Dataset Information

Accuracy of F-18 FDG PET/CT with optimal cut-offs of maximum standardized uptake value according to size for diagnosis of regional lymph node metastasis in patients with rectal cancer.

ABSTRACT: The low sensitivity of F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for the evaluation of metastatic lymph nodes (LNs) is mainly due to the partial volume effect in patients with rectal cancer. This retrospective study evaluated the diagnostic accuracy of F-18 FDG PET/CT with optimal cut-off values of the maximum standardized uptake value (SUVmax), according to LN size, for the evaluation of regional LN in rectal cancer patients.This study included 176 patients with rectal cancer who underwent F-18 FDG PET/CT for initial staging. Patients were classified based on the long-axis diameter of the regional LN on CT images as small (? 7 mm; n?=?118) and large (>?7 mm; n?=?58) LN groups. The optimal cut-off value of SUVmax was determined for each group, using receiver operating characteristic curve analysis. Areas under the curve (AUC) were compared by C-statistics using two methods: the cut-off value of SUVmax optimized according to LN size, and a fixed SUVmax cut-off value of 2.5.The optimal cut-off values of SUVmax for the small and large LN groups were 1.1, and 2.1, respectively. The sensitivity, specificity, and accuracy of F-18 FDG PET/CT using the optimal cut-off values were 90.6, 70.9, and 76.3% in the small LN group, and 68.6, 78.3, and 72.4% in the large LN group. The sensitivity, specificity, and accuracy of F-18 FDG PET/CT using the fixed cut-off value were 18.8, 100, and 78.0% in the small LN group, and 51.4, 87.0, and 65.5% in the large LN group. The AUC was significantly higher using the optimal cut-off values than the fixed cut-off value (0.808 vs. 0.594, p?=?0.005) in the small LN group, but not in the large LN group (0.734 vs. 0.692, p?=?0.429).Application of the lower cut-off value of SUVmax improves the diagnostic performance of F-18 FDG PET/CT for the evaluation of small regional LNs in patients with rectal cancer.

SUBMITTER: Bae SU

PROVIDER: S-EPMC6137872 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

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Accuracy of F-18 FDG PET/CT with optimal cut-offs of maximum standardized uptake value according to size for diagnosis of regional lymph node metastasis in patients with rectal cancer.

Bae Sung Uk SU Won Kyoung Sook KS Song Bong-Il BI Jeong Woon Kyung WK Baek Seong Kyu SK Kim Hae Won HW

Cancer imaging : the official publication of the International Cancer Imaging Society 20180914 1

<h4>Background</h4>The low sensitivity of F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for the evaluation of metastatic lymph nodes (LNs) is mainly due to the partial volume effect in patients with rectal cancer. This retrospective study evaluated the diagnostic accuracy of F-18 FDG PET/CT with optimal cut-off values of the maximum standardized uptake value (SUV<sub>max</sub>), according to LN size, for the evaluation of regional LN in rectal cancer pat ...[more]

PMID: 30217167

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Project description:ObjectivesThe accurate assessment of lymph node metastases (LNMs) and the preoperative nodal (N) stage are critical for the precise treatment of patients with gastric cancer (GC). The diagnostic performance, however, of current imaging procedures used for this assessment is sub-optimal. Our aim was to investigate the value of preoperative 18F-FDG PET/CT radiomic features to predict LNMs and the N stage.MethodsWe retrospectively collected clinical and 18F-FDG PET/CT imaging data of 185 patients with GC who underwent total or partial radical gastrectomy. Patients were allocated to training and validation sets using the stratified method at a fixed ratio (8:2). There were 2,100 radiomic features extracted from the 18F-FDG PET/CT scans. After selecting radiomic features by the random forest, relevancy-based, and sequential forward selection methods, the BalancedBagging ensemble classifier was established for the preoperative prediction of LNMs, and the OneVsRest classifier for the N stage. The performance of the models was primarily evaluated by the AUC and accuracy, and validated by the independent validation methods. Analysis of the feature importance and the correlation were also conducted. We also compared the predictive performance of our radiomic models to that with the contrast-enhanced CT (CECT) and 18F-FDG PET/CT.ResultsThere were 185 patients-127 men, 58 women, with the median age of 62, and an age range of 22-86 years. One CT feature and one PET feature were selected to predict LNMs and achieved the best performance (AUC: 82.2%, accuracy: 85.2%). This radiomic model also detected some LNMs that were missed in CECT (19.6%) and 18F-FDG PET/CT (35.7%). For predicting the N stage, four CT features and one PET feature were selected (AUC: 73.7%, accuracy: 62.3%). Of note, a proportion of patients in the validation set whose LNMs were incorrectly staged by CECT (57.4%) and 18F-FDG PET/CT (55%) were diagnosed correctly by our radiomic model.ConclusionWe developed and validated two machine learning models based on the preoperative 18F-FDG PET/CT images that have a predictive value for LNMs and the N stage in GC. These predictive models show a promise to offer a potentially useful adjunct to current staging approaches for patients with GC.

Dataset Information

Accuracy of F-18 FDG PET/CT with optimal cut-offs of maximum standardized uptake value according to size for diagnosis of regional lymph node metastasis in patients with rectal cancer.

Publications

Accuracy of F-18 FDG PET/CT with optimal cut-offs of maximum standardized uptake value according to size for diagnosis of regional lymph node metastasis in patients with rectal cancer.

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