Introduction

Metabolic dysfunction-associated Steatotic Liver disease (MASLD) is complicated by various cardiometabolic (CMR) manifestations, and the prognosis is severely burdened by acute cardiovascular events (ACE).
Even though several drugs have been tested to target CMR comorbidities, lifestyle modifications remain the cornerstone intervention. Unfortunately, the dietary-behavioral prescription is constantly limited by poor compliance, with the sociodemographic-determinant factors impacting this remaining undetermined.
Moreover, despite emerging evidence supporting behavioral therapy's relevance, the real long-term benefits of offering motivational support to MASLD individuals represent an unexplored scenario in real life.

Aims & Methods

This study aimed to evaluate the efficacy of multidisciplinary management on clinical outcomes in MASLD, investigating compliance with specialist-tailored indications. We consecutively enrolled individuals presenting MASLD diagnostic criteria (n.286). After recruitment, patients were randomized into three cohorts: subjects (n.72) receiving generic hepatologist-provided advice (“H”); subjects (n.71) also receiving the nutritionists-prescribed individualized intervention (“HN”); patients (n.143) treated with an approach additionally involving cognitive/behavioral-based psychological support (“HNP”). H and HN represented the “standard of care” (n.143), whereas “HNP” constituted the “experimental group” (n.143). At baseline, anthropometric, biochemical, clinical, liver stiffness (LSM), controlled attenuation parameter (CAP), lifestyle habits (including sociodemographic determinants), and body composition values were recorded. Along 24 months, on semestral hepatological, nutritional (H and HN), and psychological (HNP) follow-ups, all parameters were reassessed, the occurrence of ACEs was recorded, and compliance was opportunely evaluated. Three time-study points were considered: baseline (T0), intermediate (i.e., after 18 months – T1), and end of the study (T2).

Results

Intermediate evaluations revealed a significantly higher prevalence of individuals achieving a >10% decrease in body weight (HNP:62.09%; HN:44.9%; H:35.8%; chi-square, HNP vs. HN, p:0.01; HNP vs. H, p:0.0002) after 18 months (T1) in the experimental group. At the end of the observation period (T2), in this setting, a further increase in the proportion of individuals presenting and preserving >10% decrease in body weight was reported (HNP: 72.03%; HN: 46.7%; H: 39.2%, chi-square T1, HNP vs. HN, p:0.02; HNP vs. H, p:0.0001; McNemar T1-T2 HNP vs HN, p:0.001, HNP vs H, p:0.001), as wells as a significant improvement in Homeostatic-model-assessment-for-insulin-resistance (p:0.001), HDL (p < 0.0001), LSM (p:0.007), CAP (p:0.002), and fat-mass (p < 0.0001) was observed (HNP, T0 vs T2).
Relevantly, HNP patients presented a significantly lower risk of ACEs [HR: 0.497, IC (0.236-0.751) 95%, p:0.04)]. Logistic regression analysis (adjusted for sex, age, drug administration, and cardiovascular risk factors) revealed that HNP adherence was significantly associated with lower ACE occurrence (aOR: 0.81; C.I. 95%: 0.55-0.97; p:0.02). Consistently, the loss of compliance rate was significantly lower in HNP (HNP:12.08%; HN:34.7%; H:45.8%; HNP vs HN, HNP vs H, p:0.001). Finally, the analysis of sociodemographic factors revealed large family (i.e., > 3 individuals), dynamic work (i.e., < 2 hours/die dedicated to main meals), and city life as social determinants impacting compliance (OR: 2.11, 1.78, 1.12) (all p < 0.05).

Conclusion

In the era of Precision Medicine, the correct management of MASLD patients should contemplate the integration of standard hepatological-nutritional with psychological support.

References

1. Romeo M, Dallio M, Cipullo M, Coppola A, Mazzarella C, Mammone S, Iadanza G, Napolitano C, Vaia P, Ventriglia L, Federico A. Nutritional and Psychological Support as a Multidisciplinary Coordinated Approach in the Management of Chronic Liver Disease: A Scoping Review. Nutr Rev. 2025 Feb 24:nuaf001. doi: 10.1093/nutrit/nuaf001. Epub ahead of print. PMID: 39992295.
2. Righetti R, Cinque F, Volpe MT, Sebastiani G. Integrating behavioral interventions into a holistic approach to metabolic dysfunction-associated steatotic liver disease. Expert Rev Gastroenterol Hepatol. 2024 Jul;18(7):303-313. doi: 10.1080/17474124.2024.2385487. Epub 2024 Aug 5. PMID: 39075881.
3. European Association for the Study of the Liver (EASL) Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024 Sep;81(3):492-542. doi: 10.1016/j.jhep.2024.04.031. Epub 2024 Jun 7. PMID: 38851997.

Introduction

Colorectal anastomotic dehiscence (CAD) is one of the most severe postoperative complications in colorectal surgery, significantly impacting patient morbidity and mortality. Current risk assessment methods rely on clinical and intraoperative factors, but no real-time predictive tool exists. This study aimed to develop an artificial intelligence model based on intraoperative recording of the anastomosis for CAD prediction.

Aims & Methods

A convolutional neural network (CNN) was trained using a dataset of annotated frames from colorectal surgery videos across three international high-volume centers: IPO Lisboa, Hospital das Clínicas de Ribeirão Preto, São Paulo, Brazil and Royal Liverpool University Hospital. The dataset included a total of 5,356 annotated frames from 25 patients, 2,007 frames classified as CAD-positive and 3,349 as CAD-negative. Four CNN architectures (EfficientNetB0, EfficientNetB7, ResNet50, and MobileNetV2) were tested. Performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUROC) curve. Heatmaps were generated to identify key regions influencing predictions.

Results

The best-performing model achieved an accuracy of 99.55%, AUROC of 99.60%, sensitivity of 99.21%, specificity of 100%, PPV of 100%, and NPV of 98.94%. The model reliably identified CAD-positive frames and provided visual explanations through heatmaps.

Conclusion

To our knowledge, this is the first AI model developed to predict CAD using intraoperative video analysis. Its accuracy suggests the potential to redefine surgical decision-making by providing real-time risk assessment. Further refinement with a larger dataset and diverse surgical techniques could enable intraoperative interventions to prevent CAD before it occurs, marking a paradigm shift in colorectal surgery.

Introduction

Large series from eastern expert centres have defined the rate of submucosal invasive cancer (SMIC) in LNPCP according to subtype along with associated clinicopathological features. Large western series are very limited and provide conflicting results leading some authors to suggest differences in invasive potential between eastern and western patients with LNPCP. Notable differences exist between studies in reported associations between incidence of SMI and clinicopathological features which can heavily influence recommendations for therapeutic strategy.

Aims & Methods

We aimed to define the true risk of SMIC by morphological subtype and clinicopathological features of LNPCP in a large cohort of patients with LNPCP referred to a tertiary centre. All patients with LNPCP referred for consideration of treatment by ER at a tertiary referral centre between January 2011 and December 2024 were included. Data collected included morphological subtype of LNPCP, size, location, sex and age. The incidence of SMIC by morphological subtype was determined and significant differences in SMIC by clinicopathological features were sought.

Results

1641 LNPCP were treated by endoscopic resection. Mean size was 48mm (median 40mm, range 20-210mm). 43% of lesions were located in the right colon, 20% in the left colon and 37% in the rectum. 45% were treated using ESD and 57% were resected en bloc. The incidence of SMIC by morphological subtype was: laterally spreading tumour granular nodular mixed type (LST G NM) 13.4%; LST granular homogenous (LST GH) 2.8%, Is 12.9%; LST non granular (LST NG) flat-elevated 17.3% and LST NG pseudodepressed type (LST NG PD) 51.6%. SMIC was similar across size groups: 20-30mm 9.8%; 31-40mm 12.3%; 41-60mm 11.4% and >60mm 12.2% (p=0.59). No association was found between SMIC and age (p=0.35) or sex (p=0.12). SMIC was associated with rectal or left colon location: right colon 5.7%; left colon 14.9% and rectum 15.3% (p<0.001). For right colon LNPCP, the incidence of SMIC in LST G NM was 7.0%, LST NG 14.3% and LST NG PD 50.0%.

Conclusion

These data, from one of the largest single institution western series, help define the risk of SMIC according to clinicopathological features of LNPCP. High rates of en bloc resection increase the validity of these results. The risk of SMIC is largely determined by morphological subtype and occurred with similar incidence in this series to large series from eastern centres, suggesting similar tumour biology. Contrary to findings from some studies, size of LNPCP, age or sex are of limited significance. Although right colon LNPCP have a lower overall risk of SMIC, certain morphological subtypes still signal a high risk of SMIC justifying en bloc resection.

Introduction

Any computer-assisted characterization (CADx) system relies on Artificial Intelligence algorithms to provide a probability score to the endoscopist, characterizing the polyp as an adenoma or a hyperplastic polyp.^1-3 However, the clinical utility of these AI-generated outputs is dependent on the endoscopist’s ability to interpret and integrate them into their final diagnostic decision-making process. This requires not only recognizing and accepting a true positive AI diagnosis, but also accurately identifying and rejecting false positives. This requires a higher level of endoscopist expertise to discern whether an AI-generated classification is accurate and should be incorporated into the final diagnosis or requires correction. The primary challenge in CADx implementation lies in this inherent complexity of human-AI interactions, which can lead to misinterpretation, resulting in the endoscopist either rejecting correct CADx outputs or accepting incorrect classifications, compromising diagnostic accuracy.

Aims & Methods

We conducted a secondary analysis from a prospective study. Endoscopists were first presented with the CADx classification for each polyp, which they could accept or reject and provide an alternative. A total of 1143 patients aged at least 45 underwent colonoscopy between June 2022 and January 2025 at the CHUM Hospital, yielding 753 polyps from 367 patients after exclusion criteria. The primary aim was to assess the agreement/rejection patterns of the CADx diagnosis by the endoscopists, stratified by categorization. Secondary outcomes included the rate at which clinicians correctly rejected inaccurate CADx outputs, as well as trends in post-rejection reclassification.

Results

A total of 89.1% (408/458, 95% CI: (85.8,91.7)) of accepted CADx neoplastic diagnoses were correct, and 68.7% (68/99, 95% CI: (58.5,77.4)) of accepted CADx hyperplastic diagnoses were accurate. However, endoscopists accepted incorrect diagnoses significantly more often when CADx predicted hyperplastic than neoplastic (%, 95% CI: 31.3% (22.6,41.5) vs 10.9% (8.3,14.2)). Regarding the post-rejection patterns, when endoscopists rejected adenoma classifications (N=47), they falsely classified 25 cases (53.2%, 95% CI: (38.2,67.6)) as a sessile serrated lesion/polyp (SSL/P). Similarly, although 43.6% of the rejected hyperplastic CADx predictions were correctly re-assigned an adenoma (31.5%, 95% CI: (24.3,39.7)) or SSL/P diagnosis (12.1%, 95% CI: (7.5,18.7)), there were 41.6% (95% CI: (33.7,50.0)) that were incorrectly re-classified as SSL/Ps.

Conclusion

Endoscopists generally accept accurate adenoma predictions provided by CADx systems but frequently misclassify hyperplastic polyps as SSLs, demonstrating a diagnostic bias toward neoplasia. Although beneficial from a safety perspective, this bias may result in unnecessary early follow-up colonoscopies, increasing associated costs.^4,5 Developing and integrating CADx systems with robust SSL recognition capabilities seems essential to overcome these diagnostic challenges and enhance clinical decision-making when using CADx assisted optical polyp diagnosis. Such advanced CADx systems are likely crucial for successful CADx integration into routine clinical practice.

References

1. Djinbachian R, Taghiakbari M, Barkun A, et al. Optimized computer-assisted technique for increasing adenoma detection during colonoscopy: a randomized controlled trial. Surg Endosc 2025;39:1120-1127.
2. Byrne MF, Von Renteln D, Barkun AN. Artificial Intelligence-Aided Colonoscopy for Characterizing and Detecting Colorectal Polyps: Required, Nice to Have, or Overhyped? Gastroenterology 2023;164:332-333.
3. Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021;27:8103-8122.à
4. Force AAT, Parasa S, Berzin T, et al. Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology. Gastrointest Endosc 2025;101:2-9 e1.
5. Halvorsen N, Barua I, Kudo SE, et al. Leaving colorectal polyps in situ with endocytoscopy assisted by computer-aided diagnosis: a cost-effectiveness study. Endoscopy 2025.

Disclosure

Daniel von Renteln has received research funding from ERBE Elektromedizin GmbH, Ventage, Pendopharm, Fujifilm and Pentax, and has received consultant or speaker fees from Boston Scientific Inc., ERBE Elektromedizin GmbH, and Pendopharm. Roupen Djinbachian has received speaker fees from Fujifilm. The remaining authors declare that they have no conflict of interest.