Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and ...oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.
Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for ...MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD.
51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups.
Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group.
These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD.
Mapping mean axon diameter and intra-axonal volume fraction may have significant clinical potential because nerve conduction velocity is directly dependent on axon diameter, and several ...neurodegenerative diseases affect axons of specific sizes and alter axon counts. Diffusion-weighted MRI methods based on the pulsed gradient spin echo (PGSE) sequence have been reported to be able to assess axon diameter and volume fraction non-invasively. However, due to the relatively long diffusion times used, e.g. >20ms, the sensitivity to small axons (diameter<2μm) is low, and the derived mean axon diameter has been reported to be overestimated. In the current study, oscillating gradient spin echo (OGSE) diffusion sequences with variable frequency gradients were used to assess rat spinal white matter tracts with relatively short effective diffusion times (1–5ms). In contrast to previous PGSE-based methods, the extra-axonal diffusion cannot be modeled as hindered (Gaussian) diffusion when short diffusion times are used. Appropriate frequency-dependent rates are therefore incorporated into our analysis and validated by histology-based computer simulation of water diffusion. OGSE data were analyzed to derive mean axon diameters and intra-axonal volume fractions of rat spinal white matter tracts (mean axon diameter of ~1.27–5.54μm). The estimated values were in good agreement with histology, including the small axon diameters (<2.5μm). This study establishes a framework for the quantification of nerve morphology using the OGSE method with high sensitivity to small axons.
•Appropriate intra- and extra-axonal diffusion models were proposed and validated.•Temporal diffusion spectra were acquired to assess rat spinal white matter tracts.•Mean axon diameter and axonal volume fraction were fit and verified with histology.•This study establishes a framework using MRI to detect white matter morphology.
Purpose
Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell‐cycle arrest. ...Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell‐size imaging for clinical applications.
Methods
We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion‐weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients.
Results
With fast acquisitions (~7 minutes), IMPULSED can provide high‐resolution (1.3 mm in‐plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size.
Conclusion
The proposed IMPULSED method can assess cell‐size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.
Cell migration is a tightly coordinated process that requires the spatiotemporal regulation of many molecular components. Because adaptor proteins can serve as integrators of cellular events, they ...are being increasingly studied as regulators of cell migration. The adaptor protein containing a pleckstrin-homology (PH) domain, phosphotyrosine binding (PTB) domain, and leucine zipper motif 1 (APPL1) is a 709 amino acid endosomal protein that plays a role in cell proliferation and survival as well as endosomal trafficking and signaling. However, its function in regulating cell migration is poorly understood. Here, we show that APPL1 hinders cell migration by modulating both trafficking and signaling events controlled by Rab5 in cancer cells. APPL1 decreases internalization and increases recycling of α5β1 integrin, leading to higher levels of α5β1 integrin at the cell surface that hinder adhesion dynamics. Furthermore, APPL1 decreases the activity of the GTPase Rac and its effector PAK, which in turn regulate cell migration. Thus, we demonstrate a novel role for the interaction between APPL1 and Rab5 in governing crosstalk between signaling and trafficking pathways on endosomes to affect cancer cell migration.This article has an associated First Person interview with the first author of the paper.
Purpose
Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee ...arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression.
Methods
From the department’s clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics.
Results
At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately mean area under the ROC curves (AUC) 0.73–0.75. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI − 0.0025, 0.002).
Conclusions
When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression—in particular, ordinal logistic regression that does not assume linearity in its predictors.
Level of evidence
Prognostic level II
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and ...termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states.
Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings.
Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones.
These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
Why are people with epilepsy not continuously having seizures? Johnson et al. use intracranial electrical recordings to analyse brain network interactions in people with epilepsy, and provide evidence that the seizure-onset network is actively suppressed by the rest of the brain during interictal states.
Large national databases have become a common source of information on patterns of cancer care in the United States, particularly for low-incidence diseases such as sarcoma. Although aggregating ...information from many hospitals can achieve statistical power, this may come at a cost when complex variables must be abstracted from the medical record. There is a current lack of understanding of the frequency of use of the Surveillance, Epidemiology, and End Results (SEER) database and the National Cancer Database (NCDB) over the last two decades in musculoskeletal sarcoma research and whether their use tends to produce papers with conflicting findings.
(1) Is the number of published studies using the SEER and NCDB databases in musculoskeletal sarcoma research increasing over time? (2) What are the author, journal, and content characteristics of these studies? (3) Do studies using the SEER and the NCDB databases for similar diagnoses and study questions report concordant or discordant key findings? (4) Are the administrative data reported by our institution to the SEER and the NCDB databases concordant with the data in our longitudinally maintained, physician-run orthopaedic oncology dataset?
To answer our first three questions, PubMed was searched from 2001 through 2020 for all studies using the SEER or the NCDB databases to evaluate sarcoma. Studies were excluded from the review if they did not use these databases or studied anatomic locations other than the extremities, nonretroperitoneal pelvis, trunk, chest wall, or spine. To answer our first question, the number of SEER and NCDB studies were counted by year. The publication rate over the 20-year span was assessed with simple linear regression modeling. The difference in the mean number of studies between 5-year intervals (2001-2005, 2006-2010, 2011-2015, 2016-2020) was also assessed with Student t-tests. To answer our second question, we recorded and summarized descriptive data regarding author, journal, and content for these studies. To answer our third question, we grouped all studies by diagnosis, and then identified studies that shared the same diagnosis and a similar major study question with at least one other study. We then categorized study questions (and their associated studies) as having concordant findings, discordant findings, or mixed findings. Proportions of studies with concordant, discordant, or mixed findings were compared. To answer our fourth question, a coding audit was performed assessing the concordance of nationally reported administrative data from our institution with data from our longitudinally maintained, physician-run orthopaedic oncology dataset in a series of patients during the past 3 years. Our orthopaedic oncology dataset is maintained on a weekly basis by the senior author who manually records data directly from the medical record and sarcoma tumor board consensus notes; this dataset served as the gold standard for data comparison. We compared date of birth, surgery date, margin status, tumor size, clinical stage, and adjuvant treatment.
The number of musculoskeletal sarcoma studies using the SEER and the NCDB databases has steadily increased over time in a linear regression model (β = 2.51; p < 0.001). The mean number of studies per year more than tripled during 2016-2020 compared with 2011-2015 (39 versus 13 studies; mean difference 26 ± 11; p = 0.03). Of the 299 studies in total, 56% (168 of 299) have been published since 2018. Nineteen institutions published more than five studies, and the most studies from one institution was 13. Orthopaedic surgeons authored 35% (104 of 299) of studies, and medical oncology journals published 44% (130 of 299). Of the 94 studies (31% of total 94 of 299) that shared a major study question with at least one other study, 35% (33 of 94) reported discordant key findings, 29% (27 of 94) reported mixed key findings, and 44% (41 of 94) reported concordant key findings. Both concordant and discordant groups included papers on prognostic factors, demographic factors, and treatment strategies. When we compared nationally reported administrative data from our institution with our orthopaedic oncology dataset, we found clinically important discrepancies in adjuvant treatment (19% 15 of 77), tumor size (21% 16 of 77), surgery date (23% 18 of 77), surgical margins (38% 29 of 77), and clinical stage (77% 59 of 77).
Appropriate use of databases in musculoskeletal cancer research is essential to promote clear interpretation of findings, as almost two-thirds of studies we evaluated that asked similar study questions produced discordant or mixed key findings. Readers should be mindful of the differences in what each database seeks to convey because asking the same questions of different databases may result in different answers depending on what information each database captures. Likewise, differences in how studies determine which patients to include or exclude, how they handle missing data, and what they choose to emphasize may result in different messages getting drawn from large-database studies. Still, given the rarity and heterogeneity of sarcomas, these databases remain particularly useful in musculoskeletal cancer research for nationwide incidence estimations, risk factor/prognostic factor assessment, patient demographic and hospital-level variable assessment, patterns of care over time, and hypothesis generation for future prospective studies.
Level III, therapeutic study.
OBJECTIVESThe purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained ...before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer.
MATERIALS AND METHODSPatients with stage II/III breast cancer were enrolled in an institutional review board–approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC.
RESULTSThe kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018–0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (−0.11 to 0.24).
CONCLUSIONSThe multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.
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