Dataset Information

Diagnostic performance of preoperative [¹⁸F]FDG-PET/CT for lymph node staging in vulvar cancer: a large single-centre study.

ABSTRACT:

Purpose

This retrospective study aimed to assess the diagnostic performance of preoperative [¹⁸F]FDG-PET/CT in predicting the groin and pelvic lymph node (LN) status in a large single-centre series of vulvar cancer patients.

Methods

Between January 2013 and October 2018, among all consecutive women with proven vulvar cancer submitted to [¹⁸F]FDG-PET/CT, 160 patients were included. LNs were analysed by two qualitative methods assessing PET information (defined as visual assessment) and a combination of PET and low-dose CT information (defined as overall assessment), respectively, as well as semi-quantitative analysis (LN-SUV_max). Sensitivity, specificity, accuracy, positive and negative predictive values (PPV and NPV) in predicting the groin and pelvic LN status were calculated in the overall study population; a subset analysis of groin parameters in clinically/ultrasonography negative patients was also performed. Histopathology was the reference standard.

Results

All patients underwent vulvar and inguinofemoral LN surgery, and 35 pelvic LN surgery. Overall, 338 LN sites (296 groins and 42 pelvic sites) were histologically examined with 30.4% prevalence of metastatic groins and 28.6% for metastatic pelvic sites. In the overall study population, sensitivity (95% confidence interval, CI), specificity (95% CI), accuracy (95% CI), PPV (95% CI) and NPV (95% CI) at the groin level were 85.6% (78.3-92.8), 65.5% (59.0-72.0), 71.6% (66.5-76.8), 52.0% (44.0-60.1) and 91.2% (86.7-95.8) for visual assessment; 78.9% (70.5-87.3), 78.2% (72.5-83.8), 78.4% (73.7-83.1), 61.2% (52.3-70.1) and 89.4% (85.0-93.9) for overall assessment; and 73.3% (64.2-82.5), 85.0% (80.1-89.8), 81.4% (77.0-85.8), 68.0% (58.8-77.3) and 87.9% (83.4-92.5) for semi-quantitative analysis (SUV_max cut-off value 1.89 achieved by ROC analysis). Similar results were observed in the pelvis-based analysis.

Conclusion

In this large single-centre series of vulvar cancer patients, [¹⁸F]FDG-PET/CT showed good values of sensitivity and NPV in discriminating metastatic from non-metastatic LNs. In routine clinical practice, qualitative analysis is a reliable interpretative criterion making unnecessary commonly used semi-quantitative methods such as SUV_max.

SUBMITTER: Rufini V

PROVIDER: S-EPMC8426310 | biostudies-literature |

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:BackgroundTo develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.MethodsA total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.ResultsRadiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634-0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676-0.900). K-M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.ConclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer. Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.

Dataset Information

Diagnostic performance of preoperative [¹⁸F]FDG-PET/CT for lymph node staging in vulvar cancer: a large single-centre study.

Purpose

Methods

Results

Conclusion

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Dataset Information

Diagnostic performance of preoperative [18F]FDG-PET/CT for lymph node staging in vulvar cancer: a large single-centre study.

Purpose

Methods

Results

Conclusion

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Diagnostic performance of preoperative [¹⁸F]FDG-PET/CT for lymph node staging in vulvar cancer: a large single-centre study.