Imaging Lung Cancer: MRI, PET or Both?
Nina F Schwenzer1

1Dept. of Radiology, University Hospital Tübingen

Synopsis

Whereas CT is mainly used for local staging, PET/CT offers additional information about tumor metabolism and distant metastases. Although MRI is still limited in the detection of small lung nodules it offers additional functional information about diffusion and perfusion of tumor masses. In the framework of personalized medicine imaging has evolved from localizing disease towards prognostic and predictive biomarkers as well as treatment response. Hybrid modalities (PET/CT and PET/MRI) have the potential to offer a broad variety of parameters (i.e. textural parameters and multiparametric information) which can be used for further analyses such as radiomics or radiogenomics.

Nowadays, a variety of imaging modalities are available for lung cancer imaging such as CT, MRI, PET/CT and lately also the new hybrid modality PET/MRI. From a technical point of view, PET/MRI differs from PET/CT in several ways: in PET/MRI, the most commonly used technical approach integrates the PET component in the MR scanner which allows for simultaneous acquisition of PET and MRI data. This is advantageous because the co-registration of PET and MRI datasets is improved and image-based motion correction methods are achievable without the need of additional devices such as breathing belts or external markers or cameras (1, 2). Another significant difference is the attenuation correction of the PET data which is based on MRI data. This can lead to underestimations of PET quantification since bone is ignored in the attenuation correction, at least for whole body imaging (3-5). For brain imaging vendors have begun to establish so-called atlas-based methods which improve the PET quantification, but this technique is not commercially available for whole body and lung imaging up to now.

In 2015, the European Society of Radiology discussed the role of imaging in personalized medicine in a white paper (6). Here, several aspects of personalized medicine are named which are important: prevention (e.g. screening), imaging in the section of treatment (location and extent of disease, prognostic and predictive biomarkers, radiomics/radiogenomics) as well as evaluation of treatment response and personalized treatment (radiotherapy, interventional radiology). In the following, we will focus on imaging in the section of treatment. Concerning location and extent of disease in lung cancer, CT is widely used and often the first imaging modality to select the appropriate procedure. However, CT offers only limited sensitivity and specificity for identifying lymph node metastases (7) and distant metastatic disease. In contrast, PET/CT with fluorodeoxyglucose (18F-FDG) offers superior information for lymph node staging and distant metastases (8, 9). Most importantly, it could be shown that the rate of unnecessary surgery could be significantly reduced if FDG-PET was added to workup (10). MRI is the modality of choice to examine the brachial plexus in superior sulcus tumors and to identify brain and liver lesions (11-13). However, for small lung nodules < 5 mm, MRI is still limited (14, 15). The same holds true for FDG-PET/MRI concerning the detection of small lung nodules. However, first patient studies showed a similar diagnostic performance of FDG-PET/MR compared to FDG-PET/CT in primary lung cancer staging (16-18) but larger prospective studies concerning PET/MR in lung cancer staging are still missing.

An emerging field is radiomics and radiogenomics where textural parameters have to be calculated. These can be derived from statistical approaches which are most commonly used but also from model-based (i.e. fractal analysis) or transformation-methods (i.e. Fourier, wavelet transforms) (19). For this purpose, several software solutions have been published (20-22). In radiogenomics the textural information is correlated with gene expression-patterns to assess prognostic phenotypes. In future, this approach could improve decision-making in cancer treatment. Most textural data is published in CT with up to 440 image features describing the tumor phenotype (23, 24). Studies investigating the use of textural data in MRI are still very limited. Up to now, no textural analyses have been performed with FDG-PET/MR data for lung cancer so far, but results from other malignancies such as glioma are promising (25). First studies aiming at tumor characterization of lung cancer using PET/MRI investigated voxelwise correlation of multiparametric imaging to identify regions of interest within tumors (26, 27). Here, the local information is not lost which could be a starting point for biopsy planning or radiation planning. Lately, first studies investigate the use of textural PET and CT parameters in prognosis: it could be shown that certain textural features in pre-treatment FDG-PET/CT are correlated with an increased risk of local recurrence (28).

In conclusion, in lung cancer, CT is well established and the method of choice for first evaluation in the clinical workup, but also for texture analysis. The hybrid modality FDG-PET/CT is superior in staging especially for lymph node staging and distant metastases. The number of radiomics/radiogenomics studies is still limited in FDG-PET/CT. MRI offers helpful information for dedicated staging situations but is limited in the detection for small lung nodules. The new hybrid modality PET/MRI is still in its infancy but promising due to the huge amount of functional methods.

Acknowledgements

No acknowledgement found.

References

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Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)