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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2504.12438 |
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| _version_ | 1866912517915148288 |
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| author | Yoeli-Bik, Roni Whitney, Heather M. Li, Hui Bilecz, Agnes Abramowicz, Jacques S. Lan, Li Longman, Ryan E. Giger, Maryellen L. Lengyel, Ernst |
| author_facet | Yoeli-Bik, Roni Whitney, Heather M. Li, Hui Bilecz, Agnes Abramowicz, Jacques S. Lan, Li Longman, Ryan E. Giger, Maryellen L. Lengyel, Ernst |
| contents | 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. Conclusions: A novel hybrid AI/CADx echogenic components-based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12438 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound Yoeli-Bik, Roni Whitney, Heather M. Li, Hui Bilecz, Agnes Abramowicz, Jacques S. Lan, Li Longman, Ryan E. Giger, Maryellen L. Lengyel, Ernst Medical Physics 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. Conclusions: A novel hybrid AI/CADx echogenic components-based ultrasound imaging pipeline can distinguish between malignant and benign adnexal masses with strong diagnostic performance. |
| title | Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2504.12438 |