Public patient involvement and engagement via 'think-in' for digital cancer surgery

New technologies for future of surgery in Ireland: An RCSI working Group report 2024

INTRODUCTION

Surgery is a cognitive discipline whose practitioners characteristically use technology during operations for patients. With accelerating technological innovation throughout society and healthcare, we sought to develop a shared position for Irish surgery via a commissioned work programme by the Royal College of Surgeons in Ireland.

METHODS

Using Stanford design principles, representative clinical specialty and academic leads and higher trainee representatives across 15 specialties were surveyed regarding sentiments, perspectives and concerns regarding now and near future technology in clinical practice, career considerations and training/education. Selected participants proceeded to semi-structured interview as deeper dive including use-case exemplar development with narrative thematic analysis.

RESULTS

New technologies matter to surgeons and surgical practice for patients as evidenced by the high level of engagement and rich perspectives provided. However there is concern, particularly among academic groups, that Irish Surgery could be better positioned for the opportunities inherent in new technologies and better prepared strategically regarding stakeholder (surgical units, healthcare institutions, universities and especially management, health service executive and government) alignment. Investment levels are felt to be insufficient to maximally leverage new technology effectively including training investment where new ways of learning are needed as technologies such as artificial intelligence and data and analytics become more important to surgical practice beyond the current prioritisation of robotics and biomaterials.

CONCLUSION

While there is high engagement, clear ambition and confidence with role of new technologies in the now and near future of Irish Surgery, there is opportunity to demonstrate leadership and synergise stake holders for better healthcare.

Clinical validation of 3D virtual modelling for laparoscopic complete mesocolic excision with central vascular ligation for proximal colon cancer

INTRODUCTION

Laparoscopic Complete Mesocolic Excision (CME) with Central Vascular Ligation (CVL) in colon cancer surgery has not been broadly adopted in part because of safety concerns. Pre-operative 3-D virtual modelling (3DVM) may help but needs validation.

METHODS

3DVM were routinely constructed from CT mesenteric angiograms (CTMA) using a commercial service (Visible Patient, Strasbourg, France) for consecutive patients during our CMECVL learning curve over three years. 3DVMs were independently checked versus CTMA and operative findings. CMECVL outcomes were compared versus other patients undergoing standard mesocolic excision (SME) surgery laparoscopically in the same hospital as control. Stakeholders were studied regarding 3DVM use and usefulness (including detail retention) versus CTMA and a physical 3D-printed model.

RESULTS

26 patients underwent 3DVM with intraoperative display during laparoscopic CMECVL within existing workflows. 3DVM accuracy was 96 % re arteriovenous variations at patient level versus CTMA/intraoperative findings including accessory middle colic artery identification in three patients. Twenty-two laparoscopic CMECVL with 3DVM cases were compared with 49 SME controls (age 69 ± 10 vs 70.9 ± 11 years, 55 % vs 53 % males). There were no intraoperative complications with CMECVL and similar 30-day postoperative morbidity (30 % vs 29 %), hospital stay (9 ± 3 vs 12 ± 13 days), 30-day readmission (6 % vs 4 %) and reoperation (0 % vs 4 %) rates. Intraoperative times were longer (215.7 ± 43.9 vs 156.9 ± 52.9 min, p=<0.01) but decreased significantly over time. 3DVM surveys (n = 98, 20 surgeons, 48 medical students, 30 patients/patient relatives) and comparative study revealed majority endorsement (90 %) and favour (87 %).

CONCLUSION

3DVM use was positively validated for laparoscopic CMECVL and valued by clinicians, students, and patients alike.

Patient public perspectives on digital colorectal cancer surgery (DALLAS)

INTRODUCTION

The importance of patient perspectives is increasingly appreciated in clinical practice and academia with formal engagement processes developing worldwide. Digital surgery encompasses intraoperative patient data (including surgical video) analysis and so requires public-patient involvement (PPI).

METHODS

Engagement events were conducted based on NIHR and GRIPP2 LF guidelines. Following informative talks on digital surgery, invited patients and patient relatives were split into focus groups regarding 1) Research; 2) Data; 3) Industry Involvement; and 4) Artificial Intelligence in surgery. Scribed feedback was thematically analysed by two researchers independently. A pre and post event survey was sought voluntarily.

RESULTS

36 participant perspectives were analysed. In general, patients were enthusiastic about having a voice in surgical research and sharing their journey, with most groups concluding that capturing this was most appropriate after treatment recovery. The use of patient data for surgical development (i.e. research and education) was endorsed unanimously for the purpose of future patient benefit when responsibly and transparently managed and the value of industry was acknowledged. From 30 pre/post surveys (all p > 0.05), participants afforded the greatest data (including video) ownership claim to the surgical team (52 %/48 %) versus patients (32 %/24 %) and the hospital (12 %/24 %). While most (73 %/80 %) agreed that AI should be applied in surgical care, most felt the surgeon most valuable (93 %/80 %) with participants disagreeing that AI should make diagnoses (57 %/64 %) or treat patients (70 %/70 %) without human input.

CONCLUSION

Patients capably represent stable views and expectations that can strengthen modern and evolving surgical development involving data privacy, ownership and management.

CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery

AIM

Treatment pathways for significant rectal polyps differ depending on the underlying pathology, but pre-excision profiling is imperfect. It has been demonstrated that differences in fluorescence perfusion signals following injection of indocyanine green (ICG) can be analysed mathematically and, with the assistance of artificial intelligence (AI), used to classify tumours endoscopically as benign or malignant. This study aims to validate this method of characterization across multiple clinical sites regarding its generalizability, usability and accuracy while developing clinical-grade software to enable it to become a useful method.

METHODS

The CLASSICA study is a prospective, unblinded multicentre European observational study aimed to validate the use of AI analysis of ICG fluorescence for intra-operative tissue characterization. Six hundred patients undergoing transanal endoscopic evaluation of significant rectal polyps and tumours will be enrolled in at least five clinical sites across the European Union over a 4-year period. Video recordings will be analysed regarding dynamic fluorescence patterns centrally as software is developed to enable analysis with automatic classification to happen locally. AI-based classification and subsequently guided intervention will be compared with the current standard of care including biopsies, final specimen pathology and patient outcomes.

DISCUSSION

CLASSICA will validate the use of AI in the analysis of ICG fluorescence for the purposes of classifying significant rectal polyps and tumours endoscopically. Follow-on studies will compare AI-guided targeted biopsy or, indeed, AI characterization alone with traditional biopsy and AI-guided local excision versus traditional excision with regard to marginal clearance and recurrence.

Deconstructing mastery in colorectal fluorescence angiography interpretation

INTRODUCTION

Indocyanine green fluorescence angiography (ICGFA) is commonly used in colorectal anastomotic practice with limited pre-training. Recent work has shown that there is considerable inconsistency in signal interpretation between surgeons with minimal or no experience versus those consciously invested in mastery of the technique. Here, we deconstruct the fluorescence signal patterns of expert-annotated surgical ICGFA videos to understand better their correlation and combine this with structured interviews to ascertain whether such interpretative capability is conscious or unconscious.

METHODS

For fluorescence signal analysis, expert-annotated ICGFA videos (n = 24) were quantitatively interrogated using a boutique intensity tracker (IBM Research) to generate signal time plots. Such fluorescence intensity data were examined for inter-observer correlation (Intraclass Correlation Coefficients, ICC) at specific curve milestones: the maximum fluorescence signal (Fmax), the times to both achieve this maximum (Tmax), as well as half this maximum (T1/2max) and the ratio between these (T1/2/Tmax). Formal tele-interview with contributing experts (n = 6) was conducted with the narrative transcripts being thematically mapped, plotted, and qualitatively analyzed.

RESULTS

Correlation by mathematical measures was excellent (ICC0.9-1.0) for Fmax, Tmax, and T1/2max (0.95, 0.938, and 0.925, respectively) and moderate (0.5-0.75) for T1/2/Tmax (0.729). While all experts narrated a deliberate viewing strategy, their specific dynamic signal appreciation differed in the manner of description.

CONCLUSION

Expert ICGFA users demonstrate high correlation in mathematical measures of their signal interpretation although do so tacitly. Computational quantification of expert behavior can help develop the necessary lexicon and training sets as well as computer vision methodology to better exploit ICGFA technology.