Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma Article Swipe

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Thazin Nwe Aung , Matthew Liu , David Su , Saba Shafi , Ceren Boyacı , Sanna Steen , Nikolaos Tsiknakis , Joan Martínez Vidal , Nigel G. Maher , Goran Micevic , Samuel Tan , Matthew D. Vesely , Saeed Nourmohammadi , Yalai Bai , Dijana Djureinovic , Pok Fai Wong , Katherine E. Bates , Nay Chan , Niki Gavirelatou , Mengni He , Sneha Burela , Robert Barna , Martina Bosić , Konstantin Bräutigam , Irineu Illa-Bochaca , Zhou Chenhao , João Martins Gama , B.E. Kreis , Réka Mohácsi , Nir Pillar , João Correia Pinto , Christos Poulios , Maria Angeliki Toli , Evangelos Tzoras , Yadriel Bracero , Francesca M. Bosisio , Gábor Cserni , Alis Dema , Francesco Fortarezza , Maria Gonzalez , Irene Gullo , Francisco Javier Queipo Gutiérrez , Ezgi Hacıhasanoğlu , Viktor Jović , Bianca Andreea Lazar , Maria Olincă , Christina Neppl , Rui Caetano Oliveira , Federica Pezzuto , Daniel Pinto , Vanda Plótár , Ovidiu Pop , Tilman T. Rau , Kristijan Skok , Wenwen Sun , Ezgi Dicle Serbes , Wiebke Solaß , Olga Stanowska , Attila Marcell Szász , K Szymoński , Franziska Thimm , Danielle Vignati , Alon Vigdorovits , Víctor G. Prieto , Tobias Sinnberg , James S. Wilmott , Shawn E. Cowper , Jonathan Warrell , Yvonne M. Saenger , Johan Hartman , Jasmine Plummer , Iman Osman , David L. Rimm , Balázs Ács ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1001/jamanetworkopen.2025.18906

Importance Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. Objective To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. Design, Setting, and Participants This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. Main Outcomes and Measures Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. Results There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin–stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W , 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). Conclusions and Relevance In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

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Medicine Intraclass correlation Melanoma H&E stain Tumor-infiltrating lymphocytes Cohort Retrospective cohort study Internal medicine Pathology Medical physics Immunotherapy Cancer Immunohistochemistry Clinical psychology Cancer research Psychometrics

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Type: article
Language: en
Landing Page: https://doi.org/10.1001/jamanetworkopen.2025.18906
PDF: https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2836021/aung_2025_oi_250588_1750958613.50939.pdf
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Cited By: 3
References: 32
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DOI: https://doi.org/10.1001/jamanetworkopen.2025.18906

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Title: Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma

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Language: en

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Publication year: 2025

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Authors: Thazin Nwe Aung, Matthew Liu, David Su, Saba Shafi, Ceren Boyacı, Sanna Steen, Nikolaos Tsiknakis, Joan Martínez Vidal, Nigel G. Maher, Goran Micevic, Samuel Tan, Matthew D. Vesely, Saeed Nourmohammadi, Yalai Bai, Dijana Djureinovic, Pok Fai Wong, Katherine E. Bates, Nay Chan, Niki Gavirelatou, Mengni He, Sneha Burela, Robert Barna, Martina Bosić, Konstantin Bräutigam, Irineu Illa-Bochaca, Zhou Chenhao, João Martins Gama, B.E. Kreis, Réka Mohácsi, Nir Pillar, João Correia Pinto, Christos Poulios, Maria Angeliki Toli, Evangelos Tzoras, Yadriel Bracero, Francesca M. Bosisio, Gábor Cserni, Alis Dema, Francesco Fortarezza, Maria Gonzalez, Irene Gullo, Francisco Javier Queipo Gutiérrez, Ezgi Hacıhasanoğlu, Viktor Jović, Bianca Andreea Lazar, Maria Olincă, Christina Neppl, Rui Caetano Oliveira, Federica Pezzuto, Daniel Pinto, Vanda Plótár, Ovidiu Pop, Tilman T. Rau, Kristijan Skok, Wenwen Sun, Ezgi Dicle Serbes, Wiebke Solaß, Olga Stanowska, Attila Marcell Szász, K Szymoński, Franziska Thimm, Danielle Vignati, Alon Vigdorovits, Víctor G. Prieto, Tobias Sinnberg, James S. Wilmott, Shawn E. Cowper, Jonathan Warrell, Yvonne M. Saenger, Johan Hartman, Jasmine Plummer, Iman Osman, David L. Rimm, Balázs Ács

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Concepts: Medicine, Intraclass correlation, Melanoma, H&E stain, Tumor-infiltrating lymphocytes, Cohort, Retrospective cohort study, Internal medicine, Pathology, Medical physics, Immunotherapy, Cancer, Immunohistochemistry, Clinical psychology, Cancer research, Psychometrics

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Cited by: 3

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abstract_inverted_index.The	99, 286
abstract_inverted_index.age	236
abstract_inverted_index.all	298
abstract_inverted_index.and	13, 23, 47, 57, 77, 93, 113, 127, 137, 142, 154, 174, 181, 202, 206, 261, 282, 353, 371, 388, 411, 421
abstract_inverted_index.are	4
abstract_inverted_index.arm	272, 278
abstract_inverted_index.for	65, 151, 165, 219, 297, 309, 318, 418
abstract_inverted_index.the	55, 152, 200, 212, 230, 270, 276, 336, 355, 382, 396, 400, 407
abstract_inverted_index.was	101
abstract_inverted_index.who	245
abstract_inverted_index.(95%	347, 364
abstract_inverted_index.(HR)	344
abstract_inverted_index.0.44	317
abstract_inverted_index.0.45	346
abstract_inverted_index.0.56	363
abstract_inverted_index.0.61	308
abstract_inverted_index.0.90	296
abstract_inverted_index.111)	334
abstract_inverted_index.16.6	201
abstract_inverted_index.2022	112
abstract_inverted_index.2023	115
abstract_inverted_index.61.0	239
abstract_inverted_index.ICCs	293
abstract_inverted_index.June	114
abstract_inverted_index.Main	140
abstract_inverted_index.TILs	257, 312
abstract_inverted_index.This	79
abstract_inverted_index.arms	156
abstract_inverted_index.from	124
abstract_inverted_index.good	189
abstract_inverted_index.into	423
abstract_inverted_index.more	45
abstract_inverted_index.than	295
abstract_inverted_index.that	43
abstract_inverted_index.this	374
abstract_inverted_index.tool	414
abstract_inverted_index.were	131, 149, 163, 184, 197, 224, 273
abstract_inverted_index.with	71, 108, 119, 227, 292, 329, 340, 359, 392
abstract_inverted_index.(ICC)	147
abstract_inverted_index.(ICC,	307
abstract_inverted_index..04).	369
abstract_inverted_index.16.6,	358
abstract_inverted_index.Clark	166, 320
abstract_inverted_index.There	223
abstract_inverted_index.data.	159
abstract_inverted_index.male)	244
abstract_inverted_index.newer	39
abstract_inverted_index.offer	415
abstract_inverted_index.prone	27
abstract_inverted_index.ratio	343
abstract_inverted_index.study	84, 100, 376
abstract_inverted_index.total	252
abstract_inverted_index.using	103, 157, 199, 335, 354
abstract_inverted_index.value	214
abstract_inverted_index.while	275
abstract_inverted_index.whole	248
abstract_inverted_index.(TILs)	3
abstract_inverted_index.(brisk	168
abstract_inverted_index..005),	352
abstract_inverted_index.across	116
abstract_inverted_index.cohort	233
abstract_inverted_index.cutoff	338, 356
abstract_inverted_index.ensure	134
abstract_inverted_index.future	419
abstract_inverted_index.hazard	342
abstract_inverted_index.higher	294
abstract_inverted_index.limits	402
abstract_inverted_index.manual	153, 271, 305, 319
abstract_inverted_index.median	203, 337
abstract_inverted_index.nature	398
abstract_inverted_index.scores	167, 217, 325
abstract_inverted_index.showed	326
abstract_inverted_index.slides	25
abstract_inverted_index.sparse	175
abstract_inverted_index.tissue	120, 249, 264
abstract_inverted_index.values	148, 162, 183
abstract_inverted_index.years;	241
abstract_inverted_index.(median	234
abstract_inverted_index.Design,	75
abstract_inverted_index.January	111
abstract_inverted_index.Kendall	160, 314
abstract_inverted_index.Results	222
abstract_inverted_index.[50.5%]	243
abstract_inverted_index.[range]	235
abstract_inverted_index.between	89, 110
abstract_inverted_index.cohorts	105, 401
abstract_inverted_index.dataset	410
abstract_inverted_index.diverse	135
abstract_inverted_index.global,	81
abstract_inverted_index.leading	31
abstract_inverted_index.machine	62, 299
abstract_inverted_index.methods	92
abstract_inverted_index.patient	330
abstract_inverted_index.produce	44
abstract_inverted_index.scoring	41, 87
abstract_inverted_index.stromal	311
abstract_inverted_index.testing	232
abstract_inverted_index.AI-based	323
abstract_inverted_index.Although	395
abstract_inverted_index.Measures	143
abstract_inverted_index.Outcomes	141
abstract_inverted_index.Setting,	76
abstract_inverted_index.adjusted	218
abstract_inverted_index.analyses	210
abstract_inverted_index.approach	339
abstract_inverted_index.assessed	211
abstract_inverted_index.clinical	34, 58, 405
abstract_inverted_index.compared	70, 85, 391
abstract_inverted_index.cutoffs.	204
abstract_inverted_index.evaluate	54
abstract_inverted_index.however,	16
abstract_inverted_index.included	279
abstract_inverted_index.learning	63, 300
abstract_inverted_index.melanoma	69, 109, 228, 247, 263, 424
abstract_inverted_index.methods.	74, 394
abstract_inverted_index.moderate	187
abstract_inverted_index.nonbrisk	171
abstract_inverted_index.outcomes	331
abstract_inverted_index.patients	107, 226
abstract_inverted_index.publicly	408
abstract_inverted_index.readouts	49
abstract_inverted_index.reliable	46
abstract_inverted_index.research	128
abstract_inverted_index.selected	132
abstract_inverted_index.superior	290, 386
abstract_inverted_index.utility,	406
abstract_inverted_index.validity	59
abstract_inverted_index.Objective	52
abstract_inverted_index.Relevance	372
abstract_inverted_index.academic,	125
abstract_inverted_index.algorithm	64, 288, 384
abstract_inverted_index.approach.	98
abstract_inverted_index.available	409
abstract_inverted_index.biomarker	7
abstract_inverted_index.clinical,	126
abstract_inverted_index.conducted	102
abstract_inverted_index.evaluated	121, 256
abstract_inverted_index.excellent	192
abstract_inverted_index.expertise	136
abstract_inverted_index.melanoma,	9, 381
abstract_inverted_index.outcomes;	15
abstract_inverted_index.scoring).	322
abstract_inverted_index.sections.	250, 265
abstract_inverted_index.0.26-0.80;	349
abstract_inverted_index.0.32-0.98;	366
abstract_inverted_index.AI-derived	310
abstract_inverted_index.Importance	0
abstract_inverted_index.Intraclass	144
abstract_inverted_index.Therefore,	36
abstract_inverted_index.analytical	56
abstract_inverted_index.approaches	42
abstract_inverted_index.artificial	95
abstract_inverted_index.assessment	20
abstract_inverted_index.calculated	150, 164
abstract_inverted_index.classified	185
abstract_inverted_index.consistent	48
abstract_inverted_index.decisions.	35
abstract_inverted_index.diagnosis,	11, 238
abstract_inverted_index.foundation	417
abstract_inverted_index.important.	51
abstract_inverted_index.prognosis,	12
abstract_inverted_index.prognostic	83, 213, 327, 375, 389
abstract_inverted_index.regression	209
abstract_inverted_index.validation	420
abstract_inverted_index.variables,	302
abstract_inverted_index.variables.	221
abstract_inverted_index.(AI)-driven	97
abstract_inverted_index.AI-assisted	155, 277
abstract_inverted_index.Conclusions	370
abstract_inverted_index.Univariable	205
abstract_inverted_index.[25.0-87.0]	240
abstract_inverted_index.assessments	306
abstract_inverted_index.coefficient	146
abstract_inverted_index.contributed	246
abstract_inverted_index.correlation	145
abstract_inverted_index.development	37
abstract_inverted_index.hematoxylin	22, 260
abstract_inverted_index.independent	231
abstract_inverted_index.influencing	10
abstract_inverted_index.integration	422
abstract_inverted_index.lymphocytes	2
abstract_inverted_index.management.	425
abstract_inverted_index.open-source	412
abstract_inverted_index.provocative	6
abstract_inverted_index.traditional	17, 72, 90, 393
abstract_inverted_index.(0.40-0.60),	188
abstract_inverted_index.(0.61-0.80),	190
abstract_inverted_index.Participants	78, 130
abstract_inverted_index.associations	328, 390
abstract_inverted_index.backgrounds.	139
abstract_inverted_index.demonstrated	289, 385
abstract_inverted_index.dichotomized	198
abstract_inverted_index.inconsistent	33
abstract_inverted_index.intelligence	96
abstract_inverted_index.measurements	196
abstract_inverted_index.participants	123, 255, 268
abstract_inverted_index.pathologists	281
abstract_inverted_index.professional	138
abstract_inverted_index.variability,	30
abstract_inverted_index.(scientists).	285
abstract_inverted_index.Reliabilities	178
abstract_inverted_index.demonstration	403
abstract_inverted_index.immunotherapy	14
abstract_inverted_index.institutions,	118
abstract_inverted_index.institutions.	129
abstract_inverted_index.interobserver	29
abstract_inverted_index.multivariable	207
abstract_inverted_index.outperforming	304
abstract_inverted_index.pathologists,	274
abstract_inverted_index.retrospective	104, 397
abstract_inverted_index.significantly	303
abstract_inverted_index.multioperator,	80
abstract_inverted_index.quantification	67, 379
abstract_inverted_index.eosin–stained	24, 262
abstract_inverted_index.log-transformed	158
abstract_inverted_index.nonpathologists	284
abstract_inverted_index.reproducibility	88, 387
abstract_inverted_index.pathologist-read	18, 73, 91
abstract_inverted_index.reproducibility,	291
abstract_inverted_index.clinicopathologic	220
abstract_inverted_index.Tumor-infiltrating	1
abstract_inverted_index.(&amp;gt;0.80).	193
abstract_inverted_index.multi-institutional	82
cited_by_percentile_year.max	97
cited_by_percentile_year.min	96
countries_distinct_count	18
institutions_distinct_count	74
citation_normalized_percentile.value	0.96139236
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True