Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas

Ahn, Yura; Choi, Young Jun; Sung, Yu Sub; Pfeuffer, Josef; Suh, Chong Hyun; Chung, Sae Rom; Baek, Jung Hwan; Lee, Jeong Hyun

doi:10.1007/s00234-021-02751-6

Cited by 9 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through our systematic literature review and meta‐analysis, we found that AI algorithms could be used to predict HPV positivity from medical imaging in HNC and manifested an acceptable performance. Our pooled results were superior to the other two non‐AI studies, the AUC and accuracy of which were 0.745% and 75% respectively 51,52 . To the best of our knowledge, no other pertinent studies are available to date.…”

Section: Discussionmentioning

confidence: 52%

“…Our pooled results were superior to the other two non-AI studies, the AUC and accuracy of which were 0.745% and 75% respectively. 51,52 To the best of our knowledge, no other pertinent studies are available to date. We also conducted a comprehensive literature search in line with the diagnostic review guidelines to ensure the rigor of the study.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

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

show abstract

“…Ahn et al evaluated the histogram TBF parameters derived from pCASL between human papillomavirus (HPV)-positive and -negative in oropharyngeal SCC [ 79 ]. Oropharyngeal SCC with HPV-positive showed significantly lower standard deviation (SD) and 95th percentile of TBF, i.e., 27.8 vs. 37.7 mL/100 g/min for SD and 111.7 vs. 147.3 mL/100 g/min for 95th percentile of TBF [ 79 ].…”

Section: Clinical Applications Of 3d Tse Pcasl In the Head And Neckmentioning

confidence: 99%

Pseudocontinuous Arterial Spin Labeling: Clinical Applications and Usefulness in Head and Neck Entities

Tanaka

Umino

Maeda

et al. 2022

Cancers

View full text Add to dashboard Cite

As functional magnetic resonance imaging, arterial spin labeling (ASL) techniques have been developed to provide quantitative tissue blood flow measurements, which can improve the performance of lesion diagnosis. ASL does not require contrast agents, thus, it can be applied to a variety of patients regardless of renal impairments and contrast agent allergic reactions. The clinical implementation of head and neck lesions is limited, although, in recent years, ASL has been increasingly utilized in brain lesions. Here, we review the development of the ASL techniques, including pseudocontinuous ASL (pCASL). We compare readout methods between three-dimensional (3D) turbo spin-echo and 2D echo planar pCASL for the clinical applications of pCASL to head and neck lesions. We demonstrate the clinical usefulness of 3D pCASL for diagnosing various entities, including inflammatory lesions, hypervascular lesions, and neoplasms; for evaluating squamous cell carcinoma (SCC) treatment responses, and for predicting SCC prognosis.

show abstract

“…A negative skewness means that the data is tilted to the left, and a positive value means that the data is tilted to the right. Kurtosis indicates whether the histogram distribution is concentrated to the average value, positive kurtosis indicates steep distribution, and negative value indicates flat distribution (14,15). We used the histogram parameters of the two radiologists to assess the interobserver consistency and used repeated measurements with an interval of one month by the younger radiologist to assess the intraobserver consistency.…”

Section: Histogram Analysesmentioning

confidence: 99%

Clinical utility of histogram analysis of grayscale sonograms in assessing sonographic suspicious malignant thyroid nodules

Zhang¹,

Wang²,

Sun³

et al. 2022

Transl Cancer Res TCR

View full text Add to dashboard Cite

Background: When sonographic suspicious malignant thyroid nodules initially proven to be inconsistent by aspiration, repeat fine-needle aspiration cytology (FNAC) is commonly recommended. Although FNAC is simple and reliable, it is also an invasive operation with inevitable false negative results. To determine which sonographic features and histogram parameters based on gray-scale ultrasonic images need repeat aspiration.Methods: All of the nodules were suspicious for malignancy by preoperative ultrasound (US), and their initial FNAC findings indicated Bethesda category II, or III. Histogram analyses were performed for grayscale ultrasonic images. Mean, variance, skewness, kurtosis, percentiles (1st, 10th, 50th, 90th, 99th), and sonographic features were compared between pathological benign nodules and malignant ones. Thereafter, receiver operator characteristic (ROC) curves were analyzed for the valuable indicators.Results: One hundred and nineteen consecutive patients with 123 histopathologically diagnosed focal thyroid nodules were included in the cohort. The factors associated with malignancy were taller-thanwide shape [odds ratio (OR) =15.165; 95% confidence interval (CI): 3.157-72.854], irregular margins (OR =11.492; 95% CI: 1.747-75.573), microcalcifications (OR =5.107; 95% CI: 1.455-17.927) and skewness (OR =25.800; 95% CI: 1.034-76.422). The skewness of malignant thyroid nodules is higher than that of benign thyroid nodules and has an area under the curve (AUC) of 0.776 in the diagnosis.Conclusions: For thyroid nodules suspicious on US but not on cytology, skewness, together with tallerthan-wide shape, irregular margins and microcalcifications might be a promising marker for clinicians to perform repetitive FNACs.

show abstract

Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas

Cited by 9 publications

References 31 publications

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

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

Pseudocontinuous Arterial Spin Labeling: Clinical Applications and Usefulness in Head and Neck Entities

Clinical utility of histogram analysis of grayscale sonograms in assessing sonographic suspicious malignant thyroid nodules

Contact Info

Product

Resources

About