Robotic Surgery and Oncologic Outcomes

The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots-providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

Proctored adoption of robotic hiatus hernia surgery: outcomes and learning curves in a high-volume UK centre

BACKGROUND

The adoption of new surgical technologies is inevitably accompanied by a learning curve. With the increasing adoption of robotic techniques in benign foregut surgery, it is imperative to define optimal learning pathways, to ensure a clinically safe introduction of such a technique. The aim of this study was to assess the learning curve for robotic hiatal hernia repair with a pre-defined adoption process and proctoring.

METHODS

The learning curve was assessed in four surgeons in a high-volume tertiary referral centre, performing over a 100 hiatal hernia repairs annually. The robotic adoption process included simulation-based training and a multi-day wet lab-based course, followed by robotic operations proctored by robotic upper GI experts. CUSUM analysis was performed to assess changes in operating time in sequential cases.

RESULTS

Each surgeon (A, B, C and D) performed between 22 and 32 cases, including a total of 109 patients. Overall, 40 cases were identified as 'complex' (36.7%), including 16 revisional cases (16/109, 14.7%). With CUSUM analysis inflection points for operating time were seen after 7 (surgeon B) to 15 cases (surgeon B).

CONCLUSION

The learning curve for robotic laparoscopic fundoplication may be as little as 7-15 cases in the setting of a clearly organized learning pathway with proctoring. By integrating these organized learning pathways learning curves may be shortened, ensuring patient safety, preventing detrimental outcomes due to longer learning curves, and accelerating adoption and integration of novel surgical techniques.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.

Results

ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.

Conclusion

Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Global trends and hotspots in research of robotic surgery in oncology: A bibliometric and visual analysis from 2002 to 2021

BACKGROUND

With the development of robotic surgery in the field of oncology, an increasing number of relevant research papers have been published. In order to explore the research hotspots and trends in this field, a bibliometric and visual analysis was performed for the first time.

METHODS

The literature records related to oncology robotic surgery were obtained from the Web of Science Core Collection database and imported into the software VOSviewer 1.6.18, CiteSpace 6.1.R3, and the Bibliometric Online Analysis Platform for analysis.

RESULTS

A total of 6,964 publications, including 5,635 articles and 1,329 reviews, were included in this study. Over the past 20 years, annual publications and citations have experienced rapid growth, particularly in the last two years. The United States was the country with the most publications, while Yonsei University in South Korea was the most productive institution. The Journal of Robotic Surgery and the Journal of Urology were the journals with the most publications and citations, respectively. Mottrie A from Belgium and Ficarra V from Italy were the authors with the highest number of publications and citations, respectively. The keywords "robotic surgical procedure", "laparoscopic surgery", "prostate cancer", "colorectal cancer", "gastric cancer", "resection", "complications classification", "open surgery", "transoral robotic surgery", "pathological outcomes", and "robot-assisted surgery" reflect the research hotspots and trends of oncology robotic surgery.

CONCLUSION

The therapeutic advantages of robotic surgery in oncology are not yet prominent, and further randomized controlled trials with multicenter and large samples are needed to evaluate the advantages of robotic surgery compared with laparoscopic surgery and open surgery in the treatment of tumors from multiple outcome indicators.

Postoperative complications after robotic resection of colorectal cancer: An analysis based on 5-year experience at a large-scale center

BACKGROUND

As a common gastrointestinal malignancy, colorectal cancer (CRC) poses a serious health threat globally. Robotic surgery is one of the future trends in surgical treatment of CRC. Robotic surgery has several technical advantages over laparoscopic surgery, including 3D visualization, elimination of the fulcrum effect, and better ergonomic positioning, which together lead to better surgical outcomes and faster recovery. However, analysis of independent factors of postoperative complications after robotic surgery is still insufficient.

AIM

To analyze the incidence and risk factors for postoperative complications after robotic surgery in patients with CRC.

METHODS

In total, 1040 patients who had undergone robotic surgical resection for CRC between May 2015 and May 2020 were analyzed retrospectively. Postoperative complications were categorized according to the Clavien-Dindo (C-D) classification, and possible risk factors were evaluated.

RESULTS

Among 1040 patients who had undergone robotic surgery for CRC, the overall, severe, local, and systemic complication rates were 12.2%, 2.4%, 8.8%, and 3.5%, respectively. Multivariate analysis revealed that multiple organ resection (P < 0.001) and level III American Society of Anesthesiologists (ASA) score (P = 0.006) were independent risk factors for overall complications. Multivariate analysis identified multiple organ resection (P < 0.001) and comorbidities (P = 0.029) as independent risk factors for severe complications (C-D grade III or higher). Regarding local complications, multiple organ resection (P = 0.002) and multiple bowel resection (P = 0.027) were independent risk factors. Multiple organ resection (P < 0.001) and level III ASA score (P = 0.007) were independent risk factors for systemic complications. Additionally, sigmoid colectomy had a lower incidence of overall complications (6.4%; P = 0.006) and local complications (4.7%; P = 0.028) than other types of colorectal surgery.

CONCLUSION

Multiple organ resection, level III ASA score, comorbidities, and multiple bowel resection were risk factors for postoperative complications, with multiple organ resection being the most likely.

Robotic versus Laparoscopic Surgery for Spleen-Preserving Distal Pancreatectomies: Systematic Review and Meta-Analysis

Background: When oncologically feasible, avoiding unnecessary splenectomies prevents patients who are undergoing distal pancreatectomy (DP) from facing significant thromboembolic and infective risks. Methods: A systematic search of MEDLINE, Embase, and Web Of Science identified 11 studies reporting outcomes of 323 patients undergoing intended spleen-preserving minimally invasive robotic DP (SP-RADP) and 362 laparoscopic DP (SP-LADP) in order to compare the spleen preservation rates of the two techniques. The risk of bias was evaluated according to the Newcastle–Ottawa Scale. Results: SP-RADP showed superior results over the laparoscopic approach, with an inferior spleen preservation failure risk difference (RD) of 0.24 (95% CI 0.15, 0.33), reduced open conversion rate (RD of −0.05 (95% CI −0.09, −0.01)), reduced blood loss (mean difference of −138 mL (95% CI −205, −71)), and mean difference in hospital length of stay of −1.5 days (95% CI −2.8, −0.2), with similar operative time, clinically relevant postoperative pancreatic fistula (ISGPS grade B/C), and Clavien–Dindo grade ≥3 postoperative complications. Conclusion: Both SP-RADP and SP-LADP proved to be safe and effective procedures, with minimal perioperative mortality and low postoperative morbidity. The robotic approach proved to be superior to the laparoscopic approach in terms of spleen preservation rate, intraoperative blood loss, and hospital length of stay.