Advancements in Ultrasound Diagnosis of Superficial Endometriosis: Current Challenges and Emerging Techniques
Notice bibliographique
Résumé
BACKGROUND: Endometriosis is a chronic disease characterized by endometrial-like tissue outside the uterus. Superficial endometriosis (SE) is the most prevalent form, yet it remains underdiagnosed due to subtle clinical and imaging presentations. Traditionally, diagnosis relies on laparoscopy, which is relatively invasive and often contributes to diagnostic delay. With advancements in imaging techniques, especially transvaginal ultrasound (TVS), a reassessment of the diagnostic approach for SE is needed. This review updates the understanding of SE diagnostics and integrates both historical perspectives and contemporary clinical insights. OBJECTIVES: The review aimed to explore advancements in the diagnosis of SE, focusing on the growing role of TVS as a non-invasive diagnostic tool. Additionally, it seeks to highlight emerging diagnostic challenges and present new approaches to managing SE to offer updated recommendations for clinicians. METHODS: A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar. The following keywords were used: "superficial endometriosis," "diagnostic pathways," "endometriosis diagnosis," "superficial lesions," "transvaginal ultrasound," "laparoscopy," "non-invasive imaging," and "diagnostic accuracy." Only English-language articles were included, focusing on original research, metanalyses, and clinical guidelines, offering historical and current perspectives. In addition to the literature review, contemporary insights were gathered from our clinical practice at a tertiary endometriosis clinic to offer real-world context to the literature findings. OUTCOME: The review highlights TVS as a promising non-invasive method for diagnosing SE. While SE has historically been diagnosed through laparoscopy, TVS is gaining recognition as a valuable tool for detecting SE lesions, particularly through the identification of key sonographic features such as hyperechoic foci and cystic spaces. These advancements help overcome the challenges posed by the variability of SE presentation on imaging. Emerging techniques, such as sonoPODography, further enhance SE diagnosis and offer the potential for broader clinical application. Despite challenges such as the need for operator expertise and variability in lesion presentation, the literature and clinical insights support the growing utility of TVS in diagnosing SE. CONCLUSIONS AND OUTLOOK: TVS has significant potential as a non-invasive diagnostic tool for SE. While limitations such as variability in sensitivity and the need for operator expertise remain, TVS can significantly reduce reliance on invasive methods like laparoscopy. Additionally, the review provides insights into managing cases, where TVS results are negative for SE. In such cases, clinicians must adopt a patient-centered approach that emphasizes symptom management, patient autonomy, and education about possible risks and treatment options. Rather than defaulting to a "watchful waiting" or a "one size fits all" strategy, it is essential to engage patients in shared decision-making, allowing them to make informed choices about further diagnostic or therapeutic interventions. This review underscores the importance of integrating TVS into routine diagnostic pathways for SE, improving early detection and enhancing patient care. Future research should focus on refining TVS techniques, establishing standardized diagnostic criteria, and exploring alternative diagnostic strategies for patients with negative imaging results. This approach has the potential to shift the paradigm of SE management, reducing diagnostic delays and empowering patients with a more proactive, informed approach to their care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».