Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence

被引：0

作者：

Keyl, Julius ^{[1
,2
]}

Keyl, Philipp ^{[3
,4
]}

Montavon, Gregoire ^{[4
,5
,6
]}

Hosch, Rene ^{[1
]}

Brehmer, Alexander ^{[1
]}

Mochmann, Liliana ^{[3
]}

Jurmeister, Philipp ^{[3
]}

Dernbach, Gabriel ^{[5
]}

Kim, Moon ^{[1
]}

Koitka, Sven ^{[1
,7
]}

Bauer, Sebastian ^{[8
,9
,10
,11
]}

Bechrakis, Nikolaos ^{[9
,10
,11
,12
]}

Forsting, Michael ^{[7
,9
,11
]}

Fuehrer-Sakel, Dagmar ^{[9
,10
,13
]}

Glas, Martin ^{[9
,10
,11
,14
,15
]}

Gruenwald, Viktor ^{[8
,9
,10
,11
,16
]}

Hadaschik, Boris ^{[9
,10
,11
,16
]}

Haubold, Johannes ^{[7
,9
]}

Herrmann, Ken ^{[9
,10
,11
,17
]}

Kasper, Stefan ^{[8
,9
,10
,11
]}

Kimmig, Rainer ^{[9
,10
,18
]}

Lang, Stephan ^{[9
,19
]}

Rassaf, Tienush ^{[9
,20
]}

Roesch, Alexander ^{[9
,10
,11
,21
]}

Schadendorf, Dirk ^{[9
,10
,11
,21
,22
]}

Siveke, Jens T. ^{[8
,9
,10
,11
,23
,24
,25
]}

Stuschke, Martin ^{[9
,10
,11
,26
]}

Sure, Ulrich ^{[9
,10
,11
,27
]}

Totzeck, Matthias ^{[9
,20
]}

Welt, Anja ^{[8
,9
,10
]}

Wiesweg, Marcel ^{[8
,9
,10
,11
]}

Baba, Hideo A. ^{[2
,9
]}

Nensa, Felix ^{[1
,7
,9
,11
]}

Egger, Jan ^{[1
]}

Mueller, Klaus-Robert ^{[4
,5
,28
,29
]}

Schuler, Martin ^{[8
,9
,10
,11
]}

Klauschen, Frederick ^{[3
,4
,30
,31
,32
]}

Kleesiek, Jens ^{[1
,9
,10
,11
]}

机构：

[1] Univ Hosp Essen AoR, Inst Artificial Intelligence Med, Essen, Germany

[2] Univ Hosp Essen AoR, Inst Pathol, Essen, Germany

[3] Ludwig Maximilians Univ Munchen, Inst Pathol, Munich, Germany

[4] BIFOLD Berlin Inst Fdn Learning & Data, Berlin, Germany

[5] Tech Univ Berlin, Machine Learning Grp, Berlin, Germany

[6] Free Univ Berlin, Dept Math & Comp Sci, Berlin, Germany

[7] Univ Hosp Essen AoR, Inst Diagnost & Intervent Radiol & Neuroradiol, Essen, Germany

[8] Univ Hosp Essen AoR, Dept Med Oncol, Essen, Germany

[9] Univ Duisburg Essen, Fac Med, Essen, Germany

[10] Univ Hosp Essen AoR, West German Canc Ctr, Essen, Germany

[11] Univ Hosp Essen AoR, German Canc Consortium DKTK, Partner Site, Essen, Germany

[12] Univ Hosp Essen AoR, Dept Nuclearmedicine, D-45147 Essen, Germany

[13] Univ Hosp Essen AoR, Dept Endocrinol Diabet & Metab, Essen, Germany

[14] Univ Duisburg Essen, Dept Neurol, Div Clin Neurooncol, Essen, Germany

[15] Univ Duisburg Essen, Univ Med Essen, Ctr Translat Neuroand Behav Sci C TNBS, Essen, Germany

[16] Univ Hosp Essen AoR, Dept Urol, Essen, Germany

[17] Univ Hosp Essen AoR, Dept Nucl Med, Essen, Germany

[18] Univ Hosp Essen AoR, Dept Gynecol & Obstet, Essen, Germany

[19] Univ Hosp Essen AoR, Dept Endocrinol & Metab, Essen, Germany

[20] Univ Hosp Essen AoR, West German Heart & Vasc Ctr Essen, Dept Cardiol & Vasc Med, Essen, Germany

[21] Univ Hosp Essen AoR, Dept Dermatol, D-45147 Essen, Germany

[22] Univ Duisburg Essen, Res Ctr One Hlth, Res Alliance Ruhr, Essen, Germany

[23] Univ Duisburg Essen, Univ Hosp Essen AoR, Bridge Inst Expt Tumor Therapy, West German Canc Ctr, Essen, Germany

[24] German Canc Consortium DKTK, Div Solid Tumor Translat Oncol, Partner Site Essen, Heidelberg, Germany

[25] German Canc Res Ctr, DKFZ, Heidelberg, Germany

[26] Univ Hosp Essen AoR, Dept Radiotherapy, Essen, Germany

[27] Univ Hosp Essen AoR, Dept Neurosurg & Spine Surg, Essen, Germany

[28] Korea Univ, Dept Artificial Intelligence, Seoul, South Korea

[29] MPI Informat, Saarbrucken, Germany

[30] German Canc Consortium DKTK, German Canc Res Ctr DKFZ, Berlin Partner Site, Berlin, Germany

[31] German Canc Consortium DKTK, German Canc Res Ctr DKFZ, Munich Partner Site, Munich, Germany

[32] Bavarian Canc Res Ctr BZKF, Erlangen, Germany

来源：

NATURE CANCER | 2025年 / 6卷 / 02期

关键词：

PANCREATIC-CANCER; PROGNOSTIC-FACTOR; NEURAL-NETWORKS; SURVIVAL;

D O I：

10.1038/s43018-024-00891-1

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Despite advances in precision oncology, clinical decision-making still relies on limited variables and expert knowledge. To address this limitation, we combined multimodal real-world data and explainable artificial intelligence (xAI) to introduce AI-derived (AID) markers for clinical decision support. We used xAI to decode the outcome of 15,726 patients across 38 solid cancer entities based on 350 markers, including clinical records, image-derived body compositions, and mutational tumor profiles. xAI determined the prognostic contribution of each clinical marker at the patient level and identified 114 key markers that accounted for 90% of the neural network's decision process. Moreover, xAI enabled us to uncover 1,373 prognostic interactions between markers. Our approach was validated in an independent cohort of 3,288 patients with lung cancer from a US nationwide electronic health record-derived database. These results show the potential of xAI to transform the assessment of clinical variables and enable personalized, data-driven cancer care.

引用

页码：307 / 322

页数：32

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