# Beyond Morphology: Quantitative MR Relaxometry in Pulmonary Lesion Classification

## TL;DR

This study shows that MR relaxometry can accurately tell if lung nodules are benign or malignant without using radiation or invasive methods.

## Contribution

The study introduces MR relaxometry as a non-invasive, radiation-free method for classifying lung lesions based on T1 and T2 relaxation times.

## Key findings

- Benign lesions had high T2 and low T1 values, while malignant lesions had low T2 and high T1 values.
- Binary classification using T1 and T2 achieved 95.7% accuracy in distinguishing benign from malignant lesions.
- Malignant subtypes could not be reliably distinguished using the same method.

## Abstract

Lung nodules are common and often difficult to classify. Many patients undergo repeated computed tomography (CT), positron emission tomography (PET), or biopsies, all of which have limitations and involve radiation or invasiveness. We investigated whether magnetic resonance relaxometry, which involves taking quantitative measurements of tissue relaxation times (T1 and T2), could help distinguish between benign and malignant lesions. In this prospective study of 64 patients, benign lesions and cancers exhibited distinct relaxation patterns. A classification approach using only T1 and T2 values accurately separated benign and malignant lesions; however, it could not reliably distinguish detailed cancer subtypes. This radiation-free, noninvasive technique may support diagnostic confidence and follow-up decisions.

Background/Objectives: Lung nodules present a common diagnostic challenge, particularly when benign and malignant lesions exhibit similar imaging characteristics. Standard evaluation relies on computed tomography (CT), positron emission tomography (PET), or biopsy, all of which have limitations. Quantitative magnetic resonance (MR) relaxometry using native longitudinal relaxation time (T1) and transverse relaxation time (T2) mapping offers a radiation-free alternative reflecting tissue-specific differences. Methods: This prospective, single-center study included 64 patients with 76 histologically or radiologically confirmed lung lesions (25 primary lung cancers, 28 metastases, 9 granulomas, and 14 pneumonic infiltrates). The patients underwent T1 and T2 mapping at 3T. Two independent readers quantified the mean values for each lesion. The pre-specified primary endpoints were (1) benign versus malignant and (2) primary lung cancer versus pulmonary metastases. Results: Significant differences in T1 and T2 values were observed across lesion types. Benign lesions exhibited high T2 values (mean 213.6 ms) and low T1 values (mean 836.6 ms), whereas malignant tumors exhibited lower T2 values (~77–78 ms) and higher T1 values (~1460–1504 ms, p < 0.001). Binary classification yielded 95.7% accuracy (sensitivity 93.8% for malignant, specificity 100% for benign) in an internal 70/30 hold-out validation (no external dataset), with consistent performance confirmed by patient-level and nested cross-validation (balanced accuracy ≈ 0.92–0.94). However, malignant subtypes could not be reliably distinguished (p > 0.05), and multiclass accuracy was 60.9%. Conclusions: Quantitative MR relaxometry allows accurate, radiation-free differentiation of benign and malignant lung lesions and may help reduce unnecessary invasive procedures.