Target Definition in MR-Guided Adaptive Radiotherapy for Head and Neck Cancer

Magnetic resonance imaging with ultra-short echo time sequence for head and neck radiotherapy planning

BACKGROUND

Radiotherapy treatments are usually planned on computed tomography (CT) images. For head and neck localizations, magnetic resonance imaging (MRI) is also increasingly used for delineation as it provides better soft-tissue contrast.

PURPOSE

Treatment planning exclusively based on MRI is currently not straightforward, as there is no direct link between MRI signal intensity and electron density. This study aims to generate a treatment planning using a UTE sequence, for regions with high anatomical variability.

METHODS

An ultra-short echo time pulse sequence (1H MRI UTE) was performed on 25 patients with head and neck cancers, treatable by radiotherapy (protocol number R201-004-314), without exclusion due to dental induced artifacts. The hydrogen tissue content, achievable with this sequence can be linked to the electron density of tissues. Generated synthetic CT (sCT) images were compared with reference CT using mean absolute error (MAE) computation. Patient dose calculations were performed on CT and sCT and compared using dose differences, Bland-Altman analysis and global gamma pass rate computation.

RESULTS

The mean MAE was 210.9 HU for all patients. The mean 3D global gamma pass rates were 93.1 %, 89.2 % and 80.9 %, for 3 %/3mm, 2 %/2mm and 1 %/1mm criteria respectively. The mean of the median dose difference for the planning tumor volume (PTV) was 0.87 % of 70 Gray.

CONCLUSIONS

The UTE sequence enables a direct physical-based method suitable for radiotherapy planning. The proposed method, based on a dedicated acquisition sequence compatible with clinical duration, provided dosimetry results similar to the reference CT, in a region with high anatomical variability.

Magnetic Resonance Imaging Sequences and Technologies in Adaptive Radiation Therapy

Radiation therapy is essential in both curative and palliative treatments for most cancers. However, traditional radiation therapy workflows using computed tomography (CT) simulation-based planning and cone beam CT image guidance face several technical challenges, including limited tumor visibility and daily fluctuations in tumor size and shape. Magnetic resonance imaging (MRI) guided linear accelerators (MR-Linacs) address these issues by enabling precise visualization of changes in tumor position and morphologic changes, as well as changes in surrounding organs-at-risk. The hybrid MR-Linac systems combine MRI with linear accelerator technology, offering enhanced soft tissue visualization and the potential for adaptive radiation therapy (ART). This narrative review provides a comprehensive introduction to MR guided ART technologies, covering protocol optimization with appropriate pulse sequence selection and parameter adjustment for clinical implementations on various disease sites.

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.

Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients

Background and purpose

In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN).

Materials and methods

In 50 HNSCC patients (UMC-Utrecht), individual lymph nodes located in lymph node levels Ib-II-III-IV-V were manually segmented on MRI by consensus of two experts, obtaining ground truth segmentations. A 3D CNN (nnU-Net) was trained on 40 patients and tested on 10. Evaluation metrics were Dice Similarity Coefficient (DSC), recall, precision, and F1-score. The segmentations of the CNN was compared to segmentations of two observers. Transfer learning was used with 20 additional patients to re-train and test the CNN in another medical center.

Results

nnU-Net produced automatic segmentations of elective lymph nodes with median DSC: 0.72, recall: 0.76, precision: 0.78, and F1-score: 0.78. The CNN had higher recall compared to both observers (p = 0.002). No difference in evaluation scores of the networks in both medical centers was found after re-training with 5 or 10 patients.

Conclusion

nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purpose

Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated.

Materials and methods

Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints.

Results

Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL.

Conclusions

The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Tetrabromobisphenol a exacerbates the overall radioactive hazard to zebrafish (Danio rerio)

The major health risks of dual exposure to two hazardous factors of plastics and radioactive contamination are obscure. In the present study, we systematically evaluated the combinational toxic effects of tetrabromobisphenol A (TBBPA), one of the most influential plastic ingredients, mainly from electronic wastes, and γ-irradiation in zebrafish for the first time. TBBPA (0.25 μg/mL for embryos and larvae, 300 μg/L for adults) contamination aggravated the radiation (6 Gy for embryos and larvae, 20 Gy for adults)-induced early dysplasia and aberrant angiogenesis of embryos, further impaired the locomotor vitality of irradiated larvae, and worsened the radioactive multiorganic histologic injury, neurobehavioural disturbances and dysgenesis of zebrafish adults as well as the inter-generational neurotoxicity in offspring. TBBPA exaggerated the radiative toxic effects not only by enhancing the inflammatory and apoptotic response but also by further unbalancing the endocrine system and disrupting the underlying gene expression profiles. In conclusion, TBBPA exacerbates radiation-induced injury in zebrafish, including embryos, larvae, adults and even the next generation. Our findings provide new insights into the toxicology of TBBPA and γ-irradiation, shedding light on the severity of cocontamination of MP components and radioactive substances and thereby inspiring novel remediation and rehabilitation strategies for radiation-injured aqueous organisms and radiotherapy patients.

Assessment of a Therapeutic X-ray Radiation Dose Measurement System Based on a Flexible Copper Indium Gallium Selenide Solar Cell

Several detectors have been developed to measure radiation doses during radiotherapy. However, most detectors are not flexible. Consequently, the airgaps between the patient surface and detector could reduce the measurement accuracy. Thus, this study proposes a dose measurement system based on a flexible copper indium gallium selenide (CIGS) solar cell. Our system comprises a customized CIGS solar cell (with a size 10 × 10 cm2 and thickness 0.33 mm), voltage amplifier, data acquisition module, and laptop with in-house software. In the study, the dosimetric characteristics, such as dose linearity, dose rate independence, energy independence, and field size output, of the dose measurement system in therapeutic X-ray radiation were quantified. For dose linearity, the slope of the linear fitted curve and the R-square value were 1.00 and 0.9999, respectively. The differences in the measured signals according to changes in the dose rates and photon energies were <2% and <3%, respectively. The field size output measured using our system exhibited a substantial increase as the field size increased, contrary to that measured using the ion chamber/film. Our findings demonstrate that our system has good dosimetric characteristics as a flexible in vivo dosimeter. Furthermore, the size and shape of the solar cell can be easily customized, which is an advantage over other flexible dosimeters based on an a-Si solar cell.