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Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response

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Abstract

Purpose

To investigate the effects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiotherapy (CRT), and its accuracy in diagnosing pathological complete responses (pCR).

Methods

We included 39 patients (men: women, 21:18; mean age ± standard deviation, 59.1 ± 9.7 years) with mid-to-lower rectal cancer who underwent a long-course of CRT and high-resolution rectal MRIs between January 2020 and April 2021. Axial T2WI was reconstructed using the conventional method (MRIconv) and DLR with two different noise reduction factors (MRIDLR30 and MRIDLR50). The signal-to-noise ratio (SNR) of the tumor was measured. Two experienced radiologists independently made a blind assessment of the complete response on MRI. The sensitivity and specificity for pCR were analyzed using a multivariable logistic regression analysis with generalized estimating equations.

Results

Thirty-four patients did not have a pCR whereas five (12.8%) had pCR. Compared with the SNR of MRIconv (mean ± SD, 7.94 ± 1.92), MRIDLR30 and MRIDLR50 showed higher SNR (9.44 ± 2.31 and 11.83 ± 3.07, respectively) (p < 0.001). Compared to MRIconv, MRIDLR30 and MRIDLR50 showed significantly higher specificity values (p < 0.036) while the sensitivity values were not significantly different (p > 0.301). The sensitivity and specificity for pCR were 48.9% and 80.8% for MRIconv; 48.9% and 88.2% for MRIDLR30; and 38.8% and 86.7% for MRIDLR50, respectively.

Conclusion

DLR produced MR images with higher resolution and SNR. The specificity of MRI for identification of pCR was significantly higher with DLR than with conventional MRI.

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Acknowledgements

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Author notes

  1. Chul-min Lee

    Present address: Department of Radiology, Hanyang University Medical Center, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Bona Kim, Chul-min Lee, Jong Keon Jang & Ah Young Kim

  2. Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Jihun Kim

  3. Division of Colon and Rectal Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Seok-Byung Lim

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Contributions

Conceptualization: BK, C-mL, JKJ, Formal analysis: JKJ, Investigation: BK, C-mL, Methodology: BK, JKJ, Project administration: JKJ, Resources: JK, S-BL, AYK, Supervision: JKJ, JK, S-BL, AYK, Visualization: BK, JKJ, Writing-original draft: BK, Writing-review & editing: C-mL, JKJ, JK, S-BL, AYK, Approval of final manuscript: All authors.

Corresponding author

Correspondence to Jong Keon Jang.

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This retrospective study was approved by the institutional review board in our center, and the requirement for written informed consent was waived.

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Kim, B., Lee, Cm., Jang, J.K. et al. Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response. Abdom Radiol 48, 201–210 (2023). https://doi.org/10.1007/s00261-022-03701-3

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