title: Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients.
abstract: 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.
  
  
  
  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.
  
  
  
  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 ( ) when fusion of two classifier-generated prediction scores using an equal weighting factor.
  
  
  
  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.
  
  
  
  We demonstrated a new approach that has potential to assist clinicians in more effectively managing stage I NSCLC patients to reduce cancer recurrence risk.
authors.forename: Nastaran
authors.initials: N
authors.lastname: Emaminejad
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
authors.forename: Yubao
authors.initials: Y
authors.lastname: Guan
authors.forename: Maxine
authors.initials: M
authors.lastname: Tan
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Qiu
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000235:genetics; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local; D011379:Prognosis; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed
chemical_list: D014408:Biomarkers, Tumor