Image-guided Radiotherapy Research Articles

Prospects for online adaptive radiation therapy (ART) for head and neck cancer

BackgroundThe aim of the present study is to examine the impact of kV-CBCT-based online adaptive radiation therapy (ART) on dosimetric parameters in comparison to image-guided-radiotherapy (IGRT) in consecutive patients with tumors in the head and neck region from a prospective registry.MethodsThe study comprises all consecutive patients with tumors in the head and neck area who were treated with kV-CBCT-based online ART or IGRT-modus at the linear-accelerator ETHOS™. As a measure of effectiveness, the equivalent-uniform-dose was calculated for the CTV (EUDCTV) and organs-at-risk (EUDOAR) and normalized to the prescribed dose. As an important determinant for the need of ART the interfractional shifts of anatomic landmarks related to the tongue were analyzed and compared to the intrafractional shifts. The latter determine the performance of the adapted dose distribution on the verification CBCT2 postadaptation.ResultsAltogether 59 consecutive patients with tumors in the head-and-neck-area were treated from 01.12.2021 to 31.01.2023. Ten of all 59 patients (10/59; 16.9%) received at least one phase within a treatment course with ART. Of 46 fractions in the adaptive mode, irradiation was conducted in 65.2% of fractions with the adaptive-plan, the scheduled-plan in the remaining. The dispersion of the distributions of EUDCTV-values from the 46 dose fractions differed significantly between the scheduled and adaptive plans (Ansari-Bradley-Test, p = 0.0158). Thus, the 2.5th percentile of the EUDCTV-values by the adaptive plans amounted 97.1% (95% CI 96.6–99.5%) and by the scheduled plans 78.1% (95% CI 61.8–88.7%). While the EUDCTV for the accumulated dose distributions stayed above 95% at PTV-margins of ≥ 3 mm for all 8 analyzed treatment phases the scheduled plans did for margins ≥ 5 mm. The intrafractional anatomic shifts of all 8 measured anatomic landmarks were smaller than the interfractional with overall median values of 8.5 mm and 5.5 mm (p < 0.0001 for five and p < 0.05 for all parameters, pairwise comparisons, signed-rank-test). The EUDOAR-values for the larynx and the parotid gland were significantly lower for the adaptive compared with the scheduled plans (Wilcoxon-test, p < 0.001).ConclusionsThe mobile tongue and tongue base showed considerable interfractional variations. While PTV-margins of 5 mm were sufficient for IGRT, ART showed the potential of decreasing PTV-margins and spare dose to the organs-at-risk.

Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance.Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains. As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy.Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods.Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon.

This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy. Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training-test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm. Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model. The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.

Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI With Simultaneous Motion Estimation and Super-Resolution.

Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the long acquisition time and patients' respiratory variations. If not managed properly, these limitations can adversely affect treatment planning and delivery in IGRT. In this study, we developed a novel deep learning framework called the coarse-super-resolution-fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution within a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to assess the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI, enhancing anatomical features and producing 4D-MR images with high spatiotemporal resolution.

Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy.

The imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed. Included in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions. The phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains. The proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

Scalp dose analysis for transient and permanent alopecia following conventional cranial irradiation using Image Guided Radiotherapy (IGRT): A prospective study.

The dosimetry of scalp dose was prospectively studied and correlated with alopecia following conventional cranial irradiation in primary brain tumors patients. Patients with primary brain tumors who required conventional radiotherapy were enrolled. A hairline marker was applied to the patient's scalp to identify the entire scalp region. The maximal dose to 2% volume of interest (D2) for the entire scalp region were obtained. The radiation dosages at the localized hair-loss areas were evaluated during the final week of RT (transient alopecia) and six months after completing RT (permanent alopecia). Kruskal-Wallis tests were used to compare the dosimetric parameter values with statistical significance set as p < 0.05. Forty-eight patients were included in the analysis. The prescribed radiation doses ranged from 50.4 to 60.0 Gy. Thirty-two patients experienced alopecia (27 transient and 5 permanent). The median D2 values adjusted for the entire scalp were higher in the alopecia group (38.40 Gy for transient alopecia and 47.84 Gy for permanent alopecia vs 11.90 Gy for no alopecia, p < 0.001). The D2 value was determined as a predictive parameter for alopecia. The threshold values for transient and permanent alopecia over the entire scalp were 22.15 Gy and 36.81 Gy, respectively. At the localized hair-loss areas, the D2 values for transient and permanent alopecia were higher at 44.82 Gy and 50.00 Gy, respectively. The radiation intensity at the localized hair-loss areas was also related to the severity of alopecia, with D2 values of 35.14 Gy and 46.39 Gy for clinically assigned grade 1 and grade 2 transient alopecia, respectively, with the D2 value being even higher for permanent alopecia. The D2 parameter value could be used to predict the type and severity of alopecia.

The Lives Saved: A Literature Review on the Role of Radiotherapy Improving Prognosis in Cancer Patients

Radiotherapy, also known as radiation therapy, is a treatment modality based on the use of high energy rays or radioactive substances, to damage tumoral cells and halt their growth and division. It is an effective tool for treating cancer of different kinds for more than 100 years with remarkable outcomes for cancer patients. This technique is characterised by notable technological innovations and methodologies, enhanced precision and effectiveness. Initially, precision and efficacy of radiotherapy are improved by technological advancements, such as the development of linear accelerators and brachytherapy. As a result, Radiotherapy has transformed the prognosis for cancer patients enhancing survival rates and improving quality of life. Also, it offers targeted and personalized treatment with advancements in imaging techniques such as Intensity- Modulated Radiation Therapy (IMRT) and Stereotactic Body Radiation Therapy (SBRT) and innovations like Image-Guided Radiation Therapy (IGRT), Adaptive Radiotherapy (ART) and Artificial Intelligence (AI). Additionally, techniques like proton therapy result in reduced side effects and collateral damage. Although, among the challenges and future perspectives we note the issue of affordability and accessibility. Ongoing research into immunotherapy and nanotechnology and their synergy with radiation therapy shows potential for further improving treatment outcomes. In conclusion, the evolution of radiotherapy has been a transformative journey, reshaping the prognosis and outcomes for cancer patients

A dosimetric evaluation of intensity modulated radiotherapy and three-dimensional conformal radiotherapy for prostate cancer in Ghana.

External beam radiotherapy incorporates treatment techniques such as three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiotherapy (IMRT), image-guided radiotherapy and volumetric modulated arc therapy to deliver high-energy radiation to cancer. The use of IMRT for cancer treatment is also associated with significant costs for patients in low-middle-income countries. The purpose of this study was to compare the dosimetric properties of 3DCRT and IMRT treatment plans for the external beam irradiation of patients with prostate cancer (Pca) to ascertain the superiority of IMRT in terms of dose homogeneity, conformity and dose limitation to organs at risk (OAR) in a resource-limited setting. One hundred and sixty treatment plans for 80 patients were created using 3DCRT and IMRT on the Eclipse treatment planning system (version 13.6). Data were collected and assessed from the dose-volume histogram of each plan. The conformity and homogeneity index (HI) for each of the plans were calculated. The doses to the OAR were also recorded and evaluated. The mean HIs for the IMRT and 3DCRT treatment techniques were 0.04 ± 0.02 (range: 0.01-0.011) and 0.09 ± 0.02 (range: 0.04-0.016), respectively. The mean conformity index (CI) for IMRT and 3DCRT techniques were 1.257 ± 0.112 (range: 0.99-1.58) and 1.302 ± 0.196 (range: 1.10-2.26). IMRT had a better significant mean HI and CI compared to 3DCRT. Generally, for this study, IMRT had better organ sparing compared to 3DCRT. The mean doses for the OARs ranged from 4.3-74.6 Gy for IMRT and 3.1-75.9 Gy for the 3DCRT technique. Overall, this study demonstrates that IMRT may offer an enhanced therapeutic profile, potentially reducing toxicity to the patient and ensuring more precise dose delivery to the target volume compared to 3DCRT in PCa external beam irradiation.

Virtual clinical trial-based study for clinical evaluation of projection-reduced low-dose cone-beam CT for image guided radiotherapy.

Repeated cone-beam CT (CBCT) scans for image-guided radiotherapy (IGRT) increase the health risk of radiation-induced malignancies. Patient-enrolled studies to optimize scan protocols are inadequate. We proposed a virtual clinical trial-based approach to evaluate projection-reduced low-dose CBCT for IGRT. A total of 71 patients were virtually enrolled with 26 head, 23 thorax and 22 pelvis scans. Projection numbers of full-dose CBCT scans were reduced to 1/2, 1/4, and 1/8 of the original to simulate low-dose scans. Contrast-to-noise ratio (CNR) values in fat and muscle were measured in the full-dose and low-dose images. CBCT images were registered to planning CT to derive 6-degree-of-freedom couch shifts. Registration errors were statistically analyzed with the Wilcoxon paired signed-rank test. As projection numbers were reduced, CNR values descended and the magnitude of registration errors increased. The mean CNR values of full-dose and half-dose CBCT were >3.0. For full-dose and low-dose CBCT (i.e. 1/2, 1/4 and 1/8 full-dose), the mean registration errors were< ± 0.4 mm in translational directions (LAT, LNG, VRT) and ±0.2 degree in rotational directions (Pitch, Roll, Yaw); the mean magnitude of registration errors were< 1 mm in translation and< 0.5 degree in rotation. The couch shift differences between full-dose and low-dose CBCT were not statistically significant (p>0.05) in all the directions. The results indicate that while the impact of dose-reduction on CBCT couch shifts is not significant, the impact on CNR values is significant. Further validation on optimizing CBCT imaging dose is required.

The image segmentation and polyp detection in colonoscopy images using computer aided detection (CAD)

Segmentation is the process through which each pixel in an image is assigned a class. Segmentation may be used to medical image analysis to assist in image-guided surgery, radiation therapy, and better radiological diagnostics. This article summarises our study towards creating a computer-assisted detection (CAD) method for colonoscopy images containing polyps. Our method is a hybrid context-shape strategy that uses shape information to accurately detect polyps and context information to eliminate non-polyp structures. To begin, a basic edge map is generated from a colonoscopy picture. Second, we use our unique feature extraction and edge classification technique to remove non-polyp edges from the edge map. Third, we use our updated edge maps and newly developed voting mechanism to identify polyp candidates with probabilistic confidence ratings. The proposed CAD system was compared to two publicly accessible polyp databases: CVC-ColonDB, which includes 300 colonoscopy pictures with 300 polyp occurrences from 15 different polyps, and ASU-Mayo, which contains 19,400 colonoscopy frames with a total of 5,200 polyp instances from 10 distinct polyps.

Evaluation of Dose Distribution at the Level of Electronic Portal Imaging Device using Treatment Planning System

Evaluation of Dose Distribution at the Level of Electronic Portal Imaging Device using Treatment Planning System

Development of a Phantom and Analysis Program for Assessment of Positional Accuracy of Image-guided Radiation Therapy

We created a phantom and analysis program for the assessment of IGRT positional accuracy. We verified the accuracy of analysis and the practicality of this evaluation method at several facilities. End-to-end test was performed using an in-house phantom, and EPID images were acquired after displacement by an arbitrary amount using a micrometer, with after image registration as the reference. The difference between the center of the target and the irradiated field was calculated using our in-house analysis program and commercial software. The end-to-end test was conducted at three facilities, and the IGRT positional accuracy evaluation was verified. The maximum difference between the displacement of the target determined from the EPID image and the arbitrary amount of micrometer displacement was 0.24 mm for the in-house analysis program and 0.30 mm for the commercial software. The maximum difference between the center of the target and the irradiation field on EPID images acquired at the three facilities was 0.97 mm. The proposed evaluation method using our in-house phantom and analysis program can be used for the assessment of IGRT positional accuracy.

Evaluation of the trend of set-up errors during the treatment period using set-up margin in prostate radiotherapy

Accurate information on set-up error during radiotherapy is essential for determining the optimal number of treatments in hypofractionated radiotherapy for prostate cancer. This necessitates careful control by the radiotherapy staff to assess the patient's condition. This study aimed to develop an evaluation method of the temporal trends in a patient's specific prostate movement during treatment using image matching and margin values. This study included 65 patients who underwent prostate volumetric modulated arc therapy (mean treatment time, 87.2 s). Set-up errors were assessed using bone, inter-, and intra-fraction marker matching across 39 fractions. The set-up margin was determined by dividing the four periods into 39 fractions using Stroom's formula and correlation coefficient. The intra-fraction set-up error was biased in the anterior-superior (AS) direction during treatment. The temporal trend of set-up errors during radiotherapy slightly increased based on bone matching and inter-fraction marker matching, with a 1.6-mm difference in the set-up margin fractions 11 to 20. The correlation coefficient of the mean prostate movement during treatment significantly decreased in the superior-inferior direction, while remaining high in the left-right and anterior-posterior directions. Image matching contributed significantly to the improvement of set-up errors; however, careful attention is needed for prostate movement in the AS direction, particularly during short treatment times. Understanding the trend of set-up errors during the treatment period is essential in numerical information sharing on patient condition and evaluating the margins for tailored hypo-fractionated radiotherapy, considering the facility's image-guided radiation therapy technology.

Evaluating the linear accelerator accuracy: Integrating Winston-Lutz measurement method and mathematical calculations

The Electronic Portal Image Device (EPID) is commonly used to evaluate the difference between the mechanical and radiation centers of a linear accelerator; however, the gantry and EPID are limited in some non-coplanar angles due to collisions with the treatment couch. This study aimed to compare the results of mathematical calculations and actual measurements to evaluate the feasibility of assessing the differences in the mechanical and radiation field isocenters. A 20 mm Stereotactic Circular Collimator was mounted on the head of the linear accelerator (LINAC), and an Elekta 8 mm ball-bearing (BB) was placed on the couch, following the Winston-Lutz (WL) measurement method. Measurements were taken with varying gantry and couch positions, and Image J software was used to analyze the images. Two regions with a difference of more than 0.1 mm were identified. The first region was between T270 and T330, with gantry angles ranging from G120 to G160. The second region was between T280 and T340, with gantry angles ranging from G290 to G330, and the two regions showed a symmetric diagonal relationship in terms of the gantry angles. The paired sample t-test showed no significant difference between the actual measurements and the calculated results (p-value = 0.11). The study concluded that mathematical calculations can complement the limitations imposed by equipment collisions, providing valuable insights for treatment planning.

CLINICAL RESULTS OF CERVIX CANCER USING VARIOUS TECHNOLOGIES IN RADIATION THERAPY

Background: clinical results of patients with cervical cancer (CC) who treated with different methodological approaches of radiotherapy (RT) in the transforming from conventional to conformal RT and brachytherapy (BT).&#x0D; Purpose: to evaluate long-term results of treatment, locoregional and distant control.&#x0D; Methods: 137 patients with CC IIb - IIIb in 2013-2016 were analysed: 70 (51%) - conventional gamma therapy and brachytherapy (BT) with two-dimensional planning (2D) 60Сo alone or in combination with cisplatin 40 mg/m2 two groups of n=35. The total dose (TD) for point A 75.08 0.57 Gy, point B 57.9 0.43 Gy. In 67 (49%) with conform radiotherapy following BT with three-dimensional planning (3D) 192Ir alone or in combination with cisplatin 40 mg/m2 - two groups n = 35 and 32, respectively. TD CTV-HR D90 95.0 Gy 0.67 EQD2.&#x0D; Results: 3 and 5-year overall survival with 3D RT versus 2D RT was 84.6 4.5% and 63.1 6.0%; 84.6 4.5% versus 56.1 6.0%, respectively (p = 0.030). It was revealed that modern technologies are important in improving overall survival with image-guided RT: 88.2 6.6% of patients are alive for 3 years versus 59.0 8.4%, respectively (p=0.027). The use of chemoradiotherapy (CRT) showed an advantage of 3-year event-free survival in groups with 2D RT - 67.9 8.4% versus 55.2 8.6% (p = 0.042) in 3D RT - no statistically significant differences. Locoregional control was higher in the 3D RT groups at 3 years follow-up: 97.0 2.9% versus 82.9 5.3%, p = 0.050. CRT allows to reduce the number of local failures in the pelvis in the follow-up period up to 6 months, regardless of the RT technologies. The number of relapses and metastases in pelvis is lower in 3D RT - 3.0 2.1% versus 2D - 15.7 4.4% (p = 0.05). The frequency of metastases did not show statistically significant differences: 2D RT - 5.7 2.8% versus 3D RT - 9.0 3.5% (p 0.05).&#x0D; Conclusion: the study proves the improvement of local control in the treatment of cervical cancer in cases of conformal RT and 3D BT.

Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry.

Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan-specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ-evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan-specific deviations may be hindered by the presence of generic deviations. To investigate whether deep learning-based tools can help in identifying γ-alerts raised by generic deviations and in improving the detectability of plan-specific deviations. A 3D U-Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ-distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head-and-neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non-transit γ-distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ-distributions obtained in the presence of plan-specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ-analysis and the network to detect (synthetically introduced) patient-related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head-and-neck, 209 prostate and 307 breast). Error detectability of patient-related deviations was better with the network than with γ-analysis. The average area under the curve values over all sites were 0.86±0.12(1SD) and 0.69±0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ-analysis and the network was 1%, 19%, 18% and 64% for lung, head-and-neck, prostate, and breast, respectively. In head-and-neck and breast cases, 45 γ-only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ-only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient-related deviations or EPID acquisition incidents. Some patient-related deviations detected by the network were not detected by γ-analysis. Deep learning-based tools trained to reconstruct underlying patterns of generic deviations in γ-distributions can be used to (i) automatically identify false positives within the set of γ-alerts and (ii) improve the detection of plan-specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ-alert management process for large scale EIVD systems.

Analysis of dose distribution reproducibility based on a fluence map of in vivo transit dose using an electronic portal imaging device

Morphological changes can affect distribution of dose in patients. Determination of the dose distribution changes for each fraction radiotherapy can be done by relative in vivo dosimetry (IVD). This study analysed the distribution of doses per fraction based on the fluence map recorded by the electronic portal imaging device (EPID) of the patient’s transit dose. This research examined cases involving the cervix, breast, and nasopharynx. Transit dose analysis was performed by calculating the gamma index (GI) with composite and field-by-field methods. The gamma passing rate (GPR) value was assessed for its correlation with the subject’s body weight. In the case of the nasopharynx, breast, and cervix, the GPR value decreased as the fraction increased. In the case of the nasopharynx, the correlation between the GPR and fraction radiotherapy showed no difference when using either composite or field-by-field methods. However, in cases involving the cervix and breast, there was a difference in the correlation values between the composite and field-by-field methods, where the subject had a significant correlation (p < 0.05) when it was done using a field-by-field method. In addition, the nasopharynx had the highest number of subjects with significant correlation (p < 0.05) between GPR and body weight, followed by the cervix and breast. In the nasopharynx, breast, and cervix, the reproducibility of the dose distribution decreased. This decreased reproducibility was associated with changes in body weight.

Survey on fan-beam computed tomography for radiotherapy: Current implementation and future perspectives of motion management and surface guidance devices

Background and purposeThis work reports on the results of a survey performed on the use of computed tomography (CT) imaging for motion management, surface guidance devices, and their quality assurance (QA). Additionally, it details the collected user insights regarding professional needs in CT for radiotherapy. The purpose of the survey is to understand current practice, professional needs and future directions in the field of fan-beam CT in radiation therapy (RT). Materials and methodsAn online institutional survey was conducted between 1-Sep-2022 and 10-Oct-2022 among medical physics experts at Belgian and Dutch radiotherapy institutions, to assess the current status, challenges, and future directions of motion management and surface image-guided radiotherapy. The survey consisted of a maximum of 143 questions, with the exact number depending on participants’ responses. ResultsThe response rate was 66 % (31/47). Respiratory management was reported as standard practice in all but one institution; surface imaging during CT-simulation was reported in ten institutions. QA procedures are applied with varying frequencies and methodologies, primarily with commercial anatomy-like phantoms. Surface guidance users report employing commercial static and dynamic phantoms. Four main subjects are considered clinically important by the respondents: surface guidance, CT protocol optimisation, implementing gated imaging (4DCT, breath-hold), and a tattoo-less workflow. ConclusionsThe survey highlights the scattered pattern of QA procedures for respiratory motion management, indicating the need for well-defined, unambiguous, and practicable guidelines. Surface guidance is considered one of the most important techniques that should be implemented in the clinical radiotherapy simulation workflow.

Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration

Objective. Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions. Approach. To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy. Main results. Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status. Significance. The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.

Hafnium (Hf)-Chelating Porphyrin-Decorated Gold Nanosensitizers for Enhanced Radio-Radiodynamic Therapy of Colon Carcinoma.

X-ray-induced radiodynamic therapy (RDT) that can significantly reduce radiation dose with an improved anticancer effect has emerged as an attractive and promising therapeutic modality for tumors. However, it is highly significant to develop safe and efficient radiosensitizing agents for tumor radiation therapy. Herein, we present a smart nanotheranostic system FA-Au-CH that consists of gold nanoradiosensitizers, photosensitizer chlorin e6 (Ce6), and folic acid (FA) as a folate-receptor-targeting ligand for improved tumor specificity. FA-Au-CH nanoparticles have been demonstrated to be able to simultaneously serve as radiosensitizers and RDT agents for enhanced computed tomography (CT) imaging-guided radiotherapy (RT) of colon carcinoma, owing to the strong X-ray attenuation capability of high-Z elements Au and Hf, as well as the characteristics of Hf that can transfer radiation energy to Ce6 to generate ROS from Ce6 under X-ray irradiation. The integration of RT and RDT in this study demonstrates great efficacy and offers a promising therapeutic modality for the treatment of malignant tumors.