A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer.

Wang, Xiangxue; Bera, Kaustav; Barrera, Cristian; Zhou, Yu; Lu, Cheng; Vaidya, Pranjal; Fu, Pingfu; Yang, Michael; Schmid, Ralph Alexander; Berezowska, Sabina; Choi, Humberto; Velcheti, Vamsidhar; Madabhushi, Anant (2021). A prognostic and predictive computational pathology image signature for added benefit of adjuvant chemotherapy in early stage non-small-cell lung cancer. EBioMedicine, 69(103481), p. 103481. Elsevier 10.1016/j.ebiom.2021.103481

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BACKGROUND

We developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC).

METHODS

1330 patients with ES-NSCLC were acquired from 3 independent sources and divided into four cohorts D1-4. D1 comprised 100 surgery treated patients and was used to identify prognostic features via an elastic-net Cox model to predict overall and disease-free survival. CoRiS was constructed using the Cox model coefficients for the top features. The prognostic performance of CoRiS was evaluated on D2 (N=331), D3 (N=657) and D4 (N=242). Patients from D2 and D3 which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups.

FINDINGS

CoRiS was found to be prognostic on univariable analysis, D2 (hazard ratio (HR) = 1.41, adjusted (adj.) P = .01) and D3 (HR = 1.35, adj. P < .001). Multivariable analysis showed CoRiS was independently prognostic, D2 (HR = 1.41, adj. P < .001) and D3 (HR = 1.35, adj. P < .001), after adjusting for clinico-pathologic factors. CoRiS was also able to identify high-risk patients who derived survival benefit from ACT D2 (HR = 0.42, adj. P = .006) and D3 (HR = 0.46, adj. P = .08).

INTERPRETATION

CoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology > Clinical Pathology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Mu50 > Forschungsgruppe Thoraxchirurgie
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Thoracic Surgery

UniBE Contributor:

Schmid, Ralph and Berezowska, Sabina Anna

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2352-3964

Publisher:

Elsevier

Language:

English

Submitter:

Thomas Michael Marti

Date Deposited:

09 Aug 2021 16:31

Last Modified:

15 Aug 2021 03:10

Publisher DOI:

10.1016/j.ebiom.2021.103481

PubMed ID:

34265509

Uncontrolled Keywords:

Computational pathology Early-stage non-small cell lung cancer Prognostic and predictive

BORIS DOI:

10.48350/157722

URI:

https://boris.unibe.ch/id/eprint/157722

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