Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients

Nastaran Emaminejad, Wei Qian, Yubao Guan, Maxine Tan, Yuchen Qiu, Hong Liu, Bin Zheng

Research output: Contribution to journalArticleResearchpeer-review

99 Citations (Scopus)


Objective: This study aims to develop a new quantitative image feature analysis scheme and investigate its role along with two genomic biomarkers, namely protein expression of the excision repair cross-complementing 1 genes and a regulatory subunit of ribonucleotide reductase (RRM1), in predicting cancer recurrence risk of stage I nonsmall-cell lung cancer (NSCLC) patients after surgery. Methods: By using chest computed tomography images, we developed a computer-aided detection scheme to segment lung tumors and computed tumor-related image features. After feature selection, we trained a Naïve Bayesian network-based classifier using eight image features and a multilayer perceptron classifier using two genomic biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and tested using a dataset with 79 stage I NSCLC cases, a synthetic minority oversampling technique and a leave-one-case-out validation method. A fusion method was also applied to combine prediction scores of two classifiers. Results: Areas under ROC curves (AUC) values are 0.78 ± 0.06 and 0.68 ± 0.07 when using the image feature and genomic biomarker-based classifiers, respectively. AUC value significantly increased to 0.84 ± 0.05 (p < 0.05) when fusion of two classifier-generated prediction scores using an equal weighting factor. Conclusion: A quantitative image feature-based classifier yielded significantly higher discriminatory power than a genomic biomarker-based classifier in predicting cancer recurrence risk. Fusion of prediction scores generated by the two classifiers further improved prediction performance. Significance: We demonstrated a new approach that has potential to assist clinicians in more effectively managing stage I NSCLC patients to reduce cancer recurrence risk.

Original languageEnglish
Pages (from-to)1034-1043
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number5
Publication statusPublished - May 2016
Externally publishedYes


  • Computer-aided diagnosis
  • Fusion of image features and genomic biomarkers
  • Prediction of lung cancer recurrence risk
  • Quantitative image feature analysis
  • Radiomics

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