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Report on Computational Assessment of Tumor Infiltrating Lymphocytes From the International Immuno-Oncology Biomarker Working Group Publisher



Amgad M1 ; Stovgaard ES2 ; Balslev E2 ; Thagaard J3, 4 ; Chen W5 ; Dudgeon S5 ; Sharma A1 ; Kerner JK6 ; Denkert C7, 8, 9 ; Yuan Y10, 11 ; Abduljabbar K10, 11 ; Wienert S7 ; Savas P12, 13 ; Voorwerk L14 Show All Authors
Authors
  1. Amgad M1
  2. Stovgaard ES2
  3. Balslev E2
  4. Thagaard J3, 4
  5. Chen W5
  6. Dudgeon S5
  7. Sharma A1
  8. Kerner JK6
  9. Denkert C7, 8, 9
  10. Yuan Y10, 11
  11. Abduljabbar K10, 11
  12. Wienert S7
  13. Savas P12, 13
  14. Voorwerk L14
  15. Beck AH6
  16. Madabhushi A15, 16
  17. Hartman J17
  18. Sebastian MM18
  19. Horlings HM19
  20. Hudecek J20
  21. Ciompi F21
  22. Moore DA22
  23. Singh R23
  24. Roblin E24
  25. Balancin ML25
  26. Mathieu MC26
  27. Lennerz JK27
  28. Kirtani P28
  29. Chen IC29
  30. Braybrooke JP30, 31
  31. Pruneri G32
  32. Demaria S33
  33. Adams S34
  34. Schnitt SJ35
  35. Lakhani SR36
  36. Rojo F37, 38
  37. Comerma L37, 38
  38. Badve SS39
  39. Khojasteh M40
  40. Symmans WF41
  41. Sotiriou C42, 43
  42. Gonzalezericsson P44
  43. Poguegeile KL45
  44. Kim RS45
  45. Rimm DL46
  46. Viale G47
  47. Hewitt SM48
  48. Bartlett JMS49, 50
  49. Penaultllorca F51, 52
  50. Goel S53
  51. Lien HC54
  52. Loibl S55
  53. Kos Z56
  54. Loi S13, 57
  55. Hanna MG58
  56. Michiels S59, 60
  57. Kok M61, 62
  58. Nielsen TO63
  59. Lazar AJ41, 64, 65, 66
  60. Bagohorvath Z67
  61. Kooreman LFS68, 69
  62. Van Der Laak JAWM21, 70
  63. Saltz J71
  64. Gallas BD5
  65. Kurkure U40
  66. Barnes M72
  67. Salgado R12, 73
  68. Cooper LAD74
  69. Hyytiainen A75
  70. Hida AI76
  71. Thompson A77
  72. Lefevre A78
  73. Gown A79
  74. Lo A80
  75. Sapino A81
  76. Moreira A82
  77. Richardson A83
  78. Vingiani A84
  79. Bellizzi AM85
  80. Tutt A86
  81. Guerrerozotano A87
  82. Grigoriadis A88, 89
  83. Ehinger A90
  84. Garridocastro AC91
  85. Vincentsalomon A92
  86. Laenkholm AV93
  87. Ciminomathews A94
  88. Srinivasan A95
  89. Acs B96
  90. Singh B97
  91. Calhoun B98
  92. Haibekans B99
  93. Solomon B100
  94. Thapa B101
  95. Nelson BH102
  96. Castaneda C103, 104
  97. Ballesteroesmerino C105
  98. Criscitiello C106
  99. Boeckx C78
  100. Colpaert C107
  101. Quinn C108
  102. Chennubhotla CS109
  103. Swanton C110
  104. Solinas C111
  105. Hiley C110
  106. Drubay D59, 60
  107. Bethmann D112
  108. Dillon DA113
  109. Larsimont D114
  110. Sabanathan D115
  111. Peeters D116
  112. Zardavas D117
  113. Hoflmayer D118
  114. Johnson DB119
  115. Thompson EA120
  116. Brogi E58
  117. Perez E121
  118. Elgabry EA122
  119. Blackley EF100
  120. Reisenbichler E46
  121. Bellolio E123, 124
  122. Chmielik E125
  123. Gaire F126
  124. Andre F127
  125. Lu FI128
  126. Azmoudehardalan F129
  127. Gruosso FT130
  128. Peale F131
  129. Hirsch FR132
  130. Klaushen F133
  131. Acostahaab G134
  132. Farshid G135
  133. Van Den Eynden G136
  134. Curigliano G137, 138
  135. Floris G139, 140
  136. Broeckx G141
  137. Koeppen H80
  138. Haynes HR142
  139. Mcarthur H143
  140. Joensuu H144
  141. Olofsson H145
  142. Cree I146
  143. Nederlof I147
  144. Frahm I148
  145. Brcic I149
  146. Chan J150
  147. Hall JA151
  148. Ziai J80
  149. Brock J152
  150. Wesseling J153
  151. Giltnane J80
  152. Lemonnier J154
  153. Zha J155
  154. M Ribeiro J156
  155. Carter JM157
  156. Hainfellner J158
  157. Quesne JL159
  158. Juco JW160
  159. Reisfilho J58, 161
  160. Van Den Berg J162
  161. Sanchez J104
  162. Sparano J163
  163. Cucherousset J164
  164. Araya JC123
  165. Adam J165
  166. Balko JM166
  167. Saeger K167
  168. Siziopikou K168
  169. Willardgallo K169
  170. Sikorska K170
  171. Weber K171
  172. Steele KE155
  173. Emancipator K160
  174. El Bairi K172
  175. Blenman KRM173
  176. Allison KH174
  177. Van De Vijver KK175
  178. Korski K176
  179. Pusztai L173
  180. Buisseret L169
  181. Shi L177
  182. Shiwei L178
  183. Molinero L131
  184. Estrada MV179
  185. Van Seijen M19
  186. Lacroixtriki M180
  187. Cheang MCU181
  188. Bakir M110
  189. Van De Vijver M182
  190. Dieci MV183
  191. Rebelatto MC155
  192. Piccart M184
  193. Goetz MP121
  194. Preusser M158
  195. Sanders ME185
  196. Regan MM186, 187
  197. Christie M188
  198. Misialek M189
  199. Ignatiadis M190
  200. De Maaker M19
  201. Van Bockstal M191
  202. Castillo M104
  203. Harbeck N192
  204. Tung N193
  205. Laudus N194
  206. Sirtaine N195
  207. Burchardi N196
  208. Ternes N197
  209. Radosevicrobin N198
  210. Gluz O199
  211. Grimm O126
  212. Nuciforo P200
  213. Jank P201
  214. Jelinic P160
  215. Watson PH202
  216. Francis PA13, 57
  217. Russell PA203
  218. Pierce RH204
  219. Hills R205
  220. Leonferre R121
  221. De Wind R195
  222. Shui R206
  223. Declercq S207
  224. Leung S63
  225. Tabbarah S208
  226. Souza SC209
  227. Otoole S210
  228. Swain S211
  229. Willis S212
  230. Ely S213
  231. Kim SR214
  232. Bedri S215
  233. Irshad S216, 217
  234. Liu SW218
  235. Hendry S219
  236. Bianchi S220
  237. Braganca S221
  238. Paik S95
  239. Fox SB219
  240. Luen SJ12
  241. Naber S222
  242. Luz S223
  243. Fineberg S224
  244. Soler T225
  245. Gevaert T226
  246. Dalfons T58
  247. John T227
  248. Sugie T228
  249. Bossuyt V229
  250. Manem V99
  251. Camaea VP230
  252. Tong W231
  253. Yang W206
  254. Tran WT208
  255. Wang Y232
  256. Allory Y233
  257. Husain Z234

Source: npj Breast Cancer Published:2020


Abstract

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring. © 2020, The Author(s).
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