Introduction

High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell cancer (ASCC) precursors. It has shown superiority over cytology for detecting high-grade squamous intraepithelial lesions (HSIL). Nevertheless, the visual characterization of these lesions remains challenging. Preliminary studies on applying deep learning models to HRA have revealed promising results. The impact of staining techniques and anal manipulation for the treatment of precursor lesions on the effectiveness of these algorithms has not been evaluated.

Aims & Methods

Our aim was to develop a deep learning system for automatic detection and differentiation of HSIL versus low-grade squamous intraepithelial lesions (LSIL) in HRA images in different subsets of patients (non-stained, acetic acid, lugol, and after manipulation).
A convolutional neural network (CNN) was developed based on 102 HRA exams from 88 patients from a Proctology high-volume center (GH Paris Saint-Joseph, Paris, France). A total of 27,770 images were included, 19,114 images containing HSIL and 8,656 LSIL. A patient-split five-fold cross-validation analysis was performed to assess the network's performance. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images with no staining (n=2,820), acetic acid (n=13,378), lugol (n=2,195), and after treatment of the anal canal (n=9,377). For these subanalyses, training, and testing datasets comprised 90% and 10% of the total number of images for each class. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve (AUC) were calculated.

Results

The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 98.3%. The algorithm had an overall sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the CNN for detecting HSIL vs. LSIL varied between 91.5% (post-manipulation) and 100% (lugol) for the four categories of subanalysis. The AUC ranged between 0.95 and 1.00

Conclusion

The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, improving these patients' therapeutic course and prognosis. Staining techniques are essential during HRA exams. Our algorithm showed excellent performance for staining with both Lugol and acetic acid. Moreover, the CNN showed high-level performance in the post-manipulation/treatment images subset. This is extremely important as real-time AI models during HRA exams can help guide local treatment or detect relapsing disease.

References

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Introduction

Resection rates for Crohn's disease (CD) have decreased over time, while re-resection rates have remained stable at about one-third in historic cohorts. Medical treatment has been shown to influence the course of the disease.

Aims & Methods

We aimed to assess the impact of disease burden categorization on medication use and risk factors. This cohort study encompassed all CD patients undergoing primary intestinal resection in Eastern Denmark from 2010 to 2020 (46% of the Danish population, n=2,730,000). We isolated all patients with five years follow up and divided them into three groups based on post-operative disease burden: high (one or more resections after the initial resection), intermediate (recurrence without need for resection), and low (no recurrence within five years). Disease recurrence was defined based on colonoscopy (SES-CD≥3, Rutgeerts score ≥i2), inflammation or stenosis on imaging (MRI, CTA, IUS), fecal calprotectin ≥ 250 mg/kg, starting corticosteroids or re-resection due to disease activity. Medication was classified by potency, with each patient assigned to only one category (no therapy<corticosteroids<immunomodulators<biologics). We used nonparametric statistics and the Kruskal-Wallis test to compare outcomes across groups.

Results

Primary resection was performed on 631 patients due to disease activity. Patients were categorized into burden groups: high burden (n=59), intermediate (n=287), and low (n=170). The median age at diagnosis was 28.2 years (range: 21.6-43.9), with no significant differences between the three disease burden groups (p=0.069). Age at primary resection differed across groups: 38 years (25-53) for high burden, 32 (24-45) for intermediate, and 36 (27-47) for low burden (p=0.019). Smoking rates were highest in the high burden group (47%) versus intermediate (37%) and low (27%) (p=0.010).
Pre-resection medical consumption was similar across the three burden groups (p=0.2). Postoperatively, the high burden group used more biologics (54%) compared to the low burden group (16%). Additionally, 57% of the low burden group remained recurrence-free and unmedicated for five years (Table 1). In the high burden group, no patients received prophylactic biologics, compared to 8% in the intermediate and low burden groups (p=0.001). Disease location varied: colon resections were most common in the high burden group (31%), and small bowel disease, including ileocecal resections, predominated in the low burden group (47%) (p<0.001). Strictures were more common in the intermediate and low burden groups, while luminal disease was mostly seen in the high-risk group (p=0.046) (Table 1)

Table 1. Medical characteristics and Montreal classification

Conclusion

High burden patients relied more on biologics, while half of the low burden patients stayed recurrence-free unmedicated for five years, suggesting tailored treatments could improve post-resection CD management.