PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma

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

doi:10.1007/s00259-020-04839-2

Cited by 44 publications

(35 citation statements)

References 40 publications

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“…Given the variable robustness of individual radiomics features to inter- and intra-observer segmentation inconsistencies [ 47 , 48 , 49 , 50 , 51 , 52 ], we determined feature stability, retaining only stable features for analysis; the methodology and results are reported in the supplementary methods and Table S4 [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…An automated image pre-processing pipeline facilitated homogenized radiomics analysis [19]. As detailed in the supplementary methods, we performed PET grey scale normalization, PET/CT voxel size homogenization, CT re-segmentation, generation of 10 derivative images per original scan, and grey scale discretization prior to radiomics feature extraction.…”

Section: Radiomics Feature Extractionmentioning

confidence: 99%

“…Given the variable robustness of individual radiomics features to inter-and intra-observer segmentation inconsistencies [47][48][49][50][51][52], we determined feature stability, retaining only stable features for analysis; the methodology and results are reported in the supplementary methods and Table S4 [19]. Random forest machine-learning models with 1000 decision trees were applied for survival prediction and risk-stratification.…”

Section: Radiomics Feature Extractionmentioning

confidence: 99%

“…Radiomics refers to automated extraction of high-dimensional, quantitative descriptor ("feature") sets from medical images for various applications, including survival modelling, treatment guidance, and biomarker design [13][14][15][16][17]. Such features correlate with clinical outcome and convey medically meaningful information describing tumor heterogeneity, microenvironment, pathophysiology, and mutational burden [13,18,19]. While prior studies demonstrated prognostic value of radiomics biomarkers in head and neck cancers [15,16,[20][21][22][23][24][25][26][27][28], none have incorporated or compared the AJCC 8th edition staging scheme in OPSCC survival modelling and stratification.…”

Section: Introductionmentioning

confidence: 99%

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

Haider

Zeevi

Baumeister

et al. 2020

Cancers

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Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.

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Section: Methodsmentioning

confidence: 99%

Section: Radiomics Feature Extractionmentioning

confidence: 99%

Section: Radiomics Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Haider

Zeevi

Baumeister

et al. 2020

Cancers

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“…Prior studies have demonstrated the predictive value of radiomic biomarkers for LRP in HNSCC, but HPV status was rarely available in all studied OPSCC patients, and subgroup analysis of HPV-associated OPSCC was not reported [16] , [17] , [18] , [19] . Radiomics analysis can predict HPV status, and thus the results of prior studies may in part reflect the differences between HPV-associated and HPV-negative subgroups [ 20 , 21 ]. In this study, we aim to apply machine-learning algorithms using combined PET and non-contrast CT radiomic features extracted from baseline clinical scans for prediction and risk stratification of post-radiotherapy LRP in an HPV-associated OPSCC cohort.…”

Section: Introductionmentioning

confidence: 99%

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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Artificial intelligence for HPV status prediction based on disease‐specific images in head and neck cancer: A systematic review and meta‐analysis

Song,

Chen,

Tang

et al. 2023

Journal of Medical Virology

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Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at‐risk populations, stratify patients, personalized treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta‐analysis aimed to evaluate the performance of AI to predict the HPV positivity through the HPV‐associated diseased images in HNC patients. A systematic literature search was conducted in databases including Ovid‐MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to October 30, 2022. Search strategies included keywords such as “artificial intelligence,” “head and neck cancer,” “HPV,” and “sensitivity & specificity.” Duplicates, articles without HPV predictions, letters, scientific reports, conference abstracts, or reviews were excluded. Binary diagnostic data were then extracted to generate contingency tables and then used to calculate the pooled sensitivity (SE), specificity (SP), area under the curve (AUC), and their 95% confidence interval (CI). A random‐effects model was used for meta‐analysis, four subgroup analyses were further explored. Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta‐analysis. The pooled SE and SP for all studies were 79% (95% CI: 75−82%) and 74% (95% CI: 69−78%) respectively, with an AUC of 0.83 (95% CI: 0.79−0.86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75−83%), SP of 75% (95% CI: 69−79%), and an AUC of 0.84 (95% CI: 0.81−0.87). The respective heterogeneities were moderate (I2 for SE and SP were 51.70% and 51.01%) and only low (35.99% and 21.44%). This evidence‐based study showed an acceptable and promising performance for AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research. Compared with previous studies, future studies anticipate to make progress in the selection of databases, improvement of international reporting guidelines, and application of high‐quality deep learning algorithms.

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PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma

Cited by 44 publications

References 40 publications

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

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

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

Artificial intelligence for HPV status prediction based on disease‐specific images in head and neck cancer: A systematic review and meta‐analysis

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