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Multicenter Study
. 2025 Mar:184:111986.
doi: 10.1016/j.ejrad.2025.111986. Epub 2025 Feb 5.

Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?

Marina J Corines  1 , Abdalla Ibrahim  1 , Akshay Baheti  2 , Peter Gibbs  1 , Shannon P Sheedy  3 , Sonia Lee  4 , Stephanie Nougaret  5 , Randy Ernst  6 , Courtney C Moreno  7 , Elena Korngold  8 , Michael Fox  1 , Joao Miranda  9 , Sergio Carlos Nahas  10 , Iva Petkovska  1 , Junting Zheng  11 , Ramon E Sosa  1 , Natalie Gangai  1 , Binsheng Zhao  1 , Lawrence H Schwartz  1 , Natally Horvat  9 , Marc J Gollub  12
Affiliations
Multicenter Study

Can MRI radiomics distinguish residual adenocarcinoma from acellular mucin in treated rectal cancer?

Marina J Corines et al. Eur J Radiol. 2025 Mar.

Abstract

Background: We explored the use of MRI T2-weighted imaging (T2WI) radiomics to help distinguish acellular mucin from cellular mucin (residual tumor) to inform patient management.

Methods: This retrospective, multi-institutional study included consecutive patients with rectal adenocarcinoma containing mucin on restaging MRI from March 2012-January 2020. Radiologists segmented 3-mm-thick T2WI. Data were split into training (n = 122), validation (n = 40), and test (n = 42) sets. Sensitivity, specificity, PPV, NPV, and AUC of the developed radiomic models were investigated on the test set. Histopathology was the reference standard. Two radiologists independently reviewed 42 patients using a unique five-point Likert scale for the probability of cellular mucin. DeLong-Delong or McNemar's test was used to compare the AUC and diagnostic performance, respectively, of the radiomics model to that of the human readers.

Results: Of 204 patients (mean age, 56.6 ± 13.3 years; 129 men and 75 women), 39/204 (19 %) had acellular mucin whereas 165/204 (81 %) had cellular mucin. The radiomics model demonstrated a sensitivity of 90 % (27/30, 95 % CI: 74-97 %), specificity of 83 % (10/12, 95 % CI: 55-95 %), PPV of 93 % (27/29, 95 % CI: 78-98 %), NPV of 77 % (10/13, 95 % CI: 50-92 %), and AUC of 0.84 (95 % CI: 0.65-0.99), whereas the human readers showed inferior sensitivities of 47 % (14/30) (95 % CI: 28-66 %) and 50 % (15/30) (95 % CI: 31-69 %) and AUCs of 0.58 (95 % CI: 0.41-0.74) (DeLong's p = 0.06 [95 % CI: -0.55, 0.01]) and 0.73 (95 % CI: 0.57-0.88) (DeLong's p = 0.43 [95 % CI: -0.39, 0.16]).

Conclusion: The developed MRI radiomics model predicts cellular mucin post neoadjuvant chemoradiation with higher sensitivity than human readers.

Keywords: Machine learning; Magnetic resonance imaging; Mucins; Neoplasm, residual; Rectal neoplasms.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Marc J Gollub reports a relationship with GlaxoSmithKline that includes: consulting or advisory. Lawrence H Schwartz reports a relationship with Bristol Myers Squibb Co that includes: board membership and funding grants. Lawrence H Schwartz reports a relationship with Merck & Co Inc that includes: board membership. Lawrence H Schwartz reports a relationship with Regeneron Pharmaceuticals Inc that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper].

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