholar : PubMed/NCBI

45 

Haider S, Yao CQ, Sabine VS, Grzadkowski M, Stimper V, Starmans MHW, Wang J, Nguyen F, Moon NC, Lin X, et al: Pathway-based subnetworks enable cross-disease biomarker discovery. Nat Commun. 9:47462018. View Article : Google Scholar : PubMed/NCBI

46 

Tabl AA, Alkhateeb A, ElMaraghy W, Rueda L and Ngom A: A machine learning approach for identifying gene biomarkers guiding the treatment of breast cancer. Front Genet. 10:2562018. View Article : Google Scholar : PubMed/NCBI

47 

Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ, Murphy T, Clark A, Busby M, Duke F, Yang A, Young L, et al: Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat Biotechnol. 37:55–63. 2019. View Article : Google Scholar

48 

Altini N, Brunetti A, Puro E, Taccogna MG, Saponaro C, Zito FA, De Summa S and Bevilacqua V: NDG-CAM: Nuclei detection in histopathology images with semantic segmentation networks and Grad-CAM. Bioengineering (Basel). 9:4752022. View Article : Google Scholar : PubMed/NCBI

49 

Gimeno M, San José-Enériz E, Villar S, Agirre X, Prosper F, Rubio A and Carazo F: Explainable artificial intelligence for precision medicine in acute myeloid leukemia. Front Immunol. 13:9773582022. View Article : Google Scholar : PubMed/NCBI

50 

Meena J and Hasija Y: Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers. Comput Biol Med. 146:1055052022. View Article : Google Scholar : PubMed/NCBI

51 

Paul A and Prasad Mukherjee D: Mitosis detection for invasive breast cancer grading in histopathological images. IEEE Transactions on Image Processing. 24:4041–4054. 2015. View Article : Google Scholar : PubMed/NCBI

52 

Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez F, Larsen AB, Vestergaard JS, Dahl AB, et al: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal. 20:237–248. 2015. View Article : Google Scholar : PubMed/NCBI

53 

Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium, ; Hermsen M, Manson QF, et al: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 318:2199–2210. 2017. View Article : Google Scholar : PubMed/NCBI

54 

Wang CW, Lee YC, Calista E, Zhou F, Zhu H, Suzuki R, Komura D, Ishikawa S and Cheng SP: A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays. Bioinformatics. 34:1767–1773. 2018. View Article : Google Scholar : PubMed/NCBI

55 

Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, Gaasenbeek M, Angelo M, Reich M, Pinkus GS, et al: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 8:68–74. 2002. View Article : Google Scholar : PubMed/NCBI

56 

Khan U, Shin H, Choi JP and Kim M: wFDT-Weighted Fuzzy Decision Trees for prognosis of breast cancer survivability. AusDM ‘08: Proceedings of the 7th Australasian Data Mining Conference. Vol 87:pp141–152. 2008.

57 

Fatakdawala H, Xu J, Basavanhally A, Bhanot G, Ganesan S, Feldman M, Tomaszewski JE and Madabhushi A: Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng. 57:1676–1689. 2010. View Article : Google Scholar : PubMed/NCBI

58 

Sertel O, Lozanski G, Shana'ah A and Gurcan MN: Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation. IEEE Trans Biomed Eng. 57:2613–2616. 2010. View Article : Google Scholar : PubMed/NCBI

59 

Dundar MM, Badve S, Bilgin G, Raykar V, Jain R, Sertel O and Gurcan MN: Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng. 58:1977–1984. 2011. View Article : Google Scholar : PubMed/NCBI

60 

Tuominen VJ, Tolonen TT and Isola J: ImmunoMembrane: A publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology. 60:758–767. 2012. View Article : Google Scholar : PubMed/NCBI

61 

Gertych A, Joseph AO, Walts AE and Bose S: Automated detection of dual p16/Ki67 nuclear immunoreactivity in liquid-based Pap tests for improved cervical cancer risk stratification. Ann Biomed Eng. 40:1192–1204. 2012. View Article : Google Scholar : PubMed/NCBI

62 

Doyle S, Feldman MD, Shih N, Tomaszewski J and Madabhushi A: Cascaded discrimination of normal abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinformatics. 13:2822012. View Article : Google Scholar : PubMed/NCBI

63 

Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F and Madabhushi A: Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging (Bellingham). 1:0340032014. View Article : Google Scholar : PubMed/NCBI

64 

Lewis JS Jr, Ali S, Luo J, Thorstad WL and Madabhushi A: A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol. 38:128–137. 2014. View Article : Google Scholar : PubMed/NCBI

65 

Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA and Rajpoot NM: A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images. Conference: 1st International Workshop on Patch-based Techniques in Medical Imaging, Held in Conjunction with MICCAI. 2015.doi: 10.1007/978-3-319-28194-0_19.

66 

Yuan Y: Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. J R Soc Interface. 12:201411532015. View Article : Google Scholar : PubMed/NCBI

67 

Xie Y, Kong X, Xing F, Liu F, Su H and Yang L: Deep Voting: A robust approach toward nucleus localization in microscopy images. Med Image Comput Comput Assist Interv. 9351:374–382. 2015.PubMed/NCBI

68 

Sirinukunwattana K, Ahmed Raza SE, Yee-Wah Tsang, Snead DR, Cree IA and Rajpoot NM: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 35:1196–1206. 2016. View Article : Google Scholar : PubMed/NCBI

69 

Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E and Madabhushi A: Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images. Sci Rep. 6:327062016. View Article : Google Scholar : PubMed/NCBI

70 

Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J and Madabhushi A: Stacked Sparse Autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging. 35:119–130. 2016. View Article : Google Scholar : PubMed/NCBI

71 

Turkki R, Linder N, Kovanen PE, Pellinen T and Lundin J: Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform. 7:382016. View Article : Google Scholar : PubMed/NCBI

72 

Ali HR, Dariush A, Provenzano E, Bardwell H, Abraham JE, Iddawela M, Vallier AL, Hiller L, Dunn JA, Bowden SJ, et al: Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer. Breast Cancer Res. 18:212016. View Article : Google Scholar : PubMed/NCBI

73 

Bartsch G Jr, Mitra AP, Mitra SA, Almal AA, Steven KE, Skinner DG, Fry DW, Lenehan PF, Worzel WP and Cote RJ: Use of artificial intelligence and machine learning algorithms with gene expression profiling to predict recurrent nonmuscle invasive urothelial carcinoma of the bladder. J Urol. 195:493–498. 2016. View Article : Google Scholar : PubMed/NCBI

74 

Yu KH, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL and Snyder M: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 7:124742016. View Article : Google Scholar : PubMed/NCBI

75 

Ali HR, Dariush A, Thomas J, Provenzano E, Dunn J, Hiller L, Vallier AL, Abraham J, Piper T, Bartlett JMS, et al: Lymphocyte density determined by computational pathology validated as a predictor of response to neoadjuvant chemotherapy in breast cancer: Secondary analysis of the ARTemis trial. Ann Oncol. 28:1832–1835. 2017. View Article : Google Scholar : PubMed/NCBI

76 

Lu C, Lewis JS Jr, Dupont WD, Plummer WD Jr, Janowczyk A and Madabhushi A: An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Mod Pathol. 30:1655–1665. 2017. View Article : Google Scholar : PubMed/NCBI

77 

Gecer B, Aksoy S, Mercan E, Shapiro LG, Weaver DL and Elmore JG: Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recognit. 84:345–356. 2018. View Article : Google Scholar : PubMed/NCBI

78 

Yoshida H, Shimazu T, Kiyuna T, Marugame A, Yamashita Y, Cosatto E, Taniguchi H, Sekine S and Ochiai A: Automated histological classification of whole-slide images of gastric biopsy specimens. Gastric Cancer. 21:249–257. 2018. View Article : Google Scholar : PubMed/NCBI

79 

Bo L, Huang M, Zhang J, Li Y and Li R: Gastric Pathology Image Recognition Based on Deep Residual Networks. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 408–412. 2018.

80 

Ichimasa K, Kudo SE, Mori Y, Misawa M, Matsudaira S, Kouyama Y, Baba T, Hidaka E, Wakamura K, Hayashi T, et al: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. 50:230–240. 2018. View Article : Google Scholar : PubMed/NCBI

81 

Mezheyeuski A, Bergsland CH, Backman M, Djureinovic D, Sjöblom T, Bruun J and Micke P: Multispectral imaging for quantitative and compartment-specific immune infiltrates reveals distinct immune profiles that classify lung cancer patients. J Pathol. 244:421–431. 2018. View Article : Google Scholar : PubMed/NCBI

82 

Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, Brat DJ and Cooper LAD: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci USA. 115:E2970–E2979. 2018. View Article : Google Scholar : PubMed/NCBI

83 

Chaudhary K, Poirion OB, Lu L and Garmire LX: Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res. 24:1248–1259. 2018. View Article : Google Scholar : PubMed/NCBI

84 

Günakan E, Atan S, Haberal AN, Küçükyıldız İA, Gökçe E and Ayhan A: A novel prediction method for lymph node involvement in endometrial cancer: Machine learning. Int J Gynecol Cancer. 29:320–324. 2019. View Article : Google Scholar : PubMed/NCBI

85 

Turkki R, Byckhov D, Lundin M, Isola J, Nordling S, Kovanen PE, Verrill C, von Smitten K, Joensuu H, Lundin J, et al: Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat. 177:41–52. 2019. View Article : Google Scholar : PubMed/NCBI

86 

Fang M, Zhang W, Dong D, Zhou J and Tian J: Predicting histopathological findings of gastric cancer via deep generalized multi-instance learning. Proceedings of the SPIE. Vol 10949:id. 109491Q. pp62019.

87 

Mori H and Miwa H: A histopathologic feature of the behavior of gastric signet-ring cell carcinoma; an image analysis study with deep learning. Pathol Int. 69:437–439. 2019. View Article : Google Scholar : PubMed/NCBI

88 

Leon F, Gelvez M, Jaimes Z, Gelvez T and Arguello H: Supervised Classification of Histopathological Images Using Convolutional Neuronal Networks for Gastric Cancer Detection. STSIVA; 2019, View Article : Google Scholar

89 

Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F, Luo X, Yang Y, Yao B, Lin S, et al: ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine. 50:103–110. 2019. View Article : Google Scholar : PubMed/NCBI

90 

Aprupe L, Litjens G, Brinker TJ, van der Laak J and Grabe N: Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks. PeerJ. 7:e63352019. View Article : Google Scholar : PubMed/NCBI

91 

Dihge L, Vallon-Christersson J, Hegardt C, Saal LH, Häkkinen J, Larsson C, Ehinger A, Loman N, Malmberg M, Bendahl PO, et al: Prediction of lymph node metastasis in breast cancer by gene expression and clinicopathological models: Development and validation within a population-based cohort. Clin Cancer Res. 25:6368–6381. 2019. View Article : Google Scholar : PubMed/NCBI

92 

Nir G, Davood K, Goldenberg SL, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G..Thompson DJS, et al: Comparison of artificial intelligence techniques to evaluate performance of a classifier for automatic grading of prostate cancer from digitized histopathologic images. JAMA Netw Open. 2:e1904422019. View Article : Google Scholar : PubMed/NCBI

93 

Courtiol P, Maussion C, Moarii M, Pronier E, Pilcer S, Sefta M, Manceron P, Toldo S, Zaslavskiy M, Le Stang N, et al: Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat Med. 25:1519–1525. 2019. View Article : Google Scholar : PubMed/NCBI

94 

Achi HE, Belousova T, Chen L, Wahed A, Wang I, Hu Z, Kanaan Z, Rios A and Nguyen AND: Automated diagnosis of lymphoma with digital pathology images using deep learning. Ann Clin Lab Sci. 49:153–160. 2019.PubMed/NCBI

95 

Rodner E, Bocklitz T, von Eggeling F, Ernst G, Chernavskaia O, Popp J, Denzler J and Guntinas-Lichius O: Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study. Head Neck. 41:116–121. 2019.PubMed/NCBI

96 

Hu L, Bell D, Antani S, Xue Z, Yu K, Horning MP, Gachuhi N, Wilson B, Jaiswal MS, Befano B, et al: An observational study of deep learning and automated evaluation of cervical images for cancer screening. J Natl Cancer Inst. 111:923–932. 2019. View Article : Google Scholar : PubMed/NCBI

97 

Tian R, Cui Z, He D, Tian X, Gao Q, Ma X, Yang JR, Wu J, Das BC, Severinov K, et al: Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles. Carcinogenesis. 40:1220–1228. 2019. View Article : Google Scholar : PubMed/NCBI

98 

Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, et al: Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 37:773–782. 2019. View Article : Google Scholar : PubMed/NCBI

99 

Saillard C, Schmauch B, Laifa O, Moarii M, Toldo S, Zaslavskiy M, Pronier E, Laurent A, Amaddeo G, Regnault H, et al: Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology. 72:2000–2013. 2020. View Article : Google Scholar : PubMed/NCBI

100 

Dietz C, Rueden CT, Helfrich S, Dobson ETA, Horn M, Eglinger J, Evans EL III, McLean DT, Novitskaya T, Ricke WA, et al: Integration of the ImageJ Ecosystem in the KNIME Analytics Platform. Front Comput Sci. 2:82020. View Article : Google Scholar : PubMed/NCBI

101 

Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, van der Laak J, Hulsbergen-van de Kaa C and Litjens G: Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. Lancet Oncol. 21:233–241. 2020. View Article : Google Scholar : PubMed/NCBI

102 

Han W, Johnson C, Gaed M, Gómez JA, Moussa M, Chin JL, Pautler S, Bauman GS and Ward AD: Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens. Sci Rep. 10:99112020. View Article : Google Scholar : PubMed/NCBI

103 

Jaber MI, Song B, Taylor C, Vaske CJ, Benz SC, Rabizadeh S, Soon-Shiong P and Szeto CW: A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival. Breast Cancer Res. 22:122020. View Article : Google Scholar : PubMed/NCBI

104 

Valieris R, Amaro L, Osório CABT, Bueno AP, Rosales Mitrowsky RA, Carraro DM, Nunes DN, Dias-Neto E and Silva ITD: Deep learning predicts underlying features on pathology images with therapeutic relevance for breast and gastric cancer. Cancers (Basel). 12:36872020. View Article : Google Scholar : PubMed/NCBI

105 

Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, Askautrud HA, Pradhan M, Nesheim JA, Albregtsen F, et al: Deep learning for prediction of colorectal cancer outcome: A discovery and validation study. Lancet. 395:350–360. 2020. View Article : Google Scholar : PubMed/NCBI

106 

Reichling C, Taieb J, Derangere V, Klopfenstein Q, Le Malicot K, Gornet JM, Becheur H, Fein F, Cojocarasu O, Kaminsky MC, et al: Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 69:681–690. 2020. View Article : Google Scholar : PubMed/NCBI

107 

Bao H, Sun X, Zhang Y, Pang B, Li H, Zhou L, Wu F, Cao D, Wang J, Turic B, et al: The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women. Cancer Med. 9:6896–6906. 2020. View Article : Google Scholar : PubMed/NCBI

108 

Xu-Monette ZY, Zhang H, Zhu F, Tzankov A, Bhagat G, Visco C, Dybkaer K, Chiu A, Tam W, Zu Y, et al: A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL. Blood Adv. 4:3391–3404. 2020. View Article : Google Scholar : PubMed/NCBI

109 

Foersch S, Eckstein M, Wagner DC, Gach F, Woerl AC, Geiger J, Glasner C, Schelbert S, Schulz S, Porubsky S, et al: Deep learning for diagnosis and survival prediction in soft tissue sarcoma. Ann Oncol. 32:1178–1187. 2021. View Article : Google Scholar : PubMed/NCBI

110 

Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu FI, Dodington D, Gandhi S, Slodkowska EA, et al: Assessment of digital pathology imaging biomarkers associated with breast cancer histologic grade. Curr Oncol. 28:4298–4316. 2021. View Article : Google Scholar : PubMed/NCBI

111 

Bychkov D, Linder N, Tiulpin A, Kücükel H, Lundin M, Nordling S, Sihto H, Isola J, Lehtimäki T, Kellokumpu-Lehtinen PL, et al: Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep. 11:40372021. View Article : Google Scholar : PubMed/NCBI

112 

Li F, Yang Y, Wei Y, He P, Chen J, Zheng Z and Bu H: Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer. J Transl Med. 19:3482021. View Article : Google Scholar : PubMed/NCBI

113 

Chakraborty D, Ivan C, Amero P, Khan M, Rodriguez-Aguayo C, Başağaoğlu H and Lopez-Berestein G: Explainable artificial intelligence reveals novel insight into tumor microenvironment conditions linked with better prognosis in patients with breast cancer. Cancers (Basel). 13:34502021. View Article : Google Scholar : PubMed/NCBI

114 

Fitzgerald J, Higgins D, Mazo Vargas C, Watson W, Mooney C, Rahman A, Aspell N, Connolly A, Aura Gonzalez C and Gallagher W: Future of biomarker evaluation in the realm of artificial intelligence algorithms: Application in improved therapeutic stratification of patients with breast and prostate cancer. J Clin Pathol. 74:429–434. 2021. View Article : Google Scholar : PubMed/NCBI

115 

Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA and El-Baz A: Role of AI: histopathological images in detecting prostate cancer: A survey. Sensors (Basel). 21:25862021. View Article : Google Scholar : PubMed/NCBI

116 

Haggenmüller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, Beltraminelli H, Berking C, Betz-Stablein B, Blum A, et al: Skin cancer classification via convolutional neural networks: Systematic review of studies involving human experts. Eur J Cancer. 156:202–216. 2021. View Article : Google Scholar : PubMed/NCBI

117 

Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, et al: Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer. 157:464–473. 2021. View Article : Google Scholar : PubMed/NCBI

118 

Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, Higgins J, Rubin DL and Shen J: Deep learning model for the prediction of microsatellite instability in colorectal cancer: A diagnostic study. Lancet Oncol. 22:132–141. 2021. View Article : Google Scholar : PubMed/NCBI

119 

Krause J, Grabsch HI, Kloor M, Jendrusch M, Echle A, Buelow RD, Boor P, Luedde T, Brinker TJ, Trautwein C, et al: Deep learning detects genetic alterations in cancer histology generated by adversarial networks. J Pathol. 254:70–79. 2021.PubMed/NCBI

120 

Saito A, Toyoda H, Kobayashi M, Koiwa Y, Fujii H, Fujita K, Maeda A, Kaneoka Y, Hazama S, Nagano H, et al: Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Mod Pathol. 34:417–425. 2021. View Article : Google Scholar : PubMed/NCBI

121 

Yamashita R, Long J, Saleem A, Rubin DL and Shen J: Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep. 11:20472021. View Article : Google Scholar : PubMed/NCBI

122 

Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, et al: Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: A retrospective study. BMC Med. 19:802021. View Article : Google Scholar : PubMed/NCBI

123 

Chen CL, Chen CC, Yu WH, Chen SH, Chang YC, Hsu TI, Hsiao M, Yeh CY and Chen CY: An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat Commun. 12:11932021. View Article : Google Scholar : PubMed/NCBI

124 

Park J, Jang BG, Kim YW, Park H, Kim BH, Kim MJ, Ko H, Gwak JM, Lee EJ, Chung YR, et al: A prospective validation and observer performance study of a deep learning algorithm for pathologic diagnosis of gastric tumors in endoscopic biopsies. Clin Cancer Res. 27:719–728. 2021. View Article : Google Scholar : PubMed/NCBI

125 

Wang X, Chen Y, Gao Y, Zhang H, Guan Z, Dong Z, Zheng Y, Jiang J, Yang H, Wang L, et al: Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat Commun. 12:16372021. View Article : Google Scholar : PubMed/NCBI

126 

Muti HS, Heij LR, Keller G, Kohlruss M, Langer R, Dislich B, Cheong JH, Kim YW, Kim H, Kook MC, et al: Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: A retrospective multicentre cohort study. Lancet Digit Health. 3:e654–e664. 2021. View Article : Google Scholar : PubMed/NCBI

127 

Yan J, Zhao Y, Chen Y, Wang W, Duan W, Wang L, Zhang S, Ding T, Liu L, Sun Q, et al: Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities. EBioMedicine. 72:1035832021. View Article : Google Scholar : PubMed/NCBI

128 

Bao H, Wang Z, Ma X, Guo W, Zhang X, Tang W, Chen X, Wang X, Chen Y, Mo S, et al: Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection. Mol Cancer. 21:1292022. View Article : Google Scholar : PubMed/NCBI

129 

Duchmann M, Wagner-Ballon O, Boyer T, Cheok M, Fournier E, Guerin E, Fenwarth L, Badaoui B, Freynet N, Benayoun E, et al: Machine learning identifies the independent role of dysplasia in the prediction of response to chemotherapy in AML. Leukemia. 36:656–663. 2022. View Article : Google Scholar : PubMed/NCBI

130 

Yen R, Grasedieck S, Wu A, Lin H, Su J, Rothe K, Nakamoto H, Forrest DL, Eaves CJ and Jiang X: Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: A sub-analysis of the ENESTxtnd clinical trial. Leukemia. 36:2443–2452. 2022. View Article : Google Scholar : PubMed/NCBI

131 

Laukhtina E, Schuettfort VM, D'Andrea D, Pradere B, Quhal F, Mori K, Sari Motlagh R, Mostafaei H, Katayama S, Grossmann NC, et al: Selection and evaluation of preoperative systemic inflammatory response biomarkers model prior to cytoreductive nephrectomy using a machine-learning approach. World J Urol. 40:747–754. 2022. View Article : Google Scholar : PubMed/NCBI

132 

Cheng N, Ren Y, Zhou J, Zhang Y, Wang D, Zhang X, Chen B, Liu F, Lv J, Cao Q, et al: Deep Learning-based classification of hepatocellular nodular lesions on whole-slide histopathologic images. Gastroenterology. 162:1948–1961. 2022. View Article : Google Scholar : PubMed/NCBI

133 

Kleppe A, Skrede OJ, De Raedt S, Hveem TS, Askautrud HA, Jacobsen JE, Church DN, Nesbakken A, Shepherd NA, Novelli M, et al: A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: A development and validation study. Lancet Oncol. 23:1221–1232. 2022. View Article : Google Scholar : PubMed/NCBI

134 

Byeon SJ, Park J, Cho YA and Cho BJ: Automated histological classification for digital pathology images of colonoscopy specimen via deep learning. Sci Rep. 12:128042022. View Article : Google Scholar : PubMed/NCBI

135 

Feng L, Liu Z, Li C, Li Z, Lou X, Shao L, Wang Y, Huang Y, Chen H, Pang X, et al: Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: A multicentre observational study. Lancet Digit Health. 4:e8–e17. 2022. View Article : Google Scholar : PubMed/NCBI

136 

Ding K, Zhou M, Wang H, Zhang S and Metaxas DN: Spatially aware graph neural networks and cross-level molecular profile prediction in colon cancer histopathology: A retrospective multi-cohort study. Lancet Digit Health. 4:e787–e795. 2022. View Article : Google Scholar : PubMed/NCBI

137 

Wang R, Dai W, Gong J, Huang M, Hu T, Li H, Lin K, Tan C, Hu H, Tong T, et al: Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 15:112022. View Article : Google Scholar : PubMed/NCBI

138 

Sundar R, Barr Kumarakulasinghe N, Huak Chan Y, Yoshida K, Yoshikawa T, Miyagi Y, Rino Y, Masuda M, Guan J, Sakamoto J, et al: Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: Results from the randomised phase III SAMIT trial. Gut. 71:676–685. 2022. View Article : Google Scholar : PubMed/NCBI

139 

Pfob A, Sidey-Gibbons C, Rauch G, Thomas B, Schaefgen B, Kuemmel S, Reimer T, Hahn M, Thill M, Blohmer JU, et al: Intelligent Vacuum-assisted biopsy to identify breast cancer patients with pathologic complete response (ypT0 and ypN0) after neoadjuvant systemic treatment for omission of breast and axillary surgery. J Clin Oncol. 40:1903–1915. 2022. View Article : Google Scholar : PubMed/NCBI

140 

Prat A, Guarneri V, Pascual T, Brasó-Maristany F, Sanfeliu E, Paré L, Schettini F, Martínez D, Jares P, Griguolo G, et al: Development and validation of the new HER2DX assay for predicting pathological response and survival outcome in early-stage HER2-positive breast cancer. EBioMedicine. 75:1038012022. View Article : Google Scholar : PubMed/NCBI

141 

Farinella F, Merone M, Bacco L, Capirchio A, Ciccozzi M and Caligiore D: Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers. Sci Rep. 12:30412022. View Article : Google Scholar : PubMed/NCBI

142 

Park S, Ock CY, Kim H, Pereira S, Park S, Ma M, Choi S, Kim S, Shin S, Aum BJ, 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. 40:1916–1928. 2022. View Article : Google Scholar : PubMed/NCBI

143 

Cheng G, Zhang F, Xing Y, Hu X, Zhang H, Chen S, Li M, Peng C, Ding G, Zhang D, et al: Artificial intelligence-assisted score analysis for predicting the expression of the immunotherapy biomarker PD-L1 in lung cancer. Front Immunol. 13:8931982022. View Article : Google Scholar : PubMed/NCBI

144 

Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, Pagano A, Araujo-Filho JAB, Geneslaw L, Rizvi H, et al: Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 3:1151–1164. 2022. View Article : Google Scholar : PubMed/NCBI

145 

Liu Y, Jia Y, Hou C, Li N, Zhang N, Yan X, Yang L, Guo Y, Chen H, Li J, et al: Pathological prognosis classification of patients with neuroblastoma using computational pathology analysis. Comput Biol Med. 149:1059802022. View Article : Google Scholar : PubMed/NCBI

146 

Foersch S, Glasner C, Woerl AC, Eckstein M, Wagner DC, Schulz S, Kellers F, Fernandez A, Tserea K, Kloth M, et al: Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med. 29:430–439. 2023. View Article : Google Scholar : PubMed/NCBI

147 

Liu XP, Jin X, Seyed Ahmadian S, Yang X, Tian SF, Cai YX, Chawla K, Snijders AM, Xia Y, van Diest PJ, et al: Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning. Neuro Oncol. 25:68–81. 2023. View Article : Google Scholar : PubMed/NCBI

148 

Cao R, Nelson SD, Davis S, Liang Y, Luo Y, Zhang Y, Crawford B and Wang LV: Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat Biomed Eng. 7:124–134. 2023. View Article : Google Scholar : PubMed/NCBI

149 

Wu S, Hong G, Xu A, Zeng H, Chen X, Wang Y, Luo Y, Wu P, Liu C, Jiang N, et al: Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: A retrospective, multicentre, diagnostic study. Lancet Oncol. 24:360–370. 2023. View Article : Google Scholar : PubMed/NCBI

150 

Barnett R: Lung cancer. Lancet. 390:9282017. View Article : Google Scholar : PubMed/NCBI

151 

Zheng Y, Gindra RH, Green EJ, Burks EJ, Betke M, Beane JE and Kolachalama VB: A Graph-transformer for whole slide image classification. IEEE Trans Med Imaging. 41:3003–3015. 2022. View Article : Google Scholar : PubMed/NCBI

152 

Qiao C, Li D, Liu Y, Zhang S, Liu K, Liu C, Guo Y, Jiang T, Fang C, Li N, et al: Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat Biotechnol. 41:367–377. 2023. View Article : Google Scholar : PubMed/NCBI

153 

Vo TH, Nguyen NTK, Kha QH and Le NQK: On the road to explainable AI in drug-drug interactions prediction: A systematic review. Comput Struct Biotechnol J. 20:2112–2123. 2022. View Article : Google Scholar : PubMed/NCBI

154 

Cheng JY, Abel JT, Balis UGJ, McClintock DS and Pantanowitz L: Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 191:1684–1692. 2021. View Article : Google Scholar : PubMed/NCBI

155 

Schmidt C: M. D. Anderson Breaks with IBM Watson, Raising Questions about artificial intelligence in oncology. J Natl Cancer Inst. 1092017.doi: 10.1093/jnci/djx113.

156 

Lecler A, Duron L and Soyer P: Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT. Diagn Interv Imaging. 104:269–274. 2023. View Article : Google Scholar : PubMed/NCBI

157 

Will ChatGPT transform healthcare? Nat Med. 29:505–506. 2023. View Article : Google Scholar : PubMed/NCBI

158 

Sinha RK, Deb Roy A, Kumar N and Mondal H: Applicability of ChatGPT in assisting to solve higher order problems in pathology. Cureus. 15:e352372023.PubMed/NCBI

159 

Chauhan C and Gullapalli RR: Ethics of AI in pathology: Current paradigms and emerging issues. Am J Pathol. 91:1673–1683. 2021. View Article : Google Scholar : PubMed/NCBI

160 

Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, et al: Swarm Learning for decentralized and confidential clinical machine learning. Nature. 594:265–270. 2021. View Article : Google Scholar : PubMed/NCBI

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Lai B, Fu J, Zhang Q, Deng N, Jiang Q and Peng J: Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine. Int J Oncol 63: 107, 2023.
APA
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., & Peng, J. (2023). Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine. International Journal of Oncology, 63, 107. https://doi.org/10.3892/ijo.2023.5555
MLA
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., Peng, J."Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine". International Journal of Oncology 63.3 (2023): 107.
Chicago
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., Peng, J."Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine". International Journal of Oncology 63, no. 3 (2023): 107. https://doi.org/10.3892/ijo.2023.5555
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Spandidos Publications style
Lai B, Fu J, Zhang Q, Deng N, Jiang Q and Peng J: Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine. Int J Oncol 63: 107, 2023.
APA
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., & Peng, J. (2023). Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine. International Journal of Oncology, 63, 107. https://doi.org/10.3892/ijo.2023.5555
MLA
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., Peng, J."Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine". International Journal of Oncology 63.3 (2023): 107.
Chicago
Lai, B., Fu, J., Zhang, Q., Deng, N., Jiang, Q., Peng, J."Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine". International Journal of Oncology 63, no. 3 (2023): 107. https://doi.org/10.3892/ijo.2023.5555
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