Descriptive study of chest x-ray examination in mandatory annual health examinations at the workplace in Japan

Survey on Findings and Utilization of Preoperative Chest Radiography in Ophthalmic Surgery

Objective: The objective of this paper is to reconsider the significance of preoperative chest radiography (CXR) before ophthalmic surgery through investigation of imaging findings and usage status. Methods: This retrospective observational clinical study involved 1616 patients who underwent ophthalmic surgery at Saga University Hospital from 1 January 2019 to 31 December 2020. The patients' radiology reports were obtained from the electronic medical records, and their CXR findings, therapeutic interventions, and progress were investigated. Results: Among all patients, 539 (33.4%) had abnormal preoperative CXR findings. Of these patients, 74 (4.6%) had newly identified abnormal findings. In both patient groups, approximately 70% of patients with abnormal findings were aged ≥70 years, and interstitial shadows were the most common finding. Among all patients with abnormal findings, three (0.19%) received preoperative therapeutic interventions, and all surgeries were performed safely. Forty-three patients with abnormal findings were referred to our hospital or other hospitals for further investigation and treatment postoperatively. Among those patients, eight (0.5%) had primary lung cancer, seven underwent surgery, and one received chemoradiation. The other patients were also followed up and received appropriate therapeutic interventions. Conclusions: Before ophthalmic surgery, few patients required actual therapeutic interventions based on their CXR results. However, many abnormal findings were revealed in elderly patients, including some serious diseases. Furthermore, research has suggested that appropriate therapeutic intervention after ophthalmologic surgery may reduce the risk of a poor life prognosis. This study clearly shows that preoperative CXR is not only useful for perioperative systemic management but also ultimately benefits patients. It is also considered particularly meaningful for patients aged ≥70 years.

Basic assessment on adding platelet measurement to legal health checkup in Japan: A cross-sectional and 20-year longitudinal study

Background

In Japan, health checkups for workers are legally compulsory. Considering legal health checkup items are important for Japanese workers' health problems. To date, the legal health checkup items for blood cell counts include only red blood cell counts and hemoglobin but not platelet counts. This study aimed to investigate the significance of measuring platelets in workers by showing the association between the FIB-4 index (FIB-4), which can be easily calculated from factors including platelet counts and viral hepatitis infection.

Method

Both cross-sectional and longitudinal analyses were conducted on the comprehensive medical examinations of male workers. In fiscal year (FY) 2019, a logistic regression model was applied to 12,918 examinees. For 13,459 examinees (mean age = 47.5 ± 9.3 SD), FY2000 was set to be followed until FY2019. A total of 149,956 records between FY2000 and FY2019 were analyzed cross-sectionally, and 8,038 men who were consecutively examined to FY2019 at the longest were analyzed longitudinally. Receiver operating characteristic (ROC) curve–area under the ROC curve (ROC–AUC) and Cox proportional methods were used to examine the association between platelet-related indices and viral hepatitis infection.

Results

Logistic regression showed that the risk of FIB-4 ≥ 2.67 was mostly associated with hepatitis C virus antibody (HCVAb) positivity [odds ratio (OR) = 2.51, 95% confidence interval (CI) = 1.08–5.86], while negatively associated with body mass index (BMI) (OR = 0.54, 95% CI = 0.30–0.97), and not associated with the presence of fatty liver. To detect HVC Ab positivity, ROC–AUC showed more effectiveness in FIB-4 than in the AST/ALT ratio (0.776, 95% CI = 0.747–0.773 vs. 0.552; 95% CI = 0.543–0.561). The Cox analysis showed that the risk of FIB-4 ≥ 2.67 was closely associated with hepatitis B virus surface antigen (HBsAg) [hazard ratio (HR) = 3.1, 95% CI = 2.0–4.6] and HCV Ab positivity (HR = 3.2, 95% CI = 2.0–5.0).

Conclusion

Our results suggest that it might be worth considering that usage of information on platelets in legal health checkups could be some help not to overlook workers with hepatitis virus carriers as a complementary countermeasure, although further investigations are needed into its practical application.

Significance of Fib4 index as an indicator of alcoholic hepatotoxicity in health examinations among Japanese male workers: a cross-sectional and retrospectively longitudinal study

BACKGROUND

Fib4 index (Fib4) is clinically used as a noninvasive marker of liver fibrosis. In this study, we aimed to preliminarily investigate whether Fib4 can be used to detect individuals who need assessment for alcoholic liver disease (ALD) in the general population by clarifying the detailed association of Fib4 with alcohol consumption and gamma-glutamyl transferase (GGT) among male workers.

METHODS

We analyzed data sets on the comprehensive medical examinations of male workers as cross-sectional and retrospectively longitudinal studies. We enrolled 10 782 males (mean age: 52.2 ± 10.2 years) in FY2019 and 7845 males (mean follow-up: 12.6 ± 6.7 years) who could be consecutively followed up for 20 years from FY2000 to FY2019. Data were evaluated using logistic regression and COX proportional analysis.

RESULTS

In the cross-sectional setting, the rate of Fib4 ≥ 2.67 in heavy drinkers (≥ 40 g of ethanol/day) was increased dose dependently in those over 65 years old, and that of body mass index ≥ 30 kg/m² was increased in those over 60 years old, but not in those with fatty liver. The odds ratio (OR) (95% confidence interval [CI]) for heavy drinking was 4.30 (95% CI = 1.90-9.72), and GGT ≥ 200 IU/L was considerably high (OR = 29.05 [95% CI = 17.03-49.56]). In the longitudinal setting, heavy drinkers and those with GGT ≥ 200 IU/L at 10 years after the baseline showed an increased risk for Fib4 ≥ 2.67 (hazard ratio = 2.17 [95% CI = 1.58-2.98] and 7.65 [95% CI 5.26-11.12], respectively).

CONCLUSIONS

The development of Fib4 ≥ 2.67 after 10 years was associated with heavy alcohol drinking and GGT level ≥ 200 IU/L. Therefore, Fib4 combined with GGT could indicate high risk of ALD. However, clinical examinations and course observations are essentially needed.

Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis

PURPOSE

To evaluate the performance of a deep learning-based computer-aided detection (CAD) software for detecting pulmonary nodules, masses, and consolidation on chest radiographs (CRs) and to examine the effect of readers' experience and data characteristics on the sensitivity and final diagnosis.

MATERIALS AND METHODS

The CRs of 453 patients were retrospectively selected from two institutions. Among these CRs, 60 images with abnormal findings (pulmonary nodules, masses, and consolidation) and 140 without abnormal findings were randomly selected for sequential observer-performance testing. In the test, 12 readers (three radiologists, three pulmonologists, three non-pulmonology physicians, and three junior residents) interpreted 200 images with and without CAD, and the findings were compared. Weighted alternative free-response receiver operating characteristic (wAFROC) figure of merit (FOM) was used to analyze observer performance. The lesions that readers initially missed but CAD detected were stratified by anatomic location and degree of subtlety, and the adoption rate was calculated. Fisher's exact test was used for comparison.

RESULTS

The mean wAFROC FOM score of the 12 readers significantly improved from 0.746 to 0.810 with software assistance (P = 0.007). In the reader group with < 6 years of experience, the mean FOM score significantly improved from 0.680 to 0.779 (P = 0.011), while that in the reader group with ≥ 6 years of experience increased from 0.811 to 0.841 (P = 0.12). The sensitivity of the CAD software and the adoption rate for the lesions with subtlety level 2 or 3 (obscure) lesions were significantly lower than for level 4 or 5 (distinct) lesions (50% vs. 93%, P < 0.001; and 55% vs. 74%, P = 0.04, respectively).

CONCLUSION

CAD software use improved doctors' performance in detecting nodules/masses and consolidation on CRs, particularly for non-expert doctors, by preventing doctors from missing distinct lesions rather than helping them to detect obscure lesions.

The deep learning algorithm estimates chest radiograph-based sex and age as independent risk factors for future cardiovascular outcomes

Objectives

Chest X-rays (CXRs) convey much illegible physiological information that deep learning model (DLM) has been reported interpreting successfully. Since the electrocardiogram age established by DLM was revealed as a heart biological marker, we hypothesize that CXR age has similar potential to describe the heart and lung states. Therefore, we developed a DLM to predict sex and age through CXR and analyzed its relation with future cardiovascular diseases (CVD).

Methods

A total of 90,396 CXRs aged 20 to 90 were collected and separated into a development set with 53,102 CXRs and demographic information pairs, a tuning set with 7073 pairs, an internal validation set with 17,364 pairs, and an external validation set with 12,857 pairs. The study trained DLM with development set for estimating age and sex and compared them to actual information.

Results

The mean absolute errors of predicted age were 4.803 and 4.313 years in the internal and external validation sets, respectively. The area under the curve of sex analysis was 0.9993 and 0.9988 in the internal and external validation sets, respectively. Patients whose CXR age was 5 years older than chronologic age lead to higher risk of all-cause mortality (hazard ratio (HR): 2.42, 95% confidence interval (CI): 2.00-2.92), cardiovascular (CV)-cause mortality (HR: 7.57, 95% CI: 4.55-12.60), new-onset heart failure (HR: 2.07, 95% CI: 1.56-2.76), new-onset chronic kidney disease (HR: 1.73, 95% CI: 1.46-2.05), new-onset acute myocardial infarction (HR: 1.80, 95% CI: 1.12-2.92), new-onset stroke (HR: 1.45, 95% CI: 1.10-1.90), new-onset coronary artery disease (HR: 1.26, 95% CI: 1.04-1.52), and new-onset atrial fibrillation (HR: 1.43, 95% CI: 1.01-2.02).

Conclusions

Using DLM to predict CXR age provided additional information for future CVDs. Older CXR age is an accessible risk classification tool for clinician use.

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort

Objective

This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment.

Materials and Methods

This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI).

Results

Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity.

Conclusion

This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

Effect of employers' concerns about cancer countermeasures on the implementation of cancer screening and support for balancing cancer treatment and work in small and medium-sized Japanese enterprises

OBJECTIVE

Japan has recently implemented screening and support to balance cancer treatment and work. The present study evaluated whether the interest of employers in small and medium-sized enterprises (SMEs) affects cancer control in the workplace.

METHODS

Cancer preparedness at work was examined by a Japanese life insurance company contracting 370 000 SMEs. The analysis targeted SMEs hiring ≤50 employees whose employer was aged ≥40 years. The endpoints were performing one or more screening for stomach, colon, or lung cancer recommended for both sexes in Japan and implementing three or more supportive measures from the nine systems listed in a questionnaire. Logistic regression analysis was performed to predict these endpoints using other factors.

RESULTS

The survey was completed from January 5 to 28, 2022 and included 5268 eligible companies. Around half were small enterprises with up to five employees. Screenings were performed for stomach (32%), colorectal (27%), and lung (26%) cancers. Sick leave (36%) was the most common support for balancing cancer treatment and work. Logistic regression analysis revealed that employer's concern was a significant predictor of screening (odds ratio [OR] = 3.59, P < .001) and support (OR = 2.55, P < .01) compared with "not concerned at all," along with industry type, annual sales, experience of employees with cancer, and employer's participation in screening.

CONCLUSION

Our findings suggested that employers' interest was a powerful predictor of implementing cancer control in SMEs. Educational intervention targeted toward the employer could play a critical role in improving SMEs.