T1 mapping-based multi-parametric MRI for subtyping and differentiation grading of non-small cell lung cancer

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and enables multi-parametric assessment of tumor biology. Longitudinal relaxation time (T1) mapping has emerged as a quantitative method capable of measuring the intrinsic T1 value of tissues, reflecting microscopic structural and compositional changes in the tumor microenvironment. This study aimed to evaluate the utility of magnetic resonance T1 mapping, alone and in combination with diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), in differentiating histologic subtypes and assessing tumor differentiation in non-small cell lung cancer (NSCLC).

A total of 76 patients with pathologically confirmed NSCLC [48 adenocarcinoma (AD), 28 squamous cell carcinoma (SCC)] were prospectively enrolled. Patients were further stratified into poorly differentiated (n=32) and moderately/highly differentiated (n=44) groups. All underwent conventional MRI, DWI, DCE-MRI, and native/post-contrast T1 mapping. Quantitative parameters included apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, T1_pre, T1_post, absolute T1 reduction (T1_d), and percentage T1 reduction (T1_d%). For parameters showing statistically significant differences between groups, receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance. The area under the curve (AUC), optimal cutoff values, sensitivity, specificity, and Youden index were calculated.

The agreement between the two readers was reasonably good with intraclass coefficient (ICC) values of 0.938 for T1_pre, 0.922 for T1_post, and 0.814 for ADC. AD demonstrated significantly higher ADC values (1,159.01 vs. 1,041.75)×10^-6 mm²/s and lower T1_pre (1,440 vs. 1,576.83) ms, T1_post (549.07 vs. 607.44) ms, and T1_d (890.93 vs. 969.39) ms values compared with SCC (P<0.05). The four-parameter model (ADC + T1_pre + T1_post + T1_d) achieved the highest performance for differentiating AD from SCC (AUC =0.805, with 75% sensitivity and 79.2% specificity). Poorly differentiated tumors showed significantly lower ADC (985.69 vs. 1,210.44)×10^-6 mm²/s and higher T1_pre (1,553.4 vs. 1,444.61) ms values than moderately/highly differentiated tumors (P<0.05), with the combination of ADC + T1_pre yielding the best diagnostic accuracy (AUC =0.866, with 77.3% sensitivity and 84.4% specificity). No DCE parameters showed significant differences between groups (All P>0.05).

Multi-parametric MRI centered on T1 mapping, particularly when combined with ADC, provides a reproducible and non-invasive tool for subtyping and grading NSCLC, underscoring its potential as a clinically useful imaging biomarker.