Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data.

Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers.

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b = 300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p = 0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p = 0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC = 0.88) and the 25th percentile α value (ICC = 0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.

Correction to: Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

The original version of this article, published on 11 June 2019, unfortunately contained a mistake. The following correction has therefore been made in the original: In section "Multiparametric MRI review," the readers mentioned in the first sentence were partly incorrect.

Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer.

OBJECTIVES: Compare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists. METHODS: A single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone. RESULTS: Diagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67-0.81) in the PZ and 0.68 (95% CI 0.61-0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69. CONCLUSION: Zone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score. KEY POINTS: • MRI diagnostic models had similar performance to experienced radiologists for classification of prostate cancer. • MRI diagnostic models may help radiologists classify tumour in patients with indeterminate Likert 3 scores.

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. METHODS: A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. RESULTS: The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). CONCLUSIONS: Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. KEY POINTS: • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Whole body magnetic resonance imaging in newly diagnosed multiple myeloma: early changes in lesional signal fat fraction predict disease response.

Cross-sectional imaging techniques are being increasingly used for disease evaluation in patients with multiple myeloma. Whole body magnetic resonance imaging (WB-MRI) scanning is superior to plain radiography in baseline assessment of patients but changes following treatment have not been systematically explored. We carried out paired WB-MRI scans in 21 newly diagnosed patients prior to, and 8-weeks after, starting chemotherapy, and analysed stringently selected focal lesions (FLs) for parametric changes. A total of 323 FLs were evaluated, median 20 per patient. At 8 weeks, there was a reduction in estimated tumour volume (eTV), and an increase in signal fat fraction (sFF) and apparent diffusion coefficient (ADC) in the group as a whole (P < 0·001). Patients who achieved complete/very good partial response (CR/VGPR) to induction had a significantly greater increase in sFF compared to those achieving ≤ partial response (PR; P = 0·001). When analysed on a per-patient basis, all patients achieving CR/VGPR had a significant sFF increase in their FL's, in contrast to patients achieving ≤PR. sFF changes in patients reaching maximal response within 100 days (fast responders) were greater compared to slow responders (P = 0·001). Receiver Operator Characteristic analysis indicated that sFF changes at 8 weeks were the best biomarker (area under the Curve 0·95) for an inferior response (≤PR). We conclude that early lesional sFF changes may provide important information on depth of response, and are worthy of further prospective study.

Prediction of Pediatric Percutaneous Nephrolithotomy Outcomes Using Contemporary Scoring Systems.

PURPOSE: We evaluate the applicability of contemporary percutaneous nephrolithotomy scoring systems in pediatric patients and compare their predictive power regarding postoperative outcomes. MATERIALS AND METHODS: We retrospectively analyzed the records of 125 children who were diagnosed with renal calculi and underwent percutaneous nephrolithotomy between March 2011 and April 2016. Predictive scores, which consisted of Guy's Stone Score, S.T.O.N.E. (stone size, tract length, obstruction, number of involved calyces and essence/stone density) nephrolithometry and CROES (Clinical Research Office of the Endourological Society) nomogram, were calculated for all patients included in the study. Patient demographics, stone-free rate and complications were all analyzed and are reported. RESULTS: Median Guy's Stone Score was 2 (IQR 2 to 3) in patients with residual stones (group 1) and 2 (1 to 2) in those who were stone-free (group 2). Median respective CROES nomogram scores were 215 (IQR 210 to 235) and 257 (240 to 264), and S.T.O.N.E. nephrolithometry scores were 8 (7 to 9) and 5 (5 to 6, all p <0.0001). S.T.O.N.E. score demonstrated the greatest accuracy in predicting stone-free rate. Guy's Stone Score was significantly correlated with complications but the CROES and S.T.O.N.E. scores were not significantly correlated with complications. CONCLUSIONS: The scoring systems analyzed could be used to predict success of percutaneous nephrolithotomy in the pediatric setting. However, further studies are needed to formulate modifications for use in children. The main variables in the scoring systems, ie stone burden, tract length and case volume, were measured using records from adult patients. Besides these variables, the relatively small pelvicalyceal system and higher incidence of anatomical malformations in children could potentially affect percutaneous nephrolithotomy outcomes.

Whole-body MRI quantitative biomarkers are associated significantly with treatment response in patients with newly diagnosed symptomatic multiple myeloma following bortezomib induction.

OBJECTIVES: To evaluate whole-body MRI (WB-MRI) parameters significantly associated with treatment response in multiple myeloma (MM). METHODS: Twenty-one MM patients underwent WB-MRI at diagnosis and after two cycles of chemotherapy. Scans acquired at 3.0 T included T2, diffusion-weighted-imaging (DWI) and mDixon pre- and post-contrast. Twenty focal lesions (FLs) matched on DWI and post-contrast mDixon were selected for each time point. Estimated tumour volume (eTV), apparent diffusion coefficient (ADC), enhancement ratio (ER) and signal fat fraction (sFF) were derived. Clinical treatment response to chemotherapy was assessed using conventional criteria. Significance of temporal parameter change was assessed by the paired t test and receiver operating characteristics/area under the curve (AUC) analysis was performed. Parameter repeatability was assessed by interclass correlation (ICC) and Bland-Altman analysis of 10 healthy volunteers scanned at two time points. RESULTS: Fifteen of 21 patients responded to treatment. Of 254 FLs analysed, sFF (p < 0.0001) and ADC (p = 0.001) significantly increased in responders but not non-responders. eTV significantly decreased in 19/21 cases. Focal lesion sFF was the best discriminator of treatment response (AUC 1.0). Bone sFF repeatability was excellent (ICC 0.98) and better than bone ADC (ICC 0.47). CONCLUSION: WB-MRI derived focal lesion sFF shows promise as an imaging biomarker of treatment response in newly diagnosed MM. KEY POINTS: • Bone signal fat fraction using mDixon is a robust quantifiable parameter • Fat fraction and ADC significantly increase in myeloma lesions responding to treatment • Bone lesion fat fraction is the best discriminator of myeloma treatment response.

Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.

OBJECTIVES: We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). METHODS: One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. RESULTS: Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. CONCLUSIONS: LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. KEY POINTS: • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.

OBJECTIVES: To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. METHODS: Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. RESULTS: The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. CONCLUSION: LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. KEY POINTS: • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.

Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?

PURPOSE: Multiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion-weighted-imaging, to correct the noise induced bias, and to assess the effect upon cancer classification. METHODS: A new adaptation of the median-absolute-deviation technique in the wavelet-domain, using a closed form approximation of convolved probability-distribution-functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion-weighted signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients. RESULTS: The adapted median-absolute-deviation method accurately predicted the noise of simulated (R(2) = 0.96) and neck diffusion-weighted-imaging (averaged once or four times). Maximum probability recovers the true apparent-diffusion-coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters. CONCLUSIONS: Perfusion-related parameters were best at cancer classification. Noise-corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal-to-noise ratio acquisitions.

Evaluation of Crohn's disease activity: initial validation of a magnetic resonance enterography global score (MEGS) against faecal calprotectin.

OBJECTIVES: To develop an MRI enterography global score (MEGS) of Crohn's disease (CD) activity compared with a reference standard of faecal calprotectin (fC), C-reactive protein (CRP) and Harvey-Bradshaw index (HBI). METHODS: Calprotectin, CRP and HBI were prospectively recorded for 71 patients (median age 33, male 35) with known/suspected CD undergoing MRI enterography. Two observers in consensus scored activity for nine bowel segments, grading mural thickness, T2 signal, mesenteric oedema, T1 enhancement and pattern, and haustral loss. Segmental scores were multiplied according to disease length. Five points each were added for lymphadenopathy, comb sign, fistulae and abscesses to derive the MEGS. A previously validated MRI CD activity score (CDAS) was also calculated. MRI scores were correlated with clinical references using Spearman's rank. A logistic regression diagnostic model was built to discriminate active (fC > 100 µg/g) from inactive disease. RESULTS: MEGS and CDAS were significantly correlated with fC (r = 0.46, P < 0.001) and (r = 0.39, P = 0.001) respectively. MEGS correlated with CRP (r = 0.39, P = 0.002). The model for discriminating active from inactive disease achieved an area under the receiver-operating curve of 0.75 and 0.66 after leave-one-out analysis. CONCLUSION: A magnetic resonance enterography global score (MEGS) of CD activity correlated significantly with fC levels. KEY POINTS: • Magnetic resonance imaging is now widely used to assess Crohn's disease. • Existing MRI activity scores depend on local segmental endoscopic/histological reference standards. • Scores including assessment of disease extent/complications better demonstrate full disease burden. • This new global Crohn's disease burden score correlates with calprotectin and CRP. • The MRI enterography score of disease activity can complement existing clinical markers.

Is Attention all You Need in Medical Image Analysis? A Review.

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling "global" pixel interactions, which restricts their generalisation ability to understand out-of-distribution data with different "global" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments ("Transf/Attention") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images.

Spine image analysis is based on the accurate segmentation and vertebrae recognition of the spine. Several deep learning models have been proposed for spine segmentation and vertebrae recognition, but they are very computationally demanding. In this research, a novel deep learning model is introduced for spine segmentation and vertebrae recognition using CT images. The proposed model works in two steps: (1) A cascaded hierarchical atrous spatial pyramid pooling residual attention U-Net (CHASPPRAU-Net), which is a modified version of U-Net, is used for the segmentation of the spine. Cascaded spatial pyramid pooling layers, along with residual blocks, are used for feature extraction, while the attention module is used for focusing on regions of interest. (2) A 3D mobile residual U-Net (MRU-Net) is used for vertebrae recognition. MobileNetv2 includes residual and attention modules to accurately extract features from the axial, sagittal, and coronal views of 3D spine images. The features from these three views are concatenated to form a 3D feature map. After that, a 3D deep learning model is used for vertebrae recognition. The VerSe 20 and VerSe 19 datasets were used to validate the proposed model. The model achieved more accurate results in spine segmentation and vertebrae recognition than the state-of-the-art methods.

A likelihood-based particle imaging filter using prior information.

BACKGROUND: Particle imaging can increase precision in proton and ion therapy. Interactions with nuclei in the imaged object increase image noise and reduce image quality, especially for multinucleon ions that can fragment, such as helium. PURPOSE: This work proposes a particle imaging filter, referred to as the Prior Filter, based on using prior information in the form of an estimated relative stopping power (RSP) map and the principles of electromagnetic interaction, to identify particles that have undergone nuclear interaction. The particles identified as having undergone nuclear interactions are then excluded from the image reconstruction, reducing the image noise. METHODS: The Prior Filter uses Fermi-Eyges scattering and Tschalär straggling theories to determine the likelihood that a particle only interacts electromagnetically. A threshold is then set to reject those particles with a low likelihood. The filter was evaluated and compared with a filter that estimates this likelihood based on the measured distribution of energy and scattering angle within pixels, commonly implemented as the 3σ filter. Reconstructed radiographs from simulated data of a 20-cm water cylinder and an anthropomorphic chest phantom were generated with both protons and helium ions to assess the effect of the filters on noise reduction. The simulation also allowed assessment of secondary particle removal through the particle histories. Experimental data were acquired of the Catphan CTP 404 Sensitometry phantom using the U.S. proton CT (pCT) collaboration prototype scanner. The proton and helium images were filtered with both the prior filtering method and a state-of-the-art method including an implementation of the 3σ filter. For both cases, a dE-E telescope filter, designed for this type of detector, was also applied. RESULTS: The proton radiographs showed a small reduction in noise (1 mm of water-equivalent thickness [WET]) but a larger reduction in helium radiographs (up to 5-6 mm of WET) due to better secondary filtering. The proton and helium CT images reflected this, with similar noise at the center of the phantom (0.02 RSP) for the proton images and an RSP noise of 0.03 for the proposed filter and 0.06 for the 3σ filter in the helium images. Images reconstructed from data with a dose reduction, up to a factor of 9, maintained a lower noise level using the Prior Filter over the state-of-the-art filtering method. CONCLUSIONS: The proposed filter results in images with equal or reduced noise compared to those that have undergone a filtering method typical of current particle imaging studies. This work also demonstrates that the proposed filter maintains better performance against the state of the art with up to a nine-fold dose reduction.

Pilot study of optical coherence tomography angiography-derived microvascular metrics in hands and feet of healthy and diabetic people.

Optical coherence tomography angiography (OCTA) is a non-invasive, high-resolution imaging modality with growing application in dermatology and microvascular assessment. Accepted reference values for OCTA-derived microvascular parameters in skin do not yet exist but need to be established to drive OCTA into the clinic. In this pilot study, we assess a range of OCTA microvascular metrics at rest and after post-occlusive reactive hyperaemia (PORH) in the hands and feet of 52 healthy people and 11 people with well-controlled type 2 diabetes mellitus (T2DM). We calculate each metric, measure test-retest repeatability, and evaluate correlation with demographic risk factors. Our study delivers extremity-specific, age-dependent reference values and coefficients of repeatability of nine microvascular metrics at baseline and at the maximum of PORH. Significant differences are not seen for age-dependent microvascular metrics in hand, but they are present for several metrics in the foot. Significant differences are observed between hand and foot, both at baseline and maximum PORH, for most of the microvascular metrics with generally higher values in the hand. Despite a large variability over a range of individuals, as is expected based on heterogeneous ageing phenotypes of the population, the test-retest repeatability is 3.5% to 18% of the mean value for all metrics, which highlights the opportunities for OCTA-based studies in larger cohorts, for longitudinal monitoring, and for assessing the efficacy of interventions. Additionally, branchpoint density in the hand and foot and changes in vessel diameter in response to PORH stood out as good discriminators between healthy and T2DM groups, which indicates their potential value as biomarkers. This study, building on our previous work, represents a further step towards standardised OCTA in clinical practice and research.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field.

OBJECTIVES: To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions. DATA SOURCES: Peer-reviewed scientific publications and expert opinion. CONCLUSION: The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services. IMPLICATIONS FOR NURSING PRACTICE: As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies.

BACKGROUND: Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality. METHODS: We developed a deep learning-based method (named "TabMLPNet") to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls. RESULTS: The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID. CONCLUSIONS: We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level.

Primary immunodeficiencies (PI) are disorders that weaken the immune system, increasing the incident of life-threatening infections, organ damage and the development of cancer and autoimmune diseases. Although PI is estimated to affect 1-2% of the global population, 70-90% of these patients remain undiagnosed. Many patients are diagnosed during adulthood, after other serious diseases have already developed. We developed a computational method to analyze the clinical history from a large group of people with and without PI. We focused on combined (CID) and common variable immunodeficiency (CVID), which are among the least studied and most common PI subtypes, respectively. We could identify people with CID or CVID and combinations of diseases and symptoms which could make it easier to identify CID or CVID. Our method could be used to more readily identify adults at risk of CID or CVID, enabling treatment to start earlier and their long-term health to be improved.

MRI-based motion correction of thoracic PET: initial comparison of acquisition protocols and correction strategies suitable for simultaneous PET/MRI systems.

OBJECTIVES: Magnetic resonance imaging (MRI) acquired on equipment capable of simultaneous MRI and positron emission tomography (PET) could potentially provide the gold standard method for motion correction of PET. To assess the latter, in this study we compared fast 2D and 3D MRI of the torso and used deformation parameters from real MRI data to correct simulated PET data for respiratory motion. METHODS: PET sinogram data were simulated using SimSET from a 4D pseudo-PET image series created by segmenting MR images acquired over a respiratory cycle. Motion-corrected PET images were produced using post-reconstruction registration (PRR) and motion-compensated image reconstruction (MCIR). RESULTS: MRI-based motion correction improved PET image quality at the lung-liver and lung-spleen boundaries and in the heart but little improvement was obtained where MRI contrast was low. The root mean square error in SUV units per voxel compared to a motion-free image was reduced from 0.0271 (no motion correction) to 0.0264 (PRR) and 0.0250 (MCIR). CONCLUSIONS: Motion correction using MRI can improve thoracic PET images but there are limitations due to the quality of fast MRI.

Registration-weighted motion correction for PET.

PURPOSE: Patient motion during a positron emission tomography (PET) scan can lead to significant resolution loss. Two of the main motion correction techniques employed to ameliorate the loss of image quality due to respiratory motion in the torso are postreconstruction registration (PRR) and motion-compensated image reconstruction (MCIR). In this study, the authors investigated whether versions of these methods that utilize registration-based weighting of the constituent respiratory gated data offer any advantage over the standard versions that use equal weighting. The registration-based weights were designed to penalize gates that were poorly registered to the reference gate. METHODS: SimSET was used to simulate data from the NCAT phantom with six lesions added in the lung and liver. Images were reconstructed using registration-weighted PRR and MCIR algorithms, where the registration weighting was based on the mutual information (MI) of each registered gate and the reference gate. More specifically, relative to equal weighting, for which the weight for each gate is the inverse of the number of gates, the weights were increased for MI greater than the average MI and reduced for gates with MI less than the average MI. A scale factor was used to increase the range of the weights, and PRR and MCIR images were produced for a range of scale factor values. RESULTS: At the optimal values of the scale factor, registration-weighted PRR produced significantly higher contrast-to-noise ratio (CNR) for each lesion than PRR (p < 0.001), with average lesion CNR increasing significantly from (2.10 ± 0.05) to (2.70 ± 0.06) for 3 mm postsmoothing (p < 0.001) and from (2.03 ± 0.06) to (2.77 ± 0.05) for 6 mm postsmoothing (p < 0.001). Likewise, for MCIR registration weighting significantly increased the average CNR from (2.38 ± 0.04) to (2.62 ± 0.07) for 3 mm postsmoothing (p < 0.001) and from (2.56 ± 0.05) to (2.84 ± 0.08) for 6 mm postsmoothing (p < 0.001). These gains in lesion CNR were obtained despite corresponding reductions in signal-to-noise ratio, as expected from the use of unequal weighting of gated data with comparable variance. CONCLUSIONS: Registration weighting can significantly improve lesion CNR in motion corrected images, especially for PRR.