In vivo micro–Computed Tomography (μCT) is commonly used tool in the study of mouse bone architecture. However, in vivo imaging of mouse cartilage has been limited. Intra-articular contrast injection ...was evaluated for its utility in detecting mouse cartilage in μCT. Clinically used iodinated contrast agent was chosen for its widespread commercial availability. Imaging protocol was developed with wild type C57BL/6 mice for its ability to detect expected cartilage thinning that occurs with sexual maturity. The protocol was then validated with transgenic mouse model with known extracellular matrix loss. μCT findings showed good correspondence with histological assessment. In conclusion, in vivo intra-articular contrast-enhanced μCT arthrography is viable technique for evaluation of mouse cartilage.
In vivo intra-articular contrast enhanced μCT of the mouse knee joint can delineate cartilage thickness and extracellular matrix content. The imaging protocol may be useful for longitudinal evaluation of cartilage anomalies in transgenicmouse model.
•The μCT arthrogram protocol was developed for imaging mouse knee cartilage.•The protocol can detect cartilage thinning with age in wild type growing mice.•The protocol can detect extracellular matrix loss in transgenic mice.
Abstract
Objective
Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect ...causal associations among diseases and imaging findings from their co-occurrence in radiology reports.
Materials and Methods
This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P < 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth.
Results
2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold.
Discussion
Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports.
Conclusion
This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.
Objectives
Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical ...illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.
Methods
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.
Results
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (
p
< 0.0001).
Conclusions
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.
Key Point
• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
"Medical Imaging and Image-Guided Interventions" is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in this field of study. This ...publication aims at providing a thorough overview of the latest research efforts and opens new possible research paths for further novel developments.
To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists.
A total of 1356 patients (2899 images) with histologically ...confirmed primary bone tumors and pre-operative radiographs were identified from five institutions’ pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests.
For benign vs. not benign, model achieved area under curve (AUC) of 0•894 and 0•877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0•907 and 0•916 on cross-validation and external testing, respectively. For three-way classification, model achieved 72•1% accuracy vs. 74•6% and 72•1% for the two subspecialists on cross-validation (p = 0•03 and p = 0•52, respectively). On external testing, model achieved 73•4% accuracy vs. 69•3%, 73•4%, 73•1%, 67•9%, and 63•4% for the two subspecialists and three junior radiologists (p = 0•14, p = 0•89, p = 0•93, p = 0•02, p < 0•01 for radiologists 1–5, respectively).
Deep learning can classify primary bone tumors using conventional radiographs in a multi-institutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists.
The project described was supported by RSNA Research & Education Foundation, through grant number RSCH2004 to Harrison X. Bai.
Objective:
This study is a follow-up to a study in 2020 that reviewed changes in the racial and ethnic composition of public health students, graduates, and faculty among Association of Schools and ...Programs of Public Health (ASPPH)–member institutions. In the current study, we evaluated how the racial and ethnic composition among biostatistics and epidemiology students, graduates, and faculty changed from 2010 to 2020.
Methods:
We analyzed data on race and ethnicity of enrolled graduate students, graduates (master’s and doctoral), and faculty at ASPPH-member institutions by using institutionally reported data from the ASPPH Data Center. We tabulated frequencies, percentages, and percentage-point changes by race and ethnicity. We measured differences between groups by using a test for difference in 2 proportions.
Results:
The number of enrolled students, graduates, and faculty in all departments increased during the study period, while the number of tenure-track faculty in biostatistics decreased. The percentage of enrolled Hispanic/Latino biostatistics graduate students increased from 5.6% in 2010 to 10.2% in 2020 (P = .007), and the percentage of epidemiology graduates increased from 8.8% to 13.8% (P = .008). We found no differences among other underrepresented racial and ethnic groups. Most biostatistics and epidemiology professors at all ranks were non-Hispanic White, despite substantial decreases. The percentage of underrepresented racial and ethnic minority biostatistics and epidemiology professors was constant across all ranks.
Conclusion:
Although more Hispanic/Latino students are enrolled in and graduating from biostatistics and epidemiology departments at ASPPH-member institutions, we found no change among faculty. More work is needed to recruit and retain other (American Indian/Alaska Native, Black or African American, Native Hawaiian/Other Pacific Islander) underrepresented students and faculty.
Although risk factors for heterotopic ossification (HO) have been defined, the effect from surgical approach is not fully understood. The primary objective of our study was to evaluate the effect ...that surgical approach has on the risk for developing severe HO after total hip arthroplasty (THA) and compare this with other known risk factors. We hypothesized that there would be no difference in HO formation based on the surgical approach.
We retrospectively reviewed all patients who underwent primary THA at our hospital between March 2011 and March 2021. Patients with HO documented in the radiology reports were cross-referenced with our THA data set and manually reviewed to determine Brooker classification. Patient demographics, medical comorbidities, surgical details, and medication information were collected from the electronic medical record and compared.
Of 3,427 patients who underwent THA, 677 (19.8%) developed HO postoperatively. A multivariable analysis confirmed that surgical approach was independently associated with increased odds for HO development. The anterolateral (odds ratio OR, 3.43; P < 0.001) and posterior (OR, 2.24; P < 0.001) approaches had increased odds for developing HO compared with the direct anterior approach. However, only the anterolateral approach (OR, 1.85; P = 0.033) demonstrated an increased association with the development of severe HO (Brooker 3, 4) postoperatively.
Although the use of the direct anterior approach had the lowest overall OR for developing HO after THA, this is likely only clinically notable when compared with the anterolateral approach.
III.
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the ...Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
Background
Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine carcinoma of the skin. As the clinical course can be variable, prognostic markers are needed to better stratify ...patients. Prior literature, composed of small series with limited sample size, has demonstrated that tumor‐infiltrating lymphocytes (TILs) are an important prognostic marker in MCC. To validate these findings on a population level, we sought to analyze and report the prognostic value of TILs in a large national data set.
Materials and Methods
A retrospective observational cohort study was conducted of patients with nonmetastatic MCC from 2010 to 2015 using the National Cancer Database. Individual variables trending toward significance using a univariable analysis were included in a multivariable Cox proportional hazards model to assess their independent effect on overall survival (OS). TILs were subclassified into none, nonbrisk, and brisk and the survival analysis was performed. Propensity score–weighted multivariable analysis (PS MVA) was performed to adjust for additional confounding.
Results
A total of 2,182 patients met inclusion criteria: 611 (28.0%) were identified as having TILs present, and 1,571 (72.0%) had TILs absent in the tumor. On MVA, subdivision of TIL status into nonbrisk (hazard ratio HR, 0.750; 95% confidence interval CI, 0.602–0.933) and brisk (HR, 0.499; 95% CI, 0.338–0.735) was associated with incrementally improved OS compared with no TILs. The association of nonbrisk and brisk TILs with improved OS was retained on PS MVA (Nonbrisk: HR, 0.720; 95% CI, 0.550–0.944; Brisk: HR, 0.483; 95% CI, 0.286–0.814).
Conclusion
The presence of nonbrisk and brisk TILs is associated with incrementally improved OS in patients with nonmetastatic MCC in a large national data set. This pathologic feature can aid with risk stratification, estimation of prognosis, and, importantly, decision‐making with respect to treatment intensification in high‐risk patients.
Implications for Practice
Merkel cell carcinoma (MCC) is an aggressive neuroendocrine cutaneous malignancy with variable clinical course. Prognostic markers are needed to better risk stratify patients. We present the largest retrospective observational cohort study of patients with nonmetastatic MCC using the National Cancer Database. Our analysis demonstrates an association between increasing degrees of tumor‐infiltrating lymphocytes and incrementally improved survival. These conclusions improve pathologic risk stratification, and decision‐making with respect to treatment intensification. Intensification may include adjuvant radiation therapy to the primary site after wide excision despite small tumor size, to the nodal basin in sentinel lymph node‐negative patients, or offering closer follow‐up.
Recent evidence suggests that tumor‐infiltrating lymphocytes (TILs) are a potential pathologic biomarker in Merkel cell carcinoma. This article evaluates the prognostic value of TILs using a large national dataset.
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