Dataset Information

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers.

ABSTRACT: The relationship between 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for staging work-ups were retrospectively enrolled. Conventional parameters and the first- and second-order textural features of AGC were extracted using PET textural analysis. Upon histopathological analysis, along with histopathological classification and staging, the degree of CD4, CD8, and CD163 cell infiltrations and expressions of interleukin-6 and matrix-metalloproteinase-11 (MMP-11) in the primary tumor were assessed. The histopathological classification, Lauren classification, lymph node metastasis, CD8 T lymphocyte and CD163 macrophage infiltrations, and MMP-11 expression were significantly associated with the textural features of AGC. The multivariate survival analysis showed that increased FDG uptake and intra-tumoral metabolic heterogeneity were significantly associated with an increased risk of recurrence after curative surgery. Textural features of AGC on FDG PET/CT showed significant correlations with the inflammatory response in the tumor microenvironment and histopathological features of AGC, and they showed significant prognostic values for predicting RFS.

SUBMITTER: Ahn H

PROVIDER: S-EPMC9406203 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers.

Ahn Hyein H Song Geum Jong GJ Jang Si-Hyong SH Lee Hyun Ju HJ Lee Moon-Soo MS Lee Ji-Hye JH Oh Mee-Hye MH Jeong Geum Cheol GC Lee Sang Mi SM Lee Jeong Won JW

Cancers 20220815 16

The relationship between 2-deoxy-2-[<sup>18</sup>F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for stag ...[more]

PMID: 36010928

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Project description:We extracted image features from serial 18F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT), in the middle of the treatment (Mid-CRT) and post-treatment (Post-CRT) were included. For each patient, Mid-CRT and Post-CRT scans were aligned to Pre-CRT scan. Comprehensive image features were extracted from CT and PET (SUV) images within manually delineated gross tumor volume, including geometry features, intensity features and texture features. The difference of feature values between two time points were also computed and analyzed. We employed univariate logistic regression model, multivariate model, and naïve Bayesian classifier to analyze the image features and identify useful tumor recurrent predictors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the accuracy of the prediction. In univariate analysis, six geometry, three intensity, and six texture features were identified as significant predictors of tumor recurrence. A geometry feature of Roundness between Post-CRT and Pre-CRT CTs was identified as the most important predictor with an AUC value of 1.00 by multivariate logistic regression model. The difference of Number of Pixels on Border (geometry feature) between Post-CRT and Pre-CRT SUVs and Elongation (geometry feature) of Post-CRT CT were identified as the most useful feature set (AUC = 1.00) by naïve Bayesian classifier. To investigate the early prediction ability, we used features only from Pre-CRT and Mid-CRT scans. Orientation (geometry feature) of Pre-CRT SUV, Mean (intensity feature) of Pre-CRT CT, and Mean of Long Run High Gray Level Emphasis (LRHGLE) (texture feature) of Pre-CRT CT were identified as the most important feature set (AUC = 1.00) by multivariate logistic regression model. Standard deviation (intensity feature) of Mid-CRT SUV and difference of Mean of LRHGLE (texture feature) between Mid-CRT and Pre-CRT SUVs were identified as the most important feature set (AUC = 0.86) by naïve Bayesian classifier. The experimental results demonstrated the potential of serial PET/CT scans in early prediction of anal tumor recurrence.

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Project description:BackgroundRadiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset.Materials and methodsThe proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.ResultsThe number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).ConclusionWhen performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

Project description:ObjectivesFluorodeoxyglucose (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.MethodsWe 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.ResultsThe 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 < 0.001), and a worse prognosis if not: multivariate hazard ratio 2.19 (1.23-3.91; p = 0.008). Prognosis worsened with mN stage.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.

Dataset Information

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers.

Publications

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers.

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