BACKGROUND

Differentiating inflammatory from malignant lung lesions continues to be challenging in clinical routine, frequently requiring invasive methods like biopsy. Therefore, we aimed to investigate if inflammatory and malignant pulmonary lesions could be distinguished noninvasively using radiomics of apparent diffusion coefficient (ADC) maps and radiomic feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) magnetic resonance imaging (MRI) of the lung.

METHODS

Fifty-four patients with an unclear pulmonary lesion on computed tomography (CT) were prospectively included and examined by 3T MRI with T2w and diffusion-weighted sequences (b values of 50 and 800). ADC maps were calculated automatically. All patients underwent biopsy or bronchoalveolar lavage (BAL). Sixteen patients were excluded (e.g., motion artifacts), leaving 19 patients each with malignant and inflammatory pulmonary lesions. Target lesions were defined by biopsy or as the largest lesion (BAL-based pathogen detection), and two readers placed volumes of interest (VOIs) around the lesions on T2w images and ADC maps. One hundred and seven features were conventionally extracted from the ADC maps using PyRadiomics. T2w images were converted to 107 parametric feature maps per patient using a PyRadiomics-based, pretested software tool developed by our group. VOIs were copied from T2w images to T2 maps for feature quantification. Features were tested for significant differences using the Mann-Whitney U-test. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and interreader agreement by intraclass correlation coefficients (ICCs).

RESULTS

Fifty-eight features derived from ADC maps differed significantly between malignant and inflammatory pulmonary lesions, with areas under the curve (AUCs) >0.90 for 5 and >0.80 for 27 features, compared with 67 features from T2 maps (5 features with AUCs >0.80). ICCs were excellent throughout.

CONCLUSIONS

ADC and T2 maps differentiate inflammatory and malignant pulmonary lesions with outstanding (ADC) and excellent (T2w derived feature maps) diagnostic performance. MRI could thus guide the further diagnostic workup and a timely initiation of the appropriate therapy.