The top 500 results for "radiotherapy" are:

We propose a new application of adaptive radiotherapy using a scanning beam, taking into account the daily regression of a tumor. No patient-specific hardware (such as collimators and compensating filters) is needed for the scanning technique; thus, it allows re-planning immediately before each fractional irradiation using the sophisticated conformations of dose distributions. We retrospectively modeled the tumor volume regression curves as a function of the dose from the CT images which were taken three times during the treatment course. The daily shape of the tumor was mathematically interpolated assuming constant continuity of the tumor deformation. We simulated the adaptive radiotherapy by optimizing the dose distribution on the estimated daily tumor volume for every fraction. The dose–volume histogram (DVH) for the organ at risk in the adaptive radiotherapy was compared with that of the current clinical protocol. We performed analysis using the CT images of cervical cancer patients who received carbon-ion radiotherapy in broad-beam irradiation. The DVH for the rectum and the sigmoid colon was improved by adaptive radiotherapy considering the inter-fractional tumor regression. The result shows that this approach has possible advantages.

The objective of the dynamic radiotherapy project `Dynarad' within the European Community has been to compare and grade treatment techniques that are currently applied or being developed at the participating institutions. Cervical cancer was selected as the tumour site on the grounds that the involved organs at risk, mainly the rectum and the bladder, are very close to the tumour and partly located inside the internal target volume. In this work, a solid phantom simulating the pelvic anatomy was used by institutions in Belgium, France, Greece, Holland, Italy, Sweden and the United Kingdom. The results were evaluated using both biological and physical criteria. The main purpose of this parallel evaluation is to test the value of biological and physical evaluations in comparing treatment techniques. It is demonstrated that the biological objective functions allow a much higher conformality and a more clinically relevant scoring of the outcome. Often external beam treatment techniques have to be combined with intracavitary therapy to give clinically acceptable results. However, recent developments can reduce or even eliminate this need by delivering more conformal dose distributions using intensity modulated external dose delivery. In these cases the reliability of the patient set-up procedure becomes critical for the effectiveness of the treatment.

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.

The relationship between rectal dose distribution and the incidence of late rectal complications following external-beam radiotherapy has been previously studied using dose-volume histograms or dose-surface histograms. However, they do not account for the spatial dose distribution. This study proposes a metric based on both surface dose and distance that can predict the incidence of rectal bleeding in prostate cancer patients treated with radical radiotherapy. One hundred and forty-four patients treated with radical radiotherapy for prostate cancer were prospectively followed to record the incidence of grade ≥2 rectal bleeding. Radiotherapy plans were used to evaluate a dose-distance metric that accounts for the dose and its spatial distribution on the rectal surface, characterized by a logistic weighting function with slope a and inflection point d₀. This was compared to the effective dose obtained from dose-surface histograms, characterized by the parameter n which describes sensitivity to hot spots. The log-rank test was used to determine statistically significant (p < 0.05) cut-off values for the dose-distance metric and effective dose that predict for the occurrence of rectal bleeding. For the dose-distance metric, only d₀ = 25 and 30 mm combined with a > 5 led to statistical significant cut-offs. For the effective dose metric, only values of n in the range 0.07–0.35 led to statistically significant cut-offs. The proposed dose-distance metric is a predictor of rectal bleeding in prostate cancer patients treated with radiotherapy. Both the dose-distance metric and the effective dose metric indicate that the incidence of grade ≥2 rectal bleeding is sensitive to localized damage to the rectal surface.

The aim of this study was to develop a model exploiting artificial neural networks (ANNs) to correlate dosimetric and clinical variables with late rectal bleeding in prostate cancer patients undergoing radical radiotherapy and to compare the ANN results with those of a standard logistic regression (LR) analysis. 718 men included in the AIROPROS 0102 trial were analyzed. This multicenter protocol was characterized by the prospective evaluation of rectal toxicity, with a minimum follow-up of 36 months. Radiotherapy doses were between 70 and 80 Gy. Information was recorded for comorbidity, previous abdominal surgery, use of drugs and hormonal therapy. For each patient, a rectal dose–volume histogram (DVH) of the whole treatment was recorded and the equivalent uniform dose (EUD) evaluated as an effective descriptor of the whole DVH. Late rectal bleeding of grade ≥ 2 was considered to define positive events in this study (52 of 718 patients). The overall population was split into training and verification sets, both of which were involved in model instruction, and a test set, used to evaluate the predictive power of the model with independent data. Fourfold cross-validation was also used to provide realistic results for the full dataset. The LR was performed on the same data. Five variables were selected to predict late rectal bleeding: EUD, abdominal surgery, presence of hemorrhoids, use of anticoagulants and androgen deprivation. Following a receiver operating characteristic analysis of the independent test set, the areas under the curves (AUCs) were 0.704 and 0.655 for ANN and LR, respectively. When evaluated with cross-validation, the AUC was 0.714 for ANN and 0.636 for LR, which differed at a significance level of p = 0.03. When a practical discrimination threshold was selected, ANN could classify data with sensitivity and specificity both equal to 68.0%, whereas these values were 61.5% for LR. These data provide reasonable evidence that results obtained with ANNs are superior to those achieved with LR when predicting late radiotherapy-related rectal bleeding. The future introduction of patient-related personal characteristics, such as gene expression profiles, might improve the predictive power of statistical classifiers. More refined morphological aspects of the dose distribution, such as dose surface mapping, might also enhance the overall performance of ANN-based predictive models.

Previous studies have shown that during cancer radiotherapy a small translation or rotation of the tumor can lead to errors in dose delivery. Current best practice in radiotherapy accounts for tumor translations, but is unable to address rotation due to a lack of a reliable real-time estimate. We have developed a method based on the iterative closest point (ICP) algorithm that can compute rotation from kilovoltage x-ray images acquired during radiation treatment delivery. A total of 11 748 kilovoltage (kV) images acquired from ten patients (one fraction for each patient) were used to evaluate our tumor rotation algorithm. For each kV image, the three dimensional coordinates of three fiducial markers inside the prostate were calculated. The three dimensional coordinates were used as input to the ICP algorithm to calculate the real-time tumor rotation and translation around three axes. The results show that the root mean square error was improved for real-time calculation of tumor displacement from a mean of 0.97 mm with the stand alone translation to a mean of 0.16 mm by adding real-time rotation and translation displacement with the ICP algorithm. The standard deviation (SD) of rotation for the ten patients was 2.3°, 0.89° and 0.72° for rotation around the right–left (RL), anterior–posterior (AP) and superior–inferior (SI) directions respectively. The correlation between all six degrees of freedom showed that the highest correlation belonged to the AP and SI translation with a correlation of 0.67. The second highest correlation in our study was between the rotation around RL and rotation around AP, with a correlation of −0.33. Our real-time algorithm for calculation of rotation also confirms previous studies that have shown the maximum SD belongs to AP translation and rotation around RL. ICP is a reliable and fast algorithm for estimating real-time tumor rotation which could create a pathway to investigational clinical treatment studies requiring real-time measurement and adaptation to tumor rotation.

Red bone marrow (RBM) is an important dose-limiting tissue that has high radiosensitivity but is difficult to identify on clinical medical images. In this study, we investigated dose distribution in RBM for prostate cancer radiotherapy. Four suborgans were identified in the skeleton of the visible Chinese human phantom: cortical bone (CB), trabecular bone (TB), RBM, and yellow bone marrow (YBM). Dose distributions in the phantom were evaluated by the Monte Carlo method. When the left os coxae was taken as the organ-at-risk (OAR), the difference in absorbed dose between RBM and each CB and TB was up to 20%, but was much less (≤3.1%) between RBM and YBM. When the left os coxae and entire bone were both taken as OARs, RBM dose also increased with increasing planning target volume size. The results indicate the validity of using dose to homogeneous bone marrow mixture for estimating dose to RBM when RBM is not available in computational phantoms. In addition, the human skeletal system developed in this study provides a model for considering RBM dose in radiotherapy planning.

Objective. Radio-opaque markers are recommended for image-guided radiotherapy in liver stereotactic ablative radiotherapy (SABR), but their implantation is invasive. We evaluate in this in-silico study the feasibility of cone-beam computed tomography-guided stereotactic online-adaptive radiotherapy (CBCT-STAR) to propagate the target volumes without implanting radio-opaque markers and assess its consequence on the margin that should be used in that context. Approach. An emulator of a CBCT-STAR-dedicated treatment planning system was used to generate plans for 32 liver SABR patients. Three target volume propagation strategies were compared, analysing the volume difference between the GTV_Propagated and the GTV_Conventional, the vector lengths between their centres of mass (l_CoM), and the 95th percentile of the Hausdorff distance between these two volumes (HD95). These propagation strategies were: (1) structure-guided deformable registration with deformable GTV propagation; (2) rigid registration with rigid GTV propagation; and (3) image-guided deformable registration with rigid GTV propagation. Adaptive margin calculation integrated propagation errors, while interfraction position errors were removed. Scheduled plans (Plan_Non-adaptive) and daily-adapted plans (Plan_Adaptive) were compared for each treatment fraction. Main results. The image-guided deformable registration with rigid GTV propagation was the best propagation strategy regarding to l_CoM (mean: 4.3 +/− 2.1 mm), HD95 (mean 4.8 +/− 3.2 mm) and volume preservation between GTV_Propagated and GTV_Conventional. This resulted in a planning target volume (PTV) margin increase (+69.1% in volume on average). Online adaptation (Plan_Adaptive) reduced the violation rate of the most important dose constraints ('priority 1 constraints', 4.2 versus 0.9%, respectively; p < 0.001) and even improved target volume coverage compared to non-adaptive plans (Plan_Non-adaptive). Significance. Markerless CBCT-STAR for liver tumours is feasible using Image-guided deformable registration with rigid GTV propagation. Despite the cost in terms of PTV volumes, daily adaptation reduces constraints violation and restores target volumes coverage.

Conformal radiotherapy for rectal cancer is associated with small bowel toxicity mainly diarrhea. Treating patients with a full bladder is one of the practical solutions to reduce small bowel toxicity. Previous studies on prostate and cervix cancer patients revealed that maintaining consistent bladder volume throughout radiotherapy treatment is challenging. The aim of this study was to measure bladder volume variation throughout radiotherapy treatment. This study also measured the association between bladder volume changes and diarrhea. Twenty two rectal cancer patients were recruited prospectively. Patients were planned for treatment with full bladder following departmental bladder filling protocol and the planning bladder volume was measured during CT-simulation. During radiotherapy, the bladder volume was measured weekly using cone-beam computed tomography (CBCT) and compared to planning bladder volume. Incidence and severity of diarrhea were recorded during the weekly patient review. There was a negative time trend for bladder volume throughout five weeks treatment. The mean bladder volume decreased 18 % from 123 mL (SD 54 mL) during CT-simulation to 101 mL (SD 71 mL) on the 5th week of radiotherapy, but the decrease is not statistically significant. However, there was a large variation of bladder volume within each patient during treatment. This study showed an association between changes of bladder volume and diarrhea (P = 0.045). In conclusion bladder volume reduced throughout radiotherapy treatment for conformal radiotherapy for rectal cancer and there was a large variation of bladder volume within patients.

This is a practical guide to the implementation of 3D/4D ultrasound imaging in radiography. Among its features are the coverage of the technology utilised for ultrasound-guided radiotherapy, clinical need and advantages of using ultrasound. It is a practical guide for users which incorporates implementation, potential errors, uncertainties and training. This is a comprehensive review of the state-of-the-art technologies which also looks at the future direction of this exciting field. Researchers, students, hospital physicists and radiographers will all find this book of use as it guides them through current clinical situation and examines the full potential of ultrasound in radiotherapy. Part of IPEM–IOP Series in Physics and Engineering in Medicine and Biology.