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For: Wulczyn E, Steiner DF, Xu Z, Sadhwani A, Wang H, Flament-Auvigne I, Mermel CH, Chen PC, Liu Y, Stumpe MC. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One. 2020;15:e0233678. [PMID: 32555646 DOI: 10.1371/journal.pone.0233678] [Cited by in Crossref: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
Number Citing Articles
1 Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical Image Analysis 2020;65:101789. [DOI: 10.1016/j.media.2020.101789] [Cited by in Crossref: 17] [Cited by in F6Publishing: 4] [Article Influence: 8.5] [Reference Citation Analysis]
2 Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol 2021;74:409-14. [PMID: 32763920 DOI: 10.1136/jclinpath-2020-206908] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
3 Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 2021;124:686-96. [PMID: 33204028 DOI: 10.1038/s41416-020-01122-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
4 Yao J, Shi Y, Cao K, Lu L, Lu J, Song Q, Jin G, Xiao J, Hou Y, Zhang L. DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing. Med Image Anal 2021;73:102150. [PMID: 34303891 DOI: 10.1016/j.media.2021.102150] [Reference Citation Analysis]
5 Hammouda K, Khalifa F, El-Melegy M, Ghazal M, Darwish HE, Abou El-Ghar M, El-Baz A. A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens. Sensors (Basel) 2021;21:6708. [PMID: 34695922 DOI: 10.3390/s21206708] [Reference Citation Analysis]
6 Mamrot J, Hall NE, Lindley RA. Predicting clinical outcomes using cancer progression associated signatures. Oncotarget 2021;12:845-58. [PMID: 33889305 DOI: 10.18632/oncotarget.27934] [Reference Citation Analysis]
7 Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27(20): 2545-2575 [PMID: 34092975 DOI: 10.3748/wjg.v27.i20.2545] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Jiang S, Zanazzi GJ, Hassanpour S. Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images. Sci Rep 2021;11:16849. [PMID: 34413349 DOI: 10.1038/s41598-021-95948-x] [Reference Citation Analysis]
9 Howard FM, Dolezal J, Kochanny S, Schulte J, Chen H, Heij L, Huo D, Nanda R, Olopade OI, Kather JN, Cipriani N, Grossman RL, Pearson AT. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun 2021;12:4423. [PMID: 34285218 DOI: 10.1038/s41467-021-24698-1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021;8:53. [PMID: 33816053 DOI: 10.1186/s40537-021-00444-8] [Cited by in Crossref: 29] [Cited by in F6Publishing: 12] [Article Influence: 29.0] [Reference Citation Analysis]
11 Bussola N, Papa B, Melaiu O, Castellano A, Fruci D, Jurman G. Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology. Int J Mol Sci 2021;22:8804. [PMID: 34445517 DOI: 10.3390/ijms22168804] [Reference Citation Analysis]
12 Wu LL, Wang JL, Huang W, Liu X, Huang YY, Zeng J, Cui CY, Lu JB, Lin P, Long H, Zhang LJ, Wei J, Lu Y, Ma GW. Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis. Front Oncol 2021;11:565755. [PMID: 33912439 DOI: 10.3389/fonc.2021.565755] [Reference Citation Analysis]
13 Nagpal K, Foote D, Tan F, Liu Y, Chen PC, Steiner DF, Manoj N, Olson N, Smith JL, Mohtashamian A, Peterson B, Amin MB, Evans AJ, Sweet JW, Cheung C, van der Kwast T, Sangoi AR, Zhou M, Allan R, Humphrey PA, Hipp JD, Gadepalli K, Corrado GS, Peng LH, Stumpe MC, Mermel CH. Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens. JAMA Oncol 2020;6:1372-80. [PMID: 32701148 DOI: 10.1001/jamaoncol.2020.2485] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 21.0] [Reference Citation Analysis]
14 Pan Y, Lei X, Zhang Y. Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach. Med Res Rev 2021. [PMID: 34346083 DOI: 10.1002/med.21847] [Reference Citation Analysis]
15 Malenová G, Rowson D, Boeva V. Exploring Pathway-Based Group Lasso for Cancer Survival Analysis: A Special Case of Multi-Task Learning. Front Genet 2021;12:771301. [PMID: 34912376 DOI: 10.3389/fgene.2021.771301] [Reference Citation Analysis]
16 Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. NPJ Digit Med. 2021;4:5. [PMID: 33420381 DOI: 10.1038/s41746-020-00376-2] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 16.0] [Reference Citation Analysis]
17 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Sadhwani A, Chang HW, Behrooz A, Brown T, Auvigne-Flament I, Patel H, Findlater R, Velez V, Tan F, Tekiela K, Wulczyn E, Yi ES, Mermel CH, Hanks D, Chen PC, Kulig K, Batenchuk C, Steiner DF, Cimermancic P. Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images. Sci Rep 2021;11:16605. [PMID: 34400666 DOI: 10.1038/s41598-021-95747-4] [Reference Citation Analysis]
19 Steiner DF, Chen PC, Mermel CH. Closing the translation gap: AI applications in digital pathology. Biochim Biophys Acta Rev Cancer 2021;1875:188452. [PMID: 33065195 DOI: 10.1016/j.bbcan.2020.188452] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Reference Citation Analysis]
21 Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer 2021;7:104. [PMID: 34362928 DOI: 10.1038/s41523-021-00310-z] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
23 Wang J, Wu LL, Zhang Y, Ma G, Lu Y. Establishing a survival prediction model for esophageal squamous cell carcinoma based on CT and histopathological images. Phys Med Biol 2021;66. [PMID: 34192686 DOI: 10.1088/1361-6560/ac1020] [Reference Citation Analysis]
24 Wen Y, Chen L, Deng Y, Zhou C. Rethinking pre-training on medical imaging. Journal of Visual Communication and Image Representation 2021;78:103145. [DOI: 10.1016/j.jvcir.2021.103145] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
25 Shim WS, Yim K, Kim TJ, Sung YE, Lee G, Hong JH, Chun SH, Kim S, An HJ, Na SJ, Kim JJ, Moon MH, Moon SW, Park S, Hong SA, Ko YH. DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks. Cancers (Basel) 2021;13:3308. [PMID: 34282757 DOI: 10.3390/cancers13133308] [Reference Citation Analysis]
26 Lee AC, Lee Y, Choi A, Lee H, Shin K, Lee H, Kim JY, Ryu HS, Kim HS, Ryu SY, Lee S, Cheun J, Yoo DK, Lee S, Choi H, Ryu T, Yeom H, Kim N, Noh J, Lee Y, Kim I, Bae S, Kim J, Lee W, Kim O, Jung Y, Kim C, Song SW, Choi Y, Chung J, Kim BG, Han W, Kwon S. Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches. Nat Commun 2022;13. [DOI: 10.1038/s41467-022-30299-3] [Reference Citation Analysis]
27 Yamashita R, Long J, Saleem A, Rubin DL, Shen J. Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Sci Rep 2021;11:2047. [PMID: 33479370 DOI: 10.1038/s41598-021-81506-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
28 Hashimoto N, Ko K, Yokota T, Kohno K, Nakaguro M, Nakamura S, Takeuchi I, Hontani H. Subtype classification of malignant lymphoma using immunohistochemical staining pattern. Int J Comput Assist Radiol Surg 2022. [PMID: 35147848 DOI: 10.1007/s11548-021-02549-0] [Reference Citation Analysis]
29 Srinidhi CL, Kim SW, Chen FD, Martel AL. Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med Image Anal 2021;75:102256. [PMID: 34717189 DOI: 10.1016/j.media.2021.102256] [Reference Citation Analysis]
30 Wulczyn E, Steiner DF, Moran M, Plass M, Reihs R, Tan F, Flament-Auvigne I, Brown T, Regitnig P, Chen PC, Hegde N, Sadhwani A, MacDonald R, Ayalew B, Corrado GS, Peng LH, Tse D, Müller H, Xu Z, Liu Y, Stumpe MC, Zatloukal K, Mermel CH. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med 2021;4:71. [PMID: 33875798 DOI: 10.1038/s41746-021-00427-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]