Trial reportEuropean journal of nuclear medicine and molecular imaging2023
Tumor lesion detection in patients with cervical cancer by indocyanine green near-infrared imaging.
Trial report in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04224467 (Real-Time Identification of Lesions and Nerves by Using Indocyanine Green Fluorescent Imaging in Gynecological Surgery), which is not on this map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Real-Time Identification of Lesions and Nerves by Using Indocyanine Green Fluorescent Imaging in Gynecological Surgery
Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Clinical fluorescent contrast agents for image-guided surgery.Fundamental research · 2026Review
- The Medical Basis for the Photoluminescence of Indocyanine Green.Molecules (Basel, Switzerland) · 2025Review
- Recent Advances in Indocyanine Green-Based Probes for Second Near-Infrared Fluorescence Imaging and Therapy.Research (Washington, D.C.) · 2025Review
- Mechanisms of delayed indocyanine green fluorescence and applications to clinical disease processes.Surgery · 2024Review
- Imaging guided adrenalectomy with Indocyanine green fluorescence in a dog with a pheochromocytoma.Journal of veterinary science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo investigate the feasibility and accuracy of near-infrared fluorescence (NIRF) imaging for detecting the extent of tumor invasion in cervical cancer using indocyanine green (ICG).
methodsWe enrolled 51 patients who were diagnosed with cervical cancer with FIGO stage IB1-IIA2 disease. Patients were administered indocyanine green (ICG) at a dose of 5 mg/kg 24 h prior to surgery. A customized near-infrared fluorescence (NIRF) imaging system was used to identify the extent of tumor invasion when radical hysterectomy specimens were harvested. The relationship between tumor fluorescence intensity and clinicopathological characteristics was analyzed.
resultsOf the 51 enrolled patients, 3 patients did not have residual tumors after cervical conization, and tumor lesions were identified by NIRF imaging in all the remaining 48 patients. The results of NIRF imaging were in agreement with the postoperative pathological findings in 95.8% of the patients with stromal invasion, 100% of those with surgical margin invasion, 100% of those with parametrial tumor involvement, and 100% of patients with uterine corpus invasion. The mean signal-to-background ratio (SBR) of the cervical tumors was 2.91 ± 1.64, and the SBR was independent of clinicopathological characteristics. Fluorescence microscopy confirmed that ICG fluorescence was present in the tumor nests.
conclusionsNIRF imaging enables objective, accurate, and safe identification of tumor invasion during cervical cancer surgery. CLINICAL
trial registrationClinicalTrials.gov NCT04224467.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.