Systemic Sentinel Lymph Node Detection Using Fluorescence Imaging After Indocyanine Green Intravenous Injection in Colorectal Cancer: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Nodal staging is a major concern in colorectal cancer as it is an important prognostic factor. Several techniques that could potentially improve patient treatment and prognosis have been developed to increase the accuracy of nodal staging. Sentinel lymph node detection has been shown to accurately reflect nodal status in various tumors and has become the standard procedure in nodal staging of breast cancer and melanoma. However, in colorectal cancer, sentinel lymph node detection techniques are still controversial as the sensitivity reported in the literature varies from one study to another. Recently, indocyanine green fluorescence-guided surgery has been reported to be a useful technique for detection of macroscopic and microscopic metastatic deposits in lymph nodes after intravenous administration of indocyanine green dye. However, no studies have focused on the potential role of sentinel lymph node detection after systemic administration of indocyanine green dye, so-called systemic sentinel lymph nodes, or on the correspondence between the identification of the sentinel lymph node by standard local injection techniques and the detection of fluorescent lymph nodes with this new approach. OBJECTIVE: The aim of this protocol is to validate the concept of sentinel lymph nodes identified by fluorescence imaging after intravenous injection of indocyanine green dye and to compare the sentinel lymph nodes identified by fluorescence imaging with sentinel lymph nodes detected by the standard blue dye technique. METHODS: This study (SeLyNoFI; Sentinel Lymph Nodes Fluorescence Imaging) is a diagnostic, single-arm, open-label feasibility study, including patients with colorectal adenocarcinoma with or without metastatic disease who are admitted for elective colorectal resection of the primary tumor. This study evaluates the feasibility of a new approach for improving the accuracy of nodal staging using fluorescence imaging after intravenous administration of indocyanine green dye. Sensitivity, positive predictive value, and accuracy of the classical blue dye technique and of the investigatory fluorescence imaging technique will be calculated. Translational research will be proposed, if applicable. RESULTS: As of June 2020, this study has been registered. Submission for ethical review is planned for September 2020. CONCLUSIONS: The potential correlation between the two different approaches to detect sentinel lymph nodes offers new strategies for improving the accuracy of nodal staging in colorectal cancer. This new concept of the systemic sentinel lymph node and a greater understanding of the interactions between systemic sentinel lymph nodes and standard sentinel lymph nodes may provide important information regarding the underlying mechanism of primary tumor lymphatic drainage. The enhanced permeability and retention effect can also play a role in the fluorescence of systemic sentinel lymph nodes, especially if these lymph nodes are inflamed. In this case, we can even imagine that this new technique will highlight more instances of lymph node-positive colorectal cancer. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17976.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".