Support Vector Machines Model of Computed Tomography for Assessing Lymph Node Metastasis in Esophageal Cancer with Neoadjuvant Chemotherapy

Abstract

ObjectiveThe aim of this study was to diagnose lymph node metastasis of esophageal cancer by support vector machines model based on computed tomography.Materials and MethodsA total of 131 esophageal cancer patients with preoperative chemotherapy and radical surgery were included. Various indicators (tumor thickness, tumor length, tumor CT value, total number of lymph nodes, and long axis and short axis sizes of largest lymph node) on CT images before and after neoadjuvant chemotherapy were recorded. A support vector machines model based on these CT indicators was built to predict lymph node metastasis.ResultsSupport vector machines model diagnosed lymph node metastasis better than preoperative short axis size of largest lymph node on CT. The area under the receiver operating characteristic curves were 0.887 and 0.705, respectively.ConclusionsThe support vector machine model of CT images can help diagnose lymph node metastasis in esophageal cancer with preoperative chemotherapy.

Machine Learning Principles for Radiology Investigators

“…SVMs have been actively explored for imaging tasks, with some good results (33,45À47). A chief drawback of these models are the extreme narrowness of the application and probable reliance upon human feature labeling, as seen in a paper using a SVM to assess lymph node metastases (48). In this paper, the radiologists needed to measure six different variables on CT to generate a predictive model that surpassed measurement of the largest short axis lymph node in mm as a predictor.…”

Artificial intelligence-assisted esophageal cancer management: Now and future

“…Finally, the SVM model with four clinicopathological features and nine immunomarkers had better performance, with accuracy, SEN, SPE, positive predictive value, and negative predictive value of 78.7%, 56.6%, 97.7%, 95.6%, and 72.3%, respectively. Another least squares SVM model was also proposed to predict post-operative lymph node metastasis in patients who received chemotherapy preoperatively, by exploiting preoperative CT radiomics[ 43 ]. Tumor length, thickness, CT value, long axis and short axis size of the largest regional lymph node were analyzed.…”

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

“…(2017) [80] NR NR 26/NR 26(NR)/0(NR) 58(14; NR) 54% Zhi-Long Wang et al. (2017) [81] NR Pathologically proven adenocarcinoma, small cell carcinoma, mixed cancer, or other diseases; other preoperative therapies simultaneously; esophageal multiple primary carcinoma; death within 30 days after surgery; enhanced CT data before preoperative chemotherapy not obtained or images not interpretable; non-suitability for radical esophagectomy 131/NR 51(NR)/80(NR) 58(NR;42–75) 77.90% Tuan D. Pham et al. (2017) [82] * Biopsy-proven primary lung malignancy with pathological mediastinal nodal staging; Patients with nodal biopsy more than three months from CT 148/NR Test set: NR (133)/NR (138) 69.4(NR;36–84) 63% Qi Zhang et al.…”