Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.

OBJECTIVE: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC). SUMMARY BACKGROUND DATA: Recent technical advances allow complete local excision of T2 CRC, traditionally treated with surgical resection. Yet, the widespread adoption of this approach is hampered by the inability to stratify the risk of LNM. METHODS: Data from pT2 CRC patients undergoing surgical resection between April 2000 and May 2022 at one Japanese and one Italian center were analyzed. Primary goal was AI system development for accurate LNM prediction. Predictors encompassed seven variables: age, sex, tumor size and location, lympho-vascular invasion, histological differentiation, and carcinoembryonic antigen level. The tool's discriminating power was assessed via Area Under the Curve (AUC), sensitivity, and specificity. RESULTS: Out of 735 initial patients, 692 were eligible. Training and validation cohorts comprised of 492 and 200 patients, respectively. The AI model displayed an AUC of 0.75 in the combined validation dataset. Sensitivity for LNM prediction was 97.8% and specificity was 15.6%. The Positive and the Negative Predictive Value were 25.7% and 96% respectively. The False Negative (FN) rate was 2.2%, the False Positive was 84.4%. CONCLUSIONS: Our AI model, based on easily accessible clinical and pathological variables, moderately predicts LNM in T2 CRC. However, the risk of FN needs to be considered. The training of the model including more patients across Western and Eastern centers -differentiating between colon and rectal cancers- may improve its performance and accuracy.

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.

Effect of Biologics on the Risk of Advanced-Stage Inflammatory Bowel Disease-Associated Intestinal Cancer: A Nationwide Study.

INTRODUCTION: The aim of this study was to evaluate the effect of biologics on the risk of advanced-stage inflammatory bowel disease (IBD)-associated intestinal cancer from a nationwide multicenter data set. METHODS: The medical records of patients with Crohn's disease (CD) and ulcerative colitis (UC) diagnosed with IBD-associated intestinal neoplasia (dysplasia or cancer) from 1983 to 2020 were included in this study. Therapeutic agents were classified into 3 types: biologics, 5-aminosalicylic acid, and immunomodulators. The pathological cancer stage was compared based on the drug used in both patients with CD and UC. RESULTS: In total, 1,042 patients (214 CD and 828 UC patients) were included. None of the drugs were significantly associated with cancer stage in the patients with CD. In the patients with UC, an advanced cancer stage was significantly associated with less use of biologics (early stage: 7.7% vs advanced stage: 2.0%, P < 0.001), 5-aminosalicylic acid, and immunomodulators. Biologic use was associated with a lower incidence of advanced-stage cancer in patients diagnosed by regular surveillance (biologics [-] 24.5% vs [+] 9.1%, P = 0.043), but this was not the case for the other drugs. Multivariate analysis showed that biologic use was significantly associated with a lower risk of advanced-stage disease (odds ratio = 0.111 [95% confidence interval, 0.034-0.356], P < 0.001). DISCUSSION: Biologic use was associated with a lower risk of advanced IBD-associated cancer in patients with UC but not with CD. The mechanism of cancer progression between UC and CD may be different and needs to be further investigated.

Definitive S-1/mitomycin-C chemoradiotherapy for stage II/III anal canal squamous cell carcinoma: a phase I/II dose-finding and single-arm confirmatory study (JCOG0903).

BACKGROUND: Definitive chemoradiotherapy (CRT) with 5-fluorouracil plus mitomycin-C is a standard treatment for stage II/III squamous cell carcinoma of the anal canal (SCCA). We performed this dose-finding and single-arm confirmatory trial of CRT with S-1 plus mitomycin-C to determine the recommended dose (RD) of S-1 and evaluate its efficacy and safety for locally advanced SCCA. METHODS: Patients with clinical stage II/III SCCA (UICC 6th) received CRT comprising mitomycin-C (10 mg/m2 on days 1 and 29) and S-1 (60 mg/m2/day at level 0 and 80 mg/m2/day at level 1 on days 1-14 and 29-42) with concurrent radiotherapy (59.4 Gy). Dose-finding used a 3 + 3 cohort design. The primary endpoint of the confirmatory trial was 3-year event-free survival. The sample size was 65, with one-sided alpha of 5%, power of 80%, and expected and threshold values of 75% and 60%, respectively. RESULTS: Sixty-nine patients (dose-finding, n = 10; confirmatory, n = 59) were enrolled. The RD of S-1 was determined as 80 mg/m2/day. Three-year event-free survival in 63 eligible patients who received the RD was 65.0% (90% confidence interval 54.1-73.9). Three-year overall, progression-free, and colostomy-free survival rates were 87.3%, 85.7%, and 76.2%, respectively; the complete response rate was 81% on central review. Common grade 3/4 acute toxicities were leukopenia (63.1%), neutropenia (40.0%), diarrhea (20.0%), radiation dermatitis (15.4%), and febrile neutropenia (3.1%). No treatment-related deaths occurred. CONCLUSIONS: Although the primary endpoint was not met, S-1/mitomycin-C chemoradiotherapy had an acceptable toxicity profile and favorable 3-year survival and could be a treatment option for locally advanced SCCA. CLINICAL TRIAL INFORMATION: jRCTs031180002.

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).

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.

Novel "resect and analysis" approach for T2 colorectal cancer with use of artificial intelligence.

BACKGROUND AND AIMS: Because of a lack of reliable preoperative prediction of lymph node involvement in early-stage T2 colorectal cancer (CRC), surgical resection is the current standard treatment. This leads to overtreatment because only 25% of T2 CRC patients turn out to have lymph node metastasis (LNM). We assessed a novel artificial intelligence (AI) system to predict LNM in T2 CRC to ascertain patients who can be safely treated with less-invasive endoscopic resection such as endoscopic full-thickness resection and do not need surgery. METHODS: We included 511 consecutive patients who had surgical resection with T2 CRC from 2001 to 2016; 411 patients (2001-2014) were used as a training set for the random forest-based AI prediction tool, and 100 patients (2014-2016) were used to validate the AI tool performance. The AI algorithm included 8 clinicopathologic variables (patient age and sex, tumor size and location, lymphatic invasion, vascular invasion, histologic differentiation, and serum carcinoembryonic antigen level) and predicted the likelihood of LNM by receiver-operating characteristics using area under the curve (AUC) estimates. RESULTS: Rates of LNM in the training and validation datasets were 26% (106/411) and 28% (28/100), respectively. The AUC of the AI algorithm for the validation cohort was .93. With 96% sensitivity (95% confidence interval, 90%-99%), specificity was 88% (95% confidence interval, 80%-94%). In this case, 64% of patients could avoid surgery, whereas 1.6% of patients with LNM would lose a chance to receive surgery. CONCLUSIONS: Our proposed AI prediction model has a potential to reduce unnecessary surgery for patients with T2 CRC with very little risk. (Clinical trial registration number: UMIN 000038257.).

Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video).

BACKGROUND AND AIMS: Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists. METHODS: This was a propensity score-matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged ≥20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching-based comparison of the ADR between patients examined with and without CADe as the primary outcome. RESULTS: During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 nonexperts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively). CONCLUSIONS: The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists (Clinical trial registration number: UMIN000040677.).

Impact of non-curative endoscopic submucosal dissection on short- and long-term outcome of subsequent laparoscopic gastrectomy for pT1 gastric cancer.

BACKGROUND: The feasibility and oncological safety of non-curative endoscopic submucosal dissection (ESD) prior to additional gastrectomy for early gastric cancer (EGC) are still unclear. The aim of this study was to evaluate the impact of non-curative ESD on short- and long-term outcomes of subsequent laparoscopic gastrectomy (LG) for pathological T1 (pT1) EGC. METHODS: We retrospectively investigated 422 patients who underwent LG for pT1 EGC between January 2007 and December 2017 at our center. Eighty-five of these patients underwent ESD with curative intent before surgery. Using propensity-score matching for sex, age, body mass index, American society of anesthesiologists score, history of previous abdominal surgery, tumor location, mucosal/submucosal infiltration, histology, lymph node metastasis, extent of lymph node dissection, operative method, lymphatic invasion, and venous invasion, the clinicopathologic and survival data of these patients were compared. RESULTS: The median follow-up period was 60 (range 2-168) months. Using propensity-score matching from a total of 422 patients, 75 patients were selected in the Non-ESD and the ESD cohorts each. There were no significant differences in terms of characteristics and clinicopathological findings between the two groups. Furthermore, there were no significant differences in postoperative morbidity (13.3% vs. 17.3%; P = 0.497) and mortality (1.3% vs. 0%; P = 0.316). Both the 5-year overall survival ratio (88.8% vs. 86.9%; P = 0.757) and 5-year disease-specific survival ratio (97.1% vs. 98.4%; P = 0.333) were similar in the two groups. CONCLUSION: Short- and long-term outcomes of LG in patients with pT1 EGC are not related to preoperative ESD history. Even for non-curative resections, ESD prior to surgery is feasible in terms of oncological and surgical outcomes in pT1 EGC.

A technique for constructing diverting loop ileostomy to prevent outlet obstruction after rectal resection and total colectomy: a retrospective single-center study.

PURPOSE: Preventing outlet obstruction associated with a diverting stoma is important. Previously, we constructed a diverting loop ileostomy with the proximal limb of the small intestine on the caudal side, namely the oral inferior (OI) method. However, to address the issue of twisting and stenosis of the small intestine, we recently constructed a diverting loop ileostomy with the proximal limb on the cranial side, namely the oral superior (OS) method. We compared the incidence of outlet obstruction between the two methods. METHODS: The subjects of this retrospective study were 133 patients who underwent colorectal resection or total colectomy, with D2 or more lymph node dissection and diverting loop ileostomy construction, between April, 2001 and December, 2018, at our hospital. The OI method was performed in 54 patients and the OS method was performed in 79 patients. RESULTS: In the OS group, a history of laparotomy, neoadjuvant therapy, clinical stage III, and the use of anti-adhesion materials were more common, whereas blood loss and the incidence of outlet obstruction were significantly lower. Multivariate analysis identified only OS placement as a significant factor for reducing the incidence of outlet obstruction. CONCLUSION: When constructing a diverting loop ileostomy, placing the proximal limb on the cranial side is important.

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.

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.

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.

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).

Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video).

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible. METHODS: To develop the deep learning-based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps' locations with bounding boxes. RESULTS: A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively. CONCLUSIONS: Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.).

Current status and future perspective on artificial intelligence for lower endoscopy.

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms.

BACKGROUND & AIMS: Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. However, it is difficult for community-based non-experts to obtain sufficient diagnostic performance. Artificial intelligence-based systems have been developed to analyze endoscopic images; they identify neoplasms with high accuracy and low interobserver variation. We performed a multi-center study to determine the diagnostic accuracy of EndoBRAIN, an artificial intelligence-based system that analyzes cell nuclei, crypt structure, and microvessels in endoscopic images, in identification of colon neoplasms. METHODS: The EndoBRAIN system was initially trained using 69,142 endocytoscopic images, taken at 520-fold magnification, from patients with colorectal polyps who underwent endoscopy at 5 academic centers in Japan from October 2017 through March 2018. We performed a retrospective comparative analysis of the diagnostic performance of EndoBRAIN vs that of 30 endoscopists (20 trainees and 10 experts); the endoscopists assessed images from 100 cases produced via white-light microscopy, endocytoscopy with methylene blue staining, and endocytoscopy with narrow-band imaging. EndoBRAIN was used to assess endocytoscopic, but not white-light, images. The primary outcome was the accuracy of EndoBrain in distinguishing neoplasms from non-neoplasms, compared with that of endoscopists, using findings from pathology analysis as the reference standard. RESULTS: In analysis of stained endocytoscopic images, EndoBRAIN identified colon lesions with 96.9% sensitivity (95% CI, 95.8%-97.8%), 100% specificity (95% CI, 99.6%-100%), 98% accuracy (95% CI, 97.3%-98.6%), a 100% positive-predictive value (95% CI, 99.8%-100%), and a 94.6% negative-predictive (95% CI, 92.7%-96.1%); these values were all significantly greater than those of the endoscopy trainees and experts. In analysis of narrow-band images, EndoBRAIN distinguished neoplastic from non-neoplastic lesions with 96.9% sensitivity (95% CI, 95.8-97.8), 94.3% specificity (95% CI, 92.3-95.9), 96.0% accuracy (95% CI, 95.1-96.8), a 96.9% positive-predictive value, (95% CI, 95.8-97.8), and a 94.3% negative-predictive value (95% CI, 92.3-95.9); these values were all significantly higher than those of the endoscopy trainees, sensitivity and negative-predictive value were significantly higher but the other values are comparable to those of the experts. CONCLUSIONS: EndoBRAIN accurately differentiated neoplastic from non-neoplastic lesions in stained endocytoscopic images and endocytoscopic narrow-band images, when pathology findings were used as the standard. This technology has been authorized for clinical use by the Japanese regulatory agency and should be used in endoscopic evaluation of small polyps more widespread clinical settings. UMIN clinical trial no: UMIN000028843.

Endocytoscopy for the differential diagnosis of colorectal low-grade adenoma: a novel possibility for the "resect and discard" strategy.

BACKGROUND AND AIMS: Endocytoscopy, a next-generation endoscopic system, facilitates observation at a maximum magnification of ×520. To our knowledge, no study has reported high-precision diagnosis of colorectal low-grade adenoma, endoscopically. We aimed to reveal which endocytoscopic findings may be used as indicators of low-grade adenoma and to assess whether a "resect and discard" strategy using endocytoscopy is feasible. METHODS: Lesions diagnosable with endocytoscopy were examined retrospectively between May 2005 and July 2017. A normal pit-like structure in endocytoscopic images was considered a normal pit (NP) sign and used as an indicator of low-grade adenoma. The primary outcome was the diagnostic accuracy of the NP sign for low-grade adenoma. We evaluated agreement rates between endocytoscopic and pathologic diagnosis for surveillance colonoscopy interval recommendation (SCIR) and performed a validation study to verify the agreement rates. RESULTS: For 748 lesions in 573 cases diagnosed as colorectal adenoma using endocytoscopy, the results were as follows: sensitivity of the NP sign for low-grade adenoma, 85.0%; specificity, 90.7%; positive predictive value, 96.6%; negative predictive value, 66.1%; accuracy, 86.4%; and positive likelihood ratio, 9.2 (P < .001). The agreement rate between endocytoscopic and pathologic diagnosis for SCIR was 94.4% (95% confidence interval [CI], 92.2%-96.1%; P < .001) under United States guidelines and 96.3% (95% CI, 94.5%-97.7%; P < .001) under European Union guidelines. All inter- and intraobserver agreement rates for expert and nonexpert endoscopists had κ values ≥0.8 except one nonexpert pair. CONCLUSIONS: Endocytoscopy is an effective modality in determining the differential diagnosis of colorectal low-grade adenoma. (University Hospital Medical Information Network Clinical Trials database registration number: UMIN000018623.).

Treatment policy for colonic laterally spreading tumors based on each clinicopathologic feature of 4 subtypes: actual status of pseudo-depressed type.

BACKGROUND AND AIMS: Laterally spreading tumors (LSTs) are originally classified into 4 subtypes. Pseudo-depressed nongranular types (LSTs-NG-PD) are gaining attention because of their high malignancy potential. Previous studies discussed the classification of nongranular (LST-NG) and granular types (LST-G); however, the actual condition or indication for endoscopic treatment of LSTs-NG-PD remains unclear. We aimed to compare the submucosal invasion pattern of LSTs-NG-PD with the other 3 subtypes. METHODS: A total of 22,987 colonic neoplasms including 2822 LSTs were resected endoscopically or surgically at Showa University Northern Yokohama Hospital. In these LSTs, 322 (11.4%) were submucosal invasive carcinomas. We retrospectively evaluated the clinicopathologic features of LSTs divided into 4 subtypes. In 267 LSTs resected en bloc, their submucosal invasion site was further evaluated. RESULTS: The frequency of LSTs in all colonic neoplasms was significantly higher in women (14.9%) than in men (11.0%). Rates of submucosal invasive carcinoma were .8% in the granular homogenous type (LSTs-G-H), 15.2% in the granular nodular mixed type (LSTs-G-M), 8.0% in the nongranular flat elevated type (LSTs-NG-F), and 42.5% in LSTs-NG-PD. Tumor size was associated with submucosal invasion rate in LSTs-NG-F and LSTs-NG-PD (P < .001). The multifocal invasion rate of LSTs-NG-PD (46.9%) was significantly higher than that of LSTs-G-M (7.9%) or LSTs-NG-F (11.8%). In LSTs-NG-PD, the invasion was significantly deeper (≥1000 µm) if observed in 1 site. CONCLUSIONS: For LSTs-G-M and LSTs-NG-F that may have invaded the submucosa, en bloc resection could be considered. Considering that LSTs-NG-PD had a higher submucosal invasion rate, more multifocal invasive nature, and deeper invasion tendency, regardless if invasion was only observed in 1 site, than LSTs-NG-F, we should endoscopically distinguish LSTs-NG-PD from LSTs-NG-F and strictly adopt en bloc resection by endoscopic submucosal dissection or surgery for LSTs-NG-PD. (Clinical trial registration number: UMIN 000020261.).

Efficacy of screening using annual fecal immunochemical test alone versus combined with one-time colonoscopy in reducing colorectal cancer mortality: the Akita Japan population-based colonoscopy screening trial (Akita pop-colon trial).

PURPOSE: Screening programs using fecal occult blood testing help reduce mortality from colorectal cancer (CRC). Colonoscopy and colonoscopy combined with fecal occult blood testing are considered alternatives with higher sensitivity than fecal tests; however, to our knowledge, randomized controlled trials (RCTs) providing such evidence have not been reported. Therefore, this study aimed to compare screening using the fecal immunochemical test (FIT) combined with colonoscopy and FIT alone to evaluate the efficacy of colonoscopy screening in reducing CRC mortality. METHODS: This multicenter, prospective, randomized, controlled study included average-risk individuals for CRC living in the study areas and aged 40-74 years. The exclusion criteria were history of CRC, hereditary non-polyposis CRC, familial adenomatous polyposis, inflammatory bowel diseases, history of cancer other than CRC within the past 5 years, and not expected to survive from comorbid illness. The intervention group underwent one-time colonoscopy and annual FIT, while the control group underwent annual FIT. The primary endpoint was mortality from CRC, while the secondary endpoints were cumulative incidence of invasive CRC, advanced CRC (invasion into the muscle layer or deeper), invasive cancer and screening sensitivities and specificities of invasive CRC, whole CRC, advanced neoplasia, and prevalence of adverse events. The intervention and control groups comprised 4876 and 4875 participants, respectively. CONCLUSION: This explanatory RCT evaluated the efficacy of colonoscopy screening by valid statistical inference based on randomization. Data on adverse events from this kind of screening are necessary when considering implementation of future screening programs. TRIAL REGISTRATION: UMIN Clinical Trials Registry, number UMIN000001980.