Hybrid artificial intelligence echogenic components‐based diagnosis of adnexal masses on ultrasound.
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| Title: | Hybrid artificial intelligence echogenic components‐based diagnosis of adnexal masses on ultrasound. |
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| Authors: | Yoeli‐Bik, Roni1 (AUTHOR), Whitney, Heather M.2 (AUTHOR) hwhitney@uchicago.edu, Li, Hui2 (AUTHOR), Bilecz, Agnes1 (AUTHOR), Abramowicz, Jacques S.1 (AUTHOR), Lan, Li2 (AUTHOR), Longman, Ryan E.1 (AUTHOR), Giger, Maryellen L.2 (AUTHOR), Lengyel, Ernst1 (AUTHOR) |
| Source: | Medical Physics. Jul2025, Vol. 52 Issue 7, p1-14. 14p. |
| Subjects: | Artificial intelligence, Computer-aided diagnosis, Ultrasonics, Adnexal diseases, Benign tumors, Computer-assisted image analysis (Medicine), Diagnostic ultrasonic imaging |
| Abstract: | Background: Adnexal masses are heterogeneous and have varied sonographic presentations, making them difficult to diagnose correctly. Purpose: Our study aimed to develop an innovative hybrid artificial intelligence/computer‐aided diagnosis (AI/CADx)‐based pipeline to distinguish between benign and malignant adnexal masses on ultrasound imaging based upon automatic segmentation and echogenic‐based classification. Methods: The retrospective study was conducted on a consecutive dataset of patients with an adnexal mass. There was one image per mass. Mass borders were segmented from the background via a supervised U‐net algorithm. Masses were spatially subdivided automatically into their hypo‐ and hyper‐echogenic components by a physics‐driven unsupervised clustering algorithm. The dataset was separated by patient into a training/validation set (95 masses; 70%) and an independent held‐out test set (41 masses; 30%). Eight component‐based radiomic features plus a binary measure of the presence or absence of solid components were used to train a linear discriminant analysis classifier to distinguish between malignant and benign masses. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, negative predictive value, positive predictive value, and accuracy at target 95% sensitivity. Results: The cohort included 133 patients with 136 adnexal masses. In distinguishing between malignant and benign masses, the pipeline achieved an AUC of 0.90 [0.84, 0.95] on the training/validation set and 0.93 [0.83, 0.98] on the independent test set. Strong diagnostic performance was observed at the target 95% sensitivity. Conclusion: A novel hybrid AI/CADx echogenic components‐based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 186809994 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid artificial intelligence echogenic components‐based diagnosis of adnexal masses on ultrasound. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yoeli‐Bik%2C+Roni%22">Yoeli‐Bik, Roni</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Whitney%2C+Heather+M%2E%22">Whitney, Heather M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hwhitney@uchicago.edu</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Hui%22">Li, Hui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bilecz%2C+Agnes%22">Bilecz, Agnes</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abramowicz%2C+Jacques+S%2E%22">Abramowicz, Jacques S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lan%2C+Li%22">Lan, Li</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Longman%2C+Ryan+E%2E%22">Longman, Ryan E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Giger%2C+Maryellen+L%2E%22">Giger, Maryellen L.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lengyel%2C+Ernst%22">Lengyel, Ernst</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jul2025, Vol. 52 Issue 7, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+diagnosis%22">Computer-aided diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonics%22">Ultrasonics</searchLink><br /><searchLink fieldCode="DE" term="%22Adnexal+diseases%22">Adnexal diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Benign+tumors%22">Benign tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+ultrasonic+imaging%22">Diagnostic ultrasonic imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Adnexal masses are heterogeneous and have varied sonographic presentations, making them difficult to diagnose correctly. Purpose: Our study aimed to develop an innovative hybrid artificial intelligence/computer‐aided diagnosis (AI/CADx)‐based pipeline to distinguish between benign and malignant adnexal masses on ultrasound imaging based upon automatic segmentation and echogenic‐based classification. Methods: The retrospective study was conducted on a consecutive dataset of patients with an adnexal mass. There was one image per mass. Mass borders were segmented from the background via a supervised U‐net algorithm. Masses were spatially subdivided automatically into their hypo‐ and hyper‐echogenic components by a physics‐driven unsupervised clustering algorithm. The dataset was separated by patient into a training/validation set (95 masses; 70%) and an independent held‐out test set (41 masses; 30%). Eight component‐based radiomic features plus a binary measure of the presence or absence of solid components were used to train a linear discriminant analysis classifier to distinguish between malignant and benign masses. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, negative predictive value, positive predictive value, and accuracy at target 95% sensitivity. Results: The cohort included 133 patients with 136 adnexal masses. In distinguishing between malignant and benign masses, the pipeline achieved an AUC of 0.90 [0.84, 0.95] on the training/validation set and 0.93 [0.83, 0.98] on the independent test set. Strong diagnostic performance was observed at the target 95% sensitivity. Conclusion: A novel hybrid AI/CADx echogenic components‐based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.17983 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer-aided diagnosis Type: general – SubjectFull: Ultrasonics Type: general – SubjectFull: Adnexal diseases Type: general – SubjectFull: Benign tumors Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Diagnostic ultrasonic imaging Type: general Titles: – TitleFull: Hybrid artificial intelligence echogenic components‐based diagnosis of adnexal masses on ultrasound. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yoeli‐Bik, Roni – PersonEntity: Name: NameFull: Whitney, Heather M. – PersonEntity: Name: NameFull: Li, Hui – PersonEntity: Name: NameFull: Bilecz, Agnes – PersonEntity: Name: NameFull: Abramowicz, Jacques S. – PersonEntity: Name: NameFull: Lan, Li – PersonEntity: Name: NameFull: Longman, Ryan E. – PersonEntity: Name: NameFull: Giger, Maryellen L. – PersonEntity: Name: NameFull: Lengyel, Ernst IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 52 – Type: issue Value: 7 Titles: – TitleFull: Medical Physics Type: main |
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