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BACKGROUND: The delineation of intraprostatic lesions is vital for correct delivery of focal radiotherapy boost in patients with prostate cancer (PC). Errors in the delineation could translate into reduced tumour control and potentially increase the side effects. The purpose of this study is to compare PET-based delineation methods with histopathology. MATERIALS AND METHODS: The study population consisted of 15 patients with confirmed high-risk PC intended for prostatectomy. [68Ga]-PSMA-PET/MR was performed prior to surgery. Prostate lesions identified in histopathology were transferred to the in vivo [68Ga]-PSMA-PET/MR coordinate system. Four radiation oncologists manually delineated intraprostatic lesions based on PET data. Various semi-automatic segmentation methods were employed, including absolute and relative thresholds, adaptive threshold, and multi-level Otsu threshold. RESULTS: The gross tumour volumes (GTVs) delineated by the oncologists showed a moderate level of interobserver agreement with Dice similarity coefficient (DSC) of 0.68. In comparison with histopathology, manual delineations exhibited the highest median DSC and the lowest false discovery rate (FDR) among all approaches. Among semi-automatic approaches, GTVs generated using standardized uptake value (SUV) thresholds above 4 (SUV > 4) demonstrated the highest median DSC (0.41), with 0.51 median lesion coverage ratio, FDR of 0.66 and the 95th percentile of the Hausdorff distance (HD95%) of 8.22 mm. INTERPRETATION: Manual delineations showed a moderate level of interobserver agreement. Compared to histopathology, manual delineations and SUV > 4 exhibited the highest DSC and the lowest HD95% values. The methods that resulted in a high lesion coverage were associated with a large overestimation of the size of the lesions.
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Isótopos de Gálio , Radioisótopos de Gálio , Neoplasias da Próstata , Carga Tumoral , Humanos , Masculino , Neoplasias da Próstata/patologia , Neoplasias da Próstata/diagnóstico por imagem , Tomografia por Emissão de Pósitrons/métodos , Idoso , Prostatectomia , Pessoa de Meia-Idade , Compostos Radiofarmacêuticos , Oligopeptídeos , Imageamento por Ressonância Magnética/métodos , Ácido Edético/análogos & derivadosRESUMO
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification. METHODS: We analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: We find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively. CONCLUSIONS: By co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are two medical imaging methods commonly used to image prostate cancers. However, the relationship between images obtained with these methods and prostate cancer aggressiveness is not well understood. Here, we investigate whether MRI and PET can differentiate between lower- and higher-grade prostate tumors, where grade is an indicator of how aggressive the disease is likely to be. We find that the characteristics of prostate cancer tumors as seen on MRI and PET scans can help to predict tumor grade. This means that these imaging methods may be helpful when clinicians are predicting patient prognosis and deciding on appropriate treatments. However, further validation is necessary before these approaches are widely implemented for this purpose.
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OBJECTIVE: PET/CT and multiparametric MRI (mpMRI) are important diagnostic tools in clinically significant prostate cancer (csPC). The aim of this study was to compare csPC detection rates with [ 68 Ga]PSMA-11-PET (PSMA)-PET, [ 11 C]Acetate (ACE)-PET, and mpMRI with histopathology as reference, to identify the most suitable imaging modalities for subsequent hybrid imaging. An additional aim was to compare inter-reader variability to assess reproducibility. METHODS: During 2016-2019, all study participants were examined with PSMA-PET/mpMRI and ACE-PET/CT prior to radical prostatectomy. PSMA-PET, ACE-PET and mpMRI were evaluated separately by two observers, and were compared with histopathology-defined csPC. Statistical analyses included two-sided McNemar test and index of specific agreement. RESULTS: Fifty-five study participants were included, with 130 histopathological intraprostatic lesions >0.05 cc. Of these, 32% (42/130) were classified as csPC with ISUP grade ≥2 and volume >0.5 cc. PSMA-PET and mpMRI showed no difference in performance ( P â =â 0.48), with mean csPC detection rate of 70% (29.5/42) and 74% (31/42), respectively, while with ACE-PET the mean csPC detection rate was 37% (15.5/42). Interobserver agreement was higher with PSMA-PET compared to mpMRI [79% (26/33) vs 67% (24/38)]. Including all detected lesions from each pair of observers, the detection rate increased to 90% (38/42) with mpMRI, and 79% (33/42) with PSMA-PET. CONCLUSION: PSMA-PET and mpMRI showed high csPC detection rates and superior performance compared to ACE-PET. The interobserver agreement indicates higher reproducibility with PSMA-PET. The combined result of all observers in both PSMA-PET and mpMRI showed the highest detection rate, suggesting an added value of a hybrid imaging approach.
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Objective. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach. We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results. We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance. We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.
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Processamento de Imagem Assistida por Computador , Aprendizado de Máquina , Humanos , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Planejamento da Radioterapia Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Dosagem RadioterapêuticaRESUMO
The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics. To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.
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BACKGROUND: Perirectal spacers may be beneficial to reduce rectal side effects from radiotherapy (RT). Here, we present the impact of a hyaluronic acid (HA) perirectal spacer on rectal dose as well as spacer stability, long-term gastrointestinal (GI) and genitourinary (GU) toxicity and patient-reported outcome (PRO). METHODS: In this phase II study 81 patients with low- and intermediate-risk prostate cancer received transrectal injections with HA before external beam RT (78 Gy in 39 fractions). The HA spacer was evaluated with MRI four times; before (MR0) and after HA-injection (MR1), at the middle (MR2) and at the end (MR3) of RT. GI and GU toxicity was assessed by physician for up to five years according to the RTOG scale. PROs were collected using the Swedish National Prostate Cancer Registry and Prostate cancer symptom scale questionnaires. RESULTS: There was a significant reduction in rectal V70% (54.6 Gy) and V90% (70.2 Gy) between MR0 and MR1, as well as between MR0 to MR2 and MR3. From MR1 to MR2/MR3, HA thickness decreased with 28%/32% and CTV-rectum space with 19%/17% in the middle level. The cumulative late grade ≥ 2 GI toxicity at 5 years was 5% and the proportion of PRO moderate or severe overall bowel problems at 5 years follow-up was 12%. Cumulative late grade ≥ 2 GU toxicity at 5 years was 12% and moderate or severe overall urinary problems at 5 years were 10%. CONCLUSION: We show that the HA spacer reduced rectal dose and long-term toxicity.
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Ácido Hialurônico , Neoplasias da Próstata , Humanos , Masculino , Ácido Hialurônico/uso terapêutico , Medidas de Resultados Relatados pelo Paciente , Neoplasias da Próstata/tratamento farmacológico , Neoplasias da Próstata/radioterapia , Dosagem Radioterapêutica , Reto , Radioterapia/efeitos adversosRESUMO
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.
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In the last years, deep learning has dramatically improved the performances in a variety of medical image analysis applications. Among different types of deep learning models, convolutional neural networks have been among the most successful and they have been used in many applications in medical imaging. Training deep convolutional neural networks often requires large amounts of image data to generalize well to new unseen images. It is often time-consuming and expensive to collect large amounts of data in the medical image domain due to expensive imaging systems, and the need for experts to manually make ground truth annotations. A potential problem arises if new structures are added when a decision support system is already deployed and in use. Since the field of radiation therapy is constantly developing, the new structures would also have to be covered by the decision support system. In the present work, we propose a novel loss function to solve multiple problems: imbalanced datasets, partially-labeled data, and incremental learning. The proposed loss function adapts to the available data in order to utilize all available data, even when some have missing annotations. We demonstrate that the proposed loss function also works well in an incremental learning setting, where an existing model is easily adapted to semi-automatically incorporate delineations of new organs when they appear. Experiments on a large in-house dataset show that the proposed method performs on par with baseline models, while greatly reducing the training time and eliminating the hassle of maintaining multiple models in practice.
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Aprendizado Profundo , Diagnóstico por Imagem , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais de Computação , SemânticaRESUMO
Radiotherapy (RT) datasets can suffer from variations in annotation of organ at risk (OAR) and target structures. Annotation standards exist, but their description for prostate targets is limited. This restricts the use of such data for supervised machine learning purposes as it requires properly annotated data. The aim of this work was to develop a modality independent deep learning (DL) model for automatic classification and annotation of prostate RT DICOM structures. Delineated prostate organs at risk (OAR), support- and target structures (gross tumor volume [GTV]/clinical target volume [CTV]/planning target volume [PTV]), along with or without separate vesicles and/or lymph nodes, were extracted as binary masks from 1854 patients. An image modality independent 2D InceptionResNetV2 classification network was trained with varying amounts of training data using four image input channels. Channel 1-3 consisted of orthogonal 2D projections from each individual binary structure. The fourth channel contained a summation of the other available binary structure masks. Structure classification performance was assessed in independent CT (n = 200 pat) and magnetic resonance imaging (MRI) (n = 40 pat) test datasets and an external CT (n = 99 pat) dataset from another clinic. A weighted classification accuracy of 99.4% was achieved during training. The unweighted classification accuracy and the weighted average F1 score among different structures in the CT test dataset were 98.8% and 98.4% and 98.6% and 98.5% for the MRI test dataset, respectively. The external CT dataset yielded the corresponding results 98.4% and 98.7% when analyzed for trained structures only, and results from the full dataset yielded 79.6% and 75.2%. Most misclassifications in the external CT dataset occurred due to multiple CTVs and PTVs being fused together, which was not included in the training data. Our proposed DL-based method for automated renaming and standardization of prostate radiotherapy annotations shows great potential. Clinic specific contouring standards however need to be represented in the training data for successful use. Source code is available at https://github.com/jamtheim/DicomRTStructRenamerPublic.
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Aprendizado Profundo , Neoplasias da Próstata , Humanos , Imageamento por Ressonância Magnética , Masculino , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Planejamento da Radioterapia Assistida por Computador , Padrões de ReferênciaRESUMO
BACKGROUND AND PURPOSE: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region. MATERIALS AND METHODS: 4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields. RESULTS: The registration algorithm produced accurate registration result, in general DICE overlap > 0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm. CONCLUSIONS: An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.
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BACKGROUND AND PURPOSE: The diagnostic accuracy of new imaging techniques requires validation, preferably by histopathological verification. The aim of this study was to develop and present a registration procedure between histopathology and in-vivo magnetic resonance imaging (MRI) of the prostate, to estimate its uncertainty and to evaluate the benefit of adding a contour-correcting registration. MATERIALS AND METHODS: For twenty-five prostate cancer patients, planned for radical prostatectomy, a 3D-printed prostate mold based on in-vivo MRI was created and an ex-vivo MRI of the specimen, placed inside the mold, was performed. Each histopathology slice was registered to its corresponding ex-vivo MRI slice using a 2D-affine registration. The ex-vivo MRI was rigidly registered to the in-vivo MRI and the resulting transform was applied to the histopathology stack. A 2D deformable registration was used to correct for specimen distortion concerning the specimen's fit inside the mold. We estimated the spatial uncertainty by comparing positions of landmarks in the in-vivo MRI and the corresponding registered histopathology stack. RESULTS: Eighty-four landmarks were identified, located in the urethra (62%), prostatic cysts (33%), and the ejaculatory ducts (5%). The median number of landmarks was 3 per patient. We showed a median in-plane error of 1.8 mm before and 1.7 mm after the contour-correcting deformable registration. In patients with extraprostatic margins, the median in-plane error improved from 2.1 mm to 1.8 mm after the contour-correcting deformable registration. CONCLUSIONS: Our registration procedure accurately registers histopathology to in-vivo MRI, with low uncertainty. The contour-correcting registration was beneficial in patients with extraprostatic surgical margins.
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BACKGROUND AND PURPOSE: In locally advanced prostate cancer (PC), androgen deprivation therapy (ADT) in combination with whole prostate radiotherapy (RT) is the standard treatment. ADT affects the prostate as well as the tumour on multiparametric magnetic resonance imaging (MRI) with decreased PC conspicuity and impaired localisation of the prostate lesion. Image texture analysis has been suggested to be of aid in separating tumour from normal tissue. The aim of the study was to investigate the impact of ADT on baseline defined MRI features in prostate cancer with the goal to investigate if it might be of use in radiotherapy planning. MATERIALS AND METHODS: Fifty PC patients were included. Multiparametric MRI was performed before, and three months after ADT. At baseline, a tumour volume was delineated on apparent diffusion coefficient (ADC) maps with suspected tumour content and a reference volume in normal prostatic tissue. These volumes were transferred to MRIs after ADT and were analysed with first-order -and invariant Haralick -features. RESULTS: At baseline, the median value and several of the invariant Haralick features of ADC, showed a significant difference between tumour and reference volumes. After ADT, only ADC median value could significantly differentiate the two volumes. CONCLUSIONS: Invariant Haralick -features could not distinguish between baseline MRI defined PC and normal tissue after ADT. First-order median value remained significantly different in tumour and reference volumes after ADT, but the difference was less pronounced than before ADT.
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Introduction/Background. Despite growing interest in magnetic resonance imaging (MRI), integration in external beam radiotherapy (EBRT) treatment planning uptake varies globally. In order to understand the current international landscape of MRI in EBRT a survey has been performed in 11 countries. This work reports on differences and common themes identified.Methods. A multi-disciplinary Institute of Physics and Engineering in Medicine working party modified a survey previously used in the UK to understand current practice using MRI for EBRT treatment planning, investigate how MRI is currently used and managed as well as identify knowledge gaps. It was distributed electronically within 11 countries: Australia, Belgium, Denmark, Finland, France, Italy, the Netherlands, New Zealand, Sweden, the UK and the USA.Results. The survey response rate within the USA was <1% and hence these results omitted from the analysis. In the other 10 countries the survey had a median response rate of 77% per country. Direct MRI access, defined as either having a dedicated MRI scanner for radiotherapy (RT) or access to a radiology MRI scanner, varied between countries. France, Italy and the UK reported the lowest direct MRI access rates and all other countries reported direct access in ≥82% of centres. Whilst ≥83% of centres in Denmark and Sweden reported having dedicated MRI scanners for EBRT, all other countries reported ≤29%. Anatomical sites receiving MRI for EBRT varied between countries with brain, prostate, head and neck being most common. Commissioning and QA of image registration and MRI scanners varied greatly, as did MRI sequences performed, staffing models and training given to different staff groups. The lack of financial reimbursement for MR was a consistent barrier for MRI implementation for RT for all countries and MR access was a reported important barrier for all countries except Sweden and Denmark.Conclusion. No country has a comprehensive approach for MR in EBRT adoption and financial barriers are present worldwide. Variations between countries in practice, equipment, staffing models, training, QA and MRI sequences have been identified, and are likely to be due to differences in funding as well as a lack of consensus or guidelines in the literature. Access to dedicated MR for EBRT is limited in all but Sweden and Denmark, but in all countries there are financial challenges with ongoing per patient costs. Despite these challenges, significant interest exists in increasing MR guided EBRT planning over the next 5 years.
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Iodobenzenos , Humanos , Imageamento por Ressonância Magnética , Masculino , Maleimidas , Planejamento da Radioterapia Assistida por Computador/métodosRESUMO
OBJECTIVE: Recent developments on synthetically generated CTs (sCT), hybrid MRI linacs and MR-only simulations underlined the clinical feasibility and acceptance of MR guided radiation therapy. However, considering clinical application of open and low field MR with a limited field of view can result in truncation of the patient's anatomy which further affects the MR to sCT conversion. In this study an acquisition protocol and subsequent MR image stitching is proposed to overcome the limited field of view restriction of open MR scanners, for MR-only photon and proton therapy. MATERIAL AND METHODS: 12 prostate cancer patients scanned with an open 0.35T scanner were included. To obtain the full body contour an enhanced imaging protocol including two repeated scans after bilateral table movement was introduced. All required structures (patient contour, target and organ at risk) were delineated on a post-processed combined transversal image set (stitched MRI). The postprocessed MR was converted into a sCT by a pretrained neural network generator. Inversely planned photon and proton plans (VMAT and SFUD) were designed using the sCT and recalculated for rigidly and deformably registered CT images and compared based on D2%, D50%, V70Gy for organs at risk and based on D2%, D50%, D98% for the CTV and PTV. The stitched MRI and the untruncated MRI were compared to the CT, and the maximum surface distance was calculated. The sCT was evaluated with respect to delineation accuracy by comparing on stitched MRI and sCT using the DICE coefficient for femoral bones and the whole body. RESULTS: Maximum surface distance analysis revealed uncertainties in lateral direction of 1-3mm on average. DICE coefficient analysis confirms good performance of the sCT conversion, i.e. 92%, 93%, and 100% were obtained for femoral bone left and right and whole body. Dose comparison resulted in uncertainties below 1% between deformed CT and sCT and below 2% between rigidly registered CT and sCT in the CTV for photon and proton treatment plans. DISCUSSION: A newly developed acquisition protocol for open MR scanners and subsequent Sct generation revealed good acceptance for photon and proton therapy. Moreover, this protocol tackles the restriction of the limited FOVs and expands the capacities towards MR guided proton therapy with horizontal beam lines.
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Inteligência Artificial , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética , Fótons/uso terapêutico , Terapia com Prótons , HumanosRESUMO
BACKGROUND AND PURPOSE: The aim of this study was to evaluate the potential to increase the tumor control probability (TCP) with 'dose painting by numbers' (DPBN) plans optimized in a treatment planning system (TPS) compared to uniform dose plans. The DPBN optimization was based on our earlier published formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficients (ADC). MATERIAL AND METHODS: For 17 included patients, a set of DPBN plans were optimized in a TPS by maximizing the TCP for an equal average dose to the prostate volume (CTVT) as for a conventional uniform dose treatment. For the plan optimizations we applied different photon energies, different precisions for the ADC-to-Gleason mappings, and different CTVT positioning uncertainties. The TCP increasing potential was evaluated by the DPBN efficiency, defined as the ratio of TCP increases for DPBN plans by TCP increases for ideal DPBN prescriptions (optimized without considering radiation transport phenomena, uncertainties of the CTVT positioning, and uncertainties of the ADC-to-Gleason mapping). RESULTS: The median DPBN efficiency for the most conservative planning scenario optimized with a low precision ADC-to-Gleason mapping, and a positioning uncertainty of 0.6 cm was 10%, meaning that more than half of the patients had a TCP gain of at least 10% of the TCP for an ideal DPBN prescription. By increasing the precision of the ADC-to-Gleason mapping, and decreasing the positioning uncertainty the median DPBN efficiency increased by up to 40%. CONCLUSIONS: TCP increases with DPBN plans optimized in a TPS were found more likely with a high precision mapping of image data into dose-responses and a high certainty of the tumor positioning. These findings motivate further development to ensure precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor positioning when implementing DPBN clinically.
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Neoplasias da Próstata , Planejamento da Radioterapia Assistida por Computador , Humanos , Masculino , Gradação de Tumores , Probabilidade , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Dosagem RadioterapêuticaRESUMO
BACKGROUND AND PURPOSE: For skull base tumors, target definition is the key to safe high-dose treatments because surrounding normal tissues are very sensitive to radiation. In the present work we established a joint ESTRO ACROP guideline for the target volume definition of skull base tumors. MATERIAL AND METHODS: A comprehensive literature search was conducted in PubMed using various combinations of the following medical subjects headings (MeSH) and free-text words: "radiation therapy" or "stereotactic radiosurgery" or "proton therapy" or "particle beam therapy" and "skull base neoplasms" "pituitary neoplasms", "meningioma", "craniopharyngioma", "chordoma", "chondrosarcoma", "acoustic neuroma/vestibular schwannoma", "organs at risk", "gross tumor volume", "clinical tumor volume", "planning tumor volume", "target volume", "target delineation", "dose constraints". The ACROP committee identified sixteen European experts in close interaction with the ESTRO clinical committee who analyzed and discussed the body of evidence concerning target delineation. RESULTS: All experts agree that magnetic resonance (MR) images with high three-dimensional spatial accuracy and tissue-contrast definition, both T2-weighted and volumetric T1-weighted sequences, are required to improve target delineation. In detail, several key issues were identified and discussed: i) radiation techniques and immobilization, ii) imaging techniques and target delineation, and iii) technical aspects of radiation treatments including planning techniques and dose-fractionation schedules. Specific target delineation issues with regard to different skull base tumors, including pituitary adenomas, meningiomas, craniopharyngiomas, acoustic neuromas, chordomas and chondrosarcomas are presented. CONCLUSIONS: This ESTRO ACROP guideline achieved detailed recommendations on target volume definition for skull base tumors, as well as comprehensive advice about imaging modalities and radiation techniques.
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Condrossarcoma , Cordoma , Neoplasias Meníngeas , Radiocirurgia , Neoplasias da Base do Crânio , Humanos , Neoplasias da Base do Crânio/diagnóstico por imagem , Neoplasias da Base do Crânio/radioterapiaRESUMO
BACKGROUND: Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. MATERIALS AND METHOD: Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. RESULTS: The mean whole-image PET uptake error was reduced from - 5.4% for Dixon-PET to - 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from - 5.6% for Dixon-PET to - 2.3% for SDA-PET. CONCLUSION: Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.
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INTRODUCTION: This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learning algorithms as e.g. a data augmentation technique. METHODS: The StyleGAN model was trained on Computed Tomography (CT) and T2- weighted Magnetic Resonance (MR) images from 100 patients with pelvic malignancies. The resulting model was investigated with regards to three features: Image Modality, Sex, and Longitudinal Slice Position. Further, the style transfer feature of the StyleGAN was used to move images between the modalities. The root-mean-squard error (RMSE) and the Mean Absolute Error (MAE) were used to quantify errors for MR and CT, respectively. RESULTS: We demonstrate how these features can be transformed by manipulating the latent style vectors, and attempt to quantify how the errors change as we move through the latent style space. The best results were achieved by using the style transfer feature of the StyleGAN (58.7 HU MAE for MR to CT and 0.339 RMSE for CT to MR). Slices below and above an initial central slice can be predicted with an error below 75 HU MAE and 0.3 RMSE within 4cm for CT and MR, respectively. DISCUSSION: The StyleGAN is a promising model to use for generating synthetic medical images for MR and CT modalities as well as for 3D volumes.
Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética , Tomografia Computadorizada por Raios X , Algoritmos , Humanos , Razão Sinal-RuídoRESUMO
Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features. In this study, we explored the generalizability of the generator models, trained on a single field strength scanner, to data acquired with higher field strengths. T2-weighted 0.35T MRIs and CTs from 51 patients treated for prostate (40) and cervical cancer (11) were included. 25 of them were used to train four different generators (SE-ResNet, DenseNet, U-Net, and Embedded Net). Further, an ensemble model was created from the four network outputs. The models were validated on 16 patients from a 0.35T MR scanner. Further, the trained models were tested on the Gold Atlas dataset, containing T2-weighted MR scans of different field strengths; 1.5T(7) and 3T(12), and 10 patients from the 0.35T scanner. The sCTs were dosimetrically compared using clinical VMAT plans for all test patients. For the same scanner (0.35T), the results from the different models were comparable on the test set, with only minor differences in the mean absolute error (MAE) (35-51HU body). Similar results were obtained for conversions of 3T GE Signa and the 3T GE Discovery images (40-62HU MAE) for three of the models. However, larger differences were observed for the 1.5T images (48-65HU MAE). The overall best model was found to be the ensemble model. All dose differences were below 1%. This study shows that it is possible to generalize models trained on images of one scanner to other scanners and different field strengths. The best metric results were achieved by the combination of all networks.
Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/instrumentação , Tomografia Computadorizada por Raios X , Humanos , Masculino , Redes Neurais de Computação , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Radiometria , Planejamento da Radioterapia Assistida por Computador , Radioterapia de Intensidade ModuladaRESUMO
BACKGROUND: Most cancer patients undergo external radiotherapy (RT) at some stage during their treatment trajectory and RT is often associated with unfamiliar procedures in a highly technical environment. The purpose of this study was to explore how patients experience RT and the related processes, as described in free-text comments in a large Swedish survey with questionnaires including items on psychosocial climate and treatment environment. METHODS: The data consisted of free-text comments from one open-ended question: "Is there anything else you want us to know" and were analysed using qualitative content analysis. RESULTS: Of 825 returned questionnaires, 261 contained free-text comments from patients (32%). The hand-written, free-text comments reflected the patients' experience of the RT process and were abstracted into the four major categories with sub-categories: experiencing the high-tech RT environment, understanding the RT procedures and side effects, dealing with daily life during RT, and the nurses' role and performance. The categories reflect the patients' experiences and emphasize how important it is to evaluate what really matters to the patients when changing procedures, practices, and how to minimize disturbances in the patients' daily lives. CONCLUSION: The main conclusions from this study are that the involvement of patients in choosing daily appointment times, providing good information during the RT process to make the patients feel safe, experience and attitude of the staff and respect for the patient's autonomy are highly ranked values for patients. An implementation of person-centred care may help relieve many of these problems.