Dataset Information

Modified method to improve the diagnostic efficiency of ¹⁸F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

ABSTRACT:

Background

Regional lymph node (LN) metastasis is a significant factor influencing the treatment choice of esophageal squamous cell carcinoma (ESCC). The performance PET/CT as an imaging evaluation method for regional LNs in ESCC, is unsatisfactory due to the lack of logical criterion. We explored how a modified criterion improved the diagnostic value of ¹⁸F-FDG PET/CT in regional LN metastasis.

Methods

The data from 111 patients with ESCC were analyzed retrospectively. All patients underwent preoperative PET/CT examination, resection of the cancer, and regional LN dissection. The PET/CT images were interpreted by two experienced diagnosticians. LNs were allocated to five subregions. Each LN was diagnosed by two diagnostic criteria of PET/CT (traditional criterion and the modified criterion) one by one across the same field, and the accuracy of PET/CT was determined using the histopathologic results as the reference standard.

Results

A total of 4,847 LNs were dissected, of which 147 were confirmed as metastases by postoperative pathology. A total of 656 LNs were screened by ¹⁸F-FDG PET/CT imaging. The determination of all 656 LNs by PET/CT was compared with the pathological results. The diagnostic accuracy of the modified and traditional criteria for the five subregions (paraesophageal, neck, upper mediastinal, middle-lower mediastinal and ventral subregions) was: 74.60% vs. 61.90%, 86.44% vs. 81.36%, 90.26% vs. 70.78%, 96.19% vs. 75.09%, and 87.91% vs. 85.71%, respectively.

Conclusions

The modified diagnostic criterion had better diagnostic efficiency because it combined PET and CT imaging data.

SUBMITTER: Liao S

PROVIDER: S-EPMC8576671 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Modified method to improve the diagnostic efficiency of <sup>18</sup>F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

Liao Siqin S Wei Wenwei W Zhang Shuliang S Zeng Taidui T Chen Hao H Zheng Wei W Chen Chun C Ji Zhongyou Z Zheng Bin B

Annals of translational medicine 20211001 20

<h4>Background</h4>Regional lymph node (LN) metastasis is a significant factor influencing the treatment choice of esophageal squamous cell carcinoma (ESCC). The performance PET/CT as an imaging evaluation method for regional LNs in ESCC, is unsatisfactory due to the lack of logical criterion. We explored how a modified criterion improved the diagnostic value of <sup>18</sup>F-FDG PET/CT in regional LN metastasis.<h4>Methods</h4>The data from 111 patients with ESCC were analyzed retrospectively. ...[more]

PMID: 34790755

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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:PurposeThis 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.MethodsBetween 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-SUVmax). 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.ResultsAll 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 (SUVmax cut-off value 1.89 achieved by ROC analysis). Similar results were observed in the pelvis-based analysis.ConclusionIn 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 SUVmax.

Project description:BackgroundTo develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from 18F-fluorodeoxyglucose (18F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC).MethodsThe study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature.ResultsThe DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively.ConclusionsThrough the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.

Project description:BackgroundTo identify whether early metabolic responses as determined using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) during radiotherapy (RT) predict outcomes in patients with esophageal cancer.MethodsTwenty-one patients with esophageal cancer who received pre-treatment 18F-FDG PET/CT (PET1) and inter-fractional 18F-FDG PET/CT (PET2) after 11 fractions of RT (median 23.1 Gy, 2.1 Gy per fraction) were retrospectively reviewed. The region of interest for each calculation was delineated using "PET Edge". We calculated PET parameters including maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The relative changes (%) were calculated using the logarithmically transformed parameter values for the PET1 and PET2 scans. Multivariate analysis of locoregional recurrence and distant failures were performed using Cox regression analysis. After identifying statistically significant PET parameters for discriminating responders from non-responders, receiver operating characteristics curve analyses were used to assess the potentials of the studied PET parameters.ResultsAfter a median follow-up of 13 months, the 1-year overall and progression-free survival rates were 79.0% and 34.4%, respectively. Four patients developed locoregional recurrences (LRRs) and 8 had distant metastases (DMs). The 1-year overall LRR-free rate was 76.9% while the DM-free rate was 60.6%. The relative changes in MTV (ΔMTV) were significantly associated with LRR (p = 0.03). Conversely, the relative changes in SUVmean (ΔSUVmean) were associated with the risk of DM (p = 0.02). An ΔMTV threshold of 1.14 yielded a sensitivity of 60%, specificity of 94%, and an accuracy of 86% for predicting an LRR. Additionally, a ΔSUVmean threshold of a 35% decrease yielded a sensitivity of 67%, specificity of 83%, and accuracy of 76% for the prediction DM.Trial registrationRetrospectively registered.ConclusionsChanges in tumor metabolism during RT could be used to predict treatment responses, recurrences, and prognoses in patients with esophageal cancer.

Project description:ObjectivesImaging is increasingly used to assess lymph node involvement in clinically early-stage cervical cancer. This retrospective study aimed to evaluate the diagnostic accuracy of MRI, CT, and [18F]FDG-PET-CT.MethodsWomen with International Federation of Gynaecology and Obstetrics (FIGO) 2009 stage IA2-IIA cervical cancer and pretreatment imaging between 2009 and 2017 were selected from the Netherlands Cancer Registry. Patient-based and region-based (i.e. pelvic and common iliac) nodal status was extracted from radiology reports. Pathology results were considered the reference standard for calculating accuracy indices. Multiple imputation was used for missing pathology to limit verification bias risk.ResultsNodal assessment was performed in 1676 patients with MRI, 926 with CT, and 379 with [18F]FDG-PET-CT, with suspicious nodes detected in 17%, 16%, and 48%, respectively. [18F]FDG-PET-CT was used to confirm MRI/CT results in 95% of patients. Pathology results were imputed for 30% of patients. [18F]FDG-PET-CT outperformed MRI and CT in detecting patient-based nodal metastases with sensitivities of 80%, 48%, and 40%, and AUCs of 0.814, 0.706, and 0.667, respectively, but not in specificity: 79%, 92%, and 92%. Region-based analyses showed similar indices in the pelvic region, but worse performance in the common iliac region with AUCs of 0.575, 0.554, and 0.517, respectively.Conclusions[18F]FDG-PET-CT outperformed MRI and CT in detecting nodal metastases, which may be related to its use as a verification modality. However, MRI and CT had the highest specificity. As MRI is generally performed routinely to assess local and regional spread of cervical cancer, [18F]FDG-PET-CT can be used to confirm suspicious nodes.Critical relevance statementAccurate assessment of the nodal status in clinically early-stage cervical cancer is essential for tumour staging, treatment decision making and prognosis.Key points• The accuracy of MRI, CT or [18F]FDG-PET-CT for nodal staging in early cervical cancer is a subject of discussion. • Overall, [18F]FDG-PET-CT outperformed MRI, followed by CT, when used as a verification modality. • Staging with MRI and the addition of [18F]FDG-PET-CT to verify high-risk cases seems to be a good approach.

Dataset Information

Modified method to improve the diagnostic efficiency of ¹⁸F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

Background

Methods

Results

Conclusions

Publications

Modified method to improve the diagnostic efficiency of <sup>18</sup>F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Modified method to improve the diagnostic efficiency of 18F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

Background

Methods

Results

Conclusions

Publications

Modified method to improve the diagnostic efficiency of <sup>18</sup>F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Modified method to improve the diagnostic efficiency of ¹⁸F-FDG PET/CT in regional lymph node metastasis of esophageal squamous cell carcinoma.