Search

BACKGROUND: Scoliosis is spinal curvature that may progress to require surgical stabilisation. Risk factors for progression are little understood due to lack of population-based research, since radiographs cannot be performed on entire populations due to high levels of radiation. To help address this, we have previously developed and validated a method for quantification of spinal curvature from total body dual energy X-ray absorptiometry (DXA) scans. The purpose of this study was to automate this quantification of spinal curve size from DXA scans using machine learning techniques. METHODS: To develop the automation of curve size, we utilised manually annotated scans from 7298 participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) at age 9 and 5122 at age 15. To validate the automation we assessed (1) agreement between manual vs automation using the Bland-Altman limits of agreement, (2) reliability by calculating the coefficient of variation, and (3) clinical validity by running the automation on 4969 non-annotated scans at age 18 to assess the associations with physical activity, body composition, adipocyte function and backpain compared to previous literature. RESULTS: The mean difference between manual vs automated readings was less than one degree, and 90.4 % of manual vs automated readings fell within 10°. The coefficient of variation was 25.4 %. Clinical validation showed the expected relationships between curve size and physical activity, adipocyte function, height and weight. CONCLUSION: We have developed a reasonably accurate and valid automated method for quantifying spinal curvature from DXA scans for research purposes.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. Objectives: To develop and validate a deep learning method to improve the management of IPNs. Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions. Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4–90.7%) and 91.9% (95% CI, 88.7–94.7%), compared with 78.1% (95% CI, 68.7–86.4%) and 81.9 (95% CI, 76.1–87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts. Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

BackgroundEstimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines.MethodsA dataset of incidentally detected pulmonary nodules measuring 5–15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN.ResultsThe area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models.ConclusionThe LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources.

Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes. However, to allow full utilisation of population-based research cohorts, this needs to be automated. The purpose of this study was therefore to automate the identification of spinal curvature from total body DXA scans using machine learning techniques. To validate the automation, we assessed: (1) sensitivity, specificity and area under the receiver operator curve value (AUC) by comparison with 12,000 manually annotated images; (2) reliability by rerunning the automation on a subset of DXA scans repeated 2–6 weeks apart and calculating the kappa statistic; (3) validity by applying the automation to 5000 non-annotated images to assess associations with epidemiological variables. The final automated model had a sensitivity of 86.5%, specificity of 96.9% and an AUC of 0.80 (95%CI 0.74–0.87). There was almost perfect agreement of identification of those with scoliosis (kappa 0.90). Those with scoliosis identified by the automated model showed similar associations with gender, ethnicity, socioeconomic status, BMI and lean mass to previous literature. In conclusion, we have developed an accurate and valid automated method for identifying and quantifying spinal curvature from total body DXA scans.

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli. Despite extensive literature on the use of convolutional neural networks in thoracic imaging over the last few decades, we are yet to see these systems in use in clinical practice. The article reviews current state-of-the-art applications of AI and in detection, classification, and follow-up of pulmonary nodules and how deep-learning techniques might influence these going forward. Finally, we postulate the impact of these advancements on the role of radiologists and the importance of radiologists in the development and evaluation of these techniques.

This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject. Specifically, we use a large publicly-available dataset of over 20,000 subjects from the UK Biobank with both whole body Dixon technique magnetic resonance (MR) scans and also dual-energy x-ray absorptiometry (DXA) scans. We make three contributions: (i) We introduce a multi-modal image-matching contrastive framework, that is able to learn to match different-modality scans of the same subject with high accuracy. (ii) Without any adaption, we show that the correspondences learnt during this contrastive training step can be used to perform automatic cross-modal scan registration in a completely unsupervised manner. (iii) Finally, we use these registrations to transfer segmentation maps from the DXA scans to the MR scans where they are used to train a network to segment anatomical regions without requiring ground-truth MR examples. To aid further research, our code will be made publicly available., Accepted as a full paper to MICCAI 2021. Code will be made publicly available before September 27th 2021

Background: Degeneration of the intervertebral disc has long been associated with low back pain even though disc degeneration is seen in people who are asymptomatic. Investigations into the relationship between pain and disc degeneration in symptomatic and asymptomatic subjects are hampered by study sizes, by variations in definition of back pain and differences in MRI annotations of degeneration, study-to-study. Methods: We compared prevalence of disc degeneration between large symptomatic (724) and asymptomatic (701) groups of anonymised female subjects, 30-80yrs. We used SpineNet, a verified automated MRI annotation system to re-annotate MRIs onto the same objective system, irrespective of their original annotation. SpineNet rapidly annotated all MRIs for disc degeneration using the Pfirrmann scale 1-5, and for other degenerative features (herniation, endplate defects, marrow signs, spinal stenosis) as binary present/absent, taking age and spinal level into account. Findings: Severe disc degeneration (Pfirrmann 4 or 5) and degenerative features were significantly more prevalent in symptomatics than in asymptomatics in the lower (L4-S1) but not the upper (L1-L3) lumbar discs, and in subjects

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity.METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC).RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids.CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules.

Background MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiological studies of the condition. We developed and tested an automated system for grading lumbar spine MRI scans for central LSS for use in epidemiological research. Methods Using MRI scans from the large population-based cohort study (the Wakayama Spine Study), all graded by a spinal surgeon, we trained an automated system to grade central LSS in four gradings of the bone and soft tissue margins: none, mild, moderate, severe. Subsequently, we tested the automated grading against the independent readings of our observer in a test set to investigate reliability and agreement. Results Complete axial views were available for 4855 lumbar intervertebral levels from 971 participants. The machine used 4365 axial views to learn (training set) and graded the remaining 490 axial views (testing set). The agreement rate for gradings was 65.7% (322/490) and the reliability (Lin’s correlation coefficient) was 0.73. In 2.2% of scans (11/490) there was a difference in classification of 2 and in only 0.2% (1/490) was there a difference of 3. When classified into 2 groups as ‘severe’ vs ‘no/mild/moderate’. The agreement rate was 94.1% (461/490) with a kappa of 0.75. Conclusions This study showed that an automated system can “learn” to grade central LSS with excellent performance against the reference standard. Thus SpineNet offers potential to grade LSS in large-scale epidemiological studies involving a high volume of MRI spine data with a high level of consistency and objectivity.

Machine learning based lung cancer prediction models have been proposed to assist clinicians in managing incidental or screen detected indeterminate pulmonary nodules. Such systems may be able to reduce variability in nodule classification, improve decision making and ultimately reduce the number of benign nodules that are needlessly followed or worked-up. In this article, we provide an overview of the main lung cancer prediction approaches proposed to date and highlight some of their relative strengths and weaknesses. We discuss some of the challenges in the development and validation of such techniques and outline the path to clinical adoption.

Purpose To prospectively assess the repeatability of CT texture features and semi-automated volume measurements extracted from pulmonary nodules (PNs) at low-dose multi–detector row computed tomography (CT). The secondary objective was to investigate the influence of two methods of inspiratory control on the repeatability of CTTA and volumetry. Introduction Although nodule volumetry is a recognized biomarker of malignancy in pulmonary nodules (PNs), caution is needed in its interpretation because of variables such as cardiac motion, respiratory volume variation and inter-scan variability of up to 25%. The alternative technique of CT texture as a potential independent biomarker of malignancy has been suggested, but interscan variability and respiratory volume variation has not been assessed. Materials and methods 40 patients (20 with an indeterminate PN and 20 with pulmonary metastases) underwent two Low Dose CT scans (LDCT) within a 60-minute period (the “Coffee-break”). Patients were randomized to two inspiratory control groups: normal breath hold, and controlled lung volume. 20 texture features were extracted from each automatic contoured region surrounding the PN. The variability and limits of repeatability of texture measurements within individual nodules was assessed. Results The mean difference in volume between the two scans was 6.3%, SD: 29.9%. The 20 textural features displayed 95% CI below ±17.8%, and were less variable than nodule volume (95% CI ±28.9%). All features had high repeatability, calculated by the concordance correlation coefficient, (0.84 ≤ CCC ≤ 0.99). All measurements were more repeatable for the controlled lung volume group than the normal breath-hold group. Conclusion CTTA repeatability was comparable to automatic volumetric measurements, and appears to be improved using controlled volume breath holding.

Background:Magnetic resonance imaging (MRI) offers a non-invasive and objective method of early diagnosis and classification, monitoring disease burden and treatment response for patients (pts) with axial spondyloarthritis (axSpA) including ankylosing spondylitis (AS).1Numerous scoring schemes such as the AS Spine MRI Activity (ASspiMRIa) score are available for the quantitative assessment of MRI, but are subject to intra- and inter-rater variability, labor intensive and costly. Nevertheless, quantification of MRI changes has become an important tool to demonstrate treatment success of biologic drugs in axSpA.Objectives:To evaluate the performance of machine learning (ML) based software for automated Berlin grading of spinal MRI bone marrow oedema in pts with AS and compare with expert scoring.Methods:Fully automated ML software (Figure) was developed to detect and label 23 vertebrae, define vertebral units (VU) as per the Berlin modification of the ASspiMRIa score, and score each VU as either 0 (score of 0) or 1 (score of 1, 2 or 3). The ML algorithm was based on the previously developed SpineNet software.2Analysis included 108 pts from the secukinumab MEASURE 1 study3, in which imaging was done using T1 and STIR sagittal MRI at baseline and Weeks 16, 52, 104, 156 and 208. Two expert readers, blinded to treatment and visit, evaluated all images by ASspiMRIa score. The scores from Reader 2 (R2) were binned into two groups: 0 vs 1, 2, or 3. As a result of multiple pt time points and expert reading sessions, the complete dataset comprised of 10,988 VU. Ten-way cross-validation at per-VU was used to train and validate the ML software. The dataset was split into 10 randomly selected subsets, ensuring that each pt appears in only one subset, after which 8 subsets were used for training the ML software, 1 was used to check for correct training and 1 was used for validation. The process was repeated ten times such that all 10 subsets were used for validation. Accuracy weighted for the frequency of each category, sensitivity and specificity were calculated using scores from R2 as reference. Intra-reader accuracy was also calculated.Results:Accuracy of the software in relation to expert reader scores was 67% with a sensitivity of 0.63 and specificity of 0.70. The intra-reader accuracy was 71% and 77% for R1 and R2, respectively. Individual VU scoring of the Software vs. R2 are presented in the Table as a confusion matrix.Conclusion:Automated scoring of MR images in AS pts provided moderate agreement to that of expert reader-based assessments. ML software has potential to provide an automated guided-reading approach to scoring MR images, which may enable further clinical insights.References:[1]Lukas C, et al. J Rheumatol. 2007;34:862-70.[2]Jamaludin A, et al. Eur Spine J. 2017;26:1374-83.[3]Baeten D, et al. N Engl J Med. 2015;373,2534-48.Figure.Processing pipeline of automated Berlin scoring softwareTable.Confusion matrix between the software and R2SoftwareScore = 0SoftwareScore = 1, 2 or 3Total VU scoredR2 Score = 07199 (70%)3068 (30%)10,267R2 Score = 1, 2 or 3251 (35%)475 (65%)7267,4503,54310,993Percentages calculated as a fraction over the total in each row. Overall accuracy is the average of the highlighted percentages.Disclosure of Interests:Amir Jamaludin: None declared, Rhydian Windsor: None declared, Sarim Ather: None declared, Timor Kadir: None declared, Andrew Zisserman: None declared, Juergen Braun Grant/research support from: Abbvie (Abbott), Amgen, BMS, Boehringer, Celgene, Celltrion, Centocor, Chugai, Eli Lilly and Company, Medac, MSD (Schering Plough), Mundipharma, Novartis, Pfizer (Wyeth), Roche, Sanofi- Aventis, and UCB Pharma, Consultant of: Abbvie (Abbott), Amgen, BMS, Boehringer, Celgene, Celltrion, Centocor, Chugai, EBEWE Pharma, Eli Lilly and Company, Medac, MSD (Schering-Plough), Mundipharma, Novartis, Pfizer (Wyeth), Roche, Sanofi-Aventis, and UCB Pharma, Speakers bureau: Abbvie (Abbott), Amgen, BMS, Boehringer, Celgene, Celltrion, Centocor, Chugai, EBEWE Pharma, Eli Lilly and Company, Medac, MSD (Schering-Plough), Mundipharma, Novartis, Pfizer (Wyeth), Roche, Sanofi-Aventis, and UCB Pharma, Lianne S. Gensler Grant/research support from: Pfizer, Novartis, UCB, Consultant of: AbbVie, Eli Lilly, GSK, Novartis, UCB, Pedro Machado Consultant of: Abbvie, Celgene, Janssen, Lilly, MSD, BMS, Novartis, Pfizer, Roche and UCB, Speakers bureau: AbbVie, Centocor, Eli Lilly, Janssen, MSD, Novartis, Pfizer and UCB Pharma, Mikkel Ǿstergaard Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Merck, and Novartis, Consultant of: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Hospira, Janssen, Merck, Novartis, Novo Nordisk, Orion, Pfizer, Regeneron, Roche, Sandoz, Sanofi, and UCB, Denis Poddubnyy Grant/research support from: AbbVie, MSD, Novartis, and Pfizer, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, MSD, Novartis, Pfizer, Roche, UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Eli Lilly, MSD, Novartis, Pfizer, Roche, UCB, Thibaud Coroller Shareholder of: Novartis, Employee of: Novartis, Brian Porter Shareholder of: Novartis, Employee of: Novartis, Shephard Mpofu Shareholder of: Novartis, Employee of: Novartis, Aimee Readie Shareholder of: Novartis, Employee of: Novartis

We describe a method to automatically predict scoliosis in Dual-energy X-ray Absorptiometry (DXA) scans. We also show that in- termediate representations, which in our case are segments of body parts, help improve performance. Hence, we propose a two step process for pre- diction: (i) we learn to segment body parts via a segmentation Convolutional Neural Network (CNN), which we show outperforms the noisy labels it was trained on, and (ii) we predict with a classification CNN that uses as input both the raw DXA scan and also the intermediate representation, i.e. the segmented body parts. We demonstrate that this two step process can predict scoliosis with high accuracy, and can also localize the spinal curves (i.e. geometry) without additional supervision. Furthermore, we also propose a soft score of scoliosis based on the classification CNN which correlates to the severity of scoliosis.

This paper describes a novel automatic system for Modic changes classification of vertebral endplates. Modic changes are classes of vertebral degenerations visible as intensity variations in magnetic resonance images (MRI). The system operates on T1 and T2 MRI. We introduce three main novelties: 1. a vertebrae alignment scheme via precise bounding boxes obtained through corner localisation, 2. vertebral endplate classification in three dimensions, and 3. Modic changes classification. The system was trained and validated using a large dataset of 785 patients, containing MRIs sourced from a wide range of acquisition protocols. The proposed system achieved 87.8 % classification accuracy on our dataset.

Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be. This paper seeks to estimate a clinically achievable expected performance under this assumption. Assuming a perfect atlas selection, an extreme value theory has been applied to estimate the accuracy of single-atlas and multi-atlas segmentation given a large database of atlases. For this purpose, clinical contours of most common OARs on computed tomography of the head and neck ( ${N}=\textsf {316}$ ) and thoracic ( ${N}=\textsf {280}$ ) cases were used. This paper found that while for most organs, perfect segmentation cannot be reasonably expected, auto-contouring performance of a level corresponding to clinical quality could be consistently expected given a database of 5000 atlases under the assumption of perfect atlas selection.

Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes. However, to allow full utilisation of population-based research cohorts, this needs to be automated. The purpose of this study was therefore to automate the identification of spinal curvature from total body DXA scans using machine learning techniques. To validate the automation, we assessed: (1) sensitivity, specificity and area under the receiver operator curve value (AUC) by comparison with 12,000 manually annotated images; (2) reliability by rerunning the automation on a subset of DXA scans repeated 2-6 weeks apart and calculating the kappa statistic; (3) validity by applying the automation to 5000 non-annotated images to assess associations with epidemiological variables. The final automated model had a sensitivity of 86.5%, specificity of 96.9% and an AUC of 0.80 (95%CI 0.74-0.87). There was almost perfect agreement of identification of those with scoliosis (kappa 0.90). Those with scoliosis identified by the automated model showed similar associations with gender, ethnicity, socioeconomic status, BMI and lean mass to previous literature. In conclusion, we have developed an accurate and valid automated method for identifying and quantifying spinal curvature from total body DXA scans.

Positron Emission Tomography (PET) scanning in idiopathic pulmonary fibrosis (IPF) has revealed increased [18F]-fluorodeoxyglucose ([18F]-FDG) uptake in areas of the lungs that appear normal on high resolution computed tomography (HRCT). We hypothesised that “microscopic” disease identified using PET would be heterogeneous because IPF is characterised histologically by patchy fibrosis. We applied textural analysis to PET scans to evaluate heterogeneity of [18F]-FDG uptake in lung regions that appeared normal on HRCT. We identified six textural features that demonstrated significantly more heterogeneous [18F]-FDG uptake in radiologically normal lung in IPF patients compared with controls. Textural analysis of lung PET-CT imaging is a novel approach to study early changes in IPF before HRCT abnormalities are apparent. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. ALIYU has nothing to disclose. Conflict of interest: Dr. Avery has nothing to disclose. Conflict of interest: Dr. Cawthorne reports non-financial support from Optellum, non-financial support from Mirada Medical, during the conduct of the study. Conflict of interest: Dr. Archibald has nothing to disclose. Conflict of interest: Dr. Kadir reports other from Mirada Medical, during the conduct of the study; other from Optellum Ltd, outside the submitted work; . Conflict of interest: Dr. Willaime has nothing to disclose. Conflict of interest: Dr. Morice has nothing to disclose. Conflict of interest: Dr. Hart has nothing to disclose. Conflict of interest: Dr. Crooks has nothing to disclose.

Introduction Stereotactic Ablative Radiotherapy (SABR) and percutaneous microwave ablation (PMWA) are now being performed in patients deemed “medically inoperable” with non-small cell lung cancer (NSCLC). The majority of these patients are treated without ground truth histology, relying on imaging to establish the diagnosis. The purpose of this study was to investigate whether there were differences in the visible imaging features including CT Texture Analysis (CTTA) between patients referred for surgery, SABR and PMWA, which might suggest differences in underlying diagnosis. Methods 92 patients with one pulmonary nodule (PN) suspected as T1N0M0 to T2AN0M0 NSCLC on imaging were treated either with SABR (22 patients), PMWA (25) or Video-assisted thorascopic surgery (45) of which 23 had NSCLC (SURG M) and 22 had benign disease (SURG B). Patient characteristics, CT nodule morphology, presence of emphysema and percentage emphysema score, FDG avidity and CT textural features were compared. Twenty texture features (previously used in combination to create a nodule probability of malignancy score between 0–1) were extracted from each automatic contoured region surrounding the PN. The Kruskal-Wallis test was used to compare texture features between the 4 patient groups (SABR, PMWA, SURG M and SURG B). Results There was no significant difference in nodule morphology, volume at presentation (p=0.280) or volume doubling times (p=0.149), and presence of emphysema (p=0.348) or emphysema score (p=0.367) between the 4 groups. There was no statistical difference in CTTA malignancy prediction score between the SABR, PMWA and SURG M groups (p≥0.05). The probability of malignancy score was significantly lower (p-value Conclusion This is the first study to our knowledge to evaluate the radiological differences between patient groups referred for surgical and non-surgical treatments for NSCLC. On this small study, the Results support the hypothesis that the non-operative patient groups comprise the same proportion of benign and malignant as those in the operative group. The Results also demonstrate the potential clinical utility of CTTA in patient selection when histology is not obtainable. CTTA does not require volumetry detectable growth to detect change, and therefore may be a useful biomarker of malignancy at first diagnosis.

Introduction Distinguishing between benign and malignant small pulmonary nodules (PNs) detected on CT scanning is a significant challenge. Such nodules are commonly detected in clinical practice as incidental findings or in patients with a history of prior malignancy. CT texture analysis (CTTA) has been proposed as a potential imaging biomarker in tumour characterisation. Image texture refers to the statistical analysis of spatial intensity variations of the pixels within an image to produce a CT texture score. This score is then mapped onto a probability of malignancy from 0–1. Aims and Objectives To create a registry of patients with small solid PNs from an unselected population of patients. Patient demographic data were combined with information acquired from CT derived parameters such as shape, size, and texture analysis (CTTA) to develop and validate a generalised linear model to determine the probability of malignancy of PNs. A parallel prospective interventional cohort study was also conducted to assess whether CTTA repeatability was comparable to automatic volumetric measurements when a patient is scanned twice on the same day. Methods Between January 2012 to September 2014, 1008 patients presenting with small solid PNs were identified. The gold standard diagnosis of the nodules was established by histology or nodule stability at 2 years of CT follow-up. Results The prevalence of malignant PNs was 31.6% (319/1008). Significant independent predictors of malignancy included prior history of malignancy within 5 years (OR=117.4,(95% confidence interval(CI)):67.1 to 272.8, p Conclusion This study has highlighted the potential clinical utility of CTTA in the risk stratification of PNs. It has also shown that CTTA is a highly repeatable imaging biomarker of malignancy, akin to volume measurements but with the advantage of not requiring imaging follow-up.

The objective of this work is to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that this can be achieved via a Convolutional Neural Network (CNN) framework that takes intervertebral disc volumes as inputs and is trained only on disc-specific class labels. Our contributions are: (i) a CNN architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii) showing that this architecture can be trained using a multi-task loss function without requiring segmentation level annotation; and (iii) a localization method that clearly shows pathological regions in the disc volumes. We compare three visualization methods for the localization. The network is applied to a large corpus of MRI T2 sagittal spinal MRIs (using a standard clinical scan protocol) acquired from multiple machines, and is used to automatically compute disk and vertebra gradings for each MRI. These are: Pfirrmann grading, disc narrowing, upper/lower endplate defects, upper/lower marrow changes, spondylolisthesis, and central canal stenosis. We report near human performances across the eight gradings, and also visualize the evidence for these gradings localized on the original scans.

Aim To estimate the probability of malignancy in small pulmonary nodules (PNs) based on clinical and radiological characteristics in a non-screening population that includes patients with a prior history of malignancy using three validated models. Materials and methods Retrospective data on clinical and radiological characteristics was collected from the medical records of 702 patients (379 men, 323 women; range 19–94 years) with PNs ≤12 mm in diameter at a single centre. The final diagnosis was compared to the probability of malignancy calculated by one of three models (Mayo, VA, and McWilliams). Model accuracy was assessed by receiver operating characteristics (ROC). The models were calibrated by comparing predicted and observed rates of malignancy. Results The area under the ROC curve (AUC) was highest for the McWilliams model (0.82; 95% confidence interval [CI]: 0.78–0.91) and lowest for the Mayo model (0.58; 95% CI: 0.55–0.59). The VA model had an AUC of (0.62; 95% CI: 0.47–0.64). Performance of the models was significantly lower than that in the published literature. Conclusions The accuracy of the three models is lower in a non-screening population with a high prevalence of prior malignancy compared to the papers that describe their development. To the authors' knowledge, this is the largest study to validate predictive models for PNs in a non-screening clinically referred patient population, and has potential implications for the implementation of predictive models.

In this paper, we present a number of enhancements to the Kadir/Brady salient region detector which result in a significant improvement in performance. The modifications we make include: stabilising the difference between consecutive scales when calculating the inter-scale saliency, a new sampling strategy using overlap of pixels, partial volume estimation and parzen windowing. Repeatability is used as the criterion for evaluating the performance of the algorithm. We observe the repeatability for distinctive regions selected from an image and from the same image after applying a particular transformation. The transformations we use include planar rotation, pixel translation, spatial scaling, and intensity shifts and scaling. Experimental results show that the average repeatability rate is improved from 46% to approximately 78% when all the enhancements are applied. We also compare our algorithm with other region detectors on a set of sequences of real images, and our detector outperforms most of the state of the art detectors. © 2006 Elsevier Inc. All rights reserved.