Introduction

Afferent loop syndrome is a rare complication after surgical gastrojejunostomy reconstruction due to mechanical obstruction of the afferent limb. It poses a dismal prognosis, especially in cases of local cancer recurrence. Standard surgical and endoscopic treatment is often unsatisfactory. Due to poor performance status, patients frequently require immediate and less invasive treatment. Transmural stenting with lumen-apposing metal stents/LAMS/ is a new alternative to surgery and transluminal endoscopic treatment. We present our single-center experience in the management of malignant afferent loop syndrome/MALS/ with endoscopic ultrasound-guided gastroenterostomy/EUS GE/.

Aims & Methods

To evaluate the efficacy and safety of EUS GE in malignant afferent loop syndrome in cases of emergency.Methods: Two cases of MALS and severe cholangitis,managed by EUS GE in a single tertiary center.

Results

Patient 1: In the first case, a 39-year-old patient was referred to our center for recurrent episodes of severe cholangitis. He had a previous gastrojejunostomy in a different hospital, as chronic pancreatitis was suspected. After evaluation of the patient, a locally advanced pancreatic cancer with peritoneal carcinomatosis was found. Due to the poor performance status and after multidisciplinary discussion, we performed a single-step EUS GE with HOT AXIOS 15mm/10mm/Boston Scientific Marlborough, Massachusetts, USA /. After the procedure, there was complete resolution of symptoms of cholangitis and the patient started palliative chemotherapy. The patient died 56 days after the procedure due to disease progression.
Patient 2: 57 years old male, who underwent a Whipple procedure due to pancreatic cancer in 2022. During follow - up local recurrence was found. He was admitted with signs of severe cholangitis with renal and respiratory failure. Computed tomography revealed an extremely dilated afferent loop. An EUS GE was performed with,, off - label,, application of HOT SPAXUS 8mm/20mm/Taewoong/. After the procedure, the septic state was resolved, and shortly after discharge, the patient continued with regular courses of systemic chemotherapy. There are no episodes of recurrence during the current follow-up/210 days after the procedure/.
In both cases, there were no events of misdeployment. We found clinical success in both patients with fast resolution of symptoms due to cholangitis. No episodes of recurrence were found during follow-up. We didn't experience any adverse events/AE/ during either procedure, with no AE in the postprocedural period. The mean procedure time was 20 minutes. In both cases, an electro-cautery enhanced lumen apposing metal stent was used, which allowed us to create a single-step endoscopic anastomosis and obtain an internal drainage of the dilated afferent loop into the stomach.

Conclusion

Endoscopic ultrasound-guided gastroenterostomy offers a highly efficient, convenient, and safe alternative for patients presenting with malignant ALS. In our cases, no need for reintervention was found. It could be offered as a fast approach to selected patients, especially those unfit for surgery and with poor performance status.

Disclosure

No conflict of interest

Introduction

UC-SCALE[1] is an AI algorithm designed to assess endoscopic severity in ulcerative colitis (UC). It was trained on a large subset of Phase III Etrolizumab (ETRO) clinical trial data from 471 sites. In our previous work, UC-SCALE previously demonstrated excellent agreement (quadratic-weighted kappa >0.80) with centrally read Mayo Clinic Endoscopic Subscores (MCES) on an ETRO test set spanning 83 sites. While clinical trial data is highly controlled, its generalizability to real-world settings remained untested. Here, we assess UC-SCALE on a retrospective cohort from the Paris IBD Center, which we refer to as the Real World Dataset (RWD).

Aims & Methods

The RWD contained 276 unique UC patients (mean 1.17 visits/patient) from the Paris IBD center. For each colonoscopy video, two or three local gastroenterologists reviewed the recordings and reached a consensus MCES (0–3) per colonic segment (descending, sigmoid, rectum). Using the same, unmodified UC-SCALE model trained on ETRO, we evaluated both the RWD and ETRO test sets with the identical assessment protocol detailed in [1]. Quadratic-Weighted Kappa (QWK) quantified the agreement between ordinal predictions made by UC-SCALE and human scoring. Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the discriminative ability for each MCES class.

Results

On these real-world data, UC-SCALE’s section-level predictions closely mirrored local expert scoring. QWK reached ~0.804 - virtually identical to ETRO (~0.802). For class-specific discrimination (MCES 0–3), AUC values exceeded 0.85 in all classes and in several cases surpassed ETRO’s results. The average AUC across all classes approached 0.90 in the RWD, aligning well with performance in trial conditions. As in ETRO, performance was strongest for Mayo 0 and 3, but notably, predictions for intermediate scores (Mayo 1 and 2) were even more accurate in RWD. These results highlight UC-SCALE’s robust generalizability to routine clinical practice.

Conclusion

Applying the original ETRO-trained UC-SCALE model to a real-world UC cohort confirmed its high accuracy and reliability for objective MCES scoring outside of clinical trials. To date, our analysis has focused on predictions at the level of anatomical segments. Future work will expand toward full video-level scoring (closer to clinical workflows) and artificial segments, which offer a more granular topological severity map. Additionally, upcoming analyses will explore correlations between UC-SCALE and key IBD biomarkers, as well as direct comparisons with human scoring.

References

[1] Gutierrez-Becker, Benjamin, et al. "Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis." Journal of Crohn's and Colitis 19.1 (2025): jjae187.

Disclosure

Alvaro Gomariz, Bernhard Stimpel, Benjamin Gutierrez-Becker, Stefan Frässle, Emily Fisher, Steven Levitte and Teresa Karrer are employees of F. Hoffmann-La Roche Ltd.

Introduction

Placebo and treatment response rates in randomized ulcerative colitis (UC) trials impact clinical trial design and patients required to demonstrate efficacy. Prior efforts to predict endoscopic healing (EH) in UC trial patients using clinical measures have failed to produce meaningful predictive power. Using AI analysis of baseline endoscopy videos from the ustekinumab UNIFI registration Phase 3 trial in moderate to severe UC, we demonstrate robust accuracy to predict 8 week endoscopic healing in ulcerative colitis patients treated with placebo or drug.

Aims & Methods

300 screening endoscopy videos were obtained from UC patients receiving placebo, and 606 screening endoscopy videos were obtained from UC patients receiving ustekinumab (130mg or 6mg/kg) in the UNIFI induction trial. For all videos, visual features were extracted using EndoDINO, an AI foundation model pre-trained on the largest reported endoscopy video dataset. For each patient, 21 baseline clinical measures were also obtained as predictors. These included fecal calprotectin, albumin, C-reactive protein, lactoferrin, Geboes grading, prior biologics failure, prior steroids failure, UCEIS scores, and Mayo scores.
For each of the placebo and drug arms, we trained two binary classification models: one using the AI visual features and the other using the baseline clinical measures as predictors. Each model was trained to predict 8 week EH, defined as an endoscopy subscore of the Mayo score equal to 0 or 1. We performed 5-fold cross validation and reported the average Area Under the Receiver Operating Curve (AUROC).
This study, carried out under YODA Project # 2024-0668, used data obtained from the Yale University Open Data Access Project, which has an agreement with JANSSEN RESEARCH & DEVELOPMENT, L.L.C.. The interpretation and reporting of research using this data are solely the responsibility of the authors and does not necessarily represent the official views of the Yale University Open Data Access Project or JANSSEN RESEARCH & DEVELOPMENT, L.L.C.. The original proposal can be found: https://yoda.yale.edu/data-request/2024-0668/.

Results

For the placebo arm, our best performing model using AI visual features predicted 8 week endoscopic healing with an AUROC of 0.78, compared to 0.70 for our best performing model using the combination of 21 baseline clinical measures.
For the drug arm, our best performing model using AI visual features predicted 8 week endoscopic healing with an AUROC of 0.75, compared to 0.72 for our best performing model using the combination of 21 baseline clinical measures.
For models using baseline clinical measures, table 1 shows the importance factor for select measures across arms, calculated using XGBoost built in feature importance.

Conclusion

Our results demonstrate AI analysis of baseline colonoscopy videos with the EndoDINO AI foundation model provides robust accuracy for predicting 8 week EH in ulcerative colitis patients in both placebo and drug arms. AI placebo response prediction could be used in future trial design as covariate analysis to increase statistical power or reduce necessary sample size.
In future work, we aim to improve predictive power by combining our AI visual features with the most important baseline clinical measures through different modeling techniques.

References

Sands BE, Sandborn WJ, Panaccione R, et al. Ustekinumab as induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2019;381(13):1201–1214. doi:10.1056/NEJMoa1900750

Disclosure

AK, PD, MC, and MS are employees of Virgo Surgical Video Solutions, Inc., which developed technology used in this work.