Automated deep learning-assisted early detection of radiation-induced temporal lobe injury on MRI: a multicenter retrospective analysis.

OBJECTIVES

To evaluate the benefits of an automated deep learning-based tool (RTLI-DM) for early detection of radiation-induced temporal lobe injury (RTLI) on MRI.

MATERIALS AND

METHODS

A total of 396 RTLI and 3181 non-RTLI patients were retrospectively included from a cohort of 6483 NPC patients who underwent radiotherapy and MRI follow-ups at four hospitals from January 2010 to June 2021. We developed and validated an automated RTLI-DM using the initial abnormal MRI of RTLI patients and the final MRI of non-RTLI patients. The RTLI-DM consists of a bilateral temporal lobe segmentation model based on Unet++ and a diagnostic model based on a modified DenseNet-121 network. The multi-reader study was conducted to compare the performance of four radiologists (two trainees, and two experienced readers) in interpreting images without and with RTLI-DM assistance. Sensitivity, specificity, false positives (FPs), and false negatives (FNs) were compared between two reading sessions.

RESULTS

Assisted reading with RTLI-DM significantly improve the sensitivity (60.5% [95% CI: 53.6-67.0%] vs 83.5% [95% CI: 77.7-88.0%]; 79.5% [95% CI: 73.3-84.5%] vs 91.5% [95% CI: 86.7-94.7%]) and FNs (0.395 vs 0.165; 0.205 vs 0.085) for both trainees and experienced readers, although there is a slight decrease in specificity (99.0% [95% CI: 96.2-100%] vs 93.0% [95% CI: 88.5-95.9%]) and increase in FPs (0.01 vs 0.07) for trainees (p < 0.05). The averaged time for assisted reading is reduced by 29 s, compared with unassisted reading.

CONCLUSION

For early detection of RTLI on MRI, the RTLI-DM assistant significantly improved the diagnostic performance of radiologists while reducing the reading time.

KEY POINTS: Question RTLI is usually asymptomatic in its initial stage, which contributes to the challenges of early diagnosis. Findings The effectiveness of the RTLI detection model (RTLI-DM) as a diagnostic aid is evident, particularly in enhancing the detection of subtle lesions. Clinical relevance The RTLI-DM can significantly improve radiologists' diagnostic efficiency and accuracy in the early detection of RTLI in clinical workflows, but still requires validation when in clinical applications.