Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classifi...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 February 2024
|
| In: |
Nature Communications
Year: 2024, Jahrgang: 15, Pages: 1-13 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-024-45589-1 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-024-45589-1
|
| Verfasserangaben: | Omar S.M. El Nahhas, Chiara M.L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho & Jakob Nikolas Kather |
MARC
| LEADER | 00000caa a2200000 c 4500 | ||
|---|---|---|---|
| 001 | 1902533437 | ||
| 003 | DE-627 | ||
| 005 | 20250626105125.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240913s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41467-024-45589-1 |2 doi | |
| 035 | |a (DE-627)1902533437 | ||
| 035 | |a (DE-599)KXP1902533437 | ||
| 035 | |a (OCoLC)1475311741 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 33 |2 sdnb | ||
| 100 | 1 | |a El Nahhas, Omar S. M. |e VerfasserIn |0 (DE-588)1341939367 |0 (DE-627)190253512X |4 aut | |
| 245 | 1 | 0 | |a Regression-based Deep-Learning predicts molecular biomarkers from pathology slides |c Omar S.M. El Nahhas, Chiara M.L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho & Jakob Nikolas Kather |
| 264 | 1 | |c 10 February 2024 | |
| 300 | |b Illustrationen | ||
| 300 | |a 13 | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Gesehen am 13.09.2024 | ||
| 520 | |a Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology. | ||
| 650 | 4 | |a Biomarkers, Tumor | |
| 650 | 4 | |a Deep Learning | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a Neoplasms | |
| 650 | 4 | |a Technology | |
| 650 | 4 | |a Tumor Microenvironment | |
| 700 | 1 | |a Loeffler, Chiara M. L. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Carrero, Zunamys I. |e VerfasserIn |4 aut | |
| 700 | 1 | |a van Treeck, Marko |e VerfasserIn |4 aut | |
| 700 | 1 | |8 1\p |a Kolbinger, Fiona |e VerfasserIn |0 (DE-588)1201382440 |0 (DE-627)168495651X |4 aut | |
| 700 | 1 | |a Hewitt, Katherine J. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Muti, Hannah S. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Graziani, Mara |e VerfasserIn |4 aut | |
| 700 | 1 | |8 2\p |a Zeng, Qinghe |e VerfasserIn |0 (DE-588)1268840033 |0 (DE-627)1817370928 |4 aut | |
| 700 | 1 | |a Calderaro, Julien |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ortiz-Brüchle, Nadina |e VerfasserIn |4 aut | |
| 700 | 1 | |a Yuan, Tanwei |d 1995- |e VerfasserIn |0 (DE-588)1299871909 |0 (DE-627)1857588649 |4 aut | |
| 700 | 1 | |a Hoffmeister, Michael |d 1973- |e VerfasserIn |0 (DE-588)134103726 |0 (DE-627)560880820 |0 (DE-576)277089565 |4 aut | |
| 700 | 1 | |a Brenner, Hermann |e VerfasserIn |0 (DE-588)1020516445 |0 (DE-627)691247005 |0 (DE-576)360642136 |4 aut | |
| 700 | 1 | |a Brobeil, Alexander |e VerfasserIn |0 (DE-588)108137795X |0 (DE-627)846032406 |0 (DE-576)454349661 |4 aut | |
| 700 | 1 | |a Reis-Filho, Jorge S. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kather, Jakob Nikolas |d 1989- |e VerfasserIn |0 (DE-588)1064064914 |0 (DE-627)812897587 |0 (DE-576)423589091 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nature Communications |d [London] : Springer Nature, 2010 |g 15(2024), Artikel-ID 1253, Seite 1-13 |h Online-Ressource |w (DE-627)626457688 |w (DE-600)2553671-0 |w (DE-576)331555905 |x 2041-1723 |7 nnas |a Regression-based Deep-Learning predicts molecular biomarkers from pathology slides |
| 773 | 1 | 8 | |g volume:15 |g year:2024 |g elocationid:1253 |g pages:1-13 |g extent:13 |a Regression-based Deep-Learning predicts molecular biomarkers from pathology slides |
| 856 | 4 | 0 | |u https://doi.org/10.1038/s41467-024-45589-1 |x Verlag |x Resolving-System |z kostenfrei |3 Volltext |
| 883 | |8 1\p |a cgwrk |d 20241001 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | ||
| 883 | |8 2\p |a cgwrk |d 20241001 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | ||
| 951 | |a AR | ||
| 992 | |a 20240913 | ||
| 993 | |a Article | ||
| 994 | |a 2024 | ||
| 998 | |g 1064064914 |a Kather, Jakob Nikolas |m 1064064914:Kather, Jakob Nikolas |d 910000 |d 910100 |e 910000PK1064064914 |e 910100PK1064064914 |k 0/910000/ |k 1/910000/910100/ |p 17 |y j | ||
| 998 | |g 108137795X |a Brobeil, Alexander |m 108137795X:Brobeil, Alexander |d 910000 |d 912000 |e 910000PB108137795X |e 912000PB108137795X |k 0/910000/ |k 1/910000/912000/ |p 15 | ||
| 998 | |g 1020516445 |a Brenner, Hermann |m 1020516445:Brenner, Hermann |d 850000 |d 851600 |d 50000 |e 850000PB1020516445 |e 851600PB1020516445 |e 50000PB1020516445 |k 0/850000/ |k 1/850000/851600/ |k 0/50000/ |p 14 | ||
| 998 | |g 134103726 |a Hoffmeister, Michael |m 134103726:Hoffmeister, Michael |d 50000 |e 50000PH134103726 |k 0/50000/ |p 13 | ||
| 998 | |g 1299871909 |a Yuan, Tanwei |m 1299871909:Yuan, Tanwei |d 50000 |e 50000PY1299871909 |k 0/50000/ |p 12 | ||
| 999 | |a KXP-PPN1902533437 |e 457943644X | ||
| BIB | |a Y | ||
| SER | |a journal | ||
| JSO | |a {"physDesc":[{"noteIll":"Illustrationen","extent":"13 S."}],"recId":"1902533437","relHost":[{"recId":"626457688","physDesc":[{"extent":"Online-Ressource"}],"disp":"Regression-based Deep-Learning predicts molecular biomarkers from pathology slidesNature Communications","origin":[{"publisherPlace":"[London] ; [London]","dateIssuedDisp":"[2010]-","publisher":"Springer Nature ; Nature Publishing Group UK"}],"pubHistory":["2010-"],"id":{"eki":["626457688"],"issn":["2041-1723"],"zdb":["2553671-0"]},"part":{"year":"2024","pages":"1-13","extent":"13","text":"15(2024), Artikel-ID 1253, Seite 1-13","volume":"15"},"title":[{"title":"Nature Communications","title_sort":"Nature Communications"}],"language":["eng"],"type":{"media":"Online-Ressource","bibl":"periodical"},"note":["Gesehen am 13.06.24"]}],"person":[{"display":"El Nahhas, Omar S. M.","family":"El Nahhas","given":"Omar S. M.","role":"aut"},{"family":"Loeffler","role":"aut","given":"Chiara M. L.","display":"Loeffler, Chiara M. L."},{"display":"Carrero, Zunamys I.","given":"Zunamys I.","role":"aut","family":"Carrero"},{"display":"van Treeck, Marko","family":"van Treeck","given":"Marko","role":"aut"},{"family":"Kolbinger","role":"aut","given":"Fiona","display":"Kolbinger, Fiona"},{"role":"aut","given":"Katherine J.","family":"Hewitt","display":"Hewitt, Katherine J."},{"display":"Muti, Hannah S.","family":"Muti","role":"aut","given":"Hannah S."},{"given":"Mara","role":"aut","family":"Graziani","display":"Graziani, Mara"},{"family":"Zeng","role":"aut","given":"Qinghe","display":"Zeng, Qinghe"},{"family":"Calderaro","given":"Julien","role":"aut","display":"Calderaro, Julien"},{"display":"Ortiz-Brüchle, Nadina","family":"Ortiz-Brüchle","role":"aut","given":"Nadina"},{"family":"Yuan","role":"aut","given":"Tanwei","display":"Yuan, Tanwei"},{"given":"Michael","role":"aut","family":"Hoffmeister","display":"Hoffmeister, Michael"},{"display":"Brenner, Hermann","family":"Brenner","role":"aut","given":"Hermann"},{"display":"Brobeil, Alexander","role":"aut","given":"Alexander","family":"Brobeil"},{"display":"Reis-Filho, Jorge S.","role":"aut","given":"Jorge S.","family":"Reis-Filho"},{"family":"Kather","given":"Jakob Nikolas","role":"aut","display":"Kather, Jakob Nikolas"}],"origin":[{"dateIssuedDisp":"10 February 2024","dateIssuedKey":"2024"}],"note":["Gesehen am 13.09.2024"],"language":["eng"],"type":{"media":"Online-Ressource","bibl":"article-journal"},"title":[{"title_sort":"Regression-based Deep-Learning predicts molecular biomarkers from pathology slides","title":"Regression-based Deep-Learning predicts molecular biomarkers from pathology slides"}],"name":{"displayForm":["Omar S.M. El Nahhas, Chiara M.L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho & Jakob Nikolas Kather"]},"id":{"doi":["10.1038/s41467-024-45589-1"],"eki":["1902533437"]}} | ||
| SRT | |a ELNAHHASOMREGRESSION1020 | ||