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Radiomic signature of ¹⁸F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.

ABSTRACT: We aimed to evaluate whether radiomic feature-based fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits. Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores. Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy. Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC6299427 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.

Jiang Yuming Y Yuan Qingyu Q Lv Wenbing W Xi Sujuan S Huang Weicai W Sun Zepang Z Chen Hao H Zhao Liying L Liu Wei W Hu Yanfeng Y Lu Lijun L Ma Jianhua J Li Tuanjie T Yu Jiang J Wang Quanshi Q Li Guoxin G

Theranostics 20181112 21

We aimed to evaluate whether radiomic feature-based fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits. Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict diseas ...[more]

PMID: 30613271

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Project description:BackgroundIdentification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.MethodsWe retrospectively studied 18F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).ResultsTwo sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.ConclusionsIn our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

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.

Project description:PurposeTo investigate the performance of pretreatment fluorine 18 (18F) fluorodeoxyglucose PET/CT radiomics in predicting severe immune-related adverse events (irSAEs) among patients with advanced non-small cell lung cancer (NSCLC) treated with immunotherapy, which is important in optimizing treatment plans and alleviating future complications with early interventions.Materials and methodsThe retrospective arm of this study included 146 patients with histologically confirmed stage IIIB-IV NSCLC who were treated with immune checkpoint blockade between June 2011 and December 2017 and who were split into training (n = 97) and test (n = 49) cohorts. A prospective validation arm enrolled 48 patients before initiation of immunotherapy between January 2018 and June 2019 as an independent test cohort. Radiomics features extracted from baseline (preimmunotherapy treatment) PET, CT, and PET/CT fusion images were used to generate a radiomics score (RS) to quantify patient risk for developing irSAEs by an improved least absolute shrinkage and selection operator method. Weighted multivariable logistic regression analysis was then used to develop a nomogram model to predict irSAEs, which was assessed by its calibration, discrimination, and clinical usefulness.ResultsThe radiomics nomogram, incorporating the RS, type of immune checkpoint blockade, and dosing schedule, was able to predict patients with and without irSAEs with area under the receiver operating characteristic curve of 0.92 (95% confidence interval [CI]: 0.86, 0.98), 0.92 (95% CI: 0.86, 0.99), and 0.88 (95% CI: 0.78, 0.97) in the training, test, and prospective validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram model had the highest overall net benefit.ConclusionA high RS is a significant risk factor for development of irSAEs, demonstrating the value of PET/CT images in predicting irSAEs. By the identification, at baseline, of patients with NSCLC most likely to have irSAEs, treatment plans can be optimized before initiation of immunotherapy.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Yousefi.

Project description:Knowledge of the intrinsic variability of radiomic features is essential to the proper interpretation of changes in these features over time. The primary aim of this study was to assess the test-retest repeatability of radiomic features extracted from 18F-FDG PET images of cervical tumors. The impact of different image preprocessing methods was also explored. Methods: Patients with cervical cancer underwent baseline and repeat 18F-FDG PET/CT imaging within 7 d. PET images were reconstructed using 2 methods: ordered-subset expectation maximization (PETOSEM) or ordered-subset expectation maximization with point-spread function (PETPSF). Tumors were segmented to produce whole-tumor volumes of interest (VOIWT) and 40% isocontours (VOI40). Voxels were either left at the default size or resampled to 3-mm isotropic voxels. SUV was discretized to a fixed number of bins (32, 64, or 128). Radiomic features were extracted from both VOIs, and repeatability was then assessed using the Lin concordance correlation coefficient (CCC). Results: Eleven patients were enrolled and completed the test-retest PET/CT imaging protocol. Shape, neighborhood gray-level difference matrix, and gray-level cooccurrence matrix features were repeatable, with a mean CCC value of 0.81. Radiomic features extracted from PETOSEM images showed significantly better repeatability than features extracted from PETPSF images (P < 0.001). Radiomic features extracted from VOI40 were more repeatable than features extracted from VOIWT (P < 0.001). For most features (78.4%), a change in bin number or voxel size resulted in less than a 10% change in feature value. All gray-level emphasis and gray-level run emphasis features showed poor repeatability (CCC values < 0.52) when extracted from VOIWT but were highly repeatable (mean CCC values > 0.96) when extracted from VOI40 Conclusion: Shape, gray-level cooccurrence matrix, and neighborhood gray-level difference matrix radiomic features were consistently repeatable, whereas gray-level run length matrix and gray-level zone length matrix features were highly variable. Radiomic features extracted from VOI40 were more repeatable than features extracted from VOIWT Changes in voxel size or SUV discretization parameters typically resulted in relatively small differences in feature value, though several features were highly sensitive to these changes.

Project description:BackgroundAccurately and promptly predicting the response of gastrointestinal stromal tumors (GISTs) to targeted therapy is essential for optimizing treatment strategies. However, some fractions of recurrent or metastatic GISTs present as non-FDG-avid lesions, limiting the value of [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) in treatment evaluation. This study evaluated the efficacy of [18F]F-fibroblast activation protein inhibitor (FAPI)-42 [18F]FAPI-42) PET/CT for assessing the treatment response in recurrent or metastatic GISTs, in comparison to [18F]FDG PET/CT and explores a model integrating PET/CT imaging and clinical parameters to optimize the clinical use of these diagnostic tools.MethodsOur retrospective analysis included 27 patients with recurrent or metastatic GISTs who underwent [18F]FAPI-42 PET/CT and [18F]FDG PET/CT at baseline before switching targeted therapy. Treatment response status was divided into a progression group (PG) and a non-progression group (NPG) based on the Response Criteria in Solid Tumors (RECIST) 1.1, according to the contrast-enhanced computed tomography (CT) scan at six months. [18F]FAPI-42 and [18F]FDG PET/CT parameters including the mean standardized uptake value (SUVmean), the standard uptake value corrected for lean body mass (SULpeak), the maximum standardized uptake value (SUVmax), tumor-to-blood pool SUV ratio (TBR), tumor-to-liver SUV ratio (TLR), metabolic tumor volume (MTV)/FAPI-positive tumor volume (GTV-FAPI), total lesion glycolysis (TLG)/FAPI-positive total lesion accumulation (TLF) were correlated with the response status to identify indicative of treatment response. The predictive performance of them was quantified by generating receiver operating characteristic curves (ROC), calibration curves, and cross-validation.ResultsA total of 110 lesions were identified in 27 patients. Compared with PG, NPG was associated with lower levels of TBR and SUVmean in FDG PET/CT (TBR-FDG, SUVmean-FDG; P=0.033 and P=0.038, respectively), with higher SULpeak and TLF in FAPI PET/CT (SULpeak-FAPI, TLF-FAPI; P=0.10 and P=0.049, respectively). The predictive power of a composite-parameter model, including TBR-FDG, SULpeak-FAPI, gene mutation, and type of targeted therapy [area under the curve (AUC) =0.865], was superior to the few-parameter models incorporating TBR-FDG (AUC =0.637, P<0.001), SULpeak-FAPI (AUC =0.665, P<0.001) or both (AUC =0.721, P<0.001).ConclusionsBoth [18F]FAPI-42 PET/CT and [18F]FDG PET/CT have value in predicting the treatment response of recurrent or metastatic GISTs. And [18F]FAPI-42 PET/CT offers synergistic value when used in combination with [18F]FDG PET/CT. Notably, the nomogram generated from the model incorporating [18F]FAPI-42 PET/CT, [18F]FDG PET/CT parameters, gene mutation, and type of targeted therapy could yield more precise predictions of the response of recurrent metastatic GISTs.

Dataset Information

Radiomic signature of ¹⁸F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.

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Radiomic signature of <sup>18</sup>F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.

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