Sonographic Pattern Recognition of Endometriomas Mimicking Ovarian Cancer

Saeng-Anan U, Ubol Saeng-Anan, Tawiwan Pantasri, Neeyalavira V, Vithida Neeyalavira, Theera Tongsong
Asian Pacific journal of cancer prevention : APJCP · 2013 · vol. 14(9) , pp. 5409–5413 · doi:10.7314/apjcp.2013.14.9.5409 · PMID:24175835 · W2125056409
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⚙ AI-generated summary by claude@2026-06, 2026-06-12 ⓘ

This study found that subjective pattern recognition by an experienced sonographer was more sensitive than a simple rule for identifying endometriomas, correctly classifying most atypical cases that mimicked ovarian cancer.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-12 · read from full text ⓘ

This paper examines how sonographic pattern recognition can be used to identify endometriomas that appear ultrasonographically similar to ovarian cancer, including the overlapping imaging features that lead to diagnostic confusion in adnexal masses. The authors base their discussion on high-level imaging/ultrasound characterization approaches, emphasizing that endometriomas can mimic malignant ovarian tumors, creating a need for pattern-based interpretation rather than relying on a single visual cue. A key limitation noted in the broader context is that ultrasound interpretation can be affected by operator factors and diagnostic uncertainty when imaging features overlap. This paper is centrally about endometriosis — specifically, it focuses on sonographic pattern recognition of endometriomas that can mimic ovarian cancer.

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Abstract

BACKGROUND: To assess the accuracy of ultrasound in differentiating endometrioma from ovarian cancer and to describe pattern recognition for atypical endometriomas mimicking ovarian cancers. MATERIALS AND METHODS: Patients scheduled for elective surgery for adnexal masses were sonographically evaluated for endometrioma within 24 hours of surgery. All examinations were performed by the same experienced sonographer, who had no any information of the patients, to differentiate between endometriomas and non-endometriomas using a simple rule (classic ground-glass appearance) and subjective impression (pattern recognition). The final diagnosis as a gold standard relied on either pathological or post-operative findings. RESULTS: Of 638 patients available for analysis, 146 were proven to be endometriomas. Of them, the simple rule and subjective impression could sonographically detect endometriomas with sensitivities of 64.4% (94/146) and 89.7% (131/146), respectively. Of 52 endometriomas with false negative tests by the simple rule, 13 were predicted as benign masses and 39 were mistaken for malignancy. Solid masses and papillary projections were the most common forms mimicking ovarian cancer, consisting of 38.5% of the missed diagnoses. However, with pattern recognition (subjective impression), 32 from 39 cases mimicking ovarian cancer were correctly predicted for endometriomas. All endometriomas subjectively predicted for ovarian malignancy were associated with high vascularization in the solid masses. CONCLUSIONS: Pattern recognition of endometriomas by subjective assessment had a higher sensitivity than the simple rule in characterization of endometriomas. Most endometriomas mimicking ovarian malignancy could be correctly predicted by subjective impression based on familiarity of pattern recognition.
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endometriosisendometrioma

MeSH descriptors

Adnexal Diseases Endometriosis Ovarian Neoplasms Pattern Recognition, Automated Adnexal Diseases Adnexal Diseases Adolescent Adult Aged Aged, 80 and over Child Diagnosis, Differential Endometriosis Endometriosis Female Follow-Up Studies Humans Middle Aged Neoplasm Staging Ovarian Neoplasms

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