title: Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging.
abstract: OBJECTIVE
  To quantitatively evaluate regional lymph nodes in rectal cancer patients by using an automated, computer-aided approach, and to assess the accuracy of this approach in differentiating benign and malignant lymph nodes.
  
  
  METHODS
  Patients (228) with newly diagnosed rectal cancer, confirmed by biopsy, underwent enhanced computed tomography (CT). Patients were assigned to the benign node or malignant node group according to histopathological analysis of node samples. All CT-detected lymph nodes were segmented using the edge detection method, and seven quantitative parameters of each node were measured. To increase the prediction accuracy, a hierarchical model combining the merits of the support and relevance vector machines was proposed to achieve higher performance.
  
  
  RESULTS
  Of the 220 lymph nodes evaluated, 125 were positive and 95 were negative for metastases. Fractal dimension obtained by the Minkowski box-counting approach was higher in malignant nodes than in benign nodes, and there was a significant difference in heterogeneity between metastatic and non-metastatic lymph nodes. The overall performance of the proposed model is shown to have accuracy as high as 88% using morphological characterisation of lymph nodes.
  
  
  CONCLUSIONS
  Computer-aided quantitative analysis can improve the prediction of node status in rectal cancer.
authors.affiliation: State Key Laboratory of Oncology in Southern China, Imaging Diagnosis and Interventional Center, Cancer Center, Sun Yat-sen University, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People's Republic of China.
authors.forename: Chunyan
authors.initials: C
authors.lastname: Cui
authors.forename: Hongmin
authors.initials: H
authors.lastname: Cai
authors.forename: Lizhi
authors.initials: L
authors.lastname: Liu
authors.forename: Liren
authors.initials: L
authors.lastname: Li
authors.forename: Haiying
authors.initials: H
authors.lastname: Tian
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D001706:Biopsy; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008198:Lymph Nodes / Q000473:pathology; D008207:Lymphatic Metastasis / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D011871:Radiology / Q000379:methods; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*