title: A support vector machine model for predicting non-sentinel lymph node status in patients with sentinel lymph node positive breast cancer.
abstract: This study aimed to investigate the accuracy and feasibility of support vector machine (SVM) modeling in predicting non-sentinel lymph node (NSLN) status in patients with SLN-positive breast cancer. Clinicopathological data were collected from 201 cases with sentinel lymph node biopsy breast cancer and included patient age, tumor size, histological type and grade, vascular invasion, estrogen receptor status, progesterone receptor status, CerbB2 status, size and number of positive SLNs, number of negative SLNs, and positive SLN membrane invasion. Feature vector selection was based on a combination of statistical filtration and model-dependent screening. The arbitrary combination with the smallest p value for SVM input was selected, the predicative results of the model were evaluated by a 10-fold cross validation, and a training model was established. Using SLN-positive patients as a double-blind test set, 85 patients were input into the model to analyze its sensitivity and specificity. The combination with the highest cross-validation accuracy was selected for the SVM model and consisted of the following: the number and size of positive SLNs, the number of negative SLNs, and the membrane invasion of positive SLNs. The training accuracy of the model established with the four variables was 92 %, and its cross-validation veracity was 87.6 %. The accuracy of an 85-patient double-blind test of the SVM model was 91.8 %. In conclusion, this SVM model is an accurate and feasible method for the prediction of NSLN status in SLN-positive breast cancer and is conducive to guide clinical treatment.
authors: Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, 310022, China. dingxwhz@yeah.net
authors: Xiaowen
authors: X
authors: Ding
authors: Shangnao
authors: S
authors: Xie
authors: Jie
authors: J
authors: Chen
authors: Wenju
authors: W
authors: Mo
authors: Hongjian
authors: H
authors: Yang
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000473:pathology* / Q000601:surgery; D018270:Carcinoma, Ductal, Breast / Q000473:pathology* / Q000601:surgery; D018275:Carcinoma, Lobular / Q000473:pathology* / Q000601:surgery; D004311:Double-Blind Method; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D021701:Sentinel Lymph Node Biopsy; D060388:Support Vector Machine
references: J Natl Cancer Inst. 2006 May 3;98(9):599-609
references: 16670385
references: Ann Surg. 1997 Sep;226(3):271-6; discussion 276-8
references: 9339933
references: BMC Cancer. 2008 Mar 04;8:66
references: 18315887
references: N Engl J Med. 2003 Aug 7;349(6):546-53
references: 12904519
references: Chin J Cancer Res. 2013 Feb;25(1):10-21
references: 23372337
references: Gland Surg. 2012 May;1(1):5-6
references: 25083419
references: Arch Surg. 2001 Jun;136(6):688-92
references: 11387010
references: J Clin Oncol. 2000 Jul;18(13):2553-9
references: 10893286
references: J Thorac Dis. 2012 Aug;4(4):420-4
references: 22934145
references: Analyst. 2010 May;135(5):895-901
references: 20419237
references: Gland Surg. 2012 May;1(1):7-8
references: 25083420
references: Breast Cancer Res Treat. 2005 May;91(2):113-9
references: 15868438
references: Gland Surg. 2012 May;1(1):39-61
references: 25083426
references: J Surg Oncol. 2010 Aug 1;102(2):111-8
references: 20648579
references: J Clin Oncol. 2005 Jul 1;23(19):4312-21
references: 15994144
references: Br J Surg. 2008 Mar;95(3):302-9
references: 17876750
references: Ann Surg Oncol. 2003 Dec;10(10):1140-51
references: 14654469
references: Cancer Control. 2012 Oct;19(4):267-76
references: 23037494
references: Chin Clin Oncol. 2013 Mar;2(1):10
references: 25841451
references: J Clin Oncol. 2008 Nov 10;26(32):5213-9
references: 18838709