Cost-Effectiveness of Low-Dose Computed Tomography Screenings for Lung Cancer in High-Risk Populations: A Markov Model.

Background: Domestic and foreign studies on lung cancer have been oriented to the medical efficacy of low-dose computed tomography (LDCT), but there is a lack of studies on the costs, value and cost-effectiveness of the treatment. There is a scarcity of conclusive evidence regarding the cost-effectiveness of LDCT within the specific context of Taiwan. This study is designed to address this gap by conducting a comprehensive analysis of the cost-effectiveness of LDCT and chest X-ray (CXR) as screening methods for lung cancer. Methods: Markov decision model simulation was used to estimate the cost-effectiveness of biennial screening with LDCT and CXR based on a health provider perspective. Inputs are based on probabilities, health status utility (quality-adjusted life years (QALYs)), costs of lung cancer screening, diagnosis, and treatment from the literatures, and expert opinion. A total of 1,000 simulations and five cycles of Markov bootstrapping simulations were performed to compare the incremental cost-utility ratio (ICUR) of these two screening strategies. Probability and one-way sensitivity analyses were also performed. Results: The ICUR of early lung cancer screening compared LDCT to CXR is $-24,757.65/QALYs, and 100% of the probability agree to adopt it under a willingness-to-pay (WTP) threshold of the Taiwan gross domestic product (GDP) per capita ($35,513). The one-way sensitivity analysis also showed that ICUR depends heavily on recall rate. Based on the prevalence rate of 39.7 lung cancer cases per 100,000 people in 2020, it could be estimated that LDCT screening for high-risk populations could save $17,154,115. Conclusion: LDCT can detect more early lung cancers, reduce mortality and is cost-saving than CXR in a long-term simulation of Taiwan's healthcare system. This study provides valuable insights for healthcare decision-makers and suggests analyzing cost-effectiveness for additional variables in future research.

Cost-Effectiveness Analysis of Digital Breast Tomosynthesis and Mammography in Breast Cancer Screening: A Markov Modeling Study.

BACKGROUND: Mammography (MG) has demonstrated its effectiveness in diminishing mortality and advanced-stage breast cancer incidences in breast screening initiatives. Notably, research has accentuated the superior diagnostic efficacy and cost-effectiveness of digital breast tomosynthesis (DBT). However, the scope of evidence validating the cost-effectiveness of DBT remains limited, prompting a requisite for more comprehensive investigation. The present study aimed to rigorously evaluate the cost-effectiveness of DBT plus MG (DBT-MG) compared to MG alone within the framework of Taiwan's National Health Insurance program. METHODS: All parameters for the Markov decision tree model, encompassing event probabilities, costs, and utilities (quality-adjusted life years, QALYs), were sourced from reputable literature, expert opinions, and official records. With 10,000 iterations, a 2-year cycle length, a 30-year time horizon, and a 2% annual discount rate, the analysis determined the incremental cost-effectiveness ratio (ICER) to compare the cost-effectiveness of the two screening methods. Probabilistic and one-way sensitivity analyses were also conducted to demonstrate the robustness of findings. RESULTS: The ICER of DBT-MG compared to MG was US$5971.5764/QALYs. At a willingness-to-pay (WTP) threshold of US$33,004 (Gross Domestic Product of Taiwan in 2021) per QALY, more than 98% of the probabilistic simulations favored adopting DBT-MG versus MG. The one-way sensitivity analysis also shows that the ICER depended heavily on recall rates, biopsy rates, and positive predictive value (PPV2). CONCLUSION: DBT-MG shows enhanced diagnostic efficacy, potentially diminishing recall costs. While exhibiting a higher biopsy rate, DBT-MG aids in the detection of early-stage breast cancers, reduces recall rates, and exhibits notably superior cost-effectiveness.

Multitask deep learning on mammography to predict extensive intraductal component in invasive breast cancer.

OBJECTIVES: To develop a multitask deep learning (DL) algorithm to automatically classify mammography imaging findings and predict the existence of extensive intraductal component (EIC) in invasive breast cancer. METHODS: Mammograms with invasive breast cancers from 2010 to 2019 were downloaded for two radiologists performing image segmentation and imaging findings annotation. Images were randomly split into training, validation, and test datasets. A multitask approach was performed on the EfficientNet-B0 neural network mainly to predict EIC and classify imaging findings. Three more models were trained for comparison, including a single-task model (predicting EIC), a two-task model (predicting EIC and cell receptor status), and a three-task model (combining the abovementioned tasks). Additionally, these models were trained in a subgroup of invasive ductal carcinoma. The DeLong test was used to examine the difference in model performance. RESULTS: This study enrolled 1459 breast cancers on 3076 images. The EIC-positive rate was 29.0%. The three-task model was the best DL model with an area under the curve (AUC) of EIC prediction of 0.758 and 0.775 at the image and breast (patient) levels, respectively. Mass was the most accurately classified imaging finding (AUC = 0.915), followed by calcifications and mass with calcifications (AUC = 0.878 and 0.824, respectively). Cell receptor status prediction was less accurate (AUC = 0.625-0.653). The multitask approach improves the model training compared to the single-task model, but without significant effects. CONCLUSIONS: A mammography-based multitask DL model can perform simultaneous imaging finding classification and EIC prediction. CLINICAL RELEVANCE STATEMENT: The study results demonstrated the potential of deep learning to extract more information from mammography for clinical decision-making. KEY POINTS: • Extensive intraductal component (EIC) is an independent risk factor of local tumor recurrence after breast-conserving surgery. • A mammography-based deep learning model was trained to predict extensive intraductal component close to radiologists' reading. • The developed multitask deep learning model could perform simultaneous imaging finding classification and extensive intraductal component prediction.

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer.

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7−0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical−radiomics models were trained for each algorithm. Radiomics and clinical−radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.

Role of Fracture Risk Assessment Tool and Bone Turnover Markers in Predicting All-Cause and Cardiovascular Mortality in Hemodialysis Patients.

Background: Fracture Risk Assessment Tool (FRAX) and bone turnover markers (BTMs) predict fractures in the general population. However, the role of FRAX and BTMs in predicting mortality remains uncertain in hemodialysis (HD) patients. Methods: One hundred and sixty-four HD patients stratified by low or high risk of 10-year fracture probability using FRAX. High risk of fracture was defined as 10-year probability of hip fracture ≥3% or major osteoporotic fracture ≥20%. The association of high risk of fracture and BTMs with all-cause mortality and cardiovascular (CV) mortality were evaluated using multivariate-adjusted Cox regression analysis. Results: Eighty-five (51.8%) patients were classified as high risk of fracture based on FRAX among 164 HD patients. During a mean follow-up period of 3.5 ± 1.0 years, there were 39 all-cause deaths and 23 CV deaths. In multivariate-adjusted Cox regression, high risk of fracture based on FRAX was independently associated with all-cause mortality [hazard ratio (HR): 2.493, 95% confidence interval (CI): 1.026-6.056, p = 0.044) but not with CV mortality (HR: 2.129, 95% CI: 0.677-6.700, p = 0.196). There were no associations between BTMs and mortality risk. Furthermore, lower geriatric nutritional risk index (GNRI) was significantly associated with increased CV mortality (HR: 0.888, 95% CI: 0.802-0.983, p = 0.022) after adjusting by confounding variables. Conclusion: High risk of fracture using FRAX was an independent predictor of all-cause mortality in patients undergoing HD. FRAX, rather than BTMs, has an important role of prognostic significance in HD patients.

Effects of the COVID-19 pandemic on breast cancer screening in Taiwan.

The breast cancer screening program has continued in Taiwan during the COVID-19 pandemic. Our nationwide data showed that the total number of screenings decreased by 22.2%, which was more pronounced for in-hospital examinations (-37.2%), while outreach showed a 12.9% decrease. This decline in screening participation happened at all levels of hospitals, more significantly at the highest level. Our report revealed that outreach services could maintain relatively stable breast cancer screening under this kind of public health crisis. Building a flexible, outreach system into the community might need to be considered when policymakers are preparing for future possible pandemics.

Fat-Water Swaps in Iterative Decomposition of Water and Fat With Echo Asymmetry and Least-Squares Estimation Magnetic Resonance Imaging for Postinstrumentation Spine.

OBJECTIVE: To determine the frequencies of fat-water swaps in iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) water-only images of the postinstrumentation spine and discuss the efficiency of in-phase imaging in improving visibility of the thecal sac. MATERIALS AND METHODS: A total of 276 patients (167 women; mean age, 62.3 years; range, 23-89 years) with metallic devices on the lumbar spine who received complete routine 1.5 T MR imaging, including axial and sagittal images of T1-weighted, T2-weighted, and T2-weighted IDEAL sequences, were included. The exclusion criteria were significant motion artifacts and severe metallic artifacts in any one of the sequences. The images were reviewed by two radiologists to identify fat-water swaps that were divided into 3 groups: extraspinal swaps, intraspinal swaps in sagittal images, and intraspinal swaps in axial images. The qualitative evaluations for the spinal canal in axial images were performed by rating on a five-point scale. Side-by-side comparisons of T2-weighted images and IDEAL in-phase images were also performed. RESULTS: In patient-based data of 276 patients, extraspinal fat-water swaps were noted in 10 patients (3.6%) and intraspinal swaps were noted in 160 patients (58.0%). The intraspinal swaps had a higher incidence in the patients with more levels of metallic devices with screws and the trend was not noted in the extraspinal swaps. A total of 928 axial levels were evaluated in the level-based data of axial images. T1-weighted, T2-weighted, and IDEAL in-phase images had significantly better imaging quality than the IDEAL water-only images (3.9 ± 0.4, 3.9 ± 0.3, 3.8 ± 0.4 vs 3.0 ± 1.3, all P < 0.001). Compared with T2-weighted images, most of the IDEAL in-phase images (reader 1, 90.9%; reader 2, 86.7%) present similar quality. CONCLUSION: Iterative decomposition of water and fat with echo asymmetry and least-squares estimation sequence can provide good fat suppression in most spine MRI with metallic devices but the loss of cerebrospinal fluid signal intensities due to fat-water swaps are noted in more than half of postinstrumentation spine. Routine reconstruction of in-phase images is recommended to improve evaluation of the thecal sac by avoiding pitfall caused by fat-water swaps.

Progression of Aortic Arch Calcification Is Associated with Overall and Cardiovascular Mortality in Hemodialysis.

BACKGROUND: Vascular calcification is common and associated with unfavorable outcomes among patients with end-stage renal disease (ESRD). Nevertheless, little is known whether the progression of vascular calcification outweighs the baseline calcification in association with overall and cardiovascular (CV) mortality in hemodialysis (HD) patients. METHODS: This study included 140 maintenance HD patients. Vascular calcification was assessed using the aortic arch calcification (AoAC) score measured from chest radiographs at the baseline and the second year of follow-up. Progression of vascular calcification (ΔAoAC) was defined as the difference between the two measurements of AoAC. The association of ΔAoAC with overall and CV mortality was evaluated using multivariate Cox regression analysis. RESULTS: During the mean follow-up period of 5.8 years, there were 49 (35%) overall mortality and 27 (19.3%) CV mortality. High brachial-ankle pulse wave velocity was positively correlated with ΔAoAC, whereas old age was negatively correlated with ΔAoAC. In multivariate adjusted Cox analysis, increased ΔAoAC (per 1 unit), but not baseline AoAC, was significantly associated with overall mortality (HR, 1.183; 95% CI, 1.056-1.327; p = 0.004) and CV mortality (HR, 1.194; 95% CI, 1.019-1.398; p = 0.028). CONCLUSION: Progression of AoAC outperformed the baseline AoAC in association with increased risk of overall and CV mortality in HD patients. A regular follow-up of chest radiograph and AoAC score assessments are simple and cost-effective to identify the high-risk individuals of unfavorable outcomes in maintenance HD patients.

A preliminary report of longitudinal white matter alterations in patients with end-stage renal disease: A three-year diffusion tensor imaging study.

PURPOSE: End-stage renal disease (ESRD) patients exhibit silent white-matter alterations after long-term hemodialysis, which may be due to ESRD itself or the hemodialysis. The purpose of this study was to investigate the longitudinal white-matter alterations in the ESRD patients under 3-year long-term hemodialysis using voxel-wise analysis of diffusion tensor imaging (DTI). MATERIALS AND METHODS: 15 ESRD patients and 15 age-matched healthy controls participated in this study. Due to the development of abnormal brain lesions in some cases, 13 ESRD patients and 13 age-matched healthy controls were enrolled and underwent cognitive function assessment and DTI acquisition at two-time points separated by 3 years. Voxel-based analysis was performed to globally detect white-matter alterations between the two groups as well as between the two scans within the two groups. RESULTS: In the ESRD patients, diffusivity indices were significantly increased and the fractional anisotropy was significantly decreased in both scans, as compared with healthy controls. Longitudinal comparisons showed significant white-matter alterations in healthy controls in three years, but little or no significant alterations were noted in the ESRD patients after additional 3-year hemodialysis. CONCLUSION: Poorer white matter integrity and cognitive function are noted in ESRD patients and the toxic effect of ESRD may be the major factor of white matter alterations.

Associations among Geriatric Nutrition Risk Index, bone mineral density, body composition and handgrip strength in patients receiving hemodialysis.

OBJECTIVES: Malnutrition occurs as a complication of hemodialysis (HD) and has been identified as a risk factor for osteoporosis, sarcopenia, and low physical performance. The aim of this study was to assess the associations among nutrition, bone mineral density (BMD), body composition, and handgrip strength (HGS) in patients receiving HD. METHODS: We enrolled 164 patients receiving HD who have undergone the dual energy x-ray absorptiometry (DXA) examination, categorized according to baseline Geriatric Nutritional Risk Index (GNRI) values calculated by serum albumin levels and body weight index. GNRI was used to evaluate nutritional status, and DXA to investigate BMD and body composition. Additionally, HGS test was performed. Multiple stepwise linear regression analysis was used to identify the factors associated with BMD, T-score, and HGS. RESULTS: Compared with patients with tertile 1 of GNRI, those with tertile 3 of GNRI tend to have higher lumbar spine BMD and T-score, higher femoral neck BMD, higher total hip BMD, higher left distal mid-third radius BMD and T-score, higher fat mass index, higher android-to-gynoid ratio, higher lean mass index, and higher HGS. An increase in GNRI tertile was associated with an increase of BMD and a decrease of osteoporosis prevalence. The GNRI was positively correlated with BMD and T-score, body composition, and HGS. Low lean mass index and high parathyroid hormone were significantly associated with low BMD and T-score. Additionally, high GNRI and high left distal mid-third radius BMD were significantly associated with high HGS. CONCLUSIONS: The present study demonstrated that good nutrition, indicated as high GNRI, resulted in higher BMD and T-score, lean mass index, and HGS in patients receiving HD. Using GNRI to evaluate nutritional status and using DXA to investigate BMD and body composition in patients receiving HD is important to bone health and physical performance.