Role of MRI and added value of diffusion-weighted and gadolinium-enhanced MRI for the diagnosis of local recurrence from rectal cancer
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To evaluate whether the addition of gadolinium-enhanced MRI and diffusion-weighted imaging (DWI) improves T2 sequence performance for the diagnosis of local recurrence (LR) from rectal cancer and to assess which approach is better at formulating this diagnosis among readers with different experience.
Forty-three patients with suspected LR underwent pelvic MRI with T2 weighted (T2) sequences, gadolinium fat-suppressed T1 weighted sequences (post-contrast T1), and DWI sequences. Three readers (expert: G, intermediate: E, resident: V) scored the likelihood of LR on T2, T2 + post-contrast T1, T2 + DWI, and T2 + post-contrast T1 + DWI.
In total, 18/43 patients had LR; on T2 images, the expert reader achieved an area under the ROC curve (AUC) of 0.916, sensitivity of 88.9%, and specificity of 76%; the intermediate reader achieved values of 0.890, 88.9%, and 48%, respectively, and the resident achieved values of 0.852, 88.9%, and 48%, respectively. DWI significantly improved the AUC value for the expert radiologist by up to 0.999 (p = 0.04), while post-contrast T1 significantly improved the AUC for the resident by up to 0.950 (p = 0.04). For the intermediate reader, both the T2 + DWI AUC and T2 + post-contrast T1 AUC were better than the T2 AUC (0.976 and 0.980, respectively), but with no statistically significant difference. No statistically significant difference was achieved by any of the three readers by comparing either the T2 + DWI AUCs to the T2 + post-contrast T1 AUCs or the AUCs of the two pairs of sequences to those of the combined three sequences.
Furthermore, using the T2 sequences alone, all of the readers achieved a fair number of “equivocal” cases: they decreased with the addition of either DWI or post-contrast T1 sequences and, for the two less experienced readers, they decreased even more with the three combined sequences.
Both DWI and T1 post-contrast MRI increased diagnostic performance for LR diagnosis compared to T2; however, no significant difference was observed by comparing the two different pairs of sequences with the three combined sequences.
KeywordsMagnetic resonance Oncologic imaging Machine learning Rectal cancer local recurrence Diffusion weighted images
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Conflict of interest
All authors declare that they have no conflict of interest.
The authors declare that the research, with consideration of the retrospective nature of the work, complied with local regulations for work with human subject data.