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Objectives <p>Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists’ therapy management.</p> Materials and methods <p>Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection. After a primary unassisted reading based on current guidelines, a second reading with access to the classification of a radiomics-based random-forest AI-model labelling each polyp as “non-adenomatous” or “adenomatous” was performed. Performance was evaluated using polyp histopathology as the reference standard.</p> Results <p>77 polyps in 59 patients comprising 118 polyp image series (47% supine position, 53% prone position) were evaluated unassisted and AI-assisted by five independent board-certified radiologists, resulting in a total of 1180 readings (subsequent polypectomy: yes or no). AI-assisted readings had higher accuracy (76% +/− 1% vs. 84% +/− 1%), sensitivity (78% +/− 6% vs. 85% +/− 1%), and specificity (73% +/− 8% vs. 82% +/− 2%) in selecting polyps eligible for polypectomy (<i>p</i> &lt; 0.001). Inter-reader agreement was improved in the AI-assisted readings (Fleiss’ kappa 0.69 vs. 0.92).</p> Conclusion <p>AI-based characterisation of colorectal polyps at CT colonography as a second reader might enable a more precise selection of polyps eligible for subsequent endoscopic resection. However, further studies are needed to confirm this finding and histopathologic polyp evaluation is still mandatory.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>This is the first study evaluating the impact of AI-based polyp classification in CT colonography on radiologists’ therapy management.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Compared with unassisted reading, AI-assisted reading had higher accuracy, sensitivity, and specificity in selecting polyps eligible for polypectomy.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Integrating an AI tool for colorectal polyp classification in CT colonography could further improve radiologists’ therapy recommendations.</i></p> Graphical Abstract <p></p>

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Effect of artificial intelligence-aided differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists’ therapy management

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月25日

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  • Sergio Grosu,
  • Matthias P. Fabritius,
  • Michael Winkelmann,
  • Daniel Puhr-Westerheide,
  • Maria Ingenerf,
  • Stefan Maurus,
  • Anno Graser,
  • Christian Schulz,
  • Thomas Knösel,
  • Clemens C. Cyran,
  • Jens Ricke,
  • Philipp M. Kazmierczak,
  • Michael Ingrisch,
  • Philipp Wesp

摘要

Objectives

Adenomatous colorectal polyps require endoscopic resection, as opposed to non-adenomatous hyperplastic colorectal polyps. This study aims to evaluate the effect of artificial intelligence (AI)-assisted differentiation of adenomatous and non-adenomatous colorectal polyps at CT colonography on radiologists’ therapy management.

Materials and methods

Five board-certified radiologists evaluated CT colonography images with colorectal polyps of all sizes and morphologies retrospectively and decided whether the depicted polyps required endoscopic resection. After a primary unassisted reading based on current guidelines, a second reading with access to the classification of a radiomics-based random-forest AI-model labelling each polyp as “non-adenomatous” or “adenomatous” was performed. Performance was evaluated using polyp histopathology as the reference standard.

Results

77 polyps in 59 patients comprising 118 polyp image series (47% supine position, 53% prone position) were evaluated unassisted and AI-assisted by five independent board-certified radiologists, resulting in a total of 1180 readings (subsequent polypectomy: yes or no). AI-assisted readings had higher accuracy (76% +/− 1% vs. 84% +/− 1%), sensitivity (78% +/− 6% vs. 85% +/− 1%), and specificity (73% +/− 8% vs. 82% +/− 2%) in selecting polyps eligible for polypectomy (p < 0.001). Inter-reader agreement was improved in the AI-assisted readings (Fleiss’ kappa 0.69 vs. 0.92).

Conclusion

AI-based characterisation of colorectal polyps at CT colonography as a second reader might enable a more precise selection of polyps eligible for subsequent endoscopic resection. However, further studies are needed to confirm this finding and histopathologic polyp evaluation is still mandatory.

Key Points

Question This is the first study evaluating the impact of AI-based polyp classification in CT colonography on radiologists’ therapy management.

Findings Compared with unassisted reading, AI-assisted reading had higher accuracy, sensitivity, and specificity in selecting polyps eligible for polypectomy.

Clinical relevance Integrating an AI tool for colorectal polyp classification in CT colonography could further improve radiologists’ therapy recommendations.

Graphical Abstract