Enhanced Lymph Node Detection in Colon Cancer Using Indocyanine Green Fluorescence: A Systematic Review of Studies from 2020 Onwards
Notice bibliographique
Résumé
Background: Colon cancer is known as one of the most prevalent malignancies in the world. This well-known pathology requires accurate lymph node dissection to achieve effective staging and improved treatment outcomes. Indocyanine green fluorescence imaging has been used as a new technique for enhancing lymph node visualization during surgical intervention. The high rates of local recurrence in colon cancer patients require innovative methods to improve lymphatic mapping and lymph node dissection. This review evaluates the clinical utility and efficacy of ICG imaging in enhancing lymph node accuracy in colon cancer surgery. Materials and methods: A systematic search was conducted in October 2024 (last day of consulting the database was 16 November) across Web of Science, Scopus, and PubMed to identify studies published from 2020 onwards focusing on the use of indocyanine green in colon cancer surgeries. The search terms used were “indocyanine green”, “ICG”, “fluorescent imaging”, “near-infrared imaging”, “colon cancer”, “colorectal cancer”, “colon carcinoma”,” colon neoplasms”, “surgery”, “surgical procedure”, “surgical resection”, surgical precision”. The search followed PRISMA guidelines. The records underwent a two-phase independent screening process conducted by the authors, first based on the title and abstract, followed by full record evaluation. Articles were excluded following certain exclusion criteria: non-human studies; restricted access publications; other publication type than article (review, meta-analysis, questionnaire-based study, case report, etc.), studies focusing on other diseases or studies that focused on the surgical treatment of metastasis from colon cancer; foreign language (non-English); no data of interest for the current review; studies that focused on rectal cancer and that grouped rectal and colon cancer. Data extraction involved both quantitative and qualitative data, such as detection rates, sensitivity, specificity, and other surgical outcomes. Risk of bias was assessed using ROBINS-I, J Joanna Briggs Institute (JBI) Critical Appraisal Checklist, and the Newcastle–Ottawa Scale, depending on study type. The study was not preregistered in PROSPERO. However, to ensure methodological rigor and transparency, it was retrospectively registered in Open Science Framework (OSF). Results: From the 3300 records initially identified, 9 studies were included in this review. Detection rates varied from 55% to 100%, with the highest rate reported in robot-assisted surgeries. The studies showed an improved lymph node detection and lymphatic flow accuracy using ICG fluorescence. Discussion: ICG fluorescence demonstrated substantial benefits, improving staging accuracy and potentially reducing recurrence rates by guiding the lymphadenectomy. The variability observed in detection rates is largely attributed to differences in ICG administration, cancer stage, and surgical approaches. Conclusions: ICG-guided surgery for colon cancer represents a promising advancement, enhancing lymph node detection and staging accuracy. Large-scale randomized trials are essential to establish standardized protocols and validate the efficacy in improving surgical outcomes.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,012 | 0,001 |
| Bibliométrie | 0,001 | 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 | 0,001 |
| 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 ».