Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT.
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SUBMITTER: Lee JW
PROVIDER: S-EPMC8304187 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC7354146 | biostudies-literature
Project description:Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p ≤ 0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P < 0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P < 0.001); TLG, 42.24 versus 9.90 (P < 0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.
| S-EPMC6410462 | biostudies-literature
Project description: Not available
| S-EPMC10656631 | biostudies-literature
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.
| S-EPMC8812058 | biostudies-literature
Project description:Objectives Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies. Methods This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. Results Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. Conclusions Future research must prioritise prospective validation of previously proposed models to further clinical translation. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01245-0.
| S-EPMC9206060 | biostudies-literature
Project description:BackgroundThe objective of this study was to investigate the prognostic value of 2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) uptake of bone marrow (BM) and metabolic parameters of primary tumor on positron emission tomography/computed tomography (PET/CT) for predicting distant recurrence in patients with breast cancer.MethodsPretreatment [18F]FDG PET/CT images of 345 breast cancer patients were retrospectively evaluated. Maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis (TLG) of primary breast cancer and bone marrow-to-liver uptake ratio (BLR) on PET/CT were measured. A Cox proportional hazard regression model was used to evaluate the prognostic potential of parameters for predicting recurrence-free survival (RFS) and distant RFS. For Kaplan-Meier analysis, the specific cutoff values pf BLR and TLG were determined by the maximal chi-square method.ResultsThe median follow-up duration of the enrolled patients was 48.7 months, and during follow-up, 36 patients (10.4%) experienced the cancer recurrence. BLR was significantly correlated with T stage, serum inflammatory markers, and recurrence pattern (p < 0.05). Patients with high BLR and TLG showed worse RFS and distant RFS than those with low BLR and TLG. On multivariate analysis, BLR was significantly associated with both RFS and distant RFS after adjusting for T stage, estrogen receptor status, and TLG (p = 0.001 for both). Only 0.5% of patients with TLG < 9.64 g and BLR < 0.91 experienced distant recurrence. However, patients with TLG ≥ 9.64 g and BLR ≥ 0.91 had a distant recurrence rate of 40.7%.ConclusionsBLR on pretreatment [18F]FDG PET/CT were significant predictors for RFS and distant RFS in patients with breast cancer. By combining [18F]FDG uptake of BM and volumetric PET/CT index of breast cancer, the risk of distant recurrence could be stratified.
| S-EPMC7326752 | biostudies-literature
Project description:Boron-10-containing positron emission tomography (PET) radio-tracer, 18F-FBPA, has been used to evaluate the feasibility and treatment outcomes of Boron neutron capture therapy (BNCT). The clinical use of PET/MR is increasing and reveals its benefit in certain applications. However, the PET/CT is still the most widely used modality for daily PET practice due to its high quantitative accuracy and relatively low cost. Considering the different attenuation correction maps between PET/CT and PET/MR, comparison of derived image features from these two modalities is critical to identify quantitative imaging biomarkers for diagnosis and prognosis. This study aimed to investigate the comparability of image features extracted from 18F-FBPA PET/CT and PET/MR. A total of 15 patients with malignant brain tumor who underwent 18F-FBPA examinations using both PET/CT and PET/MR on the same day were retrospectively analyzed. Overall, four conventional imaging characteristics and 449 radiomic features were calculated from PET/CT and PET/MR, respectively. A linear regression model and intraclass correlation coefficient (ICC) were estimated to evaluate the comparability of derived features between two modalities. Features were classified into strong, moderate, and weak comparability based on coefficient of determination (r2) and ICC. All of the conventional features, 81.2% of histogram, 37.5% of geometry, 51.5% of texture, and 25% of wavelet-based features, showed strong comparability between PET/CT and PET/MR. With regard to the wavelet filtering, radiomic features without filtering (61.2%) or with low-pass filtering (59.2%) along three axes produced strong comparability between the two modalities. However, only 8.2% of the features with high-pass filtering showed strong comparability. The linear regression models were provided for the features with strong and moderate consensus to interchange the quantitative features between the PET/CT and the PET/MR. All of the conventional and 71% of the radiomic (mostly histogram and texture) features were sufficiently stable and could be interchanged between 18F-FBPA PET with different hybrid modalities using the proposed equations. Our findings suggested that the image features high interchangeability may facilitate future studies in comparing PET/CT and PET/MR.
| S-EPMC8615400 | biostudies-literature
Project description:BackgroundMicrovascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.MethodsIn this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models.ResultsFor HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively).Conclusions18F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.
| S-EPMC9338369 | biostudies-literature
Project description:ObjectiveTo examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer.MethodsThis retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index).ResultsTumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808).ConclusionML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer.Advances in knowledgeML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.
| S-EPMC10461278 | biostudies-literature
Project description:PurposeTo assess (1) the repeatability and (2) the impact of reconstruction methods and delineation on the repeatability of 105 radiomic features in non-small-cell lung cancer (NSCLC) 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) positron emission tomorgraphy/computed tomography (PET/CT) studies.ProceduresEleven NSCLC patients received two baseline whole-body PET/CT scans. Each scan was reconstructed twice, once using the point spread function (PSF) and once complying with the European Association for Nuclear Medicine (EANM) guidelines for tumor PET imaging. Volumes of interest (n?=?19) were delineated twice, once on PET and once on CT images.ResultsSixty-three features showed an intraclass correlation coefficient???0.90 independent of delineation or reconstruction. More features were sensitive to a change in delineation than to a change in reconstruction (25 and 3 features, respectively).ConclusionsThe majority of features in NSCLC [(18)F]FDG-PET/CT studies show a high level of repeatability that is similar or better compared to simple standardized uptake value measures.
| S-EPMC5010602 | biostudies-literature