Noninvasive prediction of microsatellite instability in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging radiomics.

Background: Colorectal cancer stands among the most prevalent digestive system malignancies. The microsatellite instability (MSI) profile plays a crucial role in determining patient outcomes and therapy responsiveness. Traditional MSI evaluation methods require invasive tissue sampling, are lengthy, and can be compromised by intratumoral heterogeneity.

Aim: To establish a non-invasive technique utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and machine learning algorithms to determine MSI status in patients with intermediate-stage rectal cancer.

Methods: This retrospective analysis examined 120 individuals diagnosed with stage II/III rectal cancer [30 MSI-high (MSI-H) and 90 microsatellite stability (MSS)/MSI-low (MSI-L) cases]. We extracted comprehensive radiomics signatures from DCE-MRI scans, encompassing textural parameters that reflect tumor heterogeneity, shape-based metrics, and histogram-derived statistical values. Least absolute shrinkage and selection operator regression facilitated feature selection, while predictive frameworks were developed using various classification algorithms (logistic regression, support vector machine, and random forest). Performance assessment utilized separate training and validation cohorts.

Results: Our investigation uncovered distinctive imaging characteristics between MSI-H and MSS/MSI-L neoplasms. MSI-H tumors exhibited significantly elevated entropy values (7.84 ± 0.92 vs 6.39 ± 0.83, P = 0.004), enhanced surface-to-volume proportions (0.72 ± 0.14 vs 0.58 ± 0.11, P = 0.008), and heightened signal intensity variation (3642 ± 782 vs 2815 ± 645, P = 0.007). The random forest model demonstrated superior classification capability with area under the curves (AUCs) of 0.891 and 0.896 across training and validation datasets, respectively. An integrated approach combining radiomics with clinical parameters further enhanced performance metrics (AUC 0.923 and 0.914), achieving 88.5% sensitivity alongside 87.2% specificity.

Conclusion: DCE-MRI radiomics features interpreted through machine learning frameworks offer an effective strategy for MSI status assessment in intermediate-stage rectal cancer.