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Purpose <p>Predicting response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is challenging. Organ preservation strategies can be offered to patients with complete clinical response. We aim to evaluate MRI-derived radiomics models in predicting complete pathological response (pCR).</p> Methods <p>Search included MEDLINE, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR) for studies published before 1st February 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess quality of included study. The research protocol was registered in PROSPERO (CRD42024512865). We calculated pooled area under the receiver operating characteristic curve (AUC) using a random-effects model. To compare AUC between subgroups the Hanley &amp; McNeil test was performed.</p> Results <p>Forty-four eligible studies (12,714 patients) were identified for inclusion in the systematic review. We selected thirty-five studies including 10,543 patients for meta-analysis. The pooled AUC for MRI radiomics predicted pCR in LARC was 0.87 (95% CI 0.84–0.89). In the subgroup analysis 3 T MRI field intensity had higher pooled AUC 0.9 (95% CI 0.87–0.94) than 1.5 T pooled AUC 0.82 (95% CI 0.80–0.83) <i>p</i> &lt; 0.001. Asian ethnicity had higher pooled AUC 0.9 (95% CI 0.87–0.93) than non-Asian pooled AUC 0.8 (95% CI 0.75–0.84) <i>p</i> &lt; 0.001.</p> Conclusion <p>We have demonstrated that 3 T MRI field intensity provides a superior predictive performance. The role of ethnicity on radiomics features needs to be explored in future studies. Further research in the field of MRI radiomics is important as accurate prediction for pCR can lead to organ preservation strategy in LARC.</p> Graphical abstract <p></p>

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MRI radiomics prediction modelling for pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Jason Rai,
  • Dinh V. C. Mai,
  • Ioanna Drami,
  • Edward T. Pring,
  • Laura E. Gould,
  • Phillip F. C. Lung,
  • Thomas Glover,
  • Joshua D. Shur,
  • Brandon Whitcher,
  • Thanos Athanasiou,
  • John T. Jenkins

摘要

Purpose

Predicting response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is challenging. Organ preservation strategies can be offered to patients with complete clinical response. We aim to evaluate MRI-derived radiomics models in predicting complete pathological response (pCR).

Methods

Search included MEDLINE, Embase and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR) for studies published before 1st February 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess quality of included study. The research protocol was registered in PROSPERO (CRD42024512865). We calculated pooled area under the receiver operating characteristic curve (AUC) using a random-effects model. To compare AUC between subgroups the Hanley & McNeil test was performed.

Results

Forty-four eligible studies (12,714 patients) were identified for inclusion in the systematic review. We selected thirty-five studies including 10,543 patients for meta-analysis. The pooled AUC for MRI radiomics predicted pCR in LARC was 0.87 (95% CI 0.84–0.89). In the subgroup analysis 3 T MRI field intensity had higher pooled AUC 0.9 (95% CI 0.87–0.94) than 1.5 T pooled AUC 0.82 (95% CI 0.80–0.83) p < 0.001. Asian ethnicity had higher pooled AUC 0.9 (95% CI 0.87–0.93) than non-Asian pooled AUC 0.8 (95% CI 0.75–0.84) p < 0.001.

Conclusion

We have demonstrated that 3 T MRI field intensity provides a superior predictive performance. The role of ethnicity on radiomics features needs to be explored in future studies. Further research in the field of MRI radiomics is important as accurate prediction for pCR can lead to organ preservation strategy in LARC.

Graphical abstract