Enhanced Lymph Node Detection in Colon Cancer Using Indocyanine Green Fluorescence: A Systematic Review of Studies from 2020 Onwards
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".