Dataset Information

¹⁸F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

ABSTRACT:

Background

Microvascular 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.

Methods

In 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.

Results

For 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).

Conclusions

¹⁸F-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.

SUBMITTER: Jiang C

PROVIDER: S-EPMC9338369 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

<sup>18</sup>F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Jiang Chunjuan C Zhao Liwei L Xin Bowen B Ma Guang G Wang Xiuying X Song Shaoli S

Quantitative imaging in medicine and surgery 20220801 8

<h4>Background</h4>Microvascular 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 <sup>18</sup>F-fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.<h4>Methods</h4>In this retrospective study, 127 (HCC: ICC =76:51) patie ...[more]

PMID: 35919043

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Project description:BackgroundFluorine-18 fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) has some limitations in diagnosis of Intrahepatic cholangiocarcinoma (ICC).Materials and methodsPatients with histologically confirmed ICC who underwent both [18F]FDG and 18F-labeled fibroblast-activation protein inhibitors ([18F]FAPI)-04 PET/CT were prospectively analyzed. The maximum standard uptake value (SUVmax), tumor-to-background ratio (TBR), metabolic tumor volume (MTV), total lesion glycolysis (TLG), [18F]FAPI-avid tumor volume (FTV), total lesion fibroblast activation protein expression (TLF) were compared between the two modalities by paired Wilcoxon signed-rank test and Mann-Whitney U test, and McNemar's test was used to assess the diagnostic accuracy between the two techniques.ResultsIn total, 23 patients with 389 lesions were included. Compared to [18F]FDG, [18F]F-FAPI-04 PET/CT demonstrated a higher detection rate for intrahepatic lesions (86.3% vs. 78.2% P = 0.040), lymph node metastases (85.2% vs. 68.2%, P = 0.007), peritoneal metastases (100% vs. 93.8%), and bone metastases (100% vs. 70.5%, P < 0.001). [18F]FAPI-04 PET showed higher SUVmax, TBR and greater tumor burden values than [18F]FDG PET in non-cholangitis intrahepatic lesions (SUVmax: 8.7 vs. 6.4, P < 0.001; TBR: 8.0 vs. 3.5, P < 0.001; FTV vs. MTV: 41.3 vs. 12.4, P < 0.001; TLF vs. TLG: 223.5 vs. 57.0, P < 0.001), lymph node metastases (SUVmax: 6.5 vs. 5.5, P = 0.042; TBR: 5.4 vs. 3.9, P < 0.001; FTV vs. MTV: 2.0 vs. 1.5, P = 0.026; TLF vs. TLG: 9.0 vs. 7.8 P = 0.024), and bone metastases (SUVmax: 9.7 vs. 5.25, P < 0.001; TBR: 10.8 vs. 3.0, P < 0.001; TLF vs. TLG: 9.8 vs. 4.2, P < 0.001). However, [18F]FDG showed higher radiotracer uptake (SUVmax: 14.7 vs. 8.4, P < 0.001; TBR: 7.4 vs. 2.8, P < 0.001) than [18F]FAPI-04 PET/CT for 6 patients with obstructive cholangitis. [18F]FAPI-04 PET/CT yielded a change in planned therapy in 6 of 23 (26.1%) patients compared with [18F]FDG.Conclusions[18F]FAPI-04 PET/CT had higher detection rate and radiotracer uptake than [18F]FDG PET/CT in intrahepatic lesions, lymph node metastases, and distant metastases, especially in bone. Therefore, [18F]FAPI-04 PET/CT may be a promising technique for diagnosis and staging of ICC.Trial registrationClinical Trials, NCT05485792. Registered 1 August 2022, retrospectively registered, https//clinicaltrials.gov/study/NCT05485792?cond=NCT05485792&rank=1.

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: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.

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Dataset Information

¹⁸F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Background

Methods

Results

Conclusions

Publications

<sup>18</sup>F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Background

Methods

Results

Conclusions

Publications

<sup>18</sup>F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

¹⁸F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.