A meta-analysis of short-term and long-term effects of indocyanine green fluorescence imaging in hepatectomy for liver cancer

A clinical assessment of three-dimensional-printed liver model navigation for thrice or more repeated hepatectomy based on a conversation analysis

PURPOSES

We performed a conversation analysis of the speech conducted among the surgical team during three-dimensional (3D)-printed liver model navigation for thrice or more repeated hepatectomy (TMRH).

METHODS

Seventeen patients underwent 3D-printed liver navigation surgery for TMRH. After transcription of the utterances recorded during surgery, the transcribed utterances were coded by the utterer, utterance object, utterance content, sensor, and surgical process during conversation. We then analyzed the utterances and clarified the association between the surgical process and conversation through the intraoperative reference of the 3D-printed liver.

RESULTS

In total, 130 conversations including 1648 segments were recorded. Utterance coding showed that the operator/assistant, 3D-printed liver/real liver, fact check (F)/plan check (Pc), visual check/tactile check, and confirmation of planned resection or preservation target (T)/confirmation of planned or ongoing resection line (L) accounted for 791/857, 885/763, 1148/500, 1208/440, and 1304/344 segments, respectively. The utterance's proportions of assistants, F, F of T on 3D-printed liver, F of T on real liver, and Pc of L on 3D-printed liver were significantly higher during non-expert surgeries than during expert surgeries. Confirming the surgical process with both 3D-printed liver and real liver and performing planning using a 3D-printed liver facilitates the safe implementation of TMRH, regardless of the surgeon's experience.

CONCLUSIONS

The present study, using a unique conversation analysis, provided the first evidence for the clinical value of 3D-printed liver for TMRH for anatomical guidance of non-expert surgeons.

Current applications of indocyanine green (ICG) in abdominal, gynecologic and urologic surgery: a meta-review and quality analysis with use of the AMSTAR 2 instrument

BACKGROUND

Indocyanine green (ICG) is an injectable fluorochrome that has recently gained popularity as a means of assisting intraoperative visualization during laparoscopic and robotic surgery. Many systematic reviews and meta-analyses have been published. We conducted a meta-review to synthesize the findings of these studies.

METHODS

PubMed and Embase were searched to identify systematic reviews and meta-analyses coping with the uses of ICG in abdominal operations, including Metabolic Bariatric Surgery, Cholecystectomy, Colorectal, Esophageal, Gastric, Hepato-Pancreato-Biliary, Obstetrics and Gynecology (OG), Pediatric Surgery, Surgical Oncology, Urology, (abdominal) Vascular Surgery, Adrenal and Splenic Surgery, and Interdisciplinary tasks, until September 2023. We submitted the retrieved meta-analyses to qualitative analysis based on the AMSTAR 2 instrument.

RESULTS

We identified 116 studies, 41 systematic reviews (SRs) and 75 meta-analyses (MAs), spanning 2013-2023. The most thoroughly investigated (sub)specialties were Colorectal (6 SRs, 25 MAs), OG (9 SRs, 15 MAs), and HPB (4 SRs, 12 MAs). Interestingly, there was high heterogeneity regarding the administered ICG doses, routes, and timing. The use of ICG offered a clear benefit regarding anastomotic leak prevention, particularly after colorectal and esophageal surgery. There was no clear benefit regarding sentinel node detection after OG. According to the AMSTAR 2 tool, most meta-analyses ranked as "critically low" (34.7%) or "low" (58.7%) quality. There were only five meta-analyses (6.7%) that qualified as "moderate" quality, whereas there were no "high" quality reviews.

CONCLUSIONS

Regardless of the abundance of pertinent literature and reviews, surgeons should be cautious when interpreting their results on ICG use in abdominal surgery. Future reviews should focus on ensuring methodological vigor; establishing clear protocols of ICG dose, route of administration, and timing; and improving reporting quality. Other sources of data (e.g., registries) and novel methods of data analysis (e.g., machine learning) might also contribute to an enhanced role of ICG as a decision-making tool in surgery.