Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

Haider, Stefan P.; Zeevi, Tal; Baumeister, Philipp; Reichel, Christoph; Sharaf, Kariem; Forghani, Reza; Kann, Benjamin H.; Judson, Benjamin L.; Prasad, Manju L.; Burtness, Barbara; Mahajan, Amit; Payabvash, Sam

doi:10.3390/cancers12071778

Cited by 38 publications

(27 citation statements)

References 60 publications

(87 reference statements)

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“…PET_RS calculated by lasso regression could directly associate with PFS. Intratumoral heterogeneity of PET/CT has been proved to be a prognostic predictor for some malignancies these years (10,31,32). The radiomics features extracted from PET/CT images allowed us to assess intratumoral and metabolic heterogeneity quantitatively.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Wang

2021

Front. Oncol.

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ObjectiveTo investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).MethodsA total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.ResultsIn univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.ConclusionPET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.

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Section: Discussionmentioning

confidence: 99%

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Wang

2021

Front. Oncol.

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“…We devised and compared three types of LRP models [15] : (1) “Radiomics” models used radiomic signatures, (2) the “clinical” model incorporated AJCC staging (T-, N- and overall-stage), patient age at initial diagnosis, and the treatment modality, and (3) “combined” models utilized the combined set of radiomics and above-mentioned clinical predictors for LRP prognostication. AJCC T-, N- and overall-stage were included as ordinal variables with four (T1-T4), four (N0-N3) and three (overall stages I-III) levels, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The potential role of radiomics for LRP risk stratification was investigated by generating radiomics risk groups (high-risk vs. low-risk) in binary classification analysis [15] . We subsequently conducted Kaplan-Meier analysis with radiomics risk groups.…”

Section: Methodsmentioning

confidence: 99%

“…Radiomics analysis has expanded the scope of “-omics” to quantitative characterization of medical images by extracting high-dimensional sets of “features” from volumes of interest (VOI) such as primary tumor lesions, which capture lesion shape, image intensity and texture patterns. The resulting imaging biomarkers may be correlated with treatment outcome, tumor microenvironment, tissue heterogeneity and pathophysiology; and may enable development of prognostic tools substituting or supplementing traditional outcome predictors such as cancer staging [11] , [12] , [13] , [14] , [15] . Depending on the imaging modality used, radiomic features can represent a variety of tumor characteristics; [ 18 F]fluorodeoxyglucose positron emission tomography (PET) radiomics may provide wholistic quantification of tumor metabolic activity and activity distribution; whereas computed tomography (CT) radiomics can describe structural properties and tissue density.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics

Haider

Sharaf

Zeevi

et al. 2021

Translational Oncology

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Highlights Radiomics quantitatively captures visually inappreciable imaging features. PET/CT radiomics provides wholistic metabolic and structural tumor characterization. Machine-learning algorithms can generate radiomics-based biomarkers for OPSCC. PET/CT radiomics can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. Such biomarkers may improve patient selection for treatment de-intensification trials.

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“…Random survival forest is suitable for integrating high-dimensional features like radiomics features for survival analysis and risk stratification. The random survival forest model was recently used to identify risk factors and generate radiomic signatures for different diseases ( 19 ). The robustness of the R-model was validated by a five-fold cross-validation approach for tuning the optimal hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

Liu

Zhang

et al. 2021

Front. Oncol.

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BackgroundTo establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma.MethodsPatients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS.ResultsIn the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01).ConclusionThe nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.

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Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

Cited by 38 publications

References 60 publications

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics

Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma

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