Radiomic analysis in prediction of Human Papilloma Virus status

Yu, Kaixian; Zhang, Youyi; Yu, Yang; Huang, Chao; Liu, Rongjie; Li, Tengfei; Yang, Liuqing; Morris, Jeffrey S.; Baladandayuthapani, Veerabhadran; Zhu, Hongtu

doi:10.1016/j.ctro.2017.10.001

Cited by 55 publications

(68 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(c)–(d), results related to the morphological features (mean breadth and spherical disproportion) suggest that HPV+ patients have tumors with more simpler, symmetric, and more rounded shapes relative to those of HPV– patients. These results are consistent with the findings reported by other investigators …”

Section: Discussionsupporting

confidence: 94%

“…The PPV and NPV of a classifier describe its performance and depend on the ratio of the positive and negative cases in the training and validation cohorts used for building and assessment of the classifier . Overall, prevalence of HPV+ and HPV− cases (positive/negative = 0.62/0.38 for 116+ vs 71−) in our dataset was considered reasonable, and was higher than in other similar studies …”

Section: Discussionmentioning

confidence: 60%

“…These results are consistent with the findings reported by other investigators. 12 As noted in Fig. 4, only 14 radiomic features (nine energies of DOST coefficients, three GLCM features, and two morphological features) from three different feature categories met the discrimination criteria > 50 times for the 100 permutation tests.…”

Section: Discussionmentioning

confidence: 91%

“…76,77 Overall, prevalence of HPV+ and HPVÀ cases (positive/negative = 0.62/0.38 for 116+ vs 71À) in our dataset was considered reasonable, and was higher than in other similar studies. [11][12][13][14] While investigators have found radiomic features to be relatively insensitive to specific image acquisition techniques among different scanners and institutions for patients, 57,78 others have shown this to be a potentially problematic issue requiring more detailed investigation. 22,79 Textural features require interpolation to isotropic voxel spacing to be rotationally invariant, and to allow comparison between image data from different samples, cohorts, or batches.…”

Section: Discussionmentioning

confidence: 99%

“…[7][8][9][10] The application of quantitative image analysis methods, such as radiomics, with reliable power in detection of the HPV status may serve as attractive alternative approaches toward determining the HPV status. [11][12][13][14] Computed tomography (CT) imaging plays a central role in early detection and prognosis for different types of cancers. [15][16][17][18][19] In particular, low dose contrast-enhanced CT (CE-CT) imaging is performed routinely for screening and diagnosis of oropharyngeal cancer patients.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

et al. 2020

View full text Add to dashboard Cite

Purpose To perform radiomic analysis of primary tumors extracted from pretreatment contrast‐enhanced computed tomography (CE‐CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. Materials and methods One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry—p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV− and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV− and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE‐CT images of the gross tumor volume (GTV). Levene and Kolmogorov–Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV− groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. Results Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV− groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. Conclusions Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity‐variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast‐enhanced diagnostic CT datasets may be potentially used for classification of HPV status.

show abstract

Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

et al. 2020

View full text Add to dashboard Cite

show abstract

Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

et al. 2020

View full text Add to dashboard Cite

Background: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables. Methods: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC). Results: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors. Conclusion: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

Radiomic analysis in prediction of Human Papilloma Virus status

Cited by 55 publications

References 27 publications

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

Application of radiomics for the prediction of HPV status for patients with head and neck cancers

Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

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

Contact Info

Product

Resources

About