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BackgroundMost MRI radiomics studies to date, even multi-centre ones, have used “pure” datasets deliberately accrued from single-vendor, single-field-strength scanners. This does not reflect aspirations for the ultimate generalisability of AI models. We therefore investigated the development of a radiomics signature from heterogeneous data originating on six different imaging platforms, for a breast cancer exemplar, in order to provide input into future discussions of the viability of radiomics in “real-world” scenarios where image data are not controlled by specific trial protocols but reflective of routine clinical practice.MethodsOne hundred fifty-six patients with pathologically proven breast cancer underwent multi-contrast MRI prior to neoadjuvant chemotherapy and/or surgery. From these, 92 patients were identified for whom T2-weighted, diffusion-weighted and contrast-enhanced T1-weighted sequences were available, as well as key clinicopathological variables. Regions-of-interest were drawn on the above image types and, from these, semantic and calculated radiomics features were derived. Classification models using a variety of methods, both with and without recursive feature elimination, were developed to predict pathological nodal status. Separately, we applied the same methods to analyse the information carried by the radiomic features regarding the originating scanner type and field strength. Repeated, ten-fold cross-validation was employed to verify the results. In parallel work, survival modelling was performed using random survival forests.ResultsPrediction of nodal status yielded mean cross-validated AUC values of 0.735 ± 0.15 (SD) for clinical variables alone, 0.673 ± 0.16 (SD) for radiomic features only, and 0.764 ± 0.16 (SD) for radiomics and clinical features together. Prediction of scanner platform from the radiomics features yielded extremely high values of AUC between 0.91 and 1 for the different classes examined indicating the presence of confounding features for the nodal status classification task. Survival analysis, gave out-of-bag prediction errors of 19.3% (clinical features only), 36.9–51.8% (radiomic features from different combinations of image contrasts), and 26.7–35.6% (clinical plus radiomics features).ConclusionsRadiomic classification models whose predictive ability was consistent with previous single-vendor, single-field strength studies have been obtained from multi-vendor, multi-field-strength data, despite clear confounding information being present. However, our sample size was too small to obtain useful survival modelling results.

Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.

Impact of curing conditions on basic dosimetric properties of silicone-based radiochromic dosimeters for photon and proton irradiation

SummaryThe National Cancer Imaging Translational Accelerator (NCITA) is creating a UK national coordinated infrastructure for accelerated translation of imaging biomarkers for clinical use. Through the development of standardised protocols, data integration tools and ongoing training programmes, NCITA provides a unique scalable infrastructure for imaging biomarker qualification using multicentre clinical studies.

Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn.

Background Textural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer. Purpose To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors. Methods Of 378 patients with Stage1–2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm3) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm3. Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features. Results Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. (p, Highlights • Texture features differed significantly between high-compared to low-volume cervical tumors (p

Intra-tumour genetic heterogeneity (ITH) fuels cancer evolution. The role of clonal diversity and genetic complexity in the progression of clear-cell renal cell carcinomas (ccRCCs) has been characterised, but the ability to predict clinically relevant evolutionary trajectories remains limited. Here, towards enhancing this ability, we investigated spatial features of clonal diversification through a combined computational modelling and experimental analysis in the TRACERx Renal study. We observe through modelling that spatial patterns of tumour growth impact the extent and trajectory of subclonal diversification. Moreover, subpopulations with high clonal diversity, and parallel evolution events, are frequently observed near the tumour margin. In-silico time-course studies further showed that budding structures on the tumour surface could indicate future steps of subclonal evolution. Such structures were evident radiologically in 15 early-stage ccRCCs, raising the possibility that spatially resolved sampling of these regions, when combined with sequencing, may enable identification of evolutionary potential in early-stage tumours.

Previous work has shown that PRESAGE® can be used successfully to perform 3D dosimetric measurements of complex radiotherapy treatments. However, measurements near the sample edges are known to be difficult to achieve. This is an issue when the doses at air-material interfaces are of interest, for example when investigating the electron return effect (ERE) present in treatments delivered by magnetic resonance (MR)-linac systems. To study this effect, a set of 3.5 cm-diameter cylindrical PRESAGE® samples was uniformly irradiated with multiple dose fractions, using either a conventional linac or an MR-linac. The samples were imaged between fractions using an optical-CT, to read out the corresponding accumulated doses. A calibration between TPS-predicted dose and optical-CT pixel value was determined for individual dosimeters as a function of radial distance from the axis of rotation. This data was used to develop a correction that was applied to four additional samples of PRESAGE® of the same formulation, irradiated with 3D-CRT and IMRT treatment plans, to recover significantly improved 3D measurements of dose. An alternative strategy was also tested, in which the outer surface of the sample was physically removed prior to irradiation. Results show that for the formulation studied here, PRESAGE® samples have a central region that responds uniformly and an edge region of 6-7 mm where there is gradual increase in dosimeter response, rising to an over-response of 24%-36% at the outer boundary. This non-uniform dose response increases in both extent and magnitude over time. Both mitigation strategies investigated were successful. In our four exemplar studies, we show how discrepancies at edges are reduced from 13%-37% of the maximum dose to between 2 and 8%. Quantitative analysis shows that the 3D gamma passing rates rise from 90.4, 69.3, 63.7 and 43.6% to 97.3, 99.9, 96.7 and 98.9% respectively.

Background: Endogenous hormones are associated with breast cancer risk, but little is known about their role on breast tissue composition, a strong risk predictor. This study aims to investigate the relationship between growth and sex hormone levels and breast tissue composition in young nulliparous women. Methods: A cross-sectional study of 415 young (age ∼21.5 years) nulliparous women from an English prebirth cohort underwent a MRI examination of their breasts to estimate percent-water (a proxy for mammographic percent density) and provided a blood sample to measure plasma levels of growth factors (insulin-like growth factor-I, insulin-like growth factor-II, insulin growth factor-binding protein-3, growth hormone) and, if not on hormonal contraception (n = 117) sex hormones (dehydroepiandrosterone, androstenedione, testosterone, estrone, estadiol, sex hormone–binding globulin, prolactin). Testosterone (n = 330) and sex hormone–binding globulin (n = 318) were also measured at age 15.5 years. Regression models were used to estimate the relative difference (RD) in percent-water associated with one SD increment in hormone levels. Results: Estradiol at age 21.5 and sex hormone–binding globulin at age 21.5 were positively associated with body mass index (BMI)-adjusted percent-water [RD (95% confidence interval (CI)): 3% (0%–7%) and 3% (1%–5%), respectively]. There was a positive nonlinear association between androstenedione at age 21.5 and percent-water. Insulin-like growth factor-I and growth hormone at age 21.5 were also positively associated with BMI-adjusted percent-water [RD (95% CI): 2% (0%–4%) and 4% (1%–7%), respectively]. Conclusions: The findings suggest that endogenous hormones affect breast tissue composition in young nulliparous women. Impact: The well-established associations of childhood growth and development with breast cancer risk may be partly mediated by the role of endogenous hormones on breast tissue composition.

With the increasing complexity of radiotherapy treatments typical 1D and 2D quality assurance (QA) detectors may fail to detect out-of-plane dose discrepancies, in particular in the presence of motion. In this work, small samples of the PRESAGE® 3D radiochromic dosimeter were used in combination with a motion phantom to measure real-time multileaf collimator (MLC)-tracked radiotherapy treatments. A different sample of PRESAGE® was irradiated for each of three different irradiation scenarios: (1) static: static sample, without tracking (2) motion: moving sample, without tracking and (3) tracking: moving sample, with tracking. Our in-house software DynaTrack dynamically moves the linac's MLC leafs based on the target position. The doses delivered to the samples were reconstructed based on the recorded positions of the MLC and phantom during the beam delivery. PRESAGE® samples were imaged with an in-house optical-CT scanner. Comparison between simulated and measured 3D dose showed good agreement for all three irradiation scenarios (static: 99.2%; motion: 99.7%; tracking: 99.3% with a 3%, 2 mm and a 10% threshold local gamma criterion), failing only at the edges of the PRESAGE® samples (~ 6 mm). Given that the dose distributions deposited using the DynaTrack system have been independently verified, this experiment demonstrates the ability of PRESAGE to measure 3D doses correctly in a tracking context. We conclude that this methodology could be used in the future to validate the delivery of dynamic MLC-tracked radiotherapy.

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07).

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role.

Dosimetric quality assurance (QA) of the new Elekta Unity (MR-linac) will differ from the QA performed of a conventional linac due to the constant magnetic field, which creates an electron return effect (ERE). In this work we aim to validate PRESAGE® dosimetry in a transverse magnetic field, and assess its use to validate the research version of the Monaco TPS of the MR-linac. Cylindrical samples of PRESAGE® 3D dosimeter separated by an air gap were irradiated with a cobalt-60 unit, while placed between the poles of an electromagnet at 0.5 T and 1.5 T. This set-up was simulated in EGSnrc/Cavity Monte Carlo (MC) code and relative dose distributions were compared with measurements using 1D and 2D gamma criteria of 3% and 1.5 mm. The irradiation conditions were adapted for the MR-linac and compared with Monaco TPS simulations. Measured and EGSnrc/Cavity simulated profiles showed good agreement with a gamma passing rate of 99.9% for 0.5 T and 99.8% for 1.5 T. Measurements on the MR-linac also compared well with Monaco TPS simulations, with a gamma passing rate of 98.4% at 1.5 T. Results demonstrated that PRESAGE® can accurately measure dose and detect the ERE, encouraging its use as a QA tool to validate the Monaco TPS of the MR-linac for clinically relevant dose distributions at tissue-air boundaries.

Purpose: To evaluate the diagnostic sensitivity of computed diffusion-weighted (DW)-MR imaging for the detection of breast cancer. Materials and Methods: Local research ethics approval was obtained. A total of 61 women (median 48 years) underwent dynamic contrast enhanced (DCE)- and DW-MR between January 2011 and March 2012, including 27 with breast cancer on core biopsy and 34 normal cases. Standard ADC maps using all four b values (0, 350, 700, 1150) were used to generate computed DW-MR images at b=1500 s/mm(2) and b=2000 s/mm(2). Four image sets were read sequentially by two readers: acquired b=1150 s/mm(2), computed b=1500 s/mm(2) and b=2000 s/mm(2), and DCE-MR at an early time point. Cancer detection was rated using a five-point scale; image quality and background suppression were rated using a four-point scale. The diagnostic sensitivity for breast cancer detection was compared using the McNemar test and inter-reader agreement with a Kappa value. Results: Computed DW-MR resulted in higher overall diagnostic sensitivity with b=2000 s/mm(2) having a mean diagnostic sensitivity of 76% (range 49.8-93.7%) and b=1500 s/mm(2) having a mean diagnostic sensitivity of 70.3% (range 32-97.7%) compared with 44.4% (range 25.5-64.7%) for acquired b=1150 s/mm(2) (both p=0.0001). Computed DW-MR images produced better image quality and background suppression (mean scores for both readers: 2.55 and 2.9 for b 1500 s/mm(2); 2.55 and 3.15 for b 2000 s/mm(2), respectively) than the acquired b value 1150 s/mm(2) images (mean scores for both readers: 2.4 and 2.45, respectively). Conclusion: Computed DW-MR imaging has the potential to improve the diagnostic sensitivity of breast cancer detection compared to acquired DW-MR.

Purpose: To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. Methods: Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T-1- and T-2-weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. Results: Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T-1- and T-2-weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. Conclusions: Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient. (C) 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P.05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Measurements near the edges of PRESAGE® 3D dosimeters will be important for validating the electron return effect (ERE) that can occur at tissue-air interfaces during radiotherapy treatment delivered with the Elekta MR-linac. We investigate and characterize the spatially non-uniform response of small samples of PRESAGE® to radiation in a conventional linac. We develop a correction to compensate for these non-uniformities and obtain dose values near the dosimeter edges. Five samples from the same batch were uniformly irradiated in a water tank with a broad beam. The non-uniform response of the samples to radiation was investigated and a radial dose-correction function was generated from each sample to obtain a correction image. We then applied these correction images to another sample from the same batch, irradiated with four beams in an inhomogeneous medium, and compared this with the relevant simulated data. Additionally, we irradiated samples after physically removed their edges (axially and top and bottom edges). Higher sensitivity to radiation was observed at the edges (~6mm) of the samples in comparison with the central region. Applying the dose correction function improved agreement between simulations and measurements, but only partial correction was possible. A uniform response was observed on the samples with the edges removed, which we propose as the best option to measure dose at the edges of PRESAGE® samples.

The combination of MRI and radiotherapy on a single platform has the potential to revolutionise image-guided delivery of radiation doses. However, in order to realise these ambitions, good dosimetry must be available. The electron return effect gives rise to significant perturbations of dose at interfaces between tissue and air within the body, and this might lead to difficulties in dose compensation if air cavities move during treatment. In this article, I review briefly the ways in which the available methods of dosimetry are affected by the presence of magnetic fields and discuss the contribution that three-dimensional measurements can make to studies in this area. The methods of MRI and optical computed tomography have well known issues in imaging close to interfaces. These are described together with progress so far in providing solutions.

Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.

Background Breast density, the amount of fibroglandular tissue in the adult breast for a women’s age and body mass index, is a strong biomarker of susceptibility to breast cancer, which may, like breast cancer risk itself, be influenced by events early in life. In the present study, we investigated the association between pre-natal exposures and breast tissue composition. Methods A sample of 500 young, nulliparous women (aged approximately 21 years) from a U.K. pre-birth cohort underwent a magnetic resonance imaging examination of their breasts to estimate percent water, a measure of the relative amount of fibroglandular tissue equivalent to mammographic percent density. Information on pre-natal exposures was collected throughout the mothers’ pregnancy and shortly after delivery. Regression models were used to investigate associations between percent water and pre-natal exposures. Mediation analysis, and a systematic review and meta-analysis of the published literature, were also conducted. Results Adjusted percent water in young women was positively associated with maternal height (p for linear trend [p t] = 0.005), maternal mammographic density in middle age (p t = 0.018) and the participant’s birth size (p t

Gold nanoparticles (AuNps), because of their high atomic number (Z), have been demonstrated to absorb low-energy X-rays preferentially, compared with tissue, and may be used to achieve localized radiation dose enhancement in tumors. The purpose of this study is to introduce the first example of a novel multicompartment radiochromic radiation dosimeter and to demonstrate its applicability for 3-dimensional (3D) dosimetry of nanoparticle-enhanced radiation therapy.A novel multicompartment phantom radiochromic dosimeter was developed. It was designed and formulated to mimic a tumor loaded with AuNps (50 nm in diameter) at a concentration of 0.5 mM, surrounded by normal tissues. The novel dosimeter is referred to as the Sensitivity Modulated Advanced Radiation Therapy (SMART) dosimeter. The dosimeters were irradiated with 100-kV and 6-MV X-ray energies. Dose enhancement produced from the interaction of X-rays with AuNps was calculated using spectrophotometric and cone-beam optical computed tomography scanning by quantitatively comparing the change in optical density and 3D datasets of the dosimetric measurements between the tissue-equivalent (TE) and TE/AuNps compartments. The interbatch and intrabatch variability and the postresponse stability of the dosimeters with AuNps were also assessed.Radiation dose enhancement factors of 1.77 and 1.11 were obtained using 100-kV and 6-MV X-ray energies, respectively. The results of this study are in good agreement with previous observations; however, for the first time we provide direct experimental confirmation and 3D visualization of the radiosensitization effect of AuNps. The dosimeters with AuNps showed small (3.5%) interbatch variability and negligible (0.5%) intrabatch variability.The SMART dosimeter yields experimental insights concerning the spatial distributions and elevated dose in nanoparticle-enhanced radiation therapy, which cannot be performed using any of the current methods. The authors concluded that it can be used as a novel independent method for nanoparticle-enhanced radiation therapy dosimetry.

Purpose: To evaluate the water and tissue equivalence of a new PRESAGE{sup Registered-Sign} 3D dosimeter for proton therapy. Methods: The GEANT4 software toolkit was used to calculate and compare total dose delivered by a proton beam with mean energy 62 MeV in a PRESAGE{sup Registered-Sign} dosimeter, water, and soft tissue. The dose delivered by primary protons and secondary particles was calculated. Depth-dose profiles and isodose contours of deposited energy were compared for the materials of interest. Results: The proton beam range was found to be Almost-Equal-To 27 mm for PRESAGE{sup Registered-Sign }, 29.9 mm for soft tissue, and 30.5 mm for water. This can be attributed to the lower collisional stopping power of water compared to soft tissue and PRESAGE{sup Registered-Sign }. The difference between total dose delivered in PRESAGE{sup Registered-Sign} and total dose delivered in water or tissue is less than 2% across the entire water/tissue equivalent range of the proton beam. The largest difference between total dose in PRESAGE{sup Registered-Sign} and total dose in water is 1.4%, while for soft tissue it is 1.8%. In both cases, this occurs at the distal end of the beam. Nevertheless, the authors find that PRESAGE{sup Registered-Sign} dosimeter is overall more tissue-equivalentmore » than water-equivalent before the Bragg peak. After the Bragg peak, the differences in the depth doses are found to be due to differences in primary proton energy deposition; PRESAGE{sup Registered-Sign} and soft tissue stop protons more rapidly than water. The dose delivered by secondary electrons in the PRESAGE{sup Registered-Sign} differs by less than 1% from that in soft tissue and water. The contribution of secondary particles to the total dose is less than 4% for electrons and Almost-Equal-To 1% for protons in all the materials of interest. Conclusions: These results demonstrate that the new PRESAGE{sup Registered-Sign} formula may be considered both a tissue- and water-equivalent 3D dosimeter for a 62 MeV proton beam. The results further suggest that tissue-equivalent thickness may provide better dosimetric and geometric accuracy than water-equivalent thickness for 3D dosimetry of this proton beam.« less

In this study, we introduce a novel, robust and accurate computerized algorithm based on volumetric principal component maps and template matching that facilitates lesion detection on dynamic contrast-enhanced MR. The study dataset comprises 24 204 contrast-enhanced breast MR images corresponding to 4034 axial slices from 47 women in the UK multi-centre study of MRI screening for breast cancer and categorized as high risk. The scans analysed here were performed on six different models of scanner from three commercial vendors, sited in 13 clinics around the UK. 1952 slices from this dataset, containing 15 benign and 13 malignant lesions, were used for training. The remaining 2082 slices, with 14 benign and 12 malignant lesions, were used for test purposes. To prevent false positives being detected from other tissues and regions of the body, breast volumes are segmented from pre-contrast images using a fast semi-automated algorithm. Principal component analysis is applied to the centred intensity vectors formed from the dynamic contrast-enhanced T1-weighted images of the segmented breasts, followed by automatic thresholding to eliminate fatty tissues and slowly enhancing normal parenchyma and a convolution and filtering process to minimize artefacts from moderately enhanced normal parenchyma and blood vessels. Finally, suspicious lesions are identified through a volumetric sixfold neighbourhood connectivity search and calculation of two morphological features: volume and volumetric eccentricity, to exclude highly enhanced blood vessels, nipples and normal parenchyma and to localize lesions. This provides satisfactory lesion localization. For a detection sensitivity of 100%, the overall false-positive detection rate of the system is 1.02/lesion, 1.17/case and 0.08/slice, comparing favourably with previous studies. This approach may facilitate detection of lesions in multi-centre and multi-instrument dynamic contrast-enhanced breast MR data. EPSRCEngineering & Physical Sciences Research Council (EPSRC) [E035736/1]; Cancer Research UKCancer Research UK; MRCMedical Research Council UK (MRC); Department of HealthDiabetes UK [C1060/A10334]; NHS; Engineering and Physical Sciences Research CouncilEngineering & Physical Sciences Research Council (EPSRC) [EP/J50077X/1, EP/E035736/1]; Medical Research CouncilMedical Research Council UK (MRC) [G0701533]; National Institute for Health ResearchNational Institute for Health Research (NIHR) [NF-SI-0512-10162] This work was supported by EPSRC (E035736/1) and also by Cancer Research UK and EPSRC with support from MRC and the Department of Health (C1060/A10334), together with support from NHS funding to the NIHR Biomedical Research Centre. We are grateful to the MARIBS study for providing data enabling us to evaluate this lesion localization method.

X-ray microbeam radiation therapy (MRT) is a novel form of treatment, currently in its preclinical stage, which uses microplanar x-ray beams from a synchrotron radiation source. It is important to perform accurate dosimetry on these microbeams, but, to date, there has been no accurate enough method available for making 3D dose measurements with isotropic, high spatial resolution to verify the results of Monte Carlo dose simulations. Here, we investigate the potential of optical computed tomography for satisfying these requirements. The construction of a simple optical CT microscopy (optical projection tomography) system from standard commercially available hardware is described. The measurement of optical densities in projection data is shown to be highly linear (r2=0.999). The depth-of-field (DOF) of the imaging system is calculated based on the previous literature and measured experimentally using a commercial DOF target. It is shown that high quality images can be acquired despite the evident lack of telecentricity and despite DOF of the system being much lower than the sample diameter. Possible reasons for this are discussed. Results are presented for a complex irradiation of a 22 mm diameter cylinder of the radiochromic polymer PRESAGE, demonstrating the exquisite 'dose-painting' abilities available in the MRT hutch of beamline ID-17 at the European Synchrotron Radiation Facility. Dose distributions in this initial experiment are equally well resolved on both an optical CT scan and a corresponding transmission image of radiochromic film, down to a line width of 83 microm (6 lp mm(-1)) with an MTF value of 0.40. A group of 33 microm wide lines was poorly resolved on both the optical CT and film images, and this is attributed to an incorrect exposure time calculation, leading to under-delivery of dose. Image artefacts in the optical CT scan are discussed. PRESAGE irradiated using the microbeam facility is proposed as a suitable material for producing phantom samples for quantitative characterization of optical CT microscopy systems.

Polymer gel dosimeters are fabricated from radiation sensitive chemicals which, upon irradiation, polymerize as a function of the absorbed radiation dose. These gel dosimeters, with the capacity to uniquely record the radiation dose distribution in three-dimensions (3D), have specific advantages when compared to one-dimensional dosimeters, such as ion chambers, and two-dimensional dosimeters, such as film. These advantages are particularly significant in dosimetry situations where steep dose gradients exist such as in intensity-modulated radiation therapy (IMRT) and stereotactic radiosurgery. Polymer gel dosimeters also have specific advantages for brachytherapy dosimetry. Potential dosimetry applications include those for low-energy x-rays, high-linear energy transfer (LET) and proton therapy, radionuclide and boron capture neutron therapy dosimetries. These 3D dosimeters are radiologically soft-tissue equivalent with properties that may be modified depending on the application. The 3D radiation dose distribution in polymer gel dosimeters may be imaged using magnetic resonance imaging (MRI), optical-computerized tomography (optical-CT), x-ray CT or ultrasound. The fundamental science underpinning polymer gel dosimetry is reviewed along with the various evaluation techniques. Clinical dosimetry applications of polymer gel dosimetry are also presented.

Over recent decades, modern protocols of external beam radiotherapy have been developed that involve very steep dose gradients and are thus extremely sensitive to errors in treatment delivery. A recent credentialling study by the Radiological Physics Center at the MD Anderson Cancer Center (Texas, USA) has noted potentially significant inaccuracies in test treatments at a variety of institutions. 3-D radiation dosimetry (often referred to as "gel dosimetry") may have an important role in commissioning new treatment protocols, to help prevent this type of error. This article discusses the various techniques of 3-D radiation dosimetry, with a focus on the types of radiosensitive samples used and on the optical computed tomography readout technique.

Anuran vocalizations are valuable in the determination of species identity and have the potential to discriminate intraspecific variation. We developed novel bioacoustic sampling techniques, based on Fast Fourier Transforms, to increase the precision and sensitivity of male advertisement call analysis and applied the method to European water frogs. This approach robustly separated the three types of north European water frogs (Rana ridibunda, R. lessonae and their viable, fertile hybrid R. esculenta) by their call subunit characteristics. The hybrid frog exhibited a high frequency call component absent from both the parental species. Furthermore, call analysis demonstrated significant intraspecific differences among populations of all three frog types. Call characters of R. ridibunda changed systematically as a function of longitude. This trend may reflect either clinal variation in selection pressures across Europe, or the consequences of drift following postglacial colonization from eastern refugia. High resolution vocalization analysis therefore provides a potentially useful method for investigating intraspecific differentiation and the phylogeographical origins of anuran distributions. © 2002 The Linnean Society of London. Biological Journal of the Linnean Society, 2002, 77, 355−365.

Rana lessonae Camerano, 1882 has a well-recorded range across Europe with its distribution recognised as far north as Sweden (lat. 60°N). In recent years new populations have been identified in Arendal. Norway and in Norfolk, England. This study aims to clarify the situation of the Norfolk frogs. Are they native or imported? This paper presents preliminary results of a new approach to this question. By means of various bioacoustic analyses calls of R. lessonae from populations across Europe and Scandinavia have been compared. The water frog species have been subjected to detailed bioacoustic analyses in the past and some characteristics for each species have been determined. We have demonstrated the use of these techniques and developed new methods of analysis that have enabled regional differences in R. lessonae calls to be determined between the populations of Norfolk, Germany and Poland. This in turn may elucidate the situation of the Norfolk Pool Frog.

Machine learning has been key to the success and advance of artificial intelligence in recent years. However, it usually requires significant quantities of labeled data with a supervised training regime, and obtaining exact labeling with expert knowledge can be very expensive and challenging, especially for medical images. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets can be widely different. Deep learning is an approach to learn representative features of data from the given dataset in an unsupervised manner using deep neural networks. This can be promising in medical image analysis, because it can be applied to automatically analyze a diverse, large set of medical images, despite the difficulty of obtaining libraries of accurately labeled example datasets and the intrinsic abnormalities present in patient datasets. This chapter investigates the concept of deep learning for medical image analysis, demonstrating the application of multiple organ detection in multiparametric magnetic resonance images of tumor patients using stacked autoencoder models.

Optical computed tomography (optical-CT) of 3D radiation dosimeters is a promising avenue for delivering an economic and reliable quality control of radiotherapy treatments such as intensity modulated radiotherapy, brachytherapy and stereotactic radiosurgery. The main problems in transferring 3D dosimeters to clinical setting have been in (1) the complexity of manufacture and behaviour of 3D dosimeters and (2) time-consuming readout and analysis of 3D dosimeters. This paper addresses the readout problem by showing that fast (20 min tomography scan), precise (projection absorbance signal-to-noise ratio is greater than 100:1 across the absorbance range 0.2 to 1.5) and accurate (good linearity in the calibration curve) measurements are possible using a novel method of optically scanning a laser beam across the 3D dosimeter.

Synchrotron microbeam radiation therapy (MRT) is an advanced form of radiotherapy for which it is extremely difficult to provide adequate quality assurance. This may delay or limit its clinical uptake, particularly in the paediatric patient populations for whom it could be especially suitable. This study investigates the extent to which new developments in 3D dosimetry using optical computed tomography (CT) can visualise MRT dose distributions, and assesses what further developments are necessary before fully quantitative 3D measurements can be achieved. Two experiments are reported. In the first cylindrical samples of the radiochromic polymer PRESAGE(®) were irradiated with different complex MRT geometries including multiport treatments of collimated 'pencil' beams, interlaced microplanar arrays and a multiport treatment using an anthropomorphic head phantom. Samples were scanned using transmission optical CT. In the second experiment, optical CT measurements of the biologically important peak-to-valley dose ratio (PVDR) were compared with expected values from Monte Carlo simulations. The depth-of-field (DOF) of the optical CT system was characterised using a knife-edge method and the possibility of spatial resolution improvement through deconvolution of a measured point spread function (PSF) was investigated. 3D datasets from the first experiment revealed excellent visualisation of the 50 μm beams and various discrepancies from the planned delivery dose were found. The optical CT PVDR measurements were found to be consistently 30% of the expected Monte Carlo values and deconvolution of the microbeam profiles was found to lead to increased noise. The reason for the underestimation of the PVDR by optical CT was attributed to lack of spatial resolution, supported by the results of the DOF characterisation. Solutions are suggested for the outstanding challenges and the data are shown already to be useful in identifying potential treatment anomalies.