Functional magnetic resonance imaging techniques and their development for radiation therapy planning and monitoring in the head and neck cancers

Adverse Radiation Therapy Effects in the Treatment of Head and Neck Tumors

Whether used as a single modality or as part of a combined approach, radiation therapy (RT) plays an essential role in the treatment of several head and neck malignancies. Despite the improvement in radiation delivery techniques, normal structures in the vicinity of the target area remain susceptible to a wide range of adverse effects. Given their high incidence, some of these effects are referred to as expected postradiation changes (eg, mucositis, sialadenitis, and edema), while others are considered true complications, meaning they should not be expected and can even represent life-threatening conditions (eg, radionecrosis, fistulas, and radiation-induced neoplasms). Also, according to their timing of onset, these deleterious effects can be divided into four groups: acute (during RT), subacute (within weeks to months), delayed onset (within months to years), and very delayed onset (after several years).The authors provide a comprehensive review of the most important radiation-induced changes related to distinct head and neck sites, focusing on their typical cross-sectional imaging features and correlating them with the time elapsed after treatment. Radiologists should not only be familiar with these imaging findings but also actively seek essential clinical data at the time of interpretation (including knowledge of the RT dose and time, target site, and manifesting symptoms) to better recognize imaging findings, avoid pitfalls and help guide appropriate management. ^© RSNA, 2022.

Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study

Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC), a group of malignancies associated with high heterogeneity. However, the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck (HN) tissues. In particular, feature repeatability of radiomics in magnetic resonance imaging (MRI) acquisition, which is considered a crucial confounding factor of radiomics feature reliability, is still sparsely investigated. This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers, aiming for potential magnetic resonance-guided radiotherapy (MRgRT) treatment of HNC. Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5 T MRI simulator. The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient (ICC), whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation (CV). The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types, tissues, and pulse sequences. Only a small fraction of features showed excellent acquisition repeatability (ICC > 0.9) and low within-subject variability. Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans. This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.

The use of deep learning technology for the detection of optic neuropathy

The emergence of computer graphics processing units (GPUs), improvements in mathematical models, and the availability of big data, has allowed artificial intelligence (AI) to use machine learning and deep learning (DL) technology to achieve robust performance in various fields of medicine. The DL system provides improved capabilities, especially in image recognition and image processing. Recent progress in the sorting of AI data sets has stimulated great interest in the development of DL algorithms. Compared with subjective evaluation and other traditional methods, DL algorithms can identify diseases faster and more accurately in diagnostic tests. Medical imaging is of great significance in the clinical diagnosis and individualized treatment of ophthalmic diseases. Based on the morphological data sets of millions of data points, various image-related diagnostic techniques can now impart high-resolution information on anatomical and functional changes, thereby providing unprecedented insights in ophthalmic clinical practice. As ophthalmology relies heavily on imaging examinations, it is one of the first medical fields to apply DL algorithms in clinical practice. Such algorithms can assist in the analysis of large amounts of data acquired from the examination of auxiliary images. In recent years, rapid advancements in imaging technology have facilitated the application of DL in the automatic identification and classification of pathologies that are characteristic of ophthalmic diseases, thereby providing high quality diagnostic information. This paper reviews the origins, development, and application of DL technology. The technical and clinical problems associated with building DL systems to meet clinical needs and the potential challenges of clinical application are discussed, especially in relation to the field of optic nerve diseases.

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging-Based Radiation Therapy Planning of Patients With Head and Neck Cancer

Purpose

Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI.

Methods and Materials

Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk.

Results

The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort.

Conclusions

We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method.

Description of normal head structures of the one-humped camel (Camelus dromedarius) by magnetic resonance imaging, computed tomography, and cross-sectional anatomy

Aim:

This study was designed for the 1^st time to describe the normal head structures of one-humped camel (Camelus dromedarius) using both magnetic resonance imaging (MRI) and computed tomography (CT) as well as cross-sectional anatomy.

Materials and Methods:

Five fresh cadaver heads were collected from clinically normal camels and then subjected to T1-weighted MR and CT imaging. Afterward, these examined heads were transversely sliced to obtain seven crossing levels.

Results:

The obtained structures per each crossing level were matched with their relevant sorted images of T1-weighted MRI and CT, then identified and labeled accordingly.

Conclusion:

The data shown herein expand our knowledge of the normal head structures of the camel and could be used as a reference for ultimate diagnosis of the surgical affections of head using MRI and/or CT.

PET and MRI based RT treatment planning: Handling uncertainties

Imaging provides the basis for radiotherapy. Multi-modality images are used for target delineation (primary tumor and nodes, boost volume) and organs at risk, treatment guidance, outcome prediction, and treatment assessment. Next to anatomical information, more and more functional imaging is being used. The current paper provides a brief overview of the different applications of imaging techniques used in the radiotherapy process, focusing on uncertainties and QA. The paper mainly focuses on PET and MRI, but also provides a short discussion on DCE-CT. A close collaboration between radiology, nuclear medicine and radiotherapy departments provides the key to improve the quality of radiotherapy. Jointly developed imaging protocols (RT position setup, immobilization tools, lasers, flat table…), and QA programs are mandatory. For PET, suitable windowing in consultation with a Nuclear Medicine Physician is crucial (differentiation benign/malignant lesions, artifacts…). A basic knowledge of MRI sequences is required, in such a way that geometrical distortions are easily recognized by all members the RT and RT physics team. If this is not the case, then the radiologist should be introduced systematically in the delineation process and multidisciplinary meetings need to be organized regularly. For each image modality and each image registration process, the associated uncertainties need to be determined and integrated in the PTV margin. When using functional information for dose painting, response assessment or outcome prediction, collaboration between the different departments is even more important. Limitations of imaging based biomarkers (specificity, sensitivity) should be known.

A pilot study of highly accelerated 3D MRI in the head and neck position verification for MR-guided radiotherapy

Background

To evaluate the performance of a highly accelerated 3D MRI on inter-fractional positional measurement for MR-guided radiotherapy (MRgRT) in the head and neck (HN).

Methods

Fourteen healthy volunteers received 159 scans on a 1.5 T MR-sim to simulate MRgRT fractions. MRI acquisition included a high-resolution (HQI-MRI, voxel-size =1.05×1.05×1.05 mm³, duration =5 min) and a highly-accelerated low-resolution (true-LQI-MRI, acceleration-factor =9, voxel-size =1.4×1.4×1.4 mm³, duration =86 s) T1w spin-echo sequence (TR/TE =420/7.2 ms). The first session HQI-MRI was used as the reference to mimic planning MRI. Other HQI-MRI was also retrospectively down-sampled in K-space and GRAPPA reconstructed to generate pseudo-LQI-MRI. Inter-sessional positional shift calculated from HQI-MRI, true-LQI-MRI and pseudo-LQI-MRI rigidly registering to the reference were analyzed and compared in the overall HN and the sub-regions of brain, nasopharynx, oropharynx and hypopharynx.

Results

The calculated SD of systematic errors (Σ) from HQI-MRI/pseudo-LQI-MRI/true-LQI-MRI images for overall HN were 1.11/1.14/1.08, 0.28/0.26/0.29, 0.43/0.44/0.60, and 0.77/0.79/0.74 mm for translation in LR, AP, SI and 3D, respectively; The corresponding RMS of random errors (σ) were 0.97/0.98/0.96, 0.28/0.27/0.26, 0.77/0.77/0.72, and 0.85/0.87/0.85 mm. For all sub-regions, brain showed the smallest Σ and σ in 3D. Other sub-regions showed direction-dependent error patterns, but the positioning results were consistent, independent of the datasets used for registration.

Conclusions

A highly-accelerated 3D-MRI could be used for MR-guided HN radiotherapy without compromising position verification accuracy.

Prognostic role of diffusion weighted and dynamic contrast-enhanced MRI in loco-regionally advanced head and neck cancer treated with concomitant chemoradiotherapy

Background In the study, the value of pre-treatment dynamic contrast-enhanced (DCE) and diffusion weighted (DW) MRI-derived parameters as well as their changes early during treatment was evaluated for predicting disease-free survival (DFS) and overall survival (OS) in patients with locoregionally advanced head and neck squamous carcinoma (HNSCC) treated with concomitant chemoradiotherapy (cCRT) with cisplatin. Patients and methods MRI scans were performed in 20 patients with locoregionally advanced HNSCC at baseline and after 10 Grays (Gy) of cCRT. Tumour apparent diffusion coefficient (ADC) and DCE parameters (volume transfer constant [Ktrans], extracellular extravascular volume fraction [ve], and plasma volume fraction [Vp]) were measured. Relative changes in parameters from baseline to 10 Gy were calculated. Univariate and multivariate Cox regression analysis were conducted. Receiver operating characteristic (ROC) curve analysis was employed to identify parameters with the best diagnostic performance. Results None of the parameters was identified to predict for DFS. On univariate analysis of OS, lower pre-treatment ADC (p = 0.012), higher pre-treatment Ktrans (p = 0.026), and higher reduction in Ktrans (p = 0.014) from baseline to 10 Gy were identified as significant predictors. Multivariate analysis identified only higher pre-treatment Ktrans (p = 0.026; 95% CI: 0.000-0.132) as an independent predictor of OS. At ROC curve analysis, pre-treatment Ktrans yielded an excellent diagnostic accuracy (area under curve [AUC] = 0.95, sensitivity 93.3%; specificity 80 %). Conclusions In our group of HNSCC patients treated with cisplatin-based cCRT, pre-treatment Ktrans was found to be a good predictor of OS.

Radiation-induced gray matter atrophy in patients with nasopharyngeal carcinoma after intensity modulated radiotherapy: a MRI magnetic resonance imaging voxel-based morphometry study

Background

Gray matter (GM) damage after radiotherapy (RT) in nasopharyngeal carcinoma (NPC) patients can result in cognitive impairment, while there may be no visible brain tissue change according to the conventional magnetic resonance imaging (MRI). This study investigated radiation-induced GM volume differences between NPC patients who received RT and those who did not.

Methods

High-resolution brain structural MRI data from two groups of patients were acquired. The pre-RT group was composed of 56 newly diagnosed but not yet medically treated NPC patients, while the after-RT group consisted of 40 NPC patients who had completed RT more than 1 year ago. Voxel-based morphometry (VBM) was applied to assess GM volumes. Two sample t-test was used to analyze GM volumes voxel-by-voxel using the VBM8 toolbox built in the SPM software. Radiation-induced cortical volume alteration in all NPC patients after RT and dosimetry of 36 patients were analyzed.

Results

Compared to pre-treatment group, cortical volumes of GM were significantly smaller in the left hippocampus, the right pulvinar and the right middle temporal gyrus (MTG, P<0.001, AlphaSim correction, cluster size ≥157). The mean dose (D_mean) for bilateral hippocampal heads were significantly higher than other different parts of the brain (P<0.001). No significant correlations between the GM volume in any brain regions and the mean dose of corresponding position of these brain regions were observed (P>0.05).

Conclusions

Radiation to the NPC patients can not only induce damage of the hippocampus, but also other secondary damages of GM.

Preliminary study on the diagnostic value of single-source dual-energy CT in diagnosing cervical lymph node metastasis of thyroid carcinoma

Background

To investigate the value of single-source dual-energy spectral CT imaging in improving the accuracy of preoperative diagnosis of lymph node metastasis of thyroid carcinoma.

Methods

Thirty-four thyroid carcinoma patients were enrolled and received spectral CT scanning before thyroidectomy and cervical lymph node dissection surgery. Iodine-based material decomposition (MD) images and 101 sets of monochromatic images from 40 to 140 keV were reconstructed after CT scans. The iodine concentrations (IC) of lymph nodes were measured on the MD images and was normalized to that of common carotid artery to obtain the normalized iodine concentration (NIC). The CT number of lymph nodes as function of photon energy was measured on the 101 sets of images to generate a spectral HU curve and to calculate its slope λ_HU. The measurements between the metastatic and non-metastatic lymph nodes were statistically compared and receiver operating characteristic (ROC) curves were used to determine the optimal thresholds of these measurements for diagnosing lymph nodes metastasis.

Results

There were 136 lymph nodes that were pathologically confirmed. Among them, 102 (75%) were metastatic and 34 (25%) were non-metastatic. The IC, NIC and the slope λ_HU of the metastatic lymph nodes were 3.93±1.58 mg/mL, 0.70±0.55 and 4.63±1.91, respectively. These values were statistically higher than the respective values of 1.77±0.71 mg/mL, 0.29±0.16 and 2.19±0.91 for the non-metastatic lymph nodes (all P<0.001). ROC analysis determined the optimal diagnostic threshold for IC as 2.56 mg/mL, with the sensitivity, specificity and accuracy of 83.3%, 91.2% and 85.3%, respectively. The optimal threshold for NIC was 0.289, with the sensitivity, specificity and accuracy of 96.1%, 76.5% and 91.2%, respectively. The optimal threshold for the spectral curve slope λ_HU was 2.692, with the sensitivity, specificity and accuracy of 88.2%, 82.4% and 86.8%, respectively.

Conclusions

The measurements obtained in dual-energy spectral CT improve the sensitivity and accuracy for preoperatively diagnosing lymph node metastasis in thyroid carcinoma.

Comparison of sonographic characteristics of primary thyroid lymphoma and anaplastic thyroid carcinoma

BACKGROUND

Although primary thyroid lymphoma (PTL) and anaplastic thyroid carcinoma (ATC) both account for a rare portion of the morbidity of all thyroid malignancies, the therapeutic methods and prognosis for these two diseases are different. The purpose of this study was to investigate the sonographic characteristics of PTL and ATC and to compare the sonographic findings of PTL and ATC.

METHODS

The study included 42 patients with histopathologically proven PTL (n=27) and ATC (n=15). The Clinical characteristics and sonographic findings were retrospectively reviewed and compared between the two groups.

RESULTS

The mean age of patients with ATC was not significantly different from that in patients with PTL (P=0.601). The female-to-male ratio of patients with ATC was significantly lower than that of patients with PTL (P=0.029). Both PTL and ATC commonly present as a relatively large, solid mass on sonography with compressive symptoms, in which hoarseness was seen more frequently in ATC group (66.7%) than in PTL group (14.8%) (P=0.001). There is no significant difference in thyroid size, nodular size, margin, shape, echo texture, echogenicity, cystic change, vascularity and local invasion on sonography between ATC and PTL groups. Echogenic strands, markedly hypoechoic and enhanced posterior echo were seen more frequently in PTL group (92.6%, 92.6%, and 85.2%, respectively) than those in ATC group (6.7%, 60.0%, and 33.3%, respectively) (P<0.05), and calcification was seen more frequently in ATC group (80.0%) than in PTL group (0%) (P<0.001). Three ultrasound patterns were observed for PTL including diffuse type (25.9%), nodular type (48.2%) and mixed type (25.9%), while all ATC cases presented with nodular type (100.0%). Associated Hashimoto's thyroiditis occurred more frequently in PTL group (59.3%) than in ATC group (20.0%) (P=0.023).

CONCLUSIONS

Certain sonographic features as a markedly hypoechogenicity, the presence of an enhanced posterior echo and linear echogenic strands, lack of calcification and associated Hashimoto's thyroiditis were valuable for distinguishing PTL from ATC. In contrast, heterogeneous echogenicity, uncircumscribed margin, irregular shape, and vascular pattern were not specific features for differential diagnosis.

Assessing Tumor Oxygenation for Predicting Outcome in Radiation Oncology: A Review of Studies Correlating Tumor Hypoxic Status and Outcome in the Preclinical and Clinical Settings

Tumor hypoxia is recognized as a limiting factor for the efficacy of radiotherapy, because it enhances tumor radioresistance. It is strongly suggested that assessing tumor oxygenation could help to predict the outcome of cancer patients undergoing radiation therapy. Strategies have also been developed to alleviate tumor hypoxia in order to radiosensitize tumors. In addition, oxygen mapping is critically needed for intensity modulated radiation therapy (IMRT), in which the most hypoxic regions require higher radiation doses and the most oxygenated regions require lower radiation doses. However, the assessment of tumor oxygenation is not yet included in day-to-day clinical practice. This is due to the lack of a method for the quantitative and non-invasive mapping of tumor oxygenation. To fully integrate tumor hypoxia parameters into effective improvements of the individually tailored radiation therapy protocols in cancer patients, methods allowing non-invasively repeated, safe, and robust mapping of changes in tissue oxygenation are required. In this review, non-invasive methods dedicated to assessing tumor oxygenation with the ultimate goal of predicting outcome in radiation oncology are presented, including positron emission tomography used with nitroimidazole tracers, magnetic resonance methods using endogenous contrasts (R₁ and R2*-based methods), and electron paramagnetic resonance oximetry; the goal is to highlight results of studies establishing correlations between tumor hypoxic status and patients’ outcome in the preclinical and clinical settings.