Prediction of Occult Lymph Node Metastasis Using Tumor-to-Blood Standardized Uptake Ratio and Metabolic Parameters in Clinical N0 Lung Adenocarcinoma

Ouyang, Mingli; Tang, Kun; Xu, Manman; Lin, Jie; Li, Tiancheng; Zheng, Xiujuan

doi:10.1097/rlu.0000000000002229

Cited by 30 publications

(26 citation statements)

References 29 publications

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“…All studies enrolled patients retrospectively. Eight studies came from China [14, 19, 21-26]. Other 6 studies were performed by non-China investigators [13, 15-18, 20].…”

Section: Resultsmentioning

confidence: 99%

“…All studies performed quantitative analysis of F-18 FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. Four studies used volumetric parameters of the primary tumor for the interpretation of F-18 FDG PET or PET/CT for the prediction of OLNM in NSCLC patients [16, 17, 21, 24]. Another 10 studies used SUV values of the primary tumor of F-18 FDG PET or PET/CT images [18, 20, 25, 26].…”

Section: Resultsmentioning

confidence: 99%

“…Another 10 studies used SUV values of the primary tumor of F-18 FDG PET or PET/CT images [18, 20, 25, 26]. Clinical N0 disease by F-18 FDG PET or PET/CT was defined as lymph nodes with FDG uptake no greater than normal background activity in two studies [21, 24], FDG uptake was not higher than the mediastinal blood pool in one study [13], lymph node SUV max <2.5 in eight studies [14, 15, 18-20, 25, 26] and ambiguous criteria in three studies [16, 17, 23].…”

Section: Resultsmentioning

confidence: 99%

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Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer

Seol

Kim

2020

Oncology

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Objective: The purpose of the current study was to investigate the diagnostic performance of 18F fluorodeoxyglucose (FDG) positron emission tomography (PET) or positron emission tomography/computed tomography (PET/CT) for the prediction of occult lymph node metastasis (OLNM) in non-small cell lung cancer (NSCLC) patients through a systematic review and meta-analysis. Methods: The PubMed, Cochrane, and EMBASE database, from the earliest available date of indexing through March 31, 2020, were searched for studies evaluating the diagnostic performance of preoperative 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. Results: Across 14 studies (3,535 patients), the pooled sensitivity for 18F FDG PET or PET/CT was 0.79 (95% CI; 0.70–0.86) with heterogeneity (I2 = 81.5, p < 0.001) and a pooled specificity of 0.65 (95% CI; 0.57–0.72) with heterogeneity (I2 = 93.7, p < 0.001). Likelihood ratio (LR) syntheses gave an overall positive likelihood ratio (LR+) of 2.3 (95% CI; 1.9–2.6) and a negative likelihood ratio (LR-) of 0.32 (95% CI; 0.23–0.44). The pooled diagnostic odds ratio (DOR) was 7 (95% CI; 5–10). The hierarchical summary receiver operating characteristic curve indicates that the area under the curve was 0.77 (95% CI; 0.74–0.81). Conclusion: The current meta-analysis showed a moderate sensitivity and specificity of 18F FDG PET or PET/CT for the prediction of OLNM in NSCLC patients. The DOR was low and the likelihood ratio scatter-gram indicated that 18F FDG PET or PET/CT might not be useful for the prediction of OLNM in NSCLC patients and not for its exclusion.

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“…All studies enrolled patients retrospectively. Eight studies came from China [14, 19, 21-26]. Other 6 studies were performed by non-China investigators [13, 15-18, 20].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer

Seol

Kim

2020

Oncology

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“…In recent years, the usefulness of metabolic parameters, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG) has been investigated. Several studies reported that MTV and TLG in the primary tumor of lung cancer, which provides information on tumor activity and volume, could predict the pathological tumor invasive size [13], LN status [14,15], and prognosis [16][17][18][19] more accurately than SUVmax. Furthermore, the LN-to-primary tumor ratio of SUVmax (LPR) was also assessed to determine whether this parameter can be a good predictor for LN metastasis in NSCLC patients [12,20].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic utility of metabolic parameters on FDG PET/CT for lymph node metastasis in patients with cN2 non-small cell lung cancer

Nakanishi

Nakamura

Sugiyama

et al. 2021

Preprint

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Background The aim of this study was to assess the diagnostic utility of metabolic parameters on fluorine-18-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET)/computed tomography (CT) for predicting lymph node (LN) metastasis in patients with cN2 non-small cell lung cancer (NSCLC).Methods We retrospectively reviewed 45 consecutive patients who underwent surgery for cN2 NSCLC between 2007 and 2020. Among them, 84 hilar and mediastinal LNs clinically diagnosed as positive and retrieved by surgery were investigated. To measure the metabolic parameters of LNs, preexisting PET data were reanalyzed, and the data according to maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and LN-to-primary tumor ratio of SUVmax (LPR) were examined. Diagnosis of each retrieved LN was confirmed based on histopathological examination of tissue specimens at surgery. Receiver operating characteristics (ROC) curves with area under the curve (AUC) calculations and multivariate analysis by logistic regression were performed.Results Of the 84 LNs clinically diagnosed as positive, 63 LNs were pathologically proven as positive (75%). The SUVmax, MTV, TLG, and LPR of LN metastasis were significantly higher than those of benign nodes. In the ROC analysis, the AUC value of LPR [AUC, 0.776; 95% confidence interval (CI), 0.640–0.913] was higher than that of LN SUVmax (AUC, 0.753; 95% CI, 0.626–0.880) or LN TLG3.5 (AUC, 0.746; 95% CI, 0.607–0.885). Using the optimal LPR cutoff value of 0.47, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 84.1%, 66.7%, 88.3%, 58.3%, and 79.8%, respectively. Multivariate analysis by logistic regression showed that LPR was an independent predictor for LN metastasis (odds ratio, 6.45; 95% CI, 1.785–23.301; P = 0.004). In the subgroup analysis of adenocarcinoma patients (n = 18; 32 LNs), TLG3.5 was a better predictor (AUC, 0.816; 95% CI, 0.639–0.985) than LPR (AUC, 0.792; 95% CI, 0.599–0.986) or LN SUVmax (AUC, 0.792; 95% CI, 0.625–0.959).Conclusions LPR on FDG-PET before surgery is a useful predictor for LN metastasis in patients with cN2 NSCLC. TLG can be a good predictor for LN metastasis in patients with adenocarcinoma.

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“…Thus, preoperative identi cation of LNM is di cult, as smaller lymph nodes cannot be detected by imaging examination. Clinical and pathological features are commonly used predictors showing that objective and quantitative clinicopathological features can be used as prognostic or predictive features for LNM patients [13][14][15][16][17]. However, pathological diagnosis conclusions are often limited by sample sampling, and the ability to judge small lesions is low, which is likely to cause missed diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

Cai

Shen

et al. 2020

Preprint

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Background: To develop and validate machine learning models for risk prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) using clinicopathologic parameters and immunohistochemical features. Methods: From January 2010 to December 2019, 639 patients' data were continuously collected in Nanfang Hospital. We exacted immunohistochemical features and clinicopathological features from the electronic medical records of patients. We established two models (a full model and a selection model) and implemented three algorithms (random forest, support vector machine and penalized logistic regression). The model performance was evaluated in terms of discrimination (receiver operating characteristic curve (AUC)), calibration, and decision curve analysis. Results: AUROC (area under receiver operating characteristic curve) analysis (also calibration curves) showed that the selection model (AUC values for training and testing, 0.843 and 0.840 respectively) and the full model constructed using random forest (AUC values for training and testing, 0.855 and 0.863 respectively) performed best among all models. Decision curve analysis depicted that the full model and the selection model using random forest was clinically useful. The model performance of the full model and the selection model were comparable. Conclusion: The random forest model using clinicopathologic- immunohistochemical features can predict the LNM of NSCLC patients.

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Prediction of Occult Lymph Node Metastasis Using Tumor-to-Blood Standardized Uptake Ratio and Metabolic Parameters in Clinical N0 Lung Adenocarcinoma

Cited by 30 publications

References 29 publications

Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer

Predictive Value of 18F-Fluorodeoxyglucose Positron Emission Tomography or Positron Emission Tomography/Computed Tomography for Assessment of Occult Lymph Node Metastasis in Non-Small Cell Lung Cancer

Diagnostic utility of metabolic parameters on FDG PET/CT for lymph node metastasis in patients with cN2 non-small cell lung cancer

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

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