Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis: A systematic review.

This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %-98 % and specificity values of 80 %-97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.

A novel artificial intelligence-assisted "vascular-healing" diagnosis for prediction of future clinical relapse in patients with ulcerative colitis: a prospective cohort study.

BACKGROUND AND AIMS: Image-enhanced endoscopy (IEE) has attracted attention as a method for detecting inflammation and predicting outcomes in patients with ulcerative colitis (UC); however, the procedure requires specialist endoscopists. Artificial intelligence (AI)-assisted IEE may help non-experts to provide objective accurate predictions using optical imaging. We aimed to develop a novel AI-based system using 8853 images from 167 patients with UC to diagnose "vascular-healing" and establish the role of AI-based vascular-healing for predicting the outcomes of patients with UC. METHODS: This open-label, prospective cohort study analyzed data for 104 patients with UC in clinical remission. Endoscopists performed colonoscopy using the AI system, which identified the target mucosa as AI-based vascular-active or vascular-healing. Mayo endoscopic subscore (MES), AI outputs, and histological assessment were recorded for six colorectal segments from each patient. Patients were followed-up for 12 months. Clinical relapse was defined as a partial Mayo score >2 RESULTS: The clinical relapse rate was significantly higher in the AI-based vascular-active group [23.9% (16/67)] compared with the AI-based vascular-healing group [3.0% (1/33)] (P=0.01). In a sub-analysis predicting clinical relapse in patients with MES ≤1, the area under the curve for the combination of complete endoscopic remission and vascular-healing (0.70) was increased compared with that for complete endoscopic remission alone (0.65). CONCLUSIONS: AI-based vascular healing diagnosis system may potentially be used to provide more confidence to physicians to accurately identify patients in remission of UC who would likely relapse rather than remain stable.

Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

OBJECTIVES: Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. METHODS: This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. RESULTS: Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). CONCLUSIONS: Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.

Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy.

Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.

Effectiveness of endoscopic resection for colorectal neoplasms in ulcerative colitis: a multicenter registration study.

BACKGROUND AND AIMS: Patients with ulcerative colitis (UC) are at risk of developing colorectal cancer. The feasibility of endoscopic resection (ER) for UC-associated neoplasia has been suggested, but its efficacy and safety remain unclear. We aimed to assess the efficacy and safety of ER for colorectal neoplasms in patients with UC. METHODS: This was a retrospective, multicenter cohort study of patients with UC who initially underwent ER or surgery for colorectal neoplasms between April 2015 and March 2021. Patients who had prior colorectal neoplastic lesions were excluded. RESULTS: Among 213 men and 123 women analyzed, the mean age at UC onset was 41.6 years, and the mean age at neoplasia diagnosis was 56.1 years for 240 cases of total colitis, 59 cases of left-sided colitis, 31 cases of proctitis, and 6 cases of segmental colitis. EMR was performed for 142 lesions, and endoscopic submucosal dissection (ESD) was performed for 96 lesions. The perforation rate was 2.5% for all 238 lesions removed by ER and 6.3% for the 96 lesions removed by ESD. Among 146 ER lesions followed up with endoscopy, the local recurrence rate was 2.7%. The incidence of metachronous neoplasia after ER was 6.1%. All patients were followed a median of 34.7 months after initial treatment, and 5 died (all surgical cases). Overall survival was significantly higher in the ER group than in the surgery group (P = .0085). CONCLUSIONS: ER for colorectal neoplasms in UC may be acceptable in selected cases, although follow-up for metachronous lesions is necessary.

Whole slide image-based prediction of lymph node metastasis in T1 colorectal cancer using unsupervised artificial intelligence.

OBJECTIVES: Lymph node metastasis (LNM) prediction for T1 colorectal cancer (CRC) is critical for determining the need for surgery after endoscopic resection because LNM occurs in 10%. We aimed to develop a novel artificial intelligence (AI) system using whole slide images (WSIs) to predict LNM. METHODS: We conducted a retrospective single center study. To train and test the AI model, we included LNM status-confirmed T1 and T2 CRC between April 2001 and October 2021. These lesions were divided into two cohorts: training (T1 and T2) and testing (T1). WSIs were cropped into small patches and clustered by unsupervised K-means. The percentage of patches belonging to each cluster was calculated from each WSI. Each cluster's percentage, sex, and tumor location were extracted and learned using the random forest algorithm. We calculated the areas under the receiver operating characteristic curves (AUCs) to identify the LNM and the rate of over-surgery of the AI model and the guidelines. RESULTS: The training cohort contained 217 T1 and 268 T2 CRCs, while 100 T1 cases (LNM-positivity 15%) were the test cohort. The AUC of the AI system for the test cohort was 0.74 (95% confidence interval [CI] 0.58-0.86), and 0.52 (95% CI 0.50-0.55) using the guidelines criteria (P = 0.0028). This AI model could reduce the 21% of over-surgery compared to the guidelines. CONCLUSION: We developed a pathologist-independent predictive model for LNM in T1 CRC using WSI for determination of the need for surgery after endoscopic resection. TRIAL REGISTRATION: UMIN Clinical Trials Registry (UMIN000046992, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053590).

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is evaluated semiquantitatively by pathologists using histological images. Therefore, the interobserver concordance is not extremely high, and an objective evaluation method is needed. This study was conducted to demonstrate that our automated quantitative method using a deep learning-based model is useful in predicting the prognosis of patients with UC. METHODS: Deep learning-based models were trained to detect goblet cell mucus area from whole slide images of biopsy specimens. This study involved 114 patients with UC in endoscopic remission with a partial Mayo score of ≤ 1. Biopsy specimens were collected during colonoscopy, and the ratio of goblet cell mucus area to the epithelial cell and goblet cell mucus area was calculated as goblet cell ratio (GCR). The follow-up time was 12 months, and the primary outcome was the relapse rate. Clinical relapse was defined as partial Mayo score of ≥ 3. RESULTS: Sixteen patients (14%) experienced clinical relapse. In the relapsed group, the GCRs of specimens obtained from the cecum, ascending colon, and rectum were significantly lower than those of specimens in the relapse-free group (p = 0.010, p = 0.027, p < 0.01). In the rectum, patients with a GCR of ≤ 12% had a significantly higher relapse rate than those with a GCR of > 12% (45% [10/22] vs. 6.5% [6/92]; p < 0.01). CONCLUSIONS: Quantifying goblet cell mucus areas using a deep learning-based model is useful in predicting the clinical relapse in patients with UC in clinical and endoscopic remission.

Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: Systematic review.

OBJECTIVES: Advances in endoscopic technology, including magnifying and image-enhanced techniques, have been attracting increasing attention for the optical characterization of colorectal lesions. These techniques are being implemented into clinical practice as cost-effective and real-time approaches. Additionally, with the recent progress in endoscopic interventions, endoscopic resection is gaining acceptance as a treatment option in patients with ulcerative colitis (UC). Therefore, accurate preoperative characterization of lesions is now required. However, lesion characterization in patients with UC may be difficult because UC is often affected by inflammation, and it may be characterized by a distinct "bottom-up" growth pattern, and even expert endoscopists have relatively little experience with such cases. In this systematic review, we assessed the current status and limitations of the use of optical characterization of lesions in patients with UC. METHODS: A literature search of online databases (MEDLINE via PubMed and CENTRAL via the Cochrane Library) was performed from 1 January 2000 to 30 November 2021. RESULTS: The database search initially identified 748 unique articles. Finally, 25 studies were included in the systematic review: 23 focused on differentiation of neoplasia from non-neoplasia, one focused on differentiation of UC-associated neoplasia from sporadic neoplasia, and one focused on differentiation of low-grade dysplasia from high-grade dysplasia and cancer. CONCLUSIONS: Optical characterization of neoplasia in patients with UC, even using advanced endoscopic technology, is still challenging and several issues remain to be addressed. We believe that the information revealed in this review will encourage researchers to commit to the improvement of optical diagnostics for UC-associated lesions.

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission. METHODS: This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy. RESULTS: Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001). CONCLUSIONS: Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.).

Combined endocytoscopy with pit pattern diagnosis in ulcerative colitis-associated neoplasia: Pilot study.

OBJECTIVES: Ulcerative colitis-associated neoplasias (UCAN) are often flat with an indistinct boundary from surrounding tissues, which makes differentiating UCAN from non-neoplasias difficult. Pit pattern (PIT) has been reported as one of the most effective indicators to identify UCAN. However, regenerated mucosa is also often diagnosed as a neoplastic PIT. Endocytoscopy (EC) allows visualization of cell nuclei. The aim of this retrospective study was to demonstrate the diagnostic ability of combined EC irregularly-formed nuclei with PIT (EC-IN-PIT) diagnosis to identify UCAN. METHODS: This study involved patients with ulcerative colitis whose lesions were observed by EC. Each lesion was diagnosed by two independent expert endoscopists, using two types of diagnostic strategies: PIT alone and EC-IN-PIT. We evaluated and compared the diagnostic abilities of PIT alone and EC-IN-PIT. We also examined the difference in the diagnostic abilities of an EC-IN-PIT diagnosis according to endoscopic inflammation severity. RESULTS: We analyzed 103 lesions from 62 patients; 23 lesions were UCAN and 80 were non-neoplastic. EC-IN-PIT diagnosis had a significantly higher specificity and accuracy compared with PIT alone: 84% versus 58% (P < 0.001), and 88% versus 67% (P < 0.01), respectively. The specificity and accuracy were significantly higher for Mayo endoscopic score (MES) 0-1 than MES 2-3: 93% versus 68% (P < 0.001) and 95% versus 74% (P < 0.001), respectively. CONCLUSIONS: Our novel EC-IN-PIT strategy had a better diagnostic ability than PIT alone to predict UCAN from suspected and initially detected lesions using conventional colonoscopy. UMIN clinical trial (UMIN000040698).

Beyond complete endoscopic healing: Goblet appearance using an endocytoscope to predict future sustained clinical remission in ulcerative colitis.

OBJECTIVES: Complete endoscopic healing, defined as Mayo endoscopic score (MES) = 0, is an optimal target in the treatment of ulcerative colitis (UC). However, some patients with MES = 0 show clinical relapse within 12 months. Histologic goblet mucin depletion has emerged as a predictor of clinical relapse in patients with MES = 0. We observed goblet depletion in vivo using an endocytoscope, and analyzed the association between goblet appearance and future prognosis in UC patients. METHODS: In this retrospective cohort study, all enrolled UC patients had MES = 0 and confirmed clinical remission between October 2016 and March 2020. We classified the patients into two groups according to the goblet appearance status: preserved-goblet and depleted-goblet groups. We followed the patients until March 2021 and evaluated the difference in cumulative clinical relapse rates between the two groups. RESULTS: We identified 125 patients with MES = 0 as the study subjects. Five patients were subsequently excluded. Thus, we analyzed the data for 120 patients, of whom 39 were classified as the preserved-goblet group and 81 as the depleted-goblet group. The patients were followed-up for a median of 549 days. During follow-up, the depleted-goblet group had a significantly higher cumulative clinical relapse rate than the preserved-goblet group (19% [15/81] vs. 5% [2/39], respectively; P = 0.02). CONCLUSIONS: Observing goblet appearance in vivo allowed us to better predict the future prognosis of UC patients with MES = 0. This approach may assist clinicians with onsite decision-making regarding treatment interventions without a biopsy.

Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: Systematic review.

With the prevalence of endoscopic submucosal dissection and endoscopic full thickness resection, which enable complete resection of T1 colorectal cancer with a negative margin, the treatment strategy following endoscopic resection has become more important. The necessity of secondary surgical resection is determined on the basis of the risk of lymph node metastasis according to the histopathological findings of resected specimens because ~10% of T1 colorectal cancer cases have lymph node metastasis. The current Japanese treatment guidelines state four risk factors for lymph node metastasis: lymphovascular invasion, histological differentiation, depth of submucosal invasion, and tumor budding. These guidelines have succeeded in stratifying the low-risk group for lymph node metastasis, in which endoscopic resection alone is acceptable for cure. On the other hand, there are some problems: there is variation in diagnosis methods and low interobserver agreement for each pathological factor and 90% of surgical resections are unnecessary, with lymph node metastasis negativity. To ensure patients with T1 colorectal cancer receive more appropriate treatment, these problems should be addressed. In this systematic review, we gave some suggestions to these practical issues of four pathological factors as predictors.

Clinicopathological features of small T1 colorectal cancers.

BACKGROUND: Although small colorectal neoplasms (< 10 mm) are often easily resected endoscopically and are considered to have less malignant potential compared with large neoplasms (≥ 10 mm), some are invasive to the submucosa. AIM: To clarify the clinicopathological features of small T1 colorectal cancers. METHODS: Of 32025 colorectal lesions between April 2001 and March 2018, a total of 1152 T1 colorectal cancers resected endoscopically or surgically were included in this study and were divided into two groups by tumor size: a small group (< 10 mm) and a large group (≥ 10 mm). We compared clinicopathological factors including lymph node metastasis (LNM) between the two groups. RESULTS: The incidence of small T1 cancers was 10.1% (116/1152). The percentage of initial endoscopic treatment in small group was significantly higher than in large group (< 10 mm 74.1% vs ≥ 10 mm 60.2%, P < 0.01). In the surgical resection cohort (n = 798), the rate of LNM did not significantly differ between the two groups (small 12.3% vs large 10.9%, P = 0.70). In addition, there were also no significant differences between the two groups in pathological factors such as histological grade, vascular invasion, or lymphatic invasion. CONCLUSION: Because there was no significant difference in the rate of LNM between small and large T1 colorectal cancers, the requirement for additional surgical resection should be determined according to pathological findings, regardless of tumor size.

Image-Enhanced Capsule Endoscopy Improves the Identification of Small Intestinal Lesions.

Image-enhanced endoscopy is useful for diagnosing and identifying lesions in the gastrointestinal tract. Recently, image-enhanced endoscopy has become a breakthrough technology that has attracted significant attention. This image enhancing technology is available for capsule endoscopy, which is an effective tool for small intestinal lesions and has been applied in flexible spectral color enhancement technology and in contrast capsule like narrow-band imaging. In this field, most researchers focus on improving the visibility and detection of small intestinal lesions. This review summarizes previous studies on image-enhanced capsule endoscopy and aims to evaluate the efficacy of this technology.

Clinical Efficacy of Endocytoscopy for Gastrointestinal Endoscopy.

Endocytoscopy (EC) is a contact-type optical endoscope that allows in vivo cellular observation during gastrointestinal endoscopy and is now commercially available not only in Japan but also in Asian, European Union, and Middle Eastern countries. EC helps conduct a highly accurate pathological prediction without biopsy. Initially, EC was reported to be effective for esophageal diseases. Subsequently, its efficacy for stomach and colorectal diseases has been reported. In this narrative review, we searched for clinical studies that investigated the efficacy of EC. EC seems to accurately diagnose gastrointestinal diseases without biopsy. Most of the studies aimed to clarify the relationship between endocytoscopic findings of gastrointestinal neoplasia and pathological diagnosis. Some studies have investigated non-epithelial lesions or diseases, such as inflammatory bowel disease or infectious diseases. However, there are few high-level pieces of evidence, such as randomized trials; thus, further studies are needed.