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Erschienen in:
17.02.2023 | Original Article
Predicting the prognosis of lower rectal cancer using preoperative magnetic resonance imaging with artificial intelligence
Erschienen in: Techniques in Coloproctology | Ausgabe 8/2023
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Background
There are various preoperative treatments that are useful for controlling local or distant metastases in lower rectal cancer. For planning perioperative management, preoperative stratification of optimal treatment strategies for each case is required. However, a stratification method has not yet been established. Therefore, we attempted to predict the prognosis of lower rectal cancer using preoperative magnetic resonance imaging (MRI) with artificial intelligence (AI).
Methods
This study included 54 patients [male:female ratio was 37:17, median age 70 years (range 49–107 years)] with lower rectal cancer who could be curatively resected without preoperative treatment at Tokyo Medical University Hospital from January 2010 to February 2017. In total, 878 preoperative T2 MRIs were analyzed. The primary endpoint was the presence or absence of recurrence, which was evaluated using the area under the receiver operating characteristic curve. The secondary endpoint was recurrence-free survival (RFS), which was evaluated using the Kaplan–Meier curve of the predicted recurrence (AI stage 1) and predicted recurrence-free (AI stage 0) groups.
Results
For recurrence prediction, the area under the curve (AUC) values for learning and test cases were 0.748 and 0.757, respectively. For prediction of recurrence in each case, the AUC values were 0.740 and 0.875, respectively. The 5-year RFS rates, according to the postoperative pathologic stage for all patients, were 100%, 64%, and 50% for stages 1, 2, and 3, respectively (p = 0.107). The 5-year RFS rates for AI stages 0 and 1 were 97% and 10%, respectively (p < 0.001 significant difference).
Conclusions
We developed a prognostic model using AI and preoperative MRI images of patients with lower rectal cancer who had not undergone preoperative treatment, and the model could be useful in comparison with pathological classification.