Computed tomography enterography-based radiomics for assessing mucosal healing in patients with small bowel Crohn's disease DOI
Hao Ding, Yuanyuan Fang, Wenjie Fan

et al.

World Journal of Gastroenterology, Journal Year: 2024, Volume and Issue: 31(3)

Published: Dec. 17, 2024

Mucosal healing (MH) is the major therapeutic target for Crohn's disease (CD). As most commonly involved intestinal segment, small bowel (SB) assessment crucial CD patients. Yet, it poses a significant challenge due to its limited accessibility through conventional endoscopic methods. To establish noninvasive radiomic model based on computed tomography enterography (CTE) MH in SBCD Seventy-three patients diagnosed with were included and divided into training cohort (n = 55) test 18). Radiomic features obtained from CTE images model. Patient demographics analysed clinical A radiomic-clinical nomogram was constructed by combining features. The diagnostic efficacy benefit evaluated via receiver operating characteristic (ROC) curve analysis decision (DCA), respectively. Of 73 enrolled, 25 achieved MH. had an area under ROC of 0.961 (95% confidence interval: 0.886-1.000) 0.958 (0.877-1.000) provided superior either or models alone, as demonstrated DCA. These results indicate that CTE-based promising imaging biomarker serves potential alternative enteroscopy

Language: Английский

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment DOI Creative Commons
Vincenza Granata, Roberta Fusco,

Maria Chiara Brunese

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(2), P. 152 - 152

Published: Jan. 9, 2024

Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in pre-surgical setting, predict tumor budding liver metastases. Methods: Patients MRI setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, 851 features extracted as median values PyRadiomics Python package. Balancing performed inter- intraclass correlation coefficients calculated between observer within reproducibility all features. A Wilcoxon–Mann–Whitney nonparametric test receiver operating characteristics (ROC) carried out. feature selection procedures performed. Linear non-logistic regression models (LRM NLRM) different learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) support vector (SVM) considered. Results: The internal training set included 49 patients 119 validation cohort consisted total 28 single lesion patients. best predictor classify original_glcm_Idn obtained T1-W VIBE sequence arterial phase an accuracy 84%; wavelet_LLH_firstorder_10Percentile portal 92%; wavelet_HHL_glcm_MaximumProbability hepatobiliary excretion 88%; wavelet_LLH_glcm_Imc1 T2-W SPACE sequences 88%. Considering linear analysis, statistically significant increase 96% weighted combination 13 radiomic from phase. Moreover, classifier KNN trained sequence, obtaining 95% AUC 0.96. reached 94%, sensitivity 86% specificity 95%. Conclusions: Machine are promising tools predicting budding. there compared feature.

Language: Английский

Citations

3
Multiparametric MRI for Staging of Bowel Inflammatory Activity in Crohn's Disease with MUSE-IVIM and DCE-MRI: A Preliminary Study DOI Creative Commons

Liangqiang Mao,

Yan Li, Bota Cui

et al.

Academic Radiology, Journal Year: 2023, Volume and Issue: 31(3), P. 880 - 888

Published: Sept. 18, 2023

•MUSE-IVIM can yield high-quality images.•MUSE-IVIM and DCE-MRI quantify the microcirculatory changes.•MUSE-IVIM stage CD lesions activity. Rationale ObjectivesTo investigate if combination of multishot diffusion imaging-based multiplexed sensitivity encoding intravoxel incoherent motion (MUSE-IVIM) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is feasible for staging Crohn's disease (CD) activity.Materials MethodsA total 65 patients were enrolled analyzed in this retrospective study. The simplified endoscopic score (SES-CD) index activity (MaRIA) used as reference. MUSE-IVIM data acquired at 3.0-T MRI scanner processed by two radiologists. Three parameters: fast apparent coefficient (ADCfast), slow (ADCslow), fractional perfusion (Fraction ADCfast), well four volume transfer constant (Ktrans), rate (Kep), extravascular extracellular fraction (Ve), plasma (Vp) generated. Intraclass correlation (ICC), non-parametric test (Kruskal–Wallis H Mann–Whitney U), logistic regression, receiver operating characteristic analysis, Delong test, Spearman's performed.ResultsAccording to SES-CD, 116 ileocolonic segments with identified as: inactive, mild, moderate severe. With multivariable regression ADCfast (p < 0.001), Fraction = 0.005), Ktrans 0.001) Kep 0.003) significant factors differentiating among three groups. Binary analyses 0.014), 0.029) independent predictors active status. ADCfast, Ktrans, K ep performed better than MaRIA 0.028), inactive was positively correlated Ve however, negatively 0.001).ConclusionThe has been demonstrated accurately inflammatory CD. To A performed. According 0.001).

Language: Английский

Citations

5
Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study DOI
Ashish P. Wasnik,

Mahmoud M Al-Hawary,

Binu Enchakalody

et al.

Clinical Imaging, Journal Year: 2024, Volume and Issue: 113, P. 110231 - 110231

Published: July 1, 2024

Language: Английский

Citations

1
Artificial Intelligence in IBD: How Will It Change Patient Management? DOI
Molly L. Stone, Ryan W. Stidham

Current Treatment Options in Gastroenterology, Journal Year: 2023, Volume and Issue: 21(4), P. 365 - 377

Published: Dec. 1, 2023

Language: Английский

Citations

2
Ultra-minimally invasive endoscopic techniques and colorectal diseases: Current status and its future DOI Creative Commons
Nalini Kanta Ghosh, Ashok Kumar

Artificial Intelligence in Gastrointestinal Endoscopy, Journal Year: 2024, Volume and Issue: 5(2)

Published: May 11, 2024

Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances colonoscope (ultrathin scope, magnetic capsule) technological gadgets (Balloon assisted third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual narrow band imaging, i-SCAN, etc. ) have made colonoscopy more comfortable efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, Besides developments diagnostic colonoscopy, therapeutic has improved manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, early colorectal cancers. The introduction of combined endo-laparoscopic surgery robotic endoscopic these interventions feasible. artificial intelligence the diagnosis management is also day by day. Hence, this article review cutting-edge principles for diseases.

Language: Английский

Citations

0
A novel computed tomography enterography radiomics combining intestinal and creeping fat features could predict surgery risk in patients with Crohn’s disease DOI
Jin-fang Du,

Fangyi Xu,

Xia Qiu

et al.

European Journal of Gastroenterology & Hepatology, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 23, 2024

Objective The objective of this study is to segment creeping fat and intestinal wall on computed tomography enterography (CTE) develop a radiomic model predict 1-year surgery risk in patients with Crohn’s disease. Methods This retrospective included 135 disease who underwent CTE between January December 2021 (training cohort) 69 June 2022 (test cohort). A total 1874 features were extracted from the respectively venous phase images, models constructed based selected using Boruta extreme gradient boosting algorithms. combined established by integrating clinical predictors models. receiver operating characteristic curve, calibration decision curve analyses used compare predictive performance Results In training test cohorts, area under (AUC) values for stratification 0.916 0.822, respectively, similar AUC 0.889 0.822. Moreover, was superior single models, showing good discrimination highest cohort: 0.963; 0.882). Addition failed significantly improve diagnostic ability. Conclusion CTE-based provided additional information model, their enables accurate prediction within 1 year CTE.

Language: Английский

Citations

0
Artificial Intelligence in Inflammatory Bowel Disease DOI
Alvin George, David T. Rubin

Gastrointestinal Endoscopy Clinics of North America, Journal Year: 2024, Volume and Issue: 35(2), P. 367 - 387

Published: Nov. 27, 2024

Language: Английский

Citations

0
Computed tomography enterography-based radiomics for assessing mucosal healing in patients with small bowel Crohn's disease DOI
Hao Ding, Yuanyuan Fang, Wenjie Fan

et al.

World Journal of Gastroenterology, Journal Year: 2024, Volume and Issue: 31(3)

Published: Dec. 17, 2024

Mucosal healing (MH) is the major therapeutic target for Crohn's disease (CD). As most commonly involved intestinal segment, small bowel (SB) assessment crucial CD patients. Yet, it poses a significant challenge due to its limited accessibility through conventional endoscopic methods. To establish noninvasive radiomic model based on computed tomography enterography (CTE) MH in SBCD Seventy-three patients diagnosed with were included and divided into training cohort (n = 55) test 18). Radiomic features obtained from CTE images model. Patient demographics analysed clinical A radiomic-clinical nomogram was constructed by combining features. The diagnostic efficacy benefit evaluated via receiver operating characteristic (ROC) curve analysis decision (DCA), respectively. Of 73 enrolled, 25 achieved MH. had an area under ROC of 0.961 (95% confidence interval: 0.886-1.000) 0.958 (0.877-1.000) provided superior either or models alone, as demonstrated DCA. These results indicate that CTE-based promising imaging biomarker serves potential alternative enteroscopy

Language: Английский

Citations

0