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C Taylor - One of the best experts on this subject based on the ideXlab platform.
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risk of heart disease in relation to Radiotherapy and chemotherapy with anthracyclines among 19 464 breast Cancer patients in denmark 1977 2005
Radiotherapy and Oncology, 2017Co-Authors: C Taylor, P Mcgale, S C Darby, Majbritt Jensen, Jens Christian Rehammar, Ebbe Laugaard Lorenzen, Lars Videbaek, Zhe WangAbstract:Abstract Background and purpose The risk of heart disease subsequent to breast Cancer Radiotherapy was examined with particular focus on women receiving anthracycline-containing chemotherapy. Material and methods Women diagnosed with early-stage breast Cancer in Denmark, 1977–2005, were identified from the register of the Danish Breast Cancer Cooperative Group, as was information on Cancer-directed treatment. Information on heart disease was sought from the Danish National Patient and Cause of Death Registries. Incidence rate ratios were estimated comparing left-sided with right-sided Cancer (IRR, LvR), stratified by calendar year, age, and time since breast Cancer Radiotherapy. Results Among 19,464 women receiving Radiotherapy, the IRR, LvR, was 1.11 (95% CI 1.03–1.20, p =0.005) for all heart disease and among those also receiving anthracyclines the IRR, LvR, was 1.32 (95% CI 1.02–1.70, p =0.03). This risk was highest if the treatment was given before the age of 50years (IRR, LvR, 1.44, (95% CI 1.04–2.01) but there was no significant trend with age or time since treatment. Conclusions Radiotherapy for left-sided breast Cancer is associated with a higher risk of heart disease than for right-sided with the largest increases seen in women who also received anthracycline-containing chemotherapy.
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estimating the risks of breast Cancer Radiotherapy evidence from modern radiation doses to the lungs and heart and from previous randomized trials
Journal of Clinical Oncology, 2017Co-Authors: C Taylor, Marianne Ewertz, C Correa, Frances K Duane, M C Aznar, Stewart J Anderson, Jonas Bergh, David Dodwell, Richard GrayAbstract:Purpose Radiotherapy reduces the absolute risk of breast Cancer mortality by a few percentage points in suitable women but can cause a second Cancer or heart disease decades later. We estimated the absolute long-term risks of modern breast Cancer Radiotherapy. Methods First, a systematic literature review was performed of lung and heart doses in breast Cancer regimens published during 2010 to 2015. Second, individual patient data meta-analyses of 40,781 women randomly assigned to breast Cancer Radiotherapy versus no Radiotherapy in 75 trials yielded rate ratios (RRs) for second primary Cancers and cause-specific mortality and excess RRs (ERRs) per Gy for incident lung Cancer and cardiac mortality. Smoking status was unavailable. Third, the lung or heart ERRs per Gy in the trials and the 2010 to 2015 doses were combined and applied to current smoker and nonsmoker lung Cancer and cardiac mortality rates in population-based data. Results Average doses from 647 regimens published during 2010 to 2015 were 5.7 Gy for whole lung and 4.4 Gy for whole heart. The median year of irradiation was 2010 (interquartile range [IQR], 2008 to 2011). Meta-analyses yielded lung Cancer incidence ≥ 10 years after Radiotherapy RR of 2.10 (95% CI, 1.48 to 2.98; P < .001) on the basis of 134 Cancers, indicating 0.11 (95% CI, 0.05 to 0.20) ERR per Gy whole-lung dose. For cardiac mortality, RR was 1.30 (95% CI, 1.15 to 1.46; P < .001) on the basis of 1,253 cardiac deaths. Detailed analyses indicated 0.04 (95% CI, 0.02 to 0.06) ERR per Gy whole-heart dose. Estimated absolute risks from modern Radiotherapy were as follows: lung Cancer, approximately 4% for long-term continuing smokers and 0.3% for nonsmokers; and cardiac mortality, approximately 1% for smokers and 0.3% for nonsmokers. Conclusion For long-term smokers, the absolute risks of modern Radiotherapy may outweigh the benefits, yet for most nonsmokers (and ex-smokers), the benefits of Radiotherapy far outweigh the risks. Hence, smoking can determine the net effect of Radiotherapy on mortality, but smoking cessation substantially reduces Radiotherapy risk.
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estimating the risks of breast Cancer Radiotherapy evidence from modern radiation doses to the lungs and heart and from previous randomized trials
Journal of Clinical Oncology, 2017Co-Authors: C Taylor, Marianne Ewertz, Frances K Duane, M C Aznar, Stewart J Anderson, Jonas Bergh, David Dodwell, Candace R Correa, Richard GrayAbstract:PurposeRadiotherapy reduces the absolute risk of breast Cancer mortality by a few percentage points in suitable women but can cause a second Cancer or heart disease decades later. We estimated the absolute long-term risks of modern breast Cancer Radiotherapy.MethodsFirst, a systematic literature review was performed of lung and heart doses in breast Cancer regimens published during 2010 to 2015. Second, individual patient data meta-analyses of 40,781 women randomly assigned to breast Cancer Radiotherapy versus no Radiotherapy in 75 trials yielded rate ratios (RRs) for second primary Cancers and cause-specific mortality and excess RRs (ERRs) per Gy for incident lung Cancer and cardiac mortality. Smoking status was unavailable. Third, the lung or heart ERRs per Gy in the trials and the 2010 to 2015 doses were combined and applied to current smoker and nonsmoker lung Cancer and cardiac mortality rates in population-based data.ResultsAverage doses from 647 regimens published during 2010 to 2015 were 5.7 Gy fo...
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cardiac dose estimates from danish and swedish breast Cancer Radiotherapy during 1977 2001
Radiotherapy and Oncology, 2011Co-Authors: C Taylor, A Nisbet, P Mcgale, S C Darby, Per Hall, Giovanna Gagliardi, Dorthe Scavenius Bronnum, Majbritt Jensen, Marianne EwertzAbstract:Background and purpose To estimate target and cardiac doses from breast Cancer Radiotherapy in Denmark and in the Stockholm and Umea areas of Sweden during 1977–2001.
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cardiac doses from swedish breast Cancer Radiotherapy since the 1950s
Radiotherapy and Oncology, 2009Co-Authors: C Taylor, A Nisbet, P Mcgale, Ulla Goldman, S C Darby, Per Hall, Giovanna GagliardiAbstract:Abstract Purpose To estimate cardiac doses from breast Cancer Radiotherapy in Sweden from the 1950s to the 1990s. These doses will contribute to deriving dose–response relationships for the risk of radiation-induced heart disease. Materials and methods The Swedish nationwide Cancer register was used to identify women irradiated for breast Cancer in the Stockholm area. Virtual simulation, computed tomography planning, and manual planning were used to reconstruct Radiotherapy regimens. Estimates of heart and coronary artery dose were derived for each woman. Results Cardiac doses were assessed in 358 women. Mean heart dose varied from Conclusions Cardiac doses from Swedish breast Cancer Radiotherapy increased from the 1950s to the 1970s, and then reduced substantially in the 1980s and 1990s. The wide range of doses observed should provide substantial statistical power for the estimation of dose–response relationships for radiation-induced heart disease.
Renaud De Crevoisier - One of the best experts on this subject based on the ideXlab platform.
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comparison of cbct based dose calculation methods in head and neck Cancer Radiotherapy from hounsfield unit to density calibration curve to deep learning
Medical Physics, 2020Co-Authors: A Barateau, Oscar Acosta, Eugenia Mylona, Renaud De Crevoisier, A Simon, A Largent, N Perichon, J Castelli, E Chajon, Jeanclaude NunesAbstract:Purpose Anatomical variations occur during head and neck (HandN) Radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in HandN Cancer Radiotherapy. Methods Forty-four patients received VMAT for HandN Cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CTref). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT(ref)and pCT of each method. Results In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and -36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon,P <= 0.05). The DLM dose discrepancies were 7 +/- 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D(mean)and 5 +/- 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D-mean). For the parotid gland D-mean, no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate +/- standard deviation was 98.1 +/- 1.2%, 91.0 +/- 5.3%, 97.9 +/- 1.6%, and 98.8 +/- 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. Conclusions For HandN Radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.
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Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate Cancer Radiotherapy
Radiotherapy and Oncology, 2017Co-Authors: Oscar Acosta, Eugenia Mylona, Mathieu Le Dain, Camille Voisin, Thibaut Lizee, Bastien Rigaud, Carolina Lafond, Khemara Gnep, Renaud De CrevoisierAbstract:Background and purpose Segmentation of intra-prostatic urethra for dose assessment from planning CT may help explaining urinary toxicity in prostate Cancer Radiotherapy. This work sought to: i) propose an automatic method for urethra segmentation in CT, ii) compare it with previously proposed surrogate models and iii) quantify the dose received by the urethra in patients treated with IMRT. Materials and methods A weighted multi-atlas-based urethra segmentation method was devised from a training data set of 55 CT scans of patients receiving brachytherapy with visible urinary catheters. Leave-one-out cross validation was performed to quantify the error between the urethra segmentation and the catheter ground truth with two scores: the centerlines distance (CLD) and the percentage of centerline within a certain distance from the catheter (PWR). The segmentation method was then applied to a second test data set of 95 prostate Cancer patients having received 78 Gy IMRT to quantify dose to the urethra. Results Mean CLD was 3.25 ± 1.2 mm for the whole urethra and 3.7 ± 1.7 mm, 2.52 ± 1.5 mm, and 3.01 ± 1.7 mm for the top, middle, and bottom thirds, respectively. In average, 53% of the segmented centerlines were within a radius < 3.5 mm from the centerline ground truth and 83% in a radius < 5 mm. The proposed method outperformed existing surrogate models. In IMRT, urethra DVH was significantly higher than prostate DVH from V74 Gy to V79 Gy. Conclusion A multi-atlas-based segmentation method was proposed enabling assessment of the dose within the prostatic urethra. .
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A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions
IEEE Journal of Biomedical and Health Informatics, 2015Co-Authors: Julie Coloigner, Oscar Acosta, Renaud De Crevoisier, Aureline Fargeas, Amar Kachenoura, Gael Drean, C. Lafond, Lu Wang, Lotfi Senhadji, Laurent AlberaAbstract:The understanding of dose/side-effects relationships in prostate Cancer Radiotherapy is crucial to define appropriate individual's constraints for the therapy planning. Most of the existing methods to predict side-effects do not fully exploit the rich spatial information conveyed by the three-dimensional planned dose distributions. We propose a new classification method for three-dimensional individuals' doses, based on a new semi-nonnegative ICA algorithm to identify patients at risk of presenting rectal bleeding from a population treated for prostate Cancer. The method first determines two bases of vectors from the population data: the two bases span vector subspaces, which characterize patients with and without rectal bleeding, respectively. The classification is then achieved by calculating the distance of a given patient to the two subspaces. The results, obtained on a cohort of 87 patients (at two year follow-up) treated with Radiotherapy, showed high performance in terms of sensitivity and specificity.
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multi atlas based segmentation of pelvic structures from ct scans for planning in prostate Cancer Radiotherapy
2014Co-Authors: Oscar Acosta, Renaud De Crevoisier, Gael Drean, Jason A Dowling, A Simon, Pascal HaigronAbstract:In prostate Cancer Radiotherapy, the accurate identification of the prostate and organs at risk in planning computer tomography (CT) images is an important part of the therapy planning and optimization. Manually contouring these organs can be a time-consuming process and subject to intra- and inter-expert variability. Automatic identification of organ boundaries from these images is challenging due to the poor soft tissue contrast. Atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new individual space; however, the interindividual variability and registration errors may lead to biased results. Multi-atlas approaches can partly overcome some of these difficulties by selecting the most similar atlases among a large data base, but the definition of similarity measure between the available atlases and the query individual has still to be addressed. The purpose of this chapter is to explain atlas-based segmentation approaches and the evaluation of different atlas-based strategies to simultaneously segment prostate, bladder, and rectum from CT images. A comparison between single and multiple atlases is performed. Experiments on atlas ranking, selection strategies, and fusion-decision rules are carried out to illustrate the presented methodology. Propagation of labels using two registration strategies is applied and the results of the comparison with manual delineations are reported.
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Voxel-based population analysis for correlating local dose and rectal toxicity in prostate Cancer Radiotherapy.
Physics in Medicine and Biology, 2013Co-Authors: Oscar Acosta, Gael Drean, Pascal Haigron, Juan David Ospina, Antoine Simon, Caroline Lafond, Renaud De CrevoisierAbstract:The majority of current models utilized for predicting toxicity in prostate Cancer Radiotherapy are based on dose-volume histograms. One of their main drawbacks is the lack of spatial accuracy, since they consider the organs as a whole volume and thus ignore the heterogeneous intra-organ radio-sensitivity. In this paper, we propose a dose-image-based framework to reveal the relationships between local dose and toxicity. In this approach, the three-dimensional (3D) planned dose distributions across a population are non-rigidly registered into a common coordinate system and compared at a voxel level, therefore enabling the identification of 3D anatomical patterns, which may be responsible for toxicity, at least to some extent. Additionally, different metrics were employed in order to assess the quality of the dose mapping. The value of this approach was demonstrated by prospectively analyzing rectal bleeding (⩾Grade 1 at 2 years) according to the CTCAE v3.0 classification in a series of 105 patients receiving 80 Gy to the prostate by intensity modulated radiation therapy (IMRT). Within the patients presenting bleeding, a significant dose excess (6 Gy on average, p < 0.01) was found in a region of the anterior rectal wall. This region, close to the prostate (1 cm), represented less than 10% of the rectum. This promising voxel-wise approach allowed subregions to be defined within the organ that may be involved in toxicity and, as such, must be considered during the inverse IMRT planning step.
Steve B Jiang - One of the best experts on this subject based on the ideXlab platform.
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deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy a feasibility study
Physics in Medicine and Biology, 2017Co-Authors: Xin Zhen, Steve B Jiang, Jiawei Chen, Zichun Zhong, Brian Hrycushko, Linghong Zhou, Kevin AlbuquerqueAbstract:Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical Cancer Radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical Cancer patients treated with combined external beam Radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical Cancer Radiotherapy.
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deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy a feasibility study
Physics in Medicine and Biology, 2017Co-Authors: Xin Zhen, Steve B Jiang, Jiawei Chen, Zichun Zhong, Brian Hrycushko, Linghong Zhou, Kevin AlbuquerqueAbstract:The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy: a feasibility study' with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.
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accurate respiration measurement using dc coupled continuous wave radar sensor for motion adaptive Cancer Radiotherapy
IEEE Transactions on Biomedical Engineering, 2012Co-Authors: Changzhan Gu, A.y.c. Fung, C. Torres, Hualiang Zhang, Steve B Jiang, Ruijiang Li, Changzhi LiAbstract:Accurate respiration measurement is crucial in motion-adaptive Cancer Radiotherapy. Conventional methods for respiration measurement are undesirable because they are either invasive to the patient or do not have sufficient accuracy. In addition, measurement of external respiration signal based on conventional approaches requires close patient contact to the physical device which often causes patient discomfort and undesirable motion during radiation dose delivery. In this paper, a dc-coupled continuous-wave radar sensor was presented to provide a noncontact and noninvasive approach for respiration measurement. The radar sensor was designed with dc-coupled adaptive tuning architectures that include RF coarse-tuning and baseband fine-tuning, which allows the radar sensor to precisely measure movement with stationary moment and always work with the maximum dynamic range. The accuracy of respiration measurement with the proposed radar sensor was experimentally evaluated using a physical phantom, human subject, and moving plate in a Radiotherapy environment. It was shown that respiration measurement with radar sensor while the radiation beam is on is feasible and the measurement has a submillimeter accuracy when compared with a commercial respiration monitoring system which requires patient contact. The proposed radar sensor provides accurate, noninvasive, and noncontact respiration measurement and therefore has a great potential in motion-adaptive Radiotherapy.
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doppler radar respiration measurement for gated lung Cancer Radiotherapy
2011 IEEE Topical Conference on Biomedical Wireless Technologies Networks and Sensing Systems, 2011Co-Authors: Changzhan Gu, Changzhi Li, Ruijiang Li, Steve B JiangAbstract:Respiration-gated radiation therapy is a promising treatment modality that precisely delivers prescribed radiation dose to the lung tumor while minimizing the incidence and severity of normal tissue complications. Conventional gating techniques either rely on implanted fiducial markers or external surrogates such as markers placed on patients' abdomen. They are either invasive to the patients or do not have sufficient accuracy. In this paper, we present a non-contact Doppler radar method to non-invasively measure the respiration signals, from which, accurate gating signals can be derived to control the linac. No marker is needed in our method, which makes it very convenient in use. We measured the respiration using a 5.8 GHz quadrature radar. Analysis of the measured signal is presented. It has been shown that the non-contact means of respiration measurement is able to supply reliable breathing motions and accurate gating signals for Radiotherapy of mobile tumors.
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fluoroscopic tumor tracking for image guided lung Cancer Radiotherapy
Physics in Medicine and Biology, 2009Co-Authors: L Cervino, Xiaoli Tang, Nuno Vasconcelos, Steve B JiangAbstract:Accurate lung tumor tracking in real time is a keystone to image-guided Radiotherapy of lung Cancers. Existing lung tumor tracking approaches can be roughly grouped into three categories: (1) deriving tumor position from external surrogates; (2) tracking implanted fiducial markers fluoroscopically or electromagnetically; (3) fluoroscopically tracking lung tumor without implanted fiducial markers. The first approach suffers from insufficient accuracy, while the second may not be widely accepted due to the risk of pneumothorax. Previous studies in fluoroscopic markerless tracking are mainly based on template matching methods, which may fail when the tumor boundary is unclear in fluoroscopic images. In this paper we propose a novel markerless tumor tracking algorithm, which employs the correlation between the tumor position and surrogate anatomic features in the image. The positions of the surrogate features are not directly tracked; instead, we use principal component analysis of regions of interest containing them to obtain parametric representations of their motion patterns. Then, the tumor position can be predicted from the parametric representations of surrogates through regression. Four regression methods were tested in this study: linear and two-degree polynomial regression, artificial neural network (ANN) and support vector machine (SVM). The experimental results based on fluoroscopic sequences of ten lung Cancer patients demonstrate a mean tracking error of 2.1 pixels and a maximum error at a 95% confidence level of 4.6 pixels (pixel size is about 0.5 mm) for the proposed tracking algorithm.
Oscar Acosta - One of the best experts on this subject based on the ideXlab platform.
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comparison of cbct based dose calculation methods in head and neck Cancer Radiotherapy from hounsfield unit to density calibration curve to deep learning
Medical Physics, 2020Co-Authors: A Barateau, Oscar Acosta, Eugenia Mylona, Renaud De Crevoisier, A Simon, A Largent, N Perichon, J Castelli, E Chajon, Jeanclaude NunesAbstract:Purpose Anatomical variations occur during head and neck (HandN) Radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in HandN Cancer Radiotherapy. Methods Forty-four patients received VMAT for HandN Cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CTref). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT(ref)and pCT of each method. Results In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and -36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon,P <= 0.05). The DLM dose discrepancies were 7 +/- 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D(mean)and 5 +/- 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D-mean). For the parotid gland D-mean, no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate +/- standard deviation was 98.1 +/- 1.2%, 91.0 +/- 5.3%, 97.9 +/- 1.6%, and 98.8 +/- 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. Conclusions For HandN Radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.
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Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate Cancer Radiotherapy
Radiotherapy and Oncology, 2017Co-Authors: Oscar Acosta, Eugenia Mylona, Mathieu Le Dain, Camille Voisin, Thibaut Lizee, Bastien Rigaud, Carolina Lafond, Khemara Gnep, Renaud De CrevoisierAbstract:Background and purpose Segmentation of intra-prostatic urethra for dose assessment from planning CT may help explaining urinary toxicity in prostate Cancer Radiotherapy. This work sought to: i) propose an automatic method for urethra segmentation in CT, ii) compare it with previously proposed surrogate models and iii) quantify the dose received by the urethra in patients treated with IMRT. Materials and methods A weighted multi-atlas-based urethra segmentation method was devised from a training data set of 55 CT scans of patients receiving brachytherapy with visible urinary catheters. Leave-one-out cross validation was performed to quantify the error between the urethra segmentation and the catheter ground truth with two scores: the centerlines distance (CLD) and the percentage of centerline within a certain distance from the catheter (PWR). The segmentation method was then applied to a second test data set of 95 prostate Cancer patients having received 78 Gy IMRT to quantify dose to the urethra. Results Mean CLD was 3.25 ± 1.2 mm for the whole urethra and 3.7 ± 1.7 mm, 2.52 ± 1.5 mm, and 3.01 ± 1.7 mm for the top, middle, and bottom thirds, respectively. In average, 53% of the segmented centerlines were within a radius < 3.5 mm from the centerline ground truth and 83% in a radius < 5 mm. The proposed method outperformed existing surrogate models. In IMRT, urethra DVH was significantly higher than prostate DVH from V74 Gy to V79 Gy. Conclusion A multi-atlas-based segmentation method was proposed enabling assessment of the dose within the prostatic urethra. .
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A Novel Classification Method for Prediction of Rectal Bleeding in Prostate Cancer Radiotherapy Based on a Semi-Nonnegative ICA of 3D Planned Dose Distributions
IEEE Journal of Biomedical and Health Informatics, 2015Co-Authors: Julie Coloigner, Oscar Acosta, Renaud De Crevoisier, Aureline Fargeas, Amar Kachenoura, Gael Drean, C. Lafond, Lu Wang, Lotfi Senhadji, Laurent AlberaAbstract:The understanding of dose/side-effects relationships in prostate Cancer Radiotherapy is crucial to define appropriate individual's constraints for the therapy planning. Most of the existing methods to predict side-effects do not fully exploit the rich spatial information conveyed by the three-dimensional planned dose distributions. We propose a new classification method for three-dimensional individuals' doses, based on a new semi-nonnegative ICA algorithm to identify patients at risk of presenting rectal bleeding from a population treated for prostate Cancer. The method first determines two bases of vectors from the population data: the two bases span vector subspaces, which characterize patients with and without rectal bleeding, respectively. The classification is then achieved by calculating the distance of a given patient to the two subspaces. The results, obtained on a cohort of 87 patients (at two year follow-up) treated with Radiotherapy, showed high performance in terms of sensitivity and specificity.
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multi atlas based segmentation of pelvic structures from ct scans for planning in prostate Cancer Radiotherapy
2014Co-Authors: Oscar Acosta, Renaud De Crevoisier, Gael Drean, Jason A Dowling, A Simon, Pascal HaigronAbstract:In prostate Cancer Radiotherapy, the accurate identification of the prostate and organs at risk in planning computer tomography (CT) images is an important part of the therapy planning and optimization. Manually contouring these organs can be a time-consuming process and subject to intra- and inter-expert variability. Automatic identification of organ boundaries from these images is challenging due to the poor soft tissue contrast. Atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new individual space; however, the interindividual variability and registration errors may lead to biased results. Multi-atlas approaches can partly overcome some of these difficulties by selecting the most similar atlases among a large data base, but the definition of similarity measure between the available atlases and the query individual has still to be addressed. The purpose of this chapter is to explain atlas-based segmentation approaches and the evaluation of different atlas-based strategies to simultaneously segment prostate, bladder, and rectum from CT images. A comparison between single and multiple atlases is performed. Experiments on atlas ranking, selection strategies, and fusion-decision rules are carried out to illustrate the presented methodology. Propagation of labels using two registration strategies is applied and the results of the comparison with manual delineations are reported.
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Voxel-based population analysis for correlating local dose and rectal toxicity in prostate Cancer Radiotherapy.
Physics in Medicine and Biology, 2013Co-Authors: Oscar Acosta, Gael Drean, Pascal Haigron, Juan David Ospina, Antoine Simon, Caroline Lafond, Renaud De CrevoisierAbstract:The majority of current models utilized for predicting toxicity in prostate Cancer Radiotherapy are based on dose-volume histograms. One of their main drawbacks is the lack of spatial accuracy, since they consider the organs as a whole volume and thus ignore the heterogeneous intra-organ radio-sensitivity. In this paper, we propose a dose-image-based framework to reveal the relationships between local dose and toxicity. In this approach, the three-dimensional (3D) planned dose distributions across a population are non-rigidly registered into a common coordinate system and compared at a voxel level, therefore enabling the identification of 3D anatomical patterns, which may be responsible for toxicity, at least to some extent. Additionally, different metrics were employed in order to assess the quality of the dose mapping. The value of this approach was demonstrated by prospectively analyzing rectal bleeding (⩾Grade 1 at 2 years) according to the CTCAE v3.0 classification in a series of 105 patients receiving 80 Gy to the prostate by intensity modulated radiation therapy (IMRT). Within the patients presenting bleeding, a significant dose excess (6 Gy on average, p < 0.01) was found in a region of the anterior rectal wall. This region, close to the prostate (1 cm), represented less than 10% of the rectum. This promising voxel-wise approach allowed subregions to be defined within the organ that may be involved in toxicity and, as such, must be considered during the inverse IMRT planning step.
Kevin Albuquerque - One of the best experts on this subject based on the ideXlab platform.
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Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical Cancer Radiotherapy
BMC, 2018Co-Authors: Jiawei Chen, Haibin Chen, Zichun Zhong, Zhuoyu Wang, Brian Hrycushko, Linghong Zhou, Steve Jiang, Kevin Albuquerque, Xin ZhenAbstract:Abstract Background Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical Cancer Radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction. Methods Forty-two cervical Cancer patients treated with combined external beam Radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0–1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D0.1/1/2cm 3. Results Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D0.1/1/2cm 3 (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity. Conclusions Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation
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deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy a feasibility study
Physics in Medicine and Biology, 2017Co-Authors: Xin Zhen, Steve B Jiang, Jiawei Chen, Zichun Zhong, Brian Hrycushko, Linghong Zhou, Kevin AlbuquerqueAbstract:Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical Cancer Radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical Cancer patients treated with combined external beam Radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical Cancer Radiotherapy.
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deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy a feasibility study
Physics in Medicine and Biology, 2017Co-Authors: Xin Zhen, Steve B Jiang, Jiawei Chen, Zichun Zhong, Brian Hrycushko, Linghong Zhou, Kevin AlbuquerqueAbstract:The application of machine learning (ML) presents tremendous opportunities for the field of oncology, thus we read 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical Cancer Radiotherapy: a feasibility study' with great interest. In this article, the authors used state of the art techniques: a pre-trained convolutional neural network (VGG-16 CNN), transfer learning, data augmentation, drop out and early stopping, all of which are directly responsible for the success and the excitement that these algorithms have created in other fields. We believe that the use of these techniques can offer tremendous opportunities in the field of Medical Physics and as such we would like to praise the authors for their pioneering application to the field of Radiation Oncology. That being said, given that the field of Medical Physics has unique characteristics that differentiate us from those fields where these techniques have been applied successfully, we would like to raise some points for future discussion and follow up studies that could help the community understand the limitations and nuances of deep learning techniques.
