Clinical Imaging最新文献

Objective

To evaluate the frequency and content of media coverage pertaining to artificial intelligence (AI) and radiology in the United States from 1998 to 2023.

Methods

The ProQuest US Newsstream database was queried for print and online articles mentioning AI and radiology published between January 1, 1998, and March 30, 2023. A Boolean search using terms related to radiology and AI was used to retrieve full text and publication information. One of 9 readers with radiology expertise independently reviewed randomly assigned articles using a standardized scoring system.

Results

379 articles met inclusion criteria, of which 290 were unique and 89 were syndicated articles. Most had a positive sentiment (74 %) towards AI, while negative sentiment was far less common (9 %). Frequency of positive sentiment was highest in articles with a focus on AI and radiology (86 %) and lowest in articles focusing on AI and non-medical topics (55 %). The net impact of AI on radiology was most commonly presented as positive (60 %). Benefits of AI were more frequently mentioned (76 %) than potential harms (46 %). Radiologists were interviewed or quoted in less than one-third of all articles.

Conclusion

Portrayal of the impact of AI on radiology in US media coverage was mostly positive, and advantages of AI were more frequently discussed than potential risks. However, articles with a general non-medical focus were more likely to have a negative sentiment regarding the impact of AI on radiology than articles with a more specific focus on medicine and radiology. Radiologists were infrequently interviewed or quoted in media coverage.

Purpose

Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements. The objective of this study was to assess the reliability of this AI tool compared to three manual readers.

Methods

A sample of 320 legs was used. Three readers' measurements were compared to AI output for hip-knee-angle (HKA), anatomical-tibiofemoral angle (aTFA), anatomical-mechanical-axis angle (AMA), joint-line-convergence angle (JLCA), mechanical-lateral-proximal-femur-angle (mLPFA), mechanical-lateral-distal-femur-angle (mLDFA), mechanical-medial-proximal-tibia-angle (mMPTA), mechanical-lateral-distal-tibia- angle (mLDTA), femur length, tibia length, full leg length, leg-length-discrepancy (LLD), and mechanical-axis-deviation (MAD). Intraclass correlation coefficients (ICCs) and Bland-Altman analysis were used to track performance.

Results

AI output was successfully produced for 272/320 legs in the study. The reader versus AI pairwise ICCs were mostly in the excellent range: 12/13, 12/13, and 9/13 variables were in the excellent range (ICC > 0.75) for readers 1, 2, and 3, respectively. There was better agreement for leg length, femur length, tibia length, LLD, and HKA than for other variables. The median reading times for the three readers and AI were 250, 282, 236, and 38 s, respectively.

Conclusion

This study showed that AI-based software provides reliable assessment of LLD and lower extremity alignment with substantial time savings.

Purpose

We aimed to evaluate the prevalence of financial hardship and Health-Related Social Needs (HRSN) among patients who missed their radiology appointment.

Methods

English-speaking adult patients, with a missed outpatient imaging appointment at any of a tertiary care imaging centers between 11/2022 and 05/2023 were eligible. We measured self-reported general financial worry using Comprehensive Score for Financial Toxicity (COST), imaging hardship (worry that the current imaging is a financial hardship to patient and their family), material hardship (e.g., medical debt), cost-related care nonadherence, and HRSNs including housing instability, food insecurity, transportation problems, and utility help needs.

Results

282 patients were included (mean age 54.7 ± 15.0 years; 70.7 % female). Majority were non-Hispanic White (52.4 %), followed by Asian (23.0 %) and Hispanic (16.0 %) racial/ethnic background. Most missed appointments were patient-initiated (74.8 %); 13.5 % due to cost or insurance coverage and 6.4 % due to transportation and parking. Mean COST score was 26.8 with 44.4 % and 28.8 % reporting their illness and imaging as a source of financial hardship. 18.3 % and 35.2 % endorsed cost-related care nonadherence and material hardship. 32.7 % had at least one HRSNs with food insecurity the most common (25.4 %). Only 12.5 % were previously screened for financial hardship or HRSNs. Having comorbidity and living in more disadvantaged neighborhoods was associated with higher report of financial hardship and HRSNs.

Conclusion

Financial hardship and HRSNs are common among those who miss radiology appointments. There needs to be more rigorous screening for financial hardship and HRSNs at every health encounter and interventions should be implemented to address these.

Purpose

Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE).

Materials and methods

Subjects with ileal CD and a CTE from a single center retrospective study between 2016 and 2021 were included. 165 CTEs were reviewed by two fellowship-trained abdominal radiologists for the presence and spatial distribution of five qualitative CD findings: mural enhancement, mural stratification, stenosis, wall thickening, and mesenteric fat stranding. A Random Forest (RF) ensemble model using automatically extracted specialist-directed bowel features and an unbiased convolutional neural network (CNN) were developed to predict the presence of qualitative findings. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, accuracy, and kappa agreement statistics.

Results

In 165 subjects with 29,895 individual qualitative finding assessments, agreement between radiologists for localization was good to very good (κ = 0.66 to 0.73), except for mesenteric fat stranding (κ = 0.47). RF prediction models had excellent performance, with an overall AUC, sensitivity, specificity of 0.91, 0.81 and 0.85, respectively. RF model and radiologist agreement for localization of CD findings approximated agreement between radiologists (κ = 0.67 to 0.76). Unbiased CNN models without benefit of disease knowledge had very similar performance to RF models which used specialist-defined imaging features.

Conclusion

Machine learning techniques for CTE image analysis can identify the presence, location, and distribution of qualitative CD findings with similar performance to experienced radiologists.

Purpose

Radiology faculty across various specialties have been reported to receive an average of 20.7 invitations to submit manuscripts to bogus journals and 4.1 invitations to speak at unsuitable events over a two-week span. Radiology trainees also receive a fair number of unsolicited invitations from unknown senders to submit manuscripts and speak at meetings. Trainees can be more vulnerable to predatory invitations due to potential naivety. We aimed to determine the prevalence of these spam invitations received by radiology trainees.

Material and methods

The designed survey for evaluating the experience of radiology trainees regarding phishing scams of predatory publications and conferences was sent to radiology residency and neuroradiology fellowship program leadership to redistribute amongst their trainees, and was advertised on social media platforms. The survey was first sent out on September 28, 2023, and was closed two weeks later October 12, 2023. Spearman’s correlation, univariable and multivariable linear regression analyses were performed.

Results

Our study included 151 respondents who completed the survey. Of the survey respondents, 53 % reported receiving unsolicited emails from predatory publications (mean = 6.76 ± 7.29), and 32 % reported receiving emails from fraudulent conferences (mean = 5.61 ± 5.77). Significant positive correlation was observed between number of unsolicited email invitations with number of PubMed indexed publications, number as corresponding author, number in open access journals and number of abstract presentations.

Conclusions

Trainees in radiology receive many unsolicited invitations to publish papers as well as to present at meetings that are not accredited. This could lead to wasted time and financial resources for unsuspecting trainees.

This article delves into the diagnostic implications of the female prostate sign, a distinctive radiological sign observed in magnetic resonance imaging of female patients with substantial urethral diverticula. We discuss the association of this sign with urethral diverticula, emphasizing its mimetic resemblance to prostatic hypertrophy observed in older males. Through a comprehensive review of clinical presentations, diagnostic imaging advancements, and treatment modalities, our article underscores the significance of magnetic resonance imaging as a superior diagnostic tool. Our findings support the enhanced recognition and understanding of the female prostate sign among healthcare professionals, facilitating accurate diagnoses and informed management of urethral diverticula.

Purpose

Assess whether a Retroaortic left renal vein (RLRV) affects vertebral osteophyte formation in the lumbar spine, compared to normal anatomy left renal vein.

Methods

We conducted a retrospective case-control study. Computed tomography (CT) scans of individuals with a RLRV (study group) were compared to age- and gender-matched normal anatomy CT scans (control group).

L1 to L4 vertebral levels were appreciated for: left renal vein level, osteophyte presence and the aorta-vertebral distance (AVD) at the left renal vein level.

Univariate analyses were conducted using Chi-square test and Fisher's test for categorical variables, and Student's t-test for continuous variables. Logistic regression was used for multivariate analyses.

Results

A total of 240 patients were included in the study - equally distributed between the study and control groups.

Normal anatomy left renal veins traversed the spine only at the L1 and L2 levels. RLRVs traversed the spine in all L1-L4 levels, mostly at the L3 and L2.

Osteophyte prevalence at the level of left renal vein was significantly higher in the study group, compared with the control group [OR 2.54, P = 0.01].

Mean AVD was greater in the study group [9.2 mm ±3.6 mm Vs. 3.5 mm ± 2.6 mm, P < 0.001].

Increased AVD was found to be associated with a higher chance of osteophyte presence at the level of the left renal vein [OR 1.282, P = 0.025].

Conclusions

Osteophytes are more prevalent at the level of the RLRV variant compared to the normal anatomy. Furthermore, the RLRV is characterized by a lower lumbar level compared to the normal anatomy.

Clinical relevance statement

This anatomic variation could assist in further understanding of osteophyte formation.

Background

Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.

Methods

A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).

Results

Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %–90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %–89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927).

Conclusion

Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.