Development and validation of tumor‐to‐blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer

Abstract

Background: To develop and validate tumor-to-blood based nomograms for preoperative prediction of lymph node (LN) metastasis in patients with lung cancer (LC). Methods: A prediction model was developed in a primary cohort comprising 330 LN stations from patients with pathologically confirmed LC, these data having been gathered from January 2016 to June 2019. Tumor-to-blood variables of LNs were calculated from positron emission tomography-computed tomography (PET-CT) images of LC and the short axis diameters of LNs were measured on CT images. Tumor-toblood variables, number of stations suspected of harboring LN metastasis according to PET, and independent clinicopathological risk factors were included in the final nomograms. After being internally validated, the nomograms were used to assess an independent validation cohort containing 101 consecutive LN stations accumulated from July 2019 to March 2020. Results: Four tumor-to-blood variables (left atrium, inferior vena cava, liver, and aortic arch) and the maximum standardized uptake value (SUVmax) for LNs were found to be significantly associated with LN status (p < 0.001 for both primary and validation cohorts). Five predictive nomograms were built. Of these, one with LN SUVmax/ left atrium SUVmax was found to be optimal for predicting LN status with AUC 0.830 (95% confidence interval [CI]: 0.774-0.886) in the primary cohort and AUC 0.865 (95% CI: 0.782-0.948) in the validation cohort. All models showed good discrimination, with a modest C-index, and good calibration in both primary and validation cohorts. Conclusions: We have developed tumor-to-blood based nomograms that incorporate identified clinicopathological risk factors and facilitate preoperative prediction of LN metastasis in LC patients.

A novel nomogram to predict lymph node metastasis in cT1 non-small-cell lung cancer based on PET/CT and peripheral blood cell parameters

“…Due to the importance of preoperative evaluation of LN status, several models based on PET/CT have been developed to predict LN metastasis [ 25 – 27 ]. Consistent with previous results, the SUVmax value of the primary tumor and the suspicious LN were identified as risk factors of LN metastasis [ 25 , 26 ].…”

A liquid biopsy assay for the noninvasive detection of lymph node metastases in T1 lung adenocarcinoma

“…While several predictive models related to pN staging or lymph node metastasis have been proposed, many have shown unsatisfactory performance in validation studies or require complex and costly testing that limits their clinical utility. [9][10][11][12] In recent years, gene expression patterns have offered a promising avenue for biomarker development. RNA, in particular, has emerged as a potential circulating tumor biomarker, providing a noninvasive, simple, and cost-effective means of diagnosing and monitoring cancer.…”

“…While several predictive models related to pN staging or lymph node metastasis have been proposed, many have shown unsatisfactory performance in validation studies or require complex and costly testing that limits their clinical utility. 9 , 10 , 11 , 12 …”

“…The development of a diagnostic biomarker for lung cancer lymph node metastasis would significantly enhance clinical decision‐making. While several predictive models related to pN staging or lymph node metastasis have been proposed, many have shown unsatisfactory performance in validation studies or require complex and costly testing that limits their clinical utility 9–12 …”

Risk profiles and a concise prediction model for lymph node metastasis in patients with lung adenocarcinoma

“…Of note, several prediction models related to pN staging or lymph node metastasis have been set up [ 8 – 13 ]. Among the previous related studies, the established prediction models either have unsatisfactory performance with C-statistics < 0.8 in the validation cohort [ 8 , 9 , 11 , 12 ] or require predictors from complex or costly tests [ 10 , 13 ]. In contrast, few studies have focused on high-accuracy prediction of pN based on features of cancer specimens for patients with LUAD after lung cancer resection without complete lymph node dissection.…”