Mathematics diagnosis model to differentiate multiple primary lung cancers from
PUBLISHED: 2015-11-26  384 total views, 1 today

Feng Li1, Wenzhao Zhong2,Feiyu Niu1, Chao Liu3, Zhihong Chen4, NingZhaoJinji Yang1

1The first department of Lung cancer, Guangdong Provincial KeyLaboratory ofLung Cancer Translational Medicine, Guangdong Lung Cancer Institute,Guangdong General Hospital, Guangzhou.

2The second department of Lung cancer, Guangdong Provincial KeyLaboratory ofLung Cancer Translational Medicine, Guangdong Lung Cancer Institute,Guangdong General Hospital, Guangzhou.

3Department of Pathology, Guangdong Provincial Key Laboratory ofLungCancer Translational Medicine, Guangdong Lung Cancer Institute, GuangdongGeneral Hospital,    Guangzhou.

4Medical Research Center, Guangdong Provincial Key Laboratory ofLungCancer Translational Medicine, Guangdong Lung Cancer Institute, GuangdongGeneral Hospital,   Guangzhou.


Objective: It is important to differentiate multiple primary lung cancers (MPLC) fromintra-pulmonary metastases (IPM) for the patients with multiple lung nodules.Currently, there are several criteria to distinguish MPLC from IPM but withconflict results. Method: Fivecriteria including Martini and Melamed guideline, American College of ChestPhysicians (ACCP) guideline, radiological evaluation, comprehensivehistological assessment (CHA) and genetic profiles (EGFR, KRAS and ALK genedetection) were applied to evaluate the origin of tumor cells in paired lungspecimens respectively. Finally, the multi-disciplinary consultation areconsidered as golden standard of the diagnosis, 65 cases of five definiteindependent results were enrolled as training set, while 9 cases of one methodwithout definite results were enrolled as validation set. Based on Bayesdiscriminant analysis, we developed mathematics diagnosis model ultimately.Kaplan-Meier analysis was used to analyze the prognosis by mathematicaldiagnosis model. Result: Totally,74consecutive patients with multiple lung nodules confirmed with adenocarcinomawere enrolled in the study from 2457 surgical resected lung cancer betweenJanuary 2007 and July 2014. Among 74 cases with multiple lung nodules, 63 caseswere identified as MPLC and 11 cases as IPM by Martini and Melamed guideline;39 cases were identified as MPLC and 29 cases as IPM by ACCP guideline, while 6cases cannot be classified; 39 cases were identified as MPLC and 35 cases asIPM by radiological evaluation; 53 cases were identified as MPLC and 21 casesas IPM by CHA; 45 cases were identified as MPLC and 26 cases as IPM by genedetection, while 3 cases cannot be identified because of triple negativeresults of gene detection, so the identification rate was 95.9% (71/74). TheBayes discriminant analysis method was applied to develop mathematics diagnosismodel as following, Y=-83.11+34.998*G+13.932*C+4.553*R+1.874*A -0.339*M    (Abbreviation, G: genetic profile; C: CHA;R: radiological evaluation; A: ACCP guideline; M: Martini and Melamedguideline). The value and significance of five criteria are as following, “1”is for MPLC and “2” is for IPM. When Y>0, the diagnosis is intended for IPM,when Y≤0, the diagnosis is intended for MPLC. The retrospective and prospectiveaccuracy rate of the model in 65 cases and 9 cases was 96.9% and 88.9%respectively. Kaplan-Meier analysis was used to explore the prognosis bymathematical model, which show that the prognosis of MPLC patients are betterthan that of IPM patients (P=0.006). Conclusion: patients with multiple lung nodules of adenocarcinoma. The prognosisof MPLC patients are better than that of IPM patients.


Keywords: Mathematics diagnosismodel  ultiple primary lung cancer


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