Dataset Information

Routinely staging gastric cancer with ¹⁸F-FDG PET-CT detects additional metastases and predicts early recurrence and death after surgery.

ABSTRACT:

Objectives

Fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) is typically considered to have minimal yield in gastric cancer, and so is not consistently recommended by international guidelines. However, its yield is considerable in esophageal and junctional cancer, identifying unsuspected metastases and risk-stratifying patients using metabolic nodal stage (mN). We aimed to determine the contemporary utility of routine ¹⁸F-FDG PET-CT in gastric cancer.

Methods

We routinely stage patients with non-junctional gastric cancer with PET-CT, provided initial CT does not demonstrate unequivocal metastases. We performed a retrospective study of all such patients staged in our institution from January 2007 to July 2016. Our primary endpoint was detection of incurable disease. Our secondary endpoint was disease-free survival following gastrectomy. Decision theory, economic, and predictive models were generated.

Results

The primary tumor was FDG-avid in 225/279 patients (80.6%). Seventy-two (25.8%) had FDG-avid nodes (resectable by D2 lymphadenectomy). This was not influenced by the Lauren classification. Unsuspected metastases were identified in 20 patients (7.2%). In 13 (4.7%), these would not have been otherwise identified. Decision theory and economic modeling supported routine PET-CT. Patients with FDG-avid nodes were more likely to have incurable disease (51.4% versus 15.5%; p?ConclusionsPET-CT appears useful when used routinely for non-junctional gastric cancer, and should be considered in international recommendations. Any extra costs appear small and offset by avoiding futile investigations and radical treatment. mN stage identifies patients at risk of early recurrence and death.

Key points

• PET-CT is typically not considered useful when staging gastric cancer. We describe a retrospective study of 279 patients routinely staged with PET-CT in the absence of metastases on CT. • The primary tumor was avid in 80% of patients. Twenty-five percent had resectable avid nodes. PET-CT identified previously unsuspected metastases in 7% of patients, which would likely not have been identified by conventional staging without PET-CT in 5%. These patients were much more likely to have avid nodes. • Beyond avoiding futile investigations and radical treatment in this 5%, we found patients with FDG-avid nodes (metabolic nodal stage, mN) to have a worse disease-free survival after gastrectomy.

SUBMITTER: Findlay JM

PROVIDER: S-EPMC6443603 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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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.

Project description:BackgroundMagnetic resonance imaging (MRI) plays an important role in the diagnosis of leptomeningeal metastases (LM); however, some sub-centimeter lesions may be missed. Positron emission tomography/computed tomography (PET/CT) has a high sensitivity and may play a synergistic role with MRI in diagnosing spinal LM (SLM). We aimed to retrospectively evaluate the detection of SLM with 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) compared to that of whole spinal cord MRI in a single center.MethodsPatients with SLM who had undergone 18F-FDG PET/CT and MRI were enrolled. 18F-FDG PET/CT imaging findings were independently reviewed by 2 nuclear medicine physicians. 18F-FDG PET/CT findings of SLMs were described. A consistency test was conducted to assess the patient-based diagnostic results obtained by the 2 physicians. Patient-based sensitivity, accuracy, and specificity in diagnosing SLM between 18F-FDG PET/CT and MRI of the whole spinal cord were compared using the chi-square or Fisher's exact test. A P value of <0.05 was considered statistically significant. The receiver operating characteristic (ROC) curve was obtained to assess the diagnostic performance of maximum standardized uptake value (SUVmax) to diagnose SLM.ResultsA total of 16 patients with SLM were included in this study from October 2010 to April 2022. The primary tumor involved the lungs, liver, ovaries, prostate, esophagus, and unknown primary site. The mean age of patients, including 13 males and 3 females, was 57.8±11.2 (range, 34-73) years. Of 16 patients with SLM, 10 had nodular diseases, 2 had linear diseases, and 4 had mixed diseases. The kappa value of the consistency test of the 2 radiologists' diagnostic results was 0.765. The patient-based sensitivity, specificity, and accuracy of 18F-FDG PET/CT in diagnosing SLM were 87.5%, 89.2%, and 88.7%, respectively and those of whole spinal cord MRI were 75.0%, 100.0%, and 92.5%, respectively. There were no significant differences in sensitivity, specificity, and accuracy between the 2 methods, with P values of 0.654, 0.115, and 0.506, respectively. However, more nodular diseases were observed on PET/CT. The area under the ROC curve (AUC) for the prediction of SLM by SUVmax was 0.907 [95% confidence interval (CI): 0.831-0.983]. When SUVmax ≥2.45, the Youden index was the largest, and the sensitivity and specificity were 89.3% and 75.7%, respectively.Conclusions18F-FDG PET/CT is a good choice of imaging modality for assessing SLM. In the diagnosis of SLMs, PET/CT and enhanced MRI can play a better synergistic role.

Project description:Objective: The purpose of this study was to explore the value of 18F-FDG PET/CT in monitoring the disease activity and predicting the prognosis of the Adult-onset Still's disease (AOSD). Methods: We retrospectively analyzed the electronic medical records of 45 AOSD patients who underwent 18F-FDG PET/CT in the Second Xiangya Hospital. PET/CT imaging and clinical information were retrospectively reviewed and analyzed. 18F-FDG uptake was assessed by measuring standard uptake value (SUV) in the spleen, liver, bone marrow, and lymph nodes. The spleen-to-liver ratio of the SUVmax (SLRmax) and SUVmean (SLRmean), the bone-to-liver ratio of the SUVmax (BLRmax), and SUVmean (BLRmean), and the lymph nodes-to-liver ratio of the SUVmax (LyLRmax) were calculated. Clinical and laboratory information were collected and evaluated for association with metabolic parameters of 18F-FDG PET/CT. The influencing factors for recurrence within 1 year were analyzed to determine whether 18F-FDG PET/CT can predict the prognosis of AOSD patients. Results: Elevated 18F-FDG uptake could be observed in bone marrow, spleen, and lymph nodes of AOSD patients. Correlation analysis between 18F-FDG uptake of organs and laboratory examinations showed that SLRmean positively correlated with LDH, AST, ferritin, and the systemic score (r = 0.572, 0.353, 0.586, and 0.424, P < 0.05). The SLRmean had the highest correlation with ferritin (r = 0586, P < 0.001). All metabolic parameters in spleen, including SUVmax, SUVmean, SLRmax, and SLRmean, are positively correlated with LDH level (r = 0.405, 0.539, 0.481, and 0.572, P < 0.05). Bone marrow SUVmax, BLRmax, and BLRmean were correlated with C-reactive protein (CRP) level (r = 0.395, 0.437, and 0.469, P < 0.05). Analysis of the influencing factors of recurrence within 1 year showed that the spleen SUVmax, spleen SUVmean, SLRmax, SLRmean, ferritin, and the systemic score of the recurrence group was significantly higher than the non-recurrence group (P < 0.05). The SLRmean cutoff of 1.66 with a sensitivity of 72.7% and specificity of 80.0% had the highest performance in predicting recurrence. Conclusion: The glucose metabolism of the liver, spleen, and bone marrow of AOSD patients were correlated with laboratory inflammatory indicators and system score, suggesting that 18F-FDG PET/CT could be applied to evaluate disease activity. Moreover, spleen 18F-FDG uptake may be a potential biomarker for predicting clinical prognosis of AOSD patients.

Project description:BackgroundIn patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment.MethodsMonocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%).ResultsN2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM.ConclusionsA machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

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Routinely staging gastric cancer with ¹⁸F-FDG PET-CT detects additional metastases and predicts early recurrence and death after surgery.

Objectives

Methods

Results

Key points

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Dataset Information

Routinely staging gastric cancer with 18F-FDG PET-CT detects additional metastases and predicts early recurrence and death after surgery.

Objectives

Methods

Results

Key points

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Routinely staging gastric cancer with ¹⁸F-FDG PET-CT detects additional metastases and predicts early recurrence and death after surgery.