Wenjun Cai

ORCID: 0000-0003-1826-4998 · 4 papers in corpus
diagnostic-accuracy 2025
Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses
Insights into imaging ·doi:10.1186/s13244-024-01874-7

OBJECTIVE: To develop an automatic segmentation model to delineate the adnexal masses and construct a machine learning model to differentiate between low malignant risk and intermediate-high malignant risk of adnexal masses based on ovarian…

meta-analysis 2025
Reproductive outcomes after fertility-sparing interventions for symptomatic adenomyosis: a systematic review and meta-analysis
BMC pregnancy and childbirth ·doi:10.1186/s12884-025-08323-3

OBJECTIVE: To systematically review studies on reproductive outcomes following fertility-sparing interventions. METHODS: Various electronic databases were searched and studies reporting reproductive outcomes following fertility-sparing int…

diagnostic-accuracy 2024
Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS
Frontiers in oncology ·doi:10.3389/fonc.2024.1377489

BACKGROUND: Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep tr…

diagnostic-accuracy 2024
Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst
Frontiers in medicine ·doi:10.3389/fmed.2024.1362588

BACKGROUND: Accurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with …