Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster viewing and lunch

167P - Deep learning-based whole slide image analysis to predict sentinel node status in the INSEMA cohort

Date

12 May 2023

Session

Poster viewing and lunch

Presenters

Sibylle Loibl

Citation

Annals of Oncology (2023) 8 (1suppl_4): 101221-101221. 10.1016/esmoop/esmoop101221

Authors

F. Marmé1, E. Krieghoff-Henning2, B. Gerber3, M. Schmitt2, D. Zahm4, D. Bauerschlag5, H. Forstbauer6, G. Hildebrandt7, B. Ataseven8, T. Brodkorb9, C. Denkert10, A. Stachs11, D. Krug12, J. Heil13, M. Golatta14, T. Kuehn15, V. Nekljudova16, S. Loibl17, T. Reimer11, T. Brinker2

Author affiliations

  • 1 UMM - Universitaetsklinikum Mannheim - Medizinische Fakultaet, Mannheim/DE
  • 2 DKFZ - German Cancer Research Center, Heidelberg/DE
  • 3 Universitaetsmedizin Rostock, Rostock/DE
  • 4 SRH Waldklinikum Gera, Gera/DE
  • 5 University Hospital UKSH, 24105 - Kiel/DE
  • 6 Praxisnetzwerk Onkologie, Troisdorf/DE
  • 7 Universitaetsmedizin Rostock - Klinik und Poliklinik fuer Strahlentherapie, Rostock/DE
  • 8 KEM | Evang. Kliniken Essen-Mitte gGmbH, Essen/DE
  • 9 Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim/DE
  • 10 UKGM - Uniklinikum Giessen und Marburg - Standort Marburg, Marburg/DE
  • 11 Universitätsfrauenklinik am Klinikum Südstadt Rostock, Rostock/DE
  • 12 University Hospital Schleswig-Holstein, Kiel/DE
  • 13 University Hospital Heidelberg, Heidelberg/DE
  • 14 UKHD - Universitätsklinikum Heidelberg, Heidelberg/DE
  • 15 Klinikum Esslingen, Esslingen am Neckar/DE
  • 16 GBG Forschungs GmbH, Neu-Isenburg/DE
  • 17 German Breast Group (GBG) Forschungs GmbH, Neu-Isenburg/DE

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 167P

Background

As further de-escalation of axillary surgery is ongoing, new biomarkers that convey the same prognostic information as sentinel node status are called for.

Methods

To predict sentinel node status, we trained a deep learning (DL) image analysis model on H&E-stained whole slide images (WSIs) of primary tumors. For training, we used cases from the INSEMA standard arm (n=762, ca. 94 % HR+/HER2-, 3.5 % G3, less than 1% pT3/4, 13 % SLN-positive) and a cohort from the Women’s Clinic in Mannheim, Germany (n=150, all HR+/HER2-, G2, pT1/2, ca. 16 % SLN positive). We also fitted a logistic regression with clinical data (pT stage, Ki-67) for this task. Models were tested on a holdout INSEMA set (n=381), and the image model also on the higher risk TCGA BRCA cohort (n=650, ca. 72% HR+, ca. 55% SLN+). Vice versa, we trained a model on TCGA WSIs and tested it on the other cohorts.

Results

During training, the image and the clinical model yielded Areas under the Receiver Operating Characteristic Curve (AUROCs) of 0.62 and of 0.77 on the Mannheim WSIs, respectively. However, performance of the image model was random on the INSEMA (determined by blinded assessment) and TCGA BRCA test sets. The clinical classifier retained an AUROC of 0.62 on the INSEMA set. Inclusion of the image classifier output in the logistic regression did not improve performance on INSEMA. The image model trained on TCGA also yielded random performance on the INSEMA and Mannheim cohorts.

Conclusions

Image analysis algorithms trained on H&E stains of the primary tumors from INSEMA or TCGA using established techniques were unable to predict sentinel status, which may suggest a lack of systematic histological differences by lymph node status in these cohorts. Thus, DL-based WSI analysis may not be a good strategy to replace sentinel node assessment, especially in low- to intermediate-risk, hormone receptor-positive breast cancer.

Clinical trial identification

NCT02466737.

Legal entity responsible for the study

The authors.

Funding

TB, MS and EKH were funded by the TPI grant to TJB by the German Federal Ministry of Health. INSEMA trial is supported by German Cancer Aid (Deutsche Krebshilfe, Bonn, Germany), Grant No. 110580 and Grant No. 70110580 to University Medicine Rostock.

Disclosure

F. Marmé: Financial Interests, Personal, Invited Speaker: AstraZeneca, GSK/Tesaro, Clovis, Pfizer, Lilly; Financial Interests, Personal, Advisory Board: AstraZeneca, MSD, Novartis, Roche, Gilead/immunomedics, EISAI, PharmaMar, GenomicHealth, Myriad, Seagen; Financial Interests, Institutional, Invited Speaker: Seagen, Daiichi Sankyo, GSK, AstraZeneca, Roche, AstraZeneca, Novartis, Roche, Eisai, Gilead/Immunomedics, MSD, German Breast Group, AGO Research GmbH, Vaccibody, GSK; Financial Interests, Institutional, Advisory Board: Roche, Immunicom; Financial Interests, Institutional, Funding: AstraZeneca, Lilly, Seagen. C. Denkert: Financial Interests, Personal, Advisory Board: MSD Oncology, Daiichi Sankyo, Molecular Health, AstraZeneca, Roche, Lilly; Financial Interests, Personal, Invited Speaker: AstraZeneca, VmScope digital pathology software; Financial Interests, Institutional, Research Grant: Roche, Myriad, German Breast Group. V. Nekljudova: Financial Interests, Institutional, Full or part-time Employment: GBG; Financial Interests, Institutional, Research Grant: AbbVie, AstraZeneca, BMS, Daichi-Sankyo,Gilead, Novartis, Pfizer, Roche; Non-Financial Interests, Institutional, Writing Engagements: Daiichi Sankyo, Gilead, Novartzis, Pfizer, Roche, Seagen; Other, Institutional, Other, EP14153692.0: Patent; Other, Institutional, Other, EP21152186.9: Patent; Other, Institutional, Other, EP15702464.7: Patent; Other, Institutional, Other, EP19808852.8: Patent; Other, Institutional, Royalties: VM Scope GmbH. S. Loibl: Financial Interests, Institutional, Advisory Board, Member: Amgen, AstraZeneca, BMS, Celgene, EirGenix, GSK, Lilly, Pierre Fabre, Roche, Seagen, AbbVie, Sanofi, Gilead, Merck, Novartis, Relay Therapeutics; Financial Interests, Institutional, Invited Speaker: AstraZeneca, DSI, Novartis, Pfizer, Roche, Gilead, Seagen; Financial Interests, Institutional, Advisory Board: DSI, Pfizer, Olema; Financial Interests, Personal, Invited Speaker: Medscape; Financial Interests, Personal, Full or part-time Employment, CEO: GBG Forschungs GmbH; Financial Interests, Institutional, Invited Speaker, Ki67: VM Scope GmbH; Financial Interests, Institutional, Research Grant: AstraZeneca, Celgene, Novartis, Immunomedics/Gilead, Pfizer, Roche, Daiichi Sankyo; Financial Interests, Institutional, Funding: AbbVie, Molecular Health; Financial Interests, Personal, Other, PIPenelope/Padma: Pfizer; Financial Interests, Personal, Other, SC PALOMA3: Pfizer; Financial Interests, Personal, Other, SC SOLAR1: Novartis; Financial Interests, Personal, Other, SC ASCENT: Immunomedics/Gilead; Financial Interests, Personal, Other, SC HERCLIMB: Seagen; Financial Interests, Personal, Other, SC Katherine: Roche; Financial Interests, Personal, Other, SC Capitello; EC Cambria 1: AstraZeneca; Financial Interests, Personal, Other, SC Inavo: Roche; Financial Interests, Personal, Other, SC Destiny B05; SC Destiny B09: Daiichi Sankyo; Non-Financial Interests, Principal Investigator, After publication of primary endpoint: PI Aphinity; Non-Financial Interests, Advisory Role, Group in Germany responsible for breast cancer guidelines: AGO Kommission Mamma; Non-Financial Interests, Member, German Gynaecological Oncology society: AGO; Non-Financial Interests, Member, German Cancer Society: DKG; Non-Financial Interests, Member: ASCO; Non-Financial Interests, Member, Member guideline committee; past chair in ESMO Breast: ESMO; Other, EP14153692.0No financial interest, Institutional: Patent; Other, EP21152186.9No financial interest, institutional: Patent; Other, EP15702464.7No financial interest, institutional: Patent; Other, EP19808852.8 No financial interest, Institutional: Patent. All other authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.