Automatic Segmentation with Deep Learning in Radiotherapy

This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.

Comparison of automated segmentation techniques for magnetic resonance images of the prostate

BACKGROUND

Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs.

METHODS

This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables.

RESULTS

The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables.

CONCLUSIONS

Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice).

Quality assurance for automatically generated contours with additional deep learning

OBJECTIVE

Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours.

METHODS

The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions.

RESULTS

Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time.

CONCLUSION

We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior.

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

Whole-body magnetic resonance imaging: technique, guidelines and key applications

Whole-body magnetic resonance imaging (WB-MRI) is an imaging method without ionising radiation that can provide WB coverage with a core protocol of essential imaging contrasts in less than 40 minutes, and it can be complemented with sequences to evaluate specific body regions as needed. In many cases, WB-MRI surpasses bone scintigraphy and computed tomography in detecting and characterising lesions, evaluating their response to therapy and in screening of high-risk patients. Consequently, international guidelines now recommend the use of WB-MRI in the management of patients with multiple myeloma, prostate cancer, melanoma and individuals with certain cancer predisposition syndromes. The use of WB-MRI is also growing for metastatic breast cancer, ovarian cancer and lymphoma as well as for cancer screening amongst the general population. In light of the increasing interest from clinicians and patients in WB-MRI as a radiation-free technique for guiding the management of cancer and for cancer screening, we review its technical basis, current international guidelines for its use and key applications.

Patients' experience with MRI-guided in-bore biopsy versus TRUS-guided biopsy in prostate cancer: a pilot study

Background

Ultrasound-guided magnetic resonance imaging (MRI)-fusion biopsy and in-bore MRI-guided biopsy (MRGB) have improved the diagnostic pathway in patients with suspected prostate cancer compared to the traditional random sampling of the prostate gland under transrectal ultrasound guidance (TRUS-Bx). The aim of our study was to assess the psychological experiences of patients undergoing MRGB and TRUS-Bx.

Method

Participants completed an ad hoc set of 11 items to be rated from 0 (not at all) to 10 (very much) on visual analogue scales and one open question on the most worrisome aspect of the procedure. The set of items evaluated satisfaction with the information received and the possibility to ask questions to the staff; the tolerability of the irritation, duration and discomfort associated with the exam; their level of worry or calm before the exam; the perceived need to undergo the exam; their satisfaction with the exam and willingness to repeat it in the future; and acceptability of the exam.

Results

Between May 2018 and June 2019, 47 participants were enrolled on the day of their MRGB; 24 had previously undergone TRUS-Bx. The MRGB was rated with high positive scores on all 11 items. The lowest ratings regarded the duration of the exam (mean = 6.6) and feeling calm (mean = 6.6). Participants were significantly more satisfied with MRGB than TRUS-Bx, rating it as less painful and more comfortable, necessary and tolerable.

Conclusion

These preliminary results indicate that the MRGB is likely to be more tolerable and acceptable to patients than TRUS-Bx.

External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis

PURPOSE

Low-dose CT screening allows early lung cancer detection, but is affected by frequent false positive results, inter/intra observer variation and uncertain diagnoses of lung nodules. Radiomics-based models have recently been introduced to overcome these issues, but limitations in demonstrating their generalizability on independent datasets are slowing their introduction to clinic. The aim of this study is to evaluate two radiomics-based models to classify malignant pulmonary nodules in low-dose CT screening, and to externally validate them on an independent cohort. The effect of a radiomics features harmonization technique is also investigated to evaluate its impact on the classification of lung nodules from a multicenter data.

METHODS

Pulmonary nodules from two independent cohorts were considered in this study; the first cohort (110 subjects, 113 nodules) was used to train prediction models, and the second cohort (72 nodules) to externally validate them. Literature-based radiomics features were extracted and, after feature selection, used as predictive variables in models for malignancy identification. An in-house prediction model based on artificial neural network (ANN) was implemented and evaluated, along with an alternative model from the literature, based on a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). External validation was performed on the second cohort to evaluate models' generalization ability. Additionally, the impact of the Combat harmonization method was investigated to compensate for multicenter datasets variabilities. A new training of the models based on harmonized features was performed on the first cohort, then tested separately on the harmonized and non-harmonized features of the second cohort.

RESULTS

Preliminary results showed a good accuracy of the investigated models in distinguishing benign from malignant pulmonary nodules with both sets of radiomics features (i.e., non-harmonized and harmonized). The performance of the models, quantified in terms of Area Under the Curve (AUC), was > 0.89 in the training set and > 0.82 in the external validation set for all the investigated scenarios, outperforming the clinical standard (AUC of 0.76). Slightly higher performance was observed for the SVM-LASSO model than the ANN in the external dataset, although they did not result significantly different. For both harmonized and non-harmonized features, no statistical difference was found between Receiver operating characteristic (ROC) curves related to training and test set for both models.

CONCLUSIONS

Although no significant improvements were observed when applying the Combat harmonization method, both in-house and literature-based models were able to classify lung nodules with good generalization to an independent dataset, thus showing their potential as tools for clinical decision-making in lung cancer screening.

PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis

PURPOSE

To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer.

METHODS

A pelvis-shaped container was filled with a MnCl₂ solution reproducing the relaxation times (T₁, T₂) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T₁ and T₂ of MnCl₂ solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients' lesions. Their repeatability was assessed by multiple measurements.

RESULTS

Muscle T₁ and T₂ were 1128 (806-1378) and 51 (40-65) ms, respectively. The phantom reproduced in vivo values within 13% (T₁) and 12% (T₂). T₁ and T₂ of tumour tissue were 1637 (1396-2121) and 94 (79-101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T₁) and 24% (T₂) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T₁ (T₂)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients' lesions.

CONCLUSIONS

We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.

A clustering approach to 4D MRI retrospective sorting for the investigation of different surrogates

PURPOSE

In retrospective 4-Dimensional Magnetic Resonance Imaging (4D MRI) sorting, respiratory surrogate selection affects the image quality of reconstructed volumes. We propose a method for retrospective 4D MRI sorting based on clustering, which allowed us to compare the performance of single or multiple internal surrogates vs. a conventional external signal.

METHODS

A k-medoids clustering algorithm was exploited for sorting 2D MRI into 4D MRI, relying on (A) multiple or (B) single automatically tracked internal landmarks or (C) respiratory belt signal. 4D MRI reconstructions for seven liver cancer patients were compared to those of the state-of-the-art mutual information (MI) approach. Sorting artifacts were measured by the root mean square error (RMSE) between the diaphragm profile and a fitted second order curve. Diaphragm and tumor motions were evaluated.

RESULTS

The median RMSEs ranged 0.97-1.66 mm, 1.24-1.89 mm, 1.43-2.27 mm, 1.74-3.72 mm for the MI, (A), (B) and (C) methods, respectively. Significant differences (Friedman, α = 5%) were found between (C) and all other methods, and between (B) and MI approaches. The discrepancies between (A) and MI approaches ranged 1.1-6.2 mm and 0.7-5.3 mm respectively in diaphragm and tumor motions. Methods (A) and (B) showed similar ranges of motion.

CONCLUSION

With multiple internal points, our method yielded the description of a higher range of motion and similar image quality with respect to the MI approach. The single point method led to more artifacts, suggesting the superior suitability of multiple internal surrogates for retrospective 4D MRI sorting. Considering internal rather than external information favored superior performance.

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.

Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA

Functional renal magnetic resonance imaging (MRI) has seen a number of recent advances, and techniques are now available that can generate quantitative imaging biomarkers with the potential to improve the management of kidney disease. Such biomarkers are sensitive to changes in renal blood flow, tissue perfusion, oxygenation and microstructure (including inflammation and fibrosis), processes that are important in a range of renal diseases including chronic kidney disease. However, several challenges remain to move these techniques towards clinical adoption, from technical validation through biological and clinical validation, to demonstration of cost-effectiveness and regulatory qualification. To address these challenges, the European Cooperation in Science and Technology Action PARENCHIMA was initiated in early 2017. PARENCHIMA is a multidisciplinary pan-European network with an overarching aim of eliminating the main barriers to the broader evaluation, commercial exploitation and clinical use of renal MRI biomarkers. This position paper lays out PARENCHIMA's vision on key clinical questions that MRI must address to become more widely used in patients with kidney disease, first within research settings and ultimately in clinical practice. We then present a series of practical recommendations to accelerate the study and translation of these techniques.

Investigating cancer patient acceptance of Whole Body MRI

BACKGROUND

Whole Body magnetic resonance imaging (WB-MRI) enables early cancer detection, without exposing the patient to ionizing radiation. Our aim was to investigate patients' acceptance of WB-MRI as a procedure for cancer staging and follow up.

MATERIALS AND METHODS

135 oncologic subjects participated to the study. An ad hoc questionnaire was administered before and after WB-MRI, to assess patient's confidence and concerns about WB-MRI, psychological reactions, experience and perceived utility of the procedure.

RESULTS

Before undergoing WB-MRI, about 58% of the patients were concerned for cancer progression outcome. 80.4% felt that they were given good information about the exam and the most informed group also perceived and higher level of utility of WB-MRI and no risk. Among people reporting discomfort with the exam (51.9%) the main reasons were noise and exam duration. Despite this, 80% of patients expressed high levels of satisfaction, and the majority (69%) judged WB-MRI more acceptable than other diagnostic exams. Patients who believed to have received more information before the exam rated their global satisfaction higher.

CONCLUSION

Our results show that WB-MRI examinations were well-accepted and perceived with high levels of satisfaction by most patients. WB-MRI appears to be equally or more tolerable than other total body imaging modalities (e.g. PET, CT), especially if they receive enough information from the radiologist.

Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research

The rapidly growing field of functional, molecular and structural bio-imaging is providing an extraordinary new opportunity to overcome the limits of invasive liver biopsy and introduce a "digital biopsy" for in vivo study of liver pathophysiology. To foster the application of bio-imaging in clinical and translational research, there is a need to standardize the methods of both acquisition and the storage of the bio-images of the liver. It can be hoped that the combination of digital, liquid and histologic liver biopsies will provide an innovative synergistic tri-dimensional approach to identifying new aetiologies, diagnostic and prognostic biomarkers and therapeutic targets for the optimization of personalized therapy of liver diseases and liver cancer. A group of experts of different disciplines (Special Interest Group for Personalized Hepatology of the Italian Association for the Study of the Liver, Institute for Biostructures and Bio-imaging of the National Research Council and Bio-banking and Biomolecular Resources Research Infrastructure) discussed criteria, methods and guidelines for facilitating the requisite application of data collection. This manuscript provides a multi-Author review of the issue with special focus on fatty liver.

Spinal cord grey matter segmentation challenge

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

Squamous cell carcinoma of the oral cavity and oropharynx: what does the apparent diffusion coefficient tell us about its histology?

Background Diffusion-weighted imaging obtained with magnetic resonance (DW-MRI) is a non-invasive imaging tool potentially able to provide information about microstructural tumor characteristics. Purpose To prospectively analyze the correlation between the apparent diffusion coefficient (ADC) and clinical-histologic characteristics of squamous cell carcinoma (SCCA) of the oral cavity and oropharynx. Material and Methods Sixty-seven patients with untreated, histologically proven SCCA of the oral cavity and oropharynx underwent conventional and diffusion-weighted (b-values 0, 50, 250, 500, and 900 s/mm²) MRI. Tumor ADC was calculated from regions of interest drawn manually on the highest b-value images using ImageJ (ImageJ, NIH) and fsl (fsl 4, University of Oxford) image processing packages. ADC was calculated in two ways: standard ADC using all b-values; and ADC^High using only b-values ≥ 250 s/mm². We assessed the correlations between both ADC and ADC^High and the clinical-histological characteristics of SCCA. Results Fifty-two patients (36 men, 16 women; mean age, 55 ± 13 years) were suitable for ADC calculation. Mean ADC was 1136.0 ± 108.5 × 10^-6mm²/s. Mean tumor ADC^High was 991.2 ± 152.1 × 10^-6mm²/s. Mean tumor size was 32.3 ± 13.4 mm (range, 14.0-69.0 mm). We observed no correlation of either ADC or ADC^High values with any of the clinical-histological tumor characteristics. Undifferentiated tumors (G3) showed lower apparent diffusion coefficient values compared to differentiated ones (G1-G2), without reaching statistical significance. Conclusion We did not observe any statistically significant correlation between ADC values and clinical-histological characteristics of SCCA of the oral cavity and oropharynx.

Neuromelanin Imaging and Dopaminergic Loss in Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder in which the major pathologic substrate is a loss of dopaminergic neurons from the substantia nigra. Our main objective was to determine the correspondence between changes in the substantia nigra, evident in neuromelanin and iron sensitive magnetic resonance imaging (MRI), and dopaminergic striatal innervation loss in patients with PD. Eighteen patients and 18 healthy control subjects were included in the study. Using neuromelanin-MRI, we measured the volume of the substantia nigra and the contrast-to-noise-ratio between substantia nigra and a background region. The apparent transverse relaxation rate and magnetic susceptibility of the substantia nigra were calculated from dual-echo MRI. Striatal dopaminergic innervation was measured as density of dopamine transporter (DAT) by means of single-photon emission computed tomography and [¹²³I] N-ω-fluoropropyl-2b-carbomethoxy-3b-(4-iodophenyl) tropane. Patients showed a reduced volume of the substantia nigra and contrast-to-noise-ratio and both positively correlated with the corresponding striatal DAT density. The apparent transverse relaxation rate and magnetic susceptibility values of the substantia nigra did not differ between patients and healthy controls. The best predictor of DAT reduction was the volume of the substantia nigra. Clinical and imaging correlations were also investigated for the locus coeruleus. Our results suggest that neuromelanin-MRI can be used for quantifying substantia nigra pathology in PD where it closely correlates with dopaminergic striatal innervation loss. Longitudinal studies should further explore the role of Neuromelanin-MRI as an imaging biomarker of PD, especially for subjects at risk of developing the disease.

Robot-assisted radical prostatectomy: Multiparametric MR imaging-directed intraoperative frozen-section analysis to reduce the rate of positive surgical margins

PURPOSE

To investigate whether use of multiparametric magnetic resonance (MR) imaging-directed intraoperative frozen-section (IFS) analysis during nerve-sparing robot-assisted radical prostatectomy reduces the rate of positive surgical margins.

MATERIALS AND METHODS

This retrospective analysis of prospectively acquired data was approved by an institutional ethics committee, and the requirement for informed consent was waived. Data were reviewed for 134 patients who underwent preoperative multiparametric MR imaging (T2 weighted, diffusion weighted, and dynamic contrast-material enhanced) and nerve-sparing robot-assisted radical prostatectomy, during which IFS analysis was used, and secondary resections were performed when IFS results were positive for cancer. Control patients (n = 134) matched for age, prostate-specific antigen level, and stage were selected from a pool of 322 patients who underwent nerve-sparing robot-assisted radical prostatectomy without multiparametric MR imaging and IFS analysis. Rates of positive surgical margins were compared by means of the McNemar test, and a multivariate conditional logistic regression model was used to estimate the odds ratio of positive surgical margins for patients who underwent MR imaging and IFS analysis compared with control subjects.

RESULTS

Eighteen patients who underwent MR imaging and IFS analysis underwent secondary resections, and 13 of these patients were found to have negative surgical margins at final pathologic examination. Positive surgical margins were found less frequently in the patients who underwent MR imaging and IFS analysis than in control patients (7.5% vs 18.7%, P = .01). When the differences in risk factors are taken into account, patients who underwent MR imaging and IFS had one-seventh the risk of having positive surgical margins relative to control patients (adjusted odds ratio: 0.15; 95% confidence interval: 0.04, 0.61).

CONCLUSION

The significantly lower rate of positive surgical margins compared with that in control patients provides preliminary evidence of the positive clinical effect of multiparametric MR imaging-directed IFS analysis for patients who undergo prostatectomy.

Risk of Rupture of Small Anterior Communicating Artery Aneurysms Is Similar to Posterior Circulation Aneurysms

Background and Purpose—

According to the International Study of Unruptured Intracranial Aneurysms (ISUIA), anterior circulation (AC) aneurysms of <7 mm in diameter have a minimal risk of rupture. It is general experience, however, that anterior communicating artery (AcoA) aneurysms are frequent and mostly rupture at <7 mm. The aim of the study was to assess whether AcoA aneurysms behave differently from other AC aneurysms.

Methods—

Information about 932 patients newly diagnosed with intracranial aneurysms between November 1, 2006, and March 31, 2012, including aneurysm status at diagnosis, its location, size, and risk factors, was collected during the multicenter @neurIST project. For each location or location and size subgroup, the odds ratio (OR) of aneurysms being ruptured at diagnosis was calculated.

Results—

The OR for aneurysms to be discovered ruptured was significantly higher for AcoA (OR, 3.5 [95% confidence interval, 2.6–4.5]) and posterior circulation (OR, 2.6 [95% confidence interval, 2.1–3.3]) than for AC excluding AcoA (OR, 0.5 [95% confidence interval, 0.4–0.6]). Although a threshold of 7 mm has been suggested by ISUIA as a threshold for aggressive treatment, AcoA aneurysms <7 mm were more frequently found ruptured (OR, 2.0 [95% confidence interval, 1.3–3.0]) than AC aneurysms of 7 to 12 mm diameter as defined in ISUIA.

Conclusions—

We found that AC aneurysms are not a homogenous group. Aneurysms between 4 and 7 mm located in AcoA or distal anterior cerebral artery present similar rupture odds to posterior circulation aneurysms. Intervention should be recommended for this high-risk lesion group.

The current state-of-the-art of spinal cord imaging: applications

A first-ever spinal cord imaging meeting was sponsored by the International Spinal Research Trust and the Wings for Life Foundation with the aim of identifying the current state-of-the-art of spinal cord imaging, the current greatest challenges, and greatest needs for future development. This meeting was attended by a small group of invited experts spanning all aspects of spinal cord imaging from basic research to clinical practice. The greatest current challenges for spinal cord imaging were identified as arising from the imaging environment itself; difficult imaging environment created by the bone surrounding the spinal canal, physiological motion of the cord and adjacent tissues, and small crosssectional dimensions of the spinal cord, exacerbated by metallic implants often present in injured patients. Challenges were also identified as a result of a lack of "critical mass" of researchers taking on the development of spinal cord imaging, affecting both the rate of progress in the field, and the demand for equipment and software to manufacturers to produce the necessary tools. Here we define the current state-of-the-art of spinal cord imaging, discuss the underlying theory and challenges, and present the evidence for the current and potential power of these methods. In two review papers (part I and part II), we propose that the challenges can be overcome with advances in methods, improving availability and effectiveness of methods, and linking existing researchers to create the necessary scientific and clinical network to advance the rate of progress and impact of the research.

The current state-of-the-art of spinal cord imaging: methods

A first-ever spinal cord imaging meeting was sponsored by the International Spinal Research Trust and the Wings for Life Foundation with the aim of identifying the current state-of-the-art of spinal cord imaging, the current greatest challenges, and greatest needs for future development. This meeting was attended by a small group of invited experts spanning all aspects of spinal cord imaging from basic research to clinical practice. The greatest current challenges for spinal cord imaging were identified as arising from the imaging environment itself; difficult imaging environment created by the bone surrounding the spinal canal, physiological motion of the cord and adjacent tissues, and small cross-sectional dimensions of the spinal cord, exacerbated by metallic implants often present in injured patients. Challenges were also identified as a result of a lack of "critical mass" of researchers taking on the development of spinal cord imaging, affecting both the rate of progress in the field, and the demand for equipment and software to manufacturers to produce the necessary tools. Here we define the current state-of-the-art of spinal cord imaging, discuss the underlying theory and challenges, and present the evidence for the current and potential power of these methods. In two review papers (part I and part II), we propose that the challenges can be overcome with advances in methods, improving availability and effectiveness of methods, and linking existing researchers to create the necessary scientific and clinical network to advance the rate of progress and impact of the research.

Quantification of variability in breath-hold perfusion CT of hepatocellular carcinoma: a step toward clinical use

PURPOSE

To assess the variability of breath-hold perfusion computed tomography (CT) parameters and to investigate whether these measurements are affected by a commercial software upgrade in patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Written informed consent was obtained from all participants in this institutional ethics committee-approved study. Perfusion CT examinations in HCC patients were prospectively analyzed by three readers. Two readers repeated their analysis after an interval of at least 4 weeks. Inter- and intraobserver agreement, as well as intersoftware agreement, were assessed with intraclass correlation coefficients (ICCs) and Bland-Altman limits of agreement (LoA), with adjustment for correlation between repeated measures.

RESULTS

Ninety-three breath-hold perfusion CT examinations were included from 23 HCC patients. The ICC between readers was very high (>0.91) for blood flow (BF), high (>0.84) for blood volume (BV), and lower (>0.30 and >0.39) for mean transit time (MTT) and permeability surface area product (PS), respectively, while ICC between readings was high (>0.80) for BF and BV, good (>0.75) for PS, and lower (>0.38) for MTT, irrespective of software version. By using the current software, the clinically relevant percentage of LoA between readers for BF were -33%; for BV, -39%; for MTT, 55%; and for PS, -93%. Between readings by the most expert reader, the clinically relevant LoA were -35% for BF,-43% for BV, 33% for MTT, and -79% for PS. BF, BV, and PS values were significantly higher and MTT values were significantly lower (P<.01) with the current software version relative to the previous version.

CONCLUSION

With the current CT perfusion software, only decreases between scans of HCC lesions of more than 35% for BF and 43% for BV, or an increase of more than 55% for MTT, could be considered beyond the analysis variability. The perfusion parameters obtained with the current and previous software versions were not exchangeable. The results of this study are specific for breath-hold perfusion CT of HCC and may not apply to different acquisition protocols and tumors.

MO-D-BRB-04: The Approval Process for the Use of Proton Therapy in NCI-Sponsored Clinical Trials

PURPOSE

To describe the approval process for the use of proton therapy in NCI- sponsored clinical trials.

METHODS

The RPC has developed a comprehensive system for the approval of proton therapy centers for participation in clinical trials. The approval process includes: 1) completion of the proton facility questionnaire, 2) participation in the RPC's annual TLD remote audit program, 3) electronic submission of treatment planning data to the Image-Guided Therapy Center (ITC), and 4) successful completion of an on-site dosimetry review visit, including the irradiation of two of the RPC's anthropomorphic proton phantoms (prostate and spine). The on-site audits allow the RPC to review the institution's treatment planning process, from simulation to treatment delivery, as well as their quality assurance practices. The RPC performs a complete set of measurements that tests the CT simulator's CT# vs. RSP conversion curve, treatment planning data, on-board imaging, and treatment delivery. These measurements detect gross errors that might lead to inaccurate proton dose delivery. The review of the institutions' QA procedures allows the RPC to encourage all proton centers to maintain a consistent level of periodic monitoring of their proton therapy delivery. Upon completion of the visit, a full report is written detailing the results from the visit, phantom irradiation, and recommendations for improving their treatment delivery and QA.

RESULTS

To date, the RPC has approved seven proton therapy centers for the use of scattered or uniform scanning proton treatment delivery in clinical trials. Results of the phantom irradiations have identified an error in the HU vs RLSP curve. The site visits have identified several lapses in QA procedures, inappropriate HU vs RLSP values, and weaknesses in treatment planning.

CONCLUSIONS

The RPC's proton therapy approval process has been developed and has identified areas of improvement for proton centers to use proton therapy in clinical trials. Work supported by grants CA10953, CA059267, and CA81647 (NCI, DHHS).

MO-D-BRB-02: The Radiological Physics Center's Quality Audit Program: Where Can We Improve?

PURPOSE

To analyze the findings of the Radiological Physics Center's (RPC) QA audits of institutions participating in NCI sponsored clinical trials.

METHODS

The RPC has developed an extensive Quality Assurance (QA) program over the past 44 years. This program includes on-site dosimetry reviews where measurements on therapy machines are made, records are reviewed and personnel are interviewed. The program's remote audit tools include mailed dosimeters (OSLD/TLD) to verify output calibration, comparison of dosimetry data with RPC 'standard' data, evaluation of benchmark and patient calculations to verify the treatment planning algorithms, review of institution's QA procedures and records, and use of anthropomorphic phantoms to verify tumor dose delivery. The RPC endeavors to assist institutions in finding the origins of any detected discrepancies, and to resolve them.

RESULTS

Ninety percent of institutions receiving dosimetry recommendations has remained level for the past 5 years. The most frequent recommendations were for not performing TG-40 QA tests, wedge factors, small field size output factors and off-axis factors. Since TG-51 was published, the number of beam calibrations audited during visits with ion chambers, that met the RPC's ±3% criterion, decreased initially but has risen to pre-TG-51 levels. The OSLD/TLD program shows that only ∼3% of the beams are outside our ±5% criteria, but these discrepancies are distributed over 12-20% of the institutions. The percent of institutions with ï,³ l beam outside the RPC's criteria is approximately the same whether OSLD/TLD or ion chambers were used. The first time passing rate for the anthropomorphic phantoms is increasing with time. The prostate phantom has the highest pass rate while the spine phantom has the lowest.

CONCLUSIONS

Numerous dosimetry errors continue to be discovered by the RPC's QA program and the RPC continues to play an important role in helping institutions resolve these errors. This work was supported by PHS grants CA10953 and CA081647 awarded by NCI.