Quantitative Multiparametric MRI May Augment the Response to Radiotherapy in Mid-Treatment Assessment of Patients with Esophageal Carcinoma

Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance

Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.

DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer

Objective

Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients.

Methods

One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups.

Results

Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models.

Conclusion

DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.

Longitudinal Correlations Between Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced (DCE) MRI During Radiotherapy in Prostate Cancer Patients

Purpose

Intravoxel incoherent motion (IVIM) is a promising technique that can acquire perfusion information without the use of contrast agent, contrary to the more established dynamic contrast-enhanced (DCE) technique. This is of interest for treatment response monitoring, where patients can be imaged on each treatment fraction. In this study, longitudinal correlations between IVIM- and DCE parameters were assessed in prostate cancer patients receiving radiation treatment.

Materials and Methods

20 prostate cancer patients were treated on a 1.5 T MR-linac with 20 x 3 or 3.1 Gy. Weekly IVIM and DCE scans were acquired. Tumors, the peripheral zone (PZ), and the transition zone (TZ) were delineated on a T₂-weighted scan acquired on the first fraction. IVIM and DCE scans were registered to this scan and the delineations were propagated. Median values from these delineations were used for further analysis. The IVIM parameters D, f, D^* and the product fD^* were calculated. The Tofts model was used to calculate the DCE parameters K^trans, k_ep and v_e. Pearson correlations were calculated for the IVIM and DCE parameters on values from the first fraction for each region of interest (ROI). For longitudinal analysis, the repeated measures correlation coefficient was used to determine correlations between IVIM and DCE parameters in each ROI.

Results

When averaging over patients, an increase during treatment in all IVIM and DCE parameters was observed in all ROIs, except for D in the PZ and TZ. No significant Pearson correlations were found between any pair of IVIM and DCE parameters measured on the first fraction. Significant but low longitudinal correlations were found for some combinations of IVIM and DCE parameters in the PZ and TZ, while no significant longitudinal correlations were found in the tumor. Notably in the TZ, for both f and fD^*, significant longitudinal correlations with all DCE parameters were found.

Conclusions

The increase in IVIM- and DCE parameters when averaging over patients indicates a measurable response to radiation treatment with both techniques. Although low, significant longitudinal correlations were found which suggests that IVIM could potentially be used as an alternative to DCE for treatment response monitoring.

Dynamic contrast-enhanced magnetic resonance imaging: A novel approach to assessing treatment in locally advanced esophageal cancer patients

Aims

This study aims to investigate the potential application of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict concurrent chemoradiation (CRT) in locally advanced esophageal carcinoma.

Patients and Methods

This study involved 33 patients with locally advanced esophageal cancer and treated with CRT. The patients underwent DCE-MRI before CRT (pre) and 3 weeks after starting CRT (mid). The patients were categorized into two groups: complete response (CR) and non-complete response (non-CR) after 3 months of treatment. The quantitative parameters of DCE-MRI (K^trans, Kep, and Ve), the changes and ratios of parameters (ΔK^trans, ΔKep, ΔVe, rΔK^trans, rΔKep, and rΔVe), and the relative ratio in the tumor area and a normal tube wall (rK^trans, rKep, and rVe) were calculated and compared between two timeframes in two groups, respectively. Moreover, the receiver operating characteristics (ROC) statistical analysis was used to assess the above parameters.

Results

We divided 33 patients into two groups: 22 in the CR group and 11 in the non-CR group. During the mid-CRT phase in the CR group, both K^trans and Kep rapidly decreased, while only Kep decreased in the non-CR group. The pre-K^trans and pre-Kep in the CR group were substantially higher compared to the non-CR group. Moreover, the rK^trans was also apparently observed as higher at pre-CRT in the CR group compared to the non-CR group. The ROC analysis demonstrated that the pre-K^trans could be the best parameter to evaluate the treatment performance (AUC = 0.74).

Conclusion

Pre-K^trans could be a promising parameter to forecast how patients with locally advanced esophageal cancer will respond to CRT.

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.