Dataset Information

Behavior of FDG-avid supradiaphragmatic lymph nodes in PET/CT throughout primary therapy in advanced serous epithelial ovarian cancer: a prospective study.

ABSTRACT: BACKGROUND:Epithelial ovarian cancer (EOC) typically spreads intra-abdominally, but preoperative evaluation with FDG PET/CT often reveals metabolically active supradiaphragmatic lymph nodes (sdLNs). Their clinical significance and behavior during treatment has not been established. METHODS:EOC patients with PET positive sdLNs at diagnosis were prospectively followed with PET/CT after primary chemotherapy and at the first recurrence. In each patient, 2 most active LNs in 5 different supradiaphramatic regions were evaluated and the size and changes in FDG uptake (SUVmax) were recorded. The patients´ overall response to primary treatment was defined with RECIST criteria. The behavior of sdLNs during chemotherapy were compared in treatment responders and non-responders. Recurrence patterns were monitored. RESULTS:Forty-one patients with 127 PET/CT scans were systematically evaluated. In pretreatment scan, 76% (31/41) of patients had FDG-avid sdLNs in multiple anatomical sites. Only a minority (22/136) of the sdLNs were enlarged in size, but their histopathologic confirmation by biopsy was not possible. Only 6/41 patients had FDG-avid sdLNs in a single surgically approachable site. The sdLNs became inactive during primary chemotherapy more often in the RECIST responders compared to the non-responders (HR 1.46 (95%CI: 1.09-1.96), p?=?0.002). The size and SUVmax values did not predict treatment outcome. In 50% of the responders the same sdLNs reactivated when recurrence occurred. Persistent post-treatment metabolic activity did not predict earlier disease relapse (p?=?0.59). CONCLUSION:The behavior of metabolically active sdLNs during chemotherapy supports their metastatic nature. Due to their distribution to multiple regions, the benefit of removal of reachable sdLNS seems unlikely. TRIAL REGISTRATION:NCT, NCT01276574 . Registered 1 September 2010.

SUBMITTER: Laasik M

PROVIDER: S-EPMC6542004 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Behavior of FDG-avid supradiaphragmatic lymph nodes in PET/CT throughout primary therapy in advanced serous epithelial ovarian cancer: a prospective study.

Laasik Maren M Kemppainen Jukka J Auranen Annika A Hietanen Sakari S Grénman Seija S Seppänen Marko M Hynninen Johanna J

Cancer imaging : the official publication of the International Cancer Imaging Society 20190529 1

<h4>Background</h4>Epithelial ovarian cancer (EOC) typically spreads intra-abdominally, but preoperative evaluation with FDG PET/CT often reveals metabolically active supradiaphragmatic lymph nodes (sdLNs). Their clinical significance and behavior during treatment has not been established.<h4>Methods</h4>EOC patients with PET positive sdLNs at diagnosis were prospectively followed with PET/CT after primary chemotherapy and at the first recurrence. In each patient, 2 most active LNs in 5 differen ...[more]

PMID: 31142357

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Project description:ObjectiveTo investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).MethodsA total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.ResultsIn univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.ConclusionPET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.

Project description:Objectives18F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer.MethodsOne hundred and eight patients with pulmonary nodules were enrolled to perform 18F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K1, k2, k3 and Ki and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was computed subsequently as a separate marker. We further divided the LNs into mediastinal LNs (N=82) and pulmonary hilar LNs (N=53). Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analysis was performed on each parameter to compare the diagnostic efficacy in differentiating lymph node metastases from inflammatory uptake. P<0.05 were considered statistically significant.ResultsAmong the 135 FDG-avid LNs confirmed by pathology, 49 LNs were non-metastatic, and 86 LNs were metastatic. LN-SUVmax, MBP-SUVmax, LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter couldn't well differentiate metastatic from non-metastatic LNs (P>0.05). However, LN-SUVmax/PT-SUVmax have good performance in the differential diagnosis of non-metastatic and metastatic LNs (P=0.039). Dynamic metabolic parameters in addition to k3, the parameters including K1, k2, Ki, and Ki/K1, on the other hand, have good performance in the differential diagnosis of metastatic and non-metastatic LNs (P=0.045, P=0.001, P=0.001, P=0.001, respectively). For ROC analysis, the metabolic parameters Ki (AUC of 0.672 [0.579-0.765], sensitivity 0.395, specificity 0.918) and Ki/K1 (AUC of 0.673 [0.580-0.767], sensitivity 0.570, specificity 0.776) have good performance in the differential diagnosis of metastatic from non-metastatic LNs than SUVmax (AUC of 0.596 [0.498-0.696], sensitivity 0.826, specificity 0.388), included the mediastinal region and pulmonary hilar region.ConclusionCompared with SUVmax, quantitative parameters such as K1, k2, Ki and Ki/K1 showed promising results for differentiation of metastatic and non-metastatic LNs with high uptake. The Ki and Ki/K1 had a high differential diagnostic value both in the mediastinal region and pulmonary hilar region.

Project description:This study assessed whether preoperative maximum standardized uptake value (SUVmax) of metastatic lymph nodes (LNs) measured by F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F-FDG PET/CT) could improve the prediction of prognosis in gastric cancer.One hundred fifty-one patients with gastric cancer and pathologically confirmed LN involvement who had undergone preoperative F-FDG PET/CT prior to curative surgical resection were retrospectively enrolled. To obtain nodal SUVmax, a transaxial image representing the highest F-FDG uptake was carefully selected, and a region of interest was manually drawn on the highest F-FDG accumulating LN. Conventional prognostic parameters and PET findings (primary tumor and nodal SUVmax) were analyzed for prediction of recurrence-free survival (RFS) and overall survival (OS). Furthermore, prognostic accuracy of survival models was assessed using c-statistics.Of the 151 patients, 38 (25%) experienced recurrence and 34 (23%) died during follow-up (median follow-up, 48 months; range, 5-74 months). Twenty-seven patients (18%) showed positive F-FDG nodal uptake (range, 2.0-22.6). In these 27 patients, a receiver-operating characteristic curve demonstrated a nodal SUVmax of 2.8 to be the optimal cutoff for predicting RFS and OS. The univariate and multivariate analyses showed that nodal SUVmax (hazard ratio [HR]?= 2.71, P < 0.0001), pathologic N (pN) stage (HR = 2.58, P = 0.0058), and pathologic T (pT) stage (HR = 1.77, P = 0.0191) were independent prognostic factors for RFS. Also, nodal SUVmax (HR = 2.80, P < 0.0001) and pN stage (HR = 2.28, P = 0.0222) were independent prognostic factors for OS. A predictive survival model incorporating conventional risk factors (pT/pN stage) gave a c-statistic of 0.833 for RFS and 0.827 for OS, whereas a model combination of nodal SUVmax with pT/pN stage gave a c-statistic of 0.871 for RFS (P = 0.0355) and 0.877 for OS (P = 0.0313).Nodal SUVmax measured by preoperative F-FDG PET/CT is an independent prognostic factor for RFS and OS. Combining nodal SUVmax with pT/pN staging can improve survival prediction precision in patients with gastric cancer.

Dataset Information

Behavior of FDG-avid supradiaphragmatic lymph nodes in PET/CT throughout primary therapy in advanced serous epithelial ovarian cancer: a prospective study.

Publications

Behavior of FDG-avid supradiaphragmatic lymph nodes in PET/CT throughout primary therapy in advanced serous epithelial ovarian cancer: a prospective study.

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