Browsing by Author "Antonelli, Giulio"

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    Combination of Mucosa-Exposure Device and Computer-Aided Detection for Adenoma Detection During Colonoscopy: A Randomized Trial
    (Elsevier, 2023-07) Spadaccini, Marco; Hassan, Cesare; Rondonotti, Emanuele; Antonelli, Giulio; Andrisani, Gianluca; Lollo, Gianluca; Auriemma, Francesco; Iacopini, Federico; Facciorusso, Antonio; Maselli, Roberta; Fugazza, Alessandro; Bambina Bergna, Irene Maria; Cereatti, Fabrizio; Mangiavillano, Benedetto; Radaelli, Franco; Di Matteo, Francesco; Gross, Seth A.; Sharma, Prateek; Mori, Yuichi; Bretthauer, Michael; Rex, Douglas K.; Repici, Alessandro; Medicine, School of Medicine
    Background & Aims Both computer-aided detection (CADe)-assisted and Endocuff-assisted colonoscopy have been found to increase adenoma detection. We investigated the performance of the combination of the 2 tools compared with CADe-assisted colonoscopy alone to detect colorectal neoplasias during colonoscopy in a multicenter randomized trial. Methods Men and women undergoing colonoscopy for colorectal cancer screening, polyp surveillance, or clincial indications at 6 centers in Italy and Switzerland were enrolled. Patients were assigned (1:1) to colonoscopy with the combinations of CADe (GI-Genius; Medtronic) and a mucosal exposure device (Endocuff Vision [ECV]; Olympus) or to CADe-assisted colonoscopy alone (control group). All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was adenoma detection rate (percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, advanced adenomas and serrated lesions detection rate, the rate of unnecessary polypectomies (polyp resection without histologically proven adenomas), and withdrawal time. Results From July 1, 2021 to May 31, 2022, there were 1316 subjects randomized and eligible for analysis; 660 to the ECV group, 656 to the control group). The adenoma detection rate was significantly higher in the ECV group (49.6%) than in the control group (44.0%) (relative risk, 1.12; 95% CI, 1.00–1.26; P = .04). Adenomas detected per colonoscopy were significantly higher in the ECV group (mean ± SD, 0.94 ± 0.54) than in the control group (0.74 ± 0.21) (incidence rate ratio, 1.26; 95% CI, 1.04–1.54; P = .02). The 2 groups did not differ in term of detection of advanced adenomas and serrated lesions. There was no significant difference between groups in mean ± SD withdrawal time (9.01 ± 2.48 seconds for the ECV group vs 8.96 ± 2.24 seconds for controls; P = .69) or proportion of subjects undergoing unnecessary polypectomies (relative risk, 0.89; 95% CI, 0.69–1.14; P = .38). Conclusions The combination of CADe and ECV during colonoscopy increases adenoma detection rate and adenomas detected per colonoscopy without increasing withdrawal time compared with CADe alone.
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    Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study
    (Elsevier, 2022) Areia, Miguel; Mori, Yuichi; Correale, Loredana; Repici, Alessandro; Bretthauer, Michael; Sharma, Prateek; Taveira, Filipe; Spadaccini, Marco; Antonelli, Giulio; Ebigbo, Alanna; Kudo, Shin-ei; Arribas, Julia; Barua, Ishita; Kaminski, Michal F.; Messmann, Helmut; Rex, Douglas K.; Dinis-Ribeiro, Mário; Hassan, Cesare; Medicine, School of Medicine
    Background: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. Methods: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. Findings: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. Interpretation: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality.
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    Dye-based chromoendoscopy for the detection of colorectal neoplasia: meta-analysis of randomized controlled trials.
    (Elsevier, 2022) Antonelli, Giulio; Correale, Loredana; Spadaccini, Marco; Maselli, Roberta; Bhandari, Pradeep; Bisschops, Raf; Cereatti, Fabrizio; Dekker, Evelien; East, James E.; Iacopini, Federico; Jover, Rodrigo; Kiesslich, Ralph; Pellise, Maria; Sharma, Prateek; Rex, Douglas K.; Repici, Alessandro; Hassan, Cesare; Medicine, School of Medicine
    Background and Aims Dye-Based chromoendoscopy (DBC) could be effective in increasing adenoma detection rate (ADR) in patients undergoing colonoscopy, but the technique is time-consuming and its uptake is limited. We aimed to assess the effect of DBC on ADR based on available randomized controlled trials (RCTs). Methods Four databases were searched up to April 2022, for RCTs comparing DBC with conventional colonoscopy (CC) in terms of ADR, advanced ADR, and sessile serrated adenoma (SSA) detection rates as well as the mean number of adenomas per patient (MAP) and non-neoplastic lesions. Relative risk (RR) for dichotomous outcomes and mean difference (MD) for continuous outcomes were calculated using random-effect models. I2 test was used for quantifying heterogeneity. Risk of bias was evaluated with Cochrane tool. Results Overall, 10 RCTs (5,334 patients) were included. Indication for colonoscopy was screening or surveillance (3 studies), and mixed (7 studies). Pooled ADR was higher in the DBC group vs. CC group, (48.1%[41.4-54.8%] vs 39.3%[33.5-46.4%]; RR=1.20[1.11- 1.29]), with low heterogeneity (I2=29%). This effect was consistent for advanced ADR (RR=1.21[1.03-1.42] I2=0.0%), and for SSA (6.1% vs 3.5%; RR, 1.68; [1.15-2.47]; I2=9.8%), as well as for MAP (MD 0.24 [0.17–0.31]) overall and in the right colon (MD, 0.28 [0.14-0.43]. High-definition white-light colonoscopy (HDWL) was more effective than standard white-light colonoscopy (SDWL) for detection of adenomas (51.6% 95% CI:47.1-56.1% vs. 34.2%; 95% CI:28.5-40.4%) and DBC (59.1%; 95% CI:54.7-63.3%) was more effective than HDWL (RR=1.14; 95% CI:1.06-1.23, I2= 0.0%]. Conclusions Meta-analysis of RCTs showed that DBC increases key quality parameters in colonoscopy, supporting its use in every-day clinical practice.
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    Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials
    (Elsevier, 2023) Mori, Yuichi; Wang, Pu; Løberg, Magnus; Misawa, Masashi; Repici, Alessandro; Spadaccini, Marco; Correale, Loredana; Antonelli, Giulio; Yu, Honggang; Gong, Dexin; Ishiyama, Misaki; Kudo, Shin-ei; Kamba, Shunsuke; Sumiyama, Kazuki; Saito, Yutaka; Nishino, Haruo; Liu, Peixi; Glissen Brown, Jeremy R.; Mansour, Nabil M.; Gross, Seth A.; Kalager, Mette; Bretthauer, Michael; Rex, Douglas K.; Sharma, Prateek; Berzin, Tyler M.; Hassan, Cesare; Medicine, School of Medicine
    Background and aims: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. Methods: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. Results: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). Conclusions: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.
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    Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis
    (Elsevier, 2020) Hassan, Cesare; Spadaccini, Marco; Iannone, Andrea; Maselli, Roberta; Jovani, Manol; Chandrasekar, Viveksandeep Thoguluva; Antonelli, Giulio; Yu, Honggang; Areia, Miguel; Dinis-Ribeiro, Mario; Bhandari, Pradeep; Sharma, Prateek; Rex, Douglas K.; Rösch, Thomas; Wallace, Michael; Repici, Alessandro; Medicine, School of Medicine
    BACKGROUND AND AIMS One fourth of colorectal neoplasia is missed at screening colonoscopy, representing the main cause of interval colorectal cancer (CRC). Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized clinical trials (RCT) reported favourable outcomes in clinical setting. Aim of this meta-analysis was to summarise available RCTs on the performance of CADe systems in colorectal neoplasia detection. METHODS We searched MEDLINE, EMBASE and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in detection of colorectal neoplasia. Primary outcome was pooled adenoma detection rate (ADR), Secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology and location, advanced APC (AAPC), as well as polyp detection rate (PDR), Polyp-per-colonoscopy (PPC), and sessile serrated lesion per colonoscopy (SPC). We calculated risk ratios (RR), performed subgroup, and sensitivity analysis, assessed heterogeneity, and publication bias. RESULTS Overall, 5 randomized controlled trials (4354 patients), were included in the final analysis. Pooled ADR was significantly higher in the CADe groups than in the control group (791/2163, 36.6% vs 558/2191, 25.2%; RR, 1.44; 95% CI, 1.27-1.62; p<0.01; I 2:42%). APC was also higher in the CADe group compared with control (1249/2163, 0.58 vs 779/2191, 0.36; RR, 1.70; 95% CI, 1.53-1.89; p<0.01;I 2:33%). APC was higher for <5 mm (RR, 1.69; 95% CI, 1.48-1.84), 6-9 mm (RR, 1.44; 95% CI, 1.19-1.75), and >10 mm adenomas (RR, 1.46; 95% CI, 1.04-2.06), as well as for proximal (RR, 1.59; 95% CI, 1.34-1.88) and distal (RR, 1.68; 95% CI, 1.50-1.88), and for flat (RR: 1.78 95% CI 1.47-2.15) and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher SPC (RR, 1.52; 95% CI,1.14-2.02), whereas a nonsignificant trend for AADR was found (RR, 1.35; 95% CI, 0.74 – 2.47; p = 0.33; I 2:69%). Level of evidence for RCTs was graded moderate. CONCLUSIONS According to available evidence, the incorporation of Artificial Intelligence as aid for detection of colorectal neoplasia results in a significant increase of the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.
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    Prophylactic Clipping After Colorectal Endoscopic Resection Prevents Bleeding of Large, Proximal Polyps: Meta-Analysis of Randomized Trials
    (Elsevier, 2020) Spadaccini, Marco; Albéniz, Eduardo; Pohl, Heiko; Maselli, Roberta; Chandrasekar, Viveksandeep Thoguluva; Correale, Loredana; Anderloni, Andrea; Carrara, Silvia; Fugazza, Alessandro; Badalamenti, Matteo; Iwatate, Mineo; Antonelli, Giulio; Enguita-Germán, Mónica; Álvarez, Marco Antonio; Sharma, Prateek; Rex, Douglas K.; Hassan, Cesare; Repici, Alessandro; Medicine, School of Medicine
    Background & Aims The benefits of prophylactic clipping to prevent bleeding after polypectomy are unclear. We conducted an updated meta-analysis of randomized trials to assess the efficacy of clipping in preventing bleeding after polypectomy, overall and according to polyp size and location. Methods We searched the Medline/PubMed, EMBASE, and Scopus databases randomized trials that compared effects of clipping vs not clipping to prevent bleeding after polypectomy. We performed a random-effects meta-analysis to generate pooled relative risks (RRs) with 95% CIs. Multilevel random-effects meta-regression analysis was used to combine data on bleeding after polypectomy and estimate associations between rates of bleeding and polyp characteristics. Results We analyzed data from 9 trials, comprising 7197 colorectal lesions (22.5% 20 mm or larger, 49.2% with proximal location). Clipping, compared with no clipping, did not significantly reduce the overall risk of post-polypectomy bleeding (2.2% with clipping vs 3.3% with no clipping; RR, 0.69; 95% CI, 0.45–1.08; P=.072). Clipping significantly reduced risk of bleeding after removal of polyps that were 20 mm or larger (4.3% had bleeding after clipping vs 7.6% had bleeding with no clipping; RR, 0.51; 95% CI, 0.33–0.78; P=.020) or that were in a proximal location (3.0% had bleeding after clipping vs 6.2% had bleeding with no clipping; RR, 0.53; 95% CI, 0.35–0.81; P<.001). In multilevel meta-regression analysis that adjusted for polyp size and location, prophylactic clipping was significantly associated with reduced risk of bleeding after removal of large proximal polyps (RR, 0.37; 95% CI, 0.22–0.61; P=.021) but not small proximal lesions (RR, 0.88; 95% CI, 0.48–1.62; P=0.581). Conclusions In a meta-analysis of randomized trials, we found that routine use of prophylactic clipping does not reduce risk of post-polypectomy bleeding, overall. However, clipping appeared to reduce bleeding after removal of large (more than 20 mm), proximal lesions.