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For: Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudeček J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD; International Immuno-Oncology Biomarker Working Group. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 2020;6:16. [PMID: 32411818 DOI: 10.1038/s41523-020-0154-2] [Cited by in Crossref: 27] [Cited by in F6Publishing: 23] [Article Influence: 13.5] [Reference Citation Analysis]
Number Citing Articles
1 Liu F, Hardiman T, Wu K, Quist J, Gazinska P, Ng T, Purushotham A, Salgado R, Guo X, Pinder SE, Grigoriadis A. Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication. NPJ Breast Cancer 2021;7:86. [PMID: 34226563 DOI: 10.1038/s41523-021-00292-y] [Reference Citation Analysis]
2 Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch 2021. [PMID: 34791536 DOI: 10.1007/s00428-021-03213-3] [Reference Citation Analysis]
3 Van Bockstal MR, Cooks M, Nederlof I, Brinkhuis M, Dutman A, Koopmans M, Kooreman L, van der Vegt B, Verhoog L, Vreuls C, Westenend P, Kok M, van Diest PJ, Nauwelaers I, Laudus N, Denkert C, Rimm D, Siziopikou KP, Ely S, Zardavas D, Roberts M, Floris G, Hartman J, Acs B, Peeters D, Bartlett JMS, Dequeker E, Salgado R, Giudici F, Michiels S, Horlings H, van Deurzen CHM. Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group. Cancers (Basel) 2021;13:4910. [PMID: 34638394 DOI: 10.3390/cancers13194910] [Reference Citation Analysis]
4 Sun P, He J, Chao X, Chen K, Xu Y, Huang Q, Yun J, Li M, Luo R, Kuang J, Wang H, Li H, Hui H, Xu S. A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer. EBioMedicine 2021;70:103492. [PMID: 34280779 DOI: 10.1016/j.ebiom.2021.103492] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Bergholtz H, Carter JM, Cesano A, Cheang MCU, Church SE, Divakar P, Fuhrman CA, Goel S, Gong J, Guerriero JL, Hoang ML, Hwang ES, Kuasne H, Lee J, Liang Y, Mittendorf EA, Perez J, Prat A, Pusztai L, Reeves JW, Riazalhosseini Y, Richer JK, Sahin Ö, Sato H, Schlam I, Sørlie T, Stover DG, Swain SM, Swarbrick A, Thompson EA, Tolaney SM, Warren SE, On Behalf Of The GeoMx Breast Cancer Consortium. Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler. Cancers (Basel) 2021;13:4456. [PMID: 34503266 DOI: 10.3390/cancers13174456] [Reference Citation Analysis]
6 Tzoras E, Zerdes I, Tsiknakis N, Manikis GC, Mezheyeuski A, Bergh J, Matikas A, Foukakis T. Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution. Cancers 2022;14:1999. [DOI: 10.3390/cancers14081999] [Reference Citation Analysis]
7 Brown LC, Salgado R, Luen SJ, Savas P, Loi S. Tumor-Infiltrating Lymphocyctes in Triple-Negative Breast Cancer: Update for 2020. Cancer J 2021;27:25-31. [PMID: 33475290 DOI: 10.1097/PPO.0000000000000501] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
8 Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R. Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance. Am J Pathol 2021:S0002-9440(21)00165-6. [PMID: 33895120 DOI: 10.1016/j.ajpath.2021.04.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Tien TZ, Lee JNLW, Lim JCT, Chen XY, Thike AA, Tan PH, Yeong JPS. Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence. Histopathology 2021;79:139-59. [PMID: 33400265 DOI: 10.1111/his.14328] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 El Bairi K, Haynes HR, Blackley E, Fineberg S, Shear J, Turner S, de Freitas JR, Sur D, Amendola LC, Gharib M, Kallala A, Arun I, Azmoudeh-Ardalan F, Fujimoto L, Sua LF, Liu SW, Lien HC, Kirtani P, Balancin M, El Attar H, Guleria P, Yang W, Shash E, Chen IC, Bautista V, Do Prado Moura JF, Rapoport BL, Castaneda C, Spengler E, Acosta-Haab G, Frahm I, Sanchez J, Castillo M, Bouchmaa N, Md Zin RR, Shui R, Onyuma T, Yang W, Husain Z, Willard-Gallo K, Coosemans A, Perez EA, Provenzano E, Ericsson PG, Richardet E, Mehrotra R, Sarancone S, Ehinger A, Rimm DL, Bartlett JMS, Viale G, Denkert C, Hida AI, Sotiriou C, Loibl S, Hewitt SM, Badve S, Symmans WF, Kim RS, Pruneri G, Goel S, Francis PA, Inurrigarro G, Yamaguchi R, Garcia-Rivello H, Horlings H, Afqir S, Salgado R, Adams S, Kok M, Dieci MV, Michiels S, Demaria S, Loi S; International Immuno-Oncology Biomarker Working Group. The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 2021;7:150. [PMID: 34853355 DOI: 10.1038/s41523-021-00346-1] [Reference Citation Analysis]
11 He J, Fu F, Wang W, Xi G, Guo W, Zheng L, Ren W, Qiu L, Huang X, Wang C, Li L, Kang D, Chen J. Prognostic value of tumour-infiltrating lymphocytes based on the evaluation of frequency in patients with oestrogen receptor-positive breast cancer. Eur J Cancer 2021;154:217-26. [PMID: 34293665 DOI: 10.1016/j.ejca.2021.06.011] [Reference Citation Analysis]
12 Abubakar M, Zhang J, Ahearn TU, Koka H, Guo C, Lawrence SM, Mutreja K, Figueroa JD, Ying J, Lissowska J, Lyu N, Garcia-Closas M, Yang XR. 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;30:1397-407. [PMID: 33952648 DOI: 10.1158/1055-9965.EPI-21-0055] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Hayward MK, Weaver VM. Improving DCIS diagnosis and predictive outcome by applying artificial intelligence. Biochim Biophys Acta Rev Cancer 2021;1876:188555. [PMID: 33933557 DOI: 10.1016/j.bbcan.2021.188555] [Reference Citation Analysis]
14 Gonzalez-Ericsson PI, Stovgaard ES, Sua LF, Reisenbichler E, Kos Z, Carter JM, Michiels S, Le Quesne J, Nielsen TO, Laenkholm AV, Fox SB, Adam J, Bartlett JM, Rimm DL, Quinn C, Peeters D, Dieci MV, Vincent-Salomon A, Cree I, Hida AI, Balko JM, Haynes HR, Frahm I, Acosta-Haab G, Balancin M, Bellolio E, Yang W, Kirtani P, Sugie T, Ehinger A, Castaneda CA, Kok M, McArthur H, Siziopikou K, Badve S, Fineberg S, Gown A, Viale G, Schnitt SJ, Pruneri G, Penault-Llorca F, Hewitt S, Thompson EA, Allison KH, Symmans WF, Bellizzi AM, Brogi E, Moore DA, Larsimont D, Dillon DA, Lazar A, Lien H, Goetz MP, Broeckx G, El Bairi K, Harbeck N, Cimino-Mathews A, Sotiriou C, Adams S, Liu SW, Loibl S, Chen IC, Lakhani SR, Juco JW, Denkert C, Blackley EF, Demaria S, Leon-Ferre R, Gluz O, Zardavas D, Emancipator K, Ely S, Loi S, Salgado R, Sanders M; International Immuno-Oncology Biomarker Working Group. The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice. J Pathol 2020;250:667-84. [PMID: 32129476 DOI: 10.1002/path.5406] [Cited by in Crossref: 48] [Cited by in F6Publishing: 48] [Article Influence: 24.0] [Reference Citation Analysis]
15 Valous NA, Moraleda RR, Jäger D, Zörnig I, Halama N. Interrogating the microenvironmental landscape of tumors with computational image analysis approaches. Semin Immunol 2020;48:101411. [PMID: 33168423 DOI: 10.1016/j.smim.2020.101411] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Kuroda H, Jamiyan T, Yamaguchi R, Kakumoto A, Abe A, Harada O, Masunaga A. Tumor-infiltrating B cells and T cells correlate with postoperative prognosis in triple-negative carcinoma of the breast. BMC Cancer 2021;21:286. [PMID: 33726701 DOI: 10.1186/s12885-021-08009-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Dudgeon SN, Wen S, Hanna MG, Gupta R, Amgad M, Sheth M, Marble H, Huang R, Herrmann MD, Szu CH, Tong D, Werness B, Szu E, Larsimont D, Madabhushi A, Hytopoulos E, Chen W, Singh R, Hart SN, Sharma A, Saltz J, Salgado R, Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform 2021;12:45. [PMID: 34881099 DOI: 10.4103/jpi.jpi_83_20] [Reference Citation Analysis]
18 Sobottka B, Nowak M, Frei AL, Haberecker M, Merki S, Levesque MP, Dummer R, Moch H, Koelzer VH; Tumor Profiler consortium. Establishing standardized immune phenotyping of metastatic melanoma by digital pathology. Lab Invest 2021;101:1561-70. [PMID: 34446805 DOI: 10.1038/s41374-021-00653-y] [Reference Citation Analysis]
19 Zerdes I, Simonetti M, Matikas A, Harbers L, Acs B, Boyaci C, Zhang N, Salgkamis D, Agartz S, Moreno-Ruiz P, Bai Y, Rimm DL, Hartman J, Mezheyeuski A, Bergh J, Crosetto N, Foukakis T. Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study. NPJ Breast Cancer 2021;7:144. [PMID: 34799582 DOI: 10.1038/s41523-021-00352-3] [Reference Citation Analysis]
20 Amgad M, Atteya L, Hussein H, Mohammed KH, Hafiz E, Elsebaie MAT, Mobadersany P, Manthey D, Gutman DA, Elfandy H, Cooper LAD. Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings. Bioinformatics 2021:btab670. [PMID: 34586355 DOI: 10.1093/bioinformatics/btab670] [Reference Citation Analysis]
21 Meirelles AL, Kurc T, Saltz J, Teodoro G. Effective Active Learning in Digital Pathology: A Case Study in Tumor Infiltrating Lymphocytes. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106828] [Reference Citation Analysis]
22 Li J, Garfinkel J, Zhang X, Wu D, Zhang Y, de Haan K, Wang H, Liu T, Bai B, Rivenson Y, Rubinstein G, Scumpia PO, Ozcan A. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl 2021;10:233. [PMID: 34795202 DOI: 10.1038/s41377-021-00674-8] [Reference Citation Analysis]
23 Graeser M, Feuerhake F, Gluz O, Volk V, Hauptmann M, Jozwiak K, Christgen M, Kuemmel S, Grischke EM, Forstbauer H, Braun M, Warm M, Hackmann J, Uleer C, Aktas B, Schumacher C, Kolberg-Liedtke C, Kates R, Wuerstlein R, Nitz U, Kreipe HH, Harbeck N. Immune cell composition and functional marker dynamics from multiplexed immunohistochemistry to predict response to neoadjuvant chemotherapy in the WSG-ADAPT-TN trial. J Immunother Cancer 2021;9:e002198. [PMID: 33963012 DOI: 10.1136/jitc-2020-002198] [Reference Citation Analysis]
24 Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ Jr. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021. [PMID: 34397396 DOI: 10.3233/BD-201011] [Reference Citation Analysis]
25 Thagaard J, Stovgaard ES, Vognsen LG, Hauberg S, Dahl A, Ebstrup T, Doré J, Vincentz RE, Jepsen RK, Roslind A, Kümler I, Nielsen D, Balslev E. Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers. Cancers (Basel) 2021;13:3050. [PMID: 34207414 DOI: 10.3390/cancers13123050] [Reference Citation Analysis]
26 Gupta R, Le H, Van Arnam J, Belinsky D, Hasan M, Samaras D, Kurc T, Saltz JH. Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics. Curr Pathobiol Rep 2020;8:133-48. [DOI: 10.1007/s40139-020-00217-7] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
27 Van Bockstal MR, François A, Altinay S, Arnould L, Balkenhol M, Broeckx G, Burguès O, Colpaert C, Dedeurwaerdere F, Dessauvagie B, Duwel V, Floris G, Fox S, Gerosa C, Hastir D, Jaffer S, Kurpershoek E, Lacroix-Triki M, Laka A, Lambein K, MacGrogan GM, Marchiò C, Martin Martinez MD, Nofech-Mozes S, Peeters D, Ravarino A, Reisenbichler E, Resetkova E, Sanati S, Schelfhout AM, Schelfhout V, Shaaban A, Sinke R, Stanciu-Pop CM, van Deurzen CHM, Van de Vijver KK, Van Rompuy AS, Vincent-Salomon A, Wen HY, Wong S, Bouzin C, Galant C. Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: the IVITA study. Mod Pathol 2021. [PMID: 34218258 DOI: 10.1038/s41379-021-00865-z] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
28 Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, Mitchell RN, Glass B, Hoffman S, Rao SK, Maheshwari C, Lahiri A, Prakash A, McLoughlin R, Kerner JK, Resnick MB, Montalto MC, Khosla A, Wapinski IN, Beck AH, Elliott HL, Taylor-Weiner A. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun 2021;12:1613. [PMID: 33712588 DOI: 10.1038/s41467-021-21896-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
29 Sobhani F, Robinson R, Hamidinekoo A, Roxanis I, Somaiah N, Yuan Y. Artificial intelligence and digital pathology: Opportunities and implications for immuno-oncology. Biochim Biophys Acta Rev Cancer 2021;1875:188520. [PMID: 33561505 DOI: 10.1016/j.bbcan.2021.188520] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
30 Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology 2021;78:791-804. [PMID: 33211332 DOI: 10.1111/his.14304] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
31 Binder A, Bockmayr M, Hägele M, Wienert S, Heim D, Hellweg K, Ishii M, Stenzinger A, Hocke A, Denkert C, Müller K, Klauschen F. Morphological and molecular breast cancer profiling through explainable machine learning. Nat Mach Intell 2021;3:355-66. [DOI: 10.1038/s42256-021-00303-4] [Cited by in Crossref: 16] [Cited by in F6Publishing: 3] [Article Influence: 16.0] [Reference Citation Analysis]
32 El Bairi K, Al Jarroudi O, Afqir S. Practical Tools and Guidelines for Young Oncologists From Resource-Limited Settings to Publish Excellence and Advance Their Career. JCO Glob Oncol 2021;7:1668-81. [PMID: 34910583 DOI: 10.1200/GO.21.00310] [Reference Citation Analysis]
33 Saw SPL, Ong BH, Chua KLM, Takano A, Tan DSW. Revisiting neoadjuvant therapy in non-small-cell lung cancer. Lancet Oncol 2021;22:e501-16. [PMID: 34735819 DOI: 10.1016/S1470-2045(21)00383-1] [Reference Citation Analysis]