Buchumschlag

Deep learning predicts HPV association in oropharyngeal squamous cell carcinomas and identifies patients with a favorable prognosis using regular H&E stains

Purpose: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared with OPSCC caused by tobacco, alcohol, and other carcinogens. Meanwhile, machine learning has evolved as a powerful tool to predict molecula...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Klein, Sebastian (VerfasserIn) , Quaas, Alexander (VerfasserIn) , Quantius, Jennifer (VerfasserIn) , Löser, Heike (VerfasserIn) , Meinel, Jörn (VerfasserIn) , Peifer, Martin (VerfasserIn) , Wagner, Steffen (VerfasserIn) , Gattenlöhner, Stefan (VerfasserIn) , Wittekindt, Claus (VerfasserIn) , Knebel Doeberitz, Magnus von (VerfasserIn) , Prigge, Elena-Sophie (VerfasserIn) , Langer, Christine (VerfasserIn) , Noh, Ka-Won (VerfasserIn) , Maltseva, Margaret (VerfasserIn) , Reinhardt, Christian (VerfasserIn) , Büttner, Reinhard (VerfasserIn) , Klußmann, Jens Peter (VerfasserIn) , Würdemann, Nora (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: February 2021
In: Clinical cancer research
Year: 2021, Jahrgang: 27, Heft: 4, Pages: 1131-1138
ISSN:1557-3265
DOI:10.1158/1078-0432.CCR-20-3596
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://clincancerres.aacrjournals.org/content/27/4/1131
Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1158/1078-0432.CCR-20-3596
Volltext
Verfasserangaben:Sebastian Klein, Alexander Quaas, Jennifer Quantius, Heike Löser, Martin Peifer, Steffen Wagner, Stefan Gattenlöhner, Claus Wittekindt, Magnus von Knebel Doeberitz, Elena-Sophie Prigge, Christine Langer, Ka-Won Noh, Margaret Maltseva, Hans Christian Reinhardt, Reinhard Büttner, Jens Peter Klussmann, Nora Wuerdemann
Beschreibung
Zusammenfassung:Purpose: Human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC) is tumorigenic and has been associated with a favorable prognosis compared with OPSCC caused by tobacco, alcohol, and other carcinogens. Meanwhile, machine learning has evolved as a powerful tool to predict molecular and cellular alterations of medical images of various sources. - Experimental Design: We generated a deep learning-based HPV prediction score (HPV-ps) on regular hematoxylin and eosin (H&E) stains and assessed its performance to predict HPV association using 273 patients from two different sites (OPSCC; Giessen, n = 163; Cologne, n = 110). Then, the prognostic relevance in a total of 594 patients (Giessen, Cologne, HNSCC TCGA) was evaluated. In addition, we investigated whether four board-certified pathologists could identify HPV association (n = 152) and compared the results to the classifier. - Results: Although pathologists were able to diagnose HPV association from H&E-stained slides (AUC = 0.74, median of four observers), the interrater reliability was minimal (Light Kappa = 0.37; P = 0.129), as compared with AUC = 0.8 using the HPV-ps within two independent cohorts (n = 273). The HPV-ps identified individuals with a favorable prognosis in a total of 594 patients from three cohorts (Giessen, OPSCC, HR = 0.55, P < 0.0001; Cologne, OPSCC, HR = 0.44, P = 0.0027; TCGA, non-OPSCC head and neck, HR = 0.69, P = 0.0073). Interestingly, the HPV-ps further stratified patients when combined with p16 status (Giessen, HR = 0.06, P < 0.0001; Cologne, HR = 0.3, P = 0.046). - Conclusions: Detection of HPV association in OPSCC using deep learning with help of regular H&E stains may either be used as a single biomarker, or in combination with p16 status, to identify patients with OPSCC with a favorable prognosis, potentially outperforming combined HPV-DNA/p16 status as a biomarker for patient stratification.
Beschreibung:Gesehen am 23.11.2021
Beschreibung:Online Resource
ISSN:1557-3265
DOI:10.1158/1078-0432.CCR-20-3596

Ähnliche Einträge