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Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT.

ABSTRACT: BACKGROUND:18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-PET/CT) has been widely applied for the imaging of head and neck squamous cell carcinoma (HNSCC). This study examined whether pre- and post-treatment 18F-PET/CT features can help predict the survival of HNSCC patients. RESULTS:Three radiomics features were identified as prognostic factors. Radiomics score calculated from these features significantly predicted overall survival (OS) and disease-free disease (DFS). The clinicopathological characteristics combined with pre- or post-treatment nomograms showed better ROC curves and decision curves than the nomogram based only on clinicopathological characteristics. CONCLUSIONS:Combining clinicopathological characteristics with radiomics features of pre-treatment PET/CT or post-treatment PET/CT assessment of primary tumor sites as positive or negative may substantially improve prediction of OS and DFS of HNSCC patients. METHODS:171 patients who received pre-treatment 18F-PET/CT scans and 154 patients who received post-treatment 18F-PET/CT scans with HNSCC in the Cancer Imaging Achieve (TCIA) were included. Nomograms that combined clinicopathological features with either pre-treatment PET/CT radiomics features or post-treatment assessment of primary tumor sites were constructed using data from 154 HNSCC patients. Receiver operating characteristic (ROC) curves and decision curves were used to compare the predictions of these models with those of a model incorporating only clinicopathological features.

SUBMITTER: Liu Z

PROVIDER: S-EPMC7425452 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment <sup>18</sup>F-PET/CT.

Liu Zheran Z Cao Yuan Y Diao Wei W Cheng Yue Y Jia Zhiyun Z Peng Xingchen X

Aging 20200716 14

<h4>Background</h4>18-fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-PET/CT) has been widely applied for the imaging of head and neck squamous cell carcinoma (HNSCC). This study examined whether pre- and post-treatment <sup>18</sup>F-PET/CT features can help predict the survival of HNSCC patients.<h4>Results</h4>Three radiomics features were identified as prognostic factors. Radiomics score calculated from these features significantly predicted overall surviv ...[more]

PMID: 32674074

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Project description:The aim of this phase II clinical trial (NCT02965001) was to evaluate the prognostic value of urokinase-type plasminogen activator receptor (uPAR) PET/CT with the novel ligand 68Ga-NOTA-AE105 in head and neck cancer and compare it with 18F-FDG. Methods: Patients with head and neck squamous cell carcinoma referred for curatively intended radiotherapy were eligible and prospectively included in this study. 68Ga-uPAR and 18F-FDG PET/CT were performed before initiation of curatively intended radiotherapy, and the SUVmax of the primary tumor was measured on both PET/CT studies by 2 independent readers. Relapse-free survival (RFS) and overall survival (OS) were calculated, and optimal cutoffs were established for 68Ga-uPAR and 18F-FDG PET independently and compared using log rank and Kaplan-Meier statistics, as well as univariate and multivariate analysis in a Cox proportional-hazards model. Results: In total, 57 patients were included and followed for a median of 33.8 mo (range, 2.30-47.2, mo). The median SUVmax of the primary tumors was 2.98 (range, 1.94-5.24) for 68Ga-uPAR and 15.7 (range, 4.24-45.5) for 18F-FDG. The optimal cutoffs for 68Ga-NOTA-AE105 SUVmax in the primary tumor were 2.63 for RFS and 2.66 for OS. A high uptake of 68Ga-NOTA-AE105 (SUVmax above cutoff) was significantly associated with poor RFS and OS (log-rank P = 0.012 and P = 0.022). 68Ga-NOTA-AE105 uptake in the primary tumor was significantly associated with poor RFS in univariate analysis (hazard ratio [HR], 8.53 [95% CI, 1.12-64.7]; P = 0.038), and borderline-associated with OS (HR, 7.44 [95% CI, 0.98-56.4]; P = 0.052). For 18F-FDG PET, the optimal cutoffs were 22.7 for RFS and 22.9 for OS. An 18F-FDG SUVmax above the cutoff was significantly associated with reduced RFS (log-rank P = 0.012) and OS (log-rank P = 0.000). 18F-FDG uptake was significantly associated with reduced RFS (HR, 3.27 [95% CI, 1.237-8.66]; P = 0.017) and OS (HR, 7.10 [95% CI, 2.60-19.4]; P < 0.001) in univariate analysis. In a multivariate analysis including 68Ga-uPAR SUVmax, 18F-FDG SUVmax, TNM stage, and p16 status, only 68Ga-uPAR SUVmax remained significant (HR, 8.51 [95% CI, 1.08-66.9]; P = 0.042) for RFS. For OS, only TNM stage and 18F-FDG remained significant. Conclusion: The current trial showed promising results for the use of 68Ga-uPAR PET SUVmax in the primary tumor to predict RFS in head and neck squamous cell carcinoma patients referred for curatively intended radiotherapy when compared with 18F-FDG PET, TNM stage, and p16 status. 68Ga-uPAR PET could potentially become valuable for identification of patients suited for deescalation of treatment and risk-stratified follow-up schemes.

Project description:PurposeTo develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC).MethodsA total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots.ResultsIn total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900-0.964), 0.901 (0.840-0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions.ConclusionThis study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

Project description:Background: Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients. We applied radiomics analysis to devise non-invasive biomarkers and accurately differentiate between well-differentiated (WD) and moderately differentiated (MD) and poorly differentiated (PD) HNSCC. Methods: This study involved 206 consecutive HNSCC patients (training cohort: n = 137; testing cohort: n = 69). In total, we extracted 670 radiomics features from contrast-enhanced computed tomography (CT) images. Radiomics signatures were constructed with a kernel principal component analysis (KPCA), random forest classifier and a variance-threshold (VT) selection. The associations between the radiomics signatures and HNSCC histological grades were investigated. A clinical model and combined model were also constructed. Areas under the receiver operating characteristic curves (AUCs) were applied to evaluate the performances of the three models. Results: In total, 670 features were selected by the KPCA and random forest methods from the CT images. The radiomics signatures had a good performance in discriminating between the two cohorts of HNSCC grades, with an AUC of 0.96 and an accuracy of 0.92. The specificity, accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of the abovementioned method with a VT selection for determining HNSCC grades were 0.83, 0.92, 0.96, 0.94, and 0.91, respectively; without VT, the corresponding results were 0.70, 0.83, 0.88, 0.80, and 0.84. The differences in accuracy, sensitivity and NPV were significant between these approaches (p < 0.05). The AUCs with VT and without VT were 0.96 and 0.89, respectively (p < 0.05). Compared to the combined model and the radiomics signatures, The clinical model had a worse performance, and the differences were significant (p < 0.05). The combined model had the best performance, but the difference between the combined model and the radiomics signature weren't significant (p > 0.05). Conclusions: The CT-based radiomics signature could discriminate between WD and MD and PD HNSCC and might serve as a biomarker for preoperative grading.

Project description:BackgroundThe aim of this study was to assess radiomics features on pre-treatment [18F]FDG positron emission tomography (PET) as potential biomarkers for response and survival in patients with metastatic colorectal cancer (mCRC).MethodsPatients with mCRC underwent [18F]FDG PET/computed tomography (CT) prior to first- or third-line palliative systemic treatment. Tumour lesions were semiautomatically delineated and standard uptake value (SUV), metabolically active tumour volume (MATV), total lesion glycolysis (TLG), entropy, area under the curve of the cumulative SUV-volume histogram (AUC-CSH), compactness and sphericity were obtained.ResultsLesions of 47 patients receiving third-line systemic treatment had higher SUVmax, SUVpeak, SUVmean, MATV and TLG, and lower AUC-CSH, compactness and sphericity compared to 52 patients receiving first-line systemic treatment. Therefore, first- and third-line groups were evaluated separately. In the first-line group, anatomical changes on CT correlated negatively with TLG (ρ = 0.31) and MATV (ρ = 0.36), and positively with compactness (ρ = -0.27) and sphericity (ρ = -0.27). Patients without benefit had higher mean entropy (p = 0.021). Progression-free survival (PFS) and overall survival (OS) were worse with a decreased mean AUC [hazard ratio (HR) 0.86, HR 0.77] and increase in mean MATV (HR 1.15, HR 1.22), sum MATV (HR 1.14, HR 1.19), mean TLG (HR 1.16, HR 1.22) and sum TLG (HT1.12, HR1.18). In the third-line group, AUC-CSH correlated negatively with anatomical change (ρ = 0.21). PFS and OS were worse with an increased mean MATV (HR 1.27, HR 1.68), sum MATV (HR 1.35, HR 2.04), mean TLG (HR 1.29, HR 1.52) and sum TLG (HT 1.27, HR 1.80). SUVmax and SUVpeak negatively correlated with OS (HR 1.19, HR 1.21). Cluster analysis of the 10 radiomics features demonstrated no complementary value in identifying aggressively growing lesions or patients with impaired survival.ConclusionWe demonstrated an association between improved clinical outcome and pre-treatment low tumour volume and heterogeneity as well as high sphericity on [18F]FDG PET. Future PET imaging research should include radiomics features that incorporate tumour volume and heterogeneity when correlating PET data with clinical outcome.

Dataset Information

Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT.

Publications

Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment <sup>18</sup>F-PET/CT.

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