Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Oct 19;7(1):13543.
doi: 10.1038/s41598-017-13773-7.

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Xiangxue Wang  1 , Andrew Janowczyk  1 , Yu Zhou  1 , Rajat Thawani  1 , Pingfu Fu  1 , Kurt Schalper  2 , Vamsidhar Velcheti  3 , Anant Madabhushi  4
Affiliations

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Xiangxue Wang et al. Sci Rep. .

Abstract

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42-67.52, P < 0.001).

PubMed Disclaimer

Conflict of interest statement

Xiangxue Wang, Andrew Janowczyk, Yu Zhou, Rajat Thawani and Pingfu Fu have no conflicts to declare. Dr. Schalper is a Consultant for Viralytics. He has received research funding from Genoptix (Novartis), Vasculox, Tesaro, Onkaido Therapeutics and Takeda Pharmaceuticals. Dr. Velcheti is a Consultant for Clovis Oncology, Genertech, Bristol-Myers Squibb, Merck, Celgene, Foundation Medicine, AstraZeneca/MedImmune and Genoptix. He has received research funding from Genentech, Trovagene, Eisai, OncoPlex Diagnostics, Alkermes, NantOmics, Genoptix, Altor BioScience, Merck, Bristol-Myers Squibb, Atreca, Heat Biologics and Leap Therapeutics. Dr. Madabhushi is an equity holder in Elucid Bioimaging and in Inspirata Inc. He is also a scientific advisory consultant for Inspirata Inc and also sits on its scientific advisory board. He is also an equity holder in Inspirata Inc. Additionally his technology has been licensed to Elucid Bioimaging and Inspirata Inc. He is also involved in a NIH U24 grant with PathCore Inc. His work is also partially sponsored by Philips Healthcare.

Figures

Figure 1
Figure 1
Inclusion and exclusion criteria for patient selection for the training and test sets.
Figure 2
Figure 2
Flowchart illustrating the procedure for training and validating the quantitative histomorphometric classifier for distinguishing early versus no/late recurrence in early stage lung cancer.
Figure 3
Figure 3
Representative TMA tissue spots of recurrent (top row) and non-recurrent (bottom row) NSCLC with corresponding feature maps: Recurrence TMA with (a,e) nuclear shape feature, (b,f) texture feature map (Haralick standard deviation intensity correlation), (c,g) nuclear cluster graph feature map, and (d,h) nuclear orientation.
Figure 4
Figure 4
ROC analysis of classifier predicting recurrence on (a) training set Cohort #1, (b) independent validation set Cohort #2, (c) independent validation set Cohort #3 batch #1 and (d) independent validation set Cohort #3 batch #2 show consistent predicting ability among different classifiers and among different tumor section.Kaplan-Meier survival analysis for (e) training set Cohort #1 and (f) validation set Cohort #2 (g,h) batch #1 and batch #2 from Cohort #3 show good visual separation and log-rank test indicates the two groups were statistically different (p-value ≪ 0.05).
See this image and copyright information in PMC

References

    1. Islami F, Torre LA, Jemal A. Global trends of lung cancer mortality and smoking prevalence. Translational lung cancer research. 2015;4:327. - PMC - PubMed
    1. Uramoto H, Tanaka F. Recurrence after surgery in patients with NSCLC. Translational lung cancer research. 2014;3:242–249. - PMC - PubMed
    1. Arriagada R, et al. Long-term results of the international adjuvant lung cancer trial evaluating adjuvant Cisplatin-based chemotherapy in resected lung cancer. Journal of clinical oncology. 2010;28:35–42. doi: 10.1200/JCO.2009.23.2272. - DOI - PubMed
    1. Liu C-H, et al. Heterogeneous prognosis and adjuvant chemotherapy in pathological stage I non-small cell lung cancer patients. Thoracic cancer. 2015;6:620–628. doi: 10.1111/1759-7714.12233. - DOI - PMC - PubMed
    1. Laskin, J. J. Adjuvant chemotherapy for non-small cell lung cancer: the new standard of care. (2005). - PubMed

Publication types

MeSH terms