AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

Age-associated mortality is partially mediated by TERT promoter mutation status in differentiated thyroid carcinoma.

BACKGROUND: Age at diagnosis (AAD) and telomerase reverse transcriptase (TERT) promoter mutations are prognostic factors in differentiated thyroid carcinoma (DTC), and the prevalence of the mutations increases with AAD. Considering this correlation, we investigated whether an interaction between AAD and the mutations is present and whether the mutation mediates the effect of AAD on the mortality rate in DTC. METHODS: The study included 393 patients with DTC who were followed-up after thyroidectomy at a single medical center in Korea from 1994 to 2004. Multivariable Cox regression was used to investigate the interaction of AAD and TERT promoter mutation. Mediation analysis was conducted using a regression-based causal mediation model. RESULTS: The age-associated mortality rate increased progressively in all DTC patients and wild-type TERT group (WT-TERT) with a linear trend (p < 0.001) contrary to mutant TERT group (M-TERT) (p = 0.301). Kaplan-Meier curves declined progressively with increasing AAD in the entire group, but the change was without significance in M-TERT. The effect of AAD on mortality was not significant (adjusted HR: 1.07, 95% CI 0.38-3.05) in M-TERT. An interaction between AAD and TERT promoter mutation (p = 0.005) was found in a multivariable Cox regression. TERT promoter mutations mediated the effect of AAD on the mortality rate by 36% in DTC in a mediation analysis. CONCLUSIONS: Considering the mediation of TERT promoter mutation on the effect of AAD on mortality, inclusion of TERT promoter mutation in a stage classification to achieve further individualized prediction in DTC is necessary.

Proposal of a New Prognostic Model for Differentiated Thyroid Cancer with TERT Promoter Mutations.

The role of telomerase reverse transcriptase (TERT) promoter mutations as an independent poor prognostic factor in differentiated thyroid cancer (DTC) patients is well known, but there is no prognostic system that combines the TERT promoter mutation status with tumor-node-metastasis (TNM) stage to predict cancer-specific survival (CSS). A total of 393 patients with pathologically confirmed DTC after thyroidectomy were enrolled. After incorporating wild-type TERT and mutant TERT with stages I, II, and III/IV of the AJCC TNM system 8th edition (TNM-8), we generated six combinations and calculated 10-year and 15-year CSS and adjusted hazard ratios (HRs) for cancer-related death using Cox regression. Then, a new mortality prediction model termed TNM-8T was derived based on the CSS and HR of each combination in the four groups. Of the 393 patients, there were 27 (6.9%) thyroid cancer-related deaths during a median follow-up of 14 years. Patients with a more advanced stage had a lower survival rate (10-year CSS for TNM-8T stage 1, 2, 3, and 4: 98.7%, 93.5%, 77.3%, and 63.0%, respectively; p < 0.001). TNM-8T showed a better spread of CSS (p < 0.001) than TNM-8 (p = 0.002) in the adjusted survival curves. The C-index for mortality risk predictability was 0.880 (95% CI, 0.665-0.957) in TNM-8T and 0.827 (95% CI, 0.622-0.930) in TNM-8 (p < 0.001). TNM-8T, a new prognostic system that incorporates the TERT mutational status into TNM-8, showed superior predictability to TNM-8 in the long-term survival of DTC patients.

TERT Promoter Mutations and the 8th Edition TNM Classification in Predicting the Survival of Thyroid Cancer Patients.

Our research group has previously shown that the presence of TERT promoter mutations is an independent prognostic factor, by applying the TERT mutation status to the variables of the AJCC 7th edition. This study aimed to determine if TERT mutations could be independent predictors of thyroid cancer-specific mortality based on the AJCC TNM 8th edition, with long-term follow-up. This was a retrospective study of 393 patients with pathologically confirmed differentiated thyroid carcinoma (DTC) after thyroidectomy at a tertiary Korean hospital from 1994 to 2004. The thyroid cancer-specific mortality rate was 6.9% (5.2% for papillary and 15.2% for follicular cancers). TERT promoter mutations were identified in 10.9% (43/393) of DTC cases (9.8% of papillary and 16.7% of follicular cancer) and were associated with older age (p < 0.001), the presence of extrathyroidal invasion (p < 0.001), distant metastasis (p = 0.001), and advanced stage at diagnosis (p < 0.001). The 10-year survival rate in mutant TERT was 67.4% for DTC patients (vs. 98% for wild-type; adjusted hazard ratio (HR) of 9.93, (95% CI: 3.67-26.90)) and 75% for patients with papillary cancer (vs. 99%; 18.55 (4.83-71.18)). In addition, TERT promoter mutations were related to poor prognosis regardless of histologic type (p < 0.001 for both papillary and follicular cancer) or initial stage (p < 0.001, p = 0.004, and p = 0.086 for stages I, II, and III and IV, respectively). TERT promoter mutations comprise an independent poor prognostic factor after adjusting for the clinicopathological risk factors of the AJCC TNM 8th edition, histologic type, and each stage at diagnosis, which could increase prognostic predictability for patients with DTC.

Poly-4-vinylphenol (PVP) and Poly(melamine-co-formaldehyde) (PMF)-Based Atomic Switching Device and Its Application to Logic Gate Circuits with Low Operating Voltage.

In this study, we demonstrate a high-performance solid polymer electrolyte (SPE) atomic switching device with low SET/RESET voltages (0.25 and -0.5 V, respectively), high on/off-current ratio (105), excellent cyclic endurance (>103), and long retention time (>104 s), where poly-4-vinylphenol (PVP)/poly(melamine-co-formaldehyde) (PMF) is used as an SPE layer. To accomplish these excellent device performance parameters, we reduce the off-current level of the PVP/PMF atomic switching device by improving the electrical insulating property of the PVP/PMF electrolyte through adjustment of the number of cross-linked chains. We then apply a titanium buffer layer to the PVP/PMF switching device for further improvement of bipolar switching behavior and device stability. In addition, we first implement SPE atomic switch-based logic AND and OR circuits with low operating voltages below 2 V by integrating 5 × 5 arrays of PVP/PMF switching devices on the flexible substrate. In particular, this low operating voltage of our logic circuits was much lower than that (>5 V) of the circuits configured by polymer resistive random access memory. This research successfully presents the feasibility of PVP/PMF atomic switches for flexible integrated circuits for next-generation electronic applications.

Prognostic significance of elevated lipoprotein(a) in coronary artery revascularization patients.

BACKGROUND: Although lipoprotein(a) [Lp(a)] has been considered a cardiovascular risk factor for many years, there is a paucity of data in regard to the potential risk of elevated Lp(a) in symptomatic patients with CAD. Therefore, we sought to evaluate whether elevated Lp(a) is associated with worse outcome in symptomatic patients with coronary artery disease (CAD), and to clarify the prognostic value of Lp(a) in the era of coronary artery revascularization. METHODS: 6252 consecutive subjects (59.2% male, mean age 61.2 ± 11.2 years) suspected of having CAD underwent coronary angiography. Laboratory values for lipid parameters including Lp(a) were obtained on the day of coronary angiography. Baseline risk factors, coronary angiographic findings, length of follow-up, and major adverse cardiovascular events (MACE), including cardiac death and non-fatal myocardial infarction were recorded. RESULTS: Over a mean follow-up period of 3.1 ± 2.2 years, there were 100 MACE (56 cardiac deaths and 44 non-fatal myocardial infarctions), with an event rate of 1.6%. In multivariate Cox regression analysis, elevated Lp(a) was a significant predictor of MACE [hazard ratio 1.773 (95% confidence interval 1.194-2.634, p=0.005)], and the addition of this factor to the model significantly increased the global х(2) value over traditional risk factors and CAD (from 79.1 to 88.7, p=0.003). CONCLUSIONS: Elevated Lp(a) is an independent prognostic risk factor for cardiovascular events, and moreover, has incremental prognostic value in symptomatic patients with coronary artery revascularization.

Correlation of a dynamic model for immunological synapse formation with effector functions: two pathways to synapse formation.

During antigen recognition by T cells different receptors and ligands form a pattern in the intercellular junction called the immunological synapse, which might be involved in T-cell activation. Recently, a synapse assembly model has been proposed, which enables the calculation of the propensity for synapse assembly driven by membrane-constrained protein binding interactions. We bring together model predictions of mature synapse assembly with data on the dependence of T-cell responses on T-cell receptor (TCR)-MHC-peptide (pMHC) binding kinetics. Predictions of mature synapse assembly, based on TCR-pMHC binding kinetics, correlate well with observed cytokine responses by T cells bearing the relevant TCR but not with cytotoxic T lymphocyte-mediated killing. We discuss the suggested different role for the synapse in pre- and post-nuclear activation events in T cells. The view of immunological synapse assembly given here emphasizes the importance of both the on and off rates for the TCR-pMHC interaction and in this context recent data on a positive role for analogs of self-peptides in synapse assembly is considered.