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Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMV-LSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin tended to decrease over time, and urine pH and prothrombin time tended to increase. Conclusions: Early surveillance of AKI outbreaks can be achieved by applying an IMV-LSTM based on time series data through an EHR-based DRN. This approach can help identify risk factors and enable early detection of adverse drug reactions when prescribing drugs that cause renal toxicity before AKI occurs.
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BACKGRUOUND: Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). METHODS: The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. RESULTS: Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781. CONCLUSION: CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.
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Absorciometría de Fotón , Densidad Ósea , Vértebras Lumbares , Osteoporosis , Tomografía Computarizada por Rayos X , Humanos , Osteoporosis/diagnóstico por imagen , Osteoporosis/diagnóstico , Tomografía Computarizada por Rayos X/métodos , Femenino , Absorciometría de Fotón/métodos , Estudios Retrospectivos , Persona de Mediana Edad , Masculino , Anciano , Vértebras Lumbares/diagnóstico por imagen , Tamizaje Masivo/métodos , República de Corea , Aprendizaje ProfundoRESUMEN
BACKGROUND: The recurrence of thyroid cancer poses challenges compounded by postoperative fibrosis and anatomic changes. By overcoming the limitations of current localizing dye techniques, indocyanine green-macroaggregated albumin-hyaluronic acid (ICG-MAA-HA) mixture dye promises improved localization. This study aimed to evaluate the efficacy and safety of the dye for recurrent thyroid cancer. METHODS: The nine patients in this study underwent surgery and postoperative ultrasonography. The dye was injected into recurrent lesions in all the patients preoperatively. During surgery, the lesions were confirmed with an imaging system before and after excision. If the lesion was unidentifiable with the naked eye, surgical excision was performed under the corresponding fluorescent guide. Side effects related to the dye injection and completeness of the surgery were evaluated. RESULTS: No side effects such as bleeding, skin tattooing, or pain during or after the dye injection were reported, and no discoloration occurred that interfered with the surgical field of view during surgery. In three cases (33.3 %), because it was difficult to localize metastatic lesions with the naked eye, the operation was successfully completed using an imaging system. The completeness of the surgical resection was confirmed by ultrasonography after an average of 5 months postoperatively. CONCLUSION: The study found that ICG-MAA-HA dye effectively located metastatic and recurrent thyroid cancer and had favorable results in terms of minimal procedural side effects and potential for assisting the surgeon. A large-scale multi-institutional study is necessary to prove the clinical significance regarding patient survival and disease control.
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Verde de Indocianina , Neoplasias de la Tiroides , Humanos , Ácido Hialurónico , Colorantes , Neoplasias de la Tiroides/diagnóstico por imagen , Neoplasias de la Tiroides/cirugía , Neoplasias de la Tiroides/patología , Albúminas , Biopsia del Ganglio Linfático Centinela/métodosRESUMEN
BACKGROUND: Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute. OBJECTIVE: We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size. METHODS: We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records. RESULTS: The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist. CONCLUSIONS: Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.
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OBJECTIVES: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. METHODS: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. RESULTS: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. CONCLUSIONS: Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.
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Background: Active surveillance (AS) is an alternative to thyroidectomy for the management of low-risk papillary thyroid microcarcinoma (PTMC). However, prospective AS data collected from diverse populations are needed. Methods: This multicenter prospective cohort study enrolled patients from three referral hospitals in Korea. The participants were self-assigned into two groups, AS or immediate surgery. All patients underwent neck ultrasound every 6-12 months to monitor for disease progression. Progression under AS was evaluated by a criterion of tumor size increment by 3 mm in one dimension (3 mm), 2 mm in two dimensions (2 × 2 mm), new extrathyroidal extension (ETE), or new lymph node metastasis (LNM), and a composite outcome was defined using all four criteria. Results: A total of 1177 eligible patients with PTMC (919 female, 78.1%) with a median age of 48 years (range 19-87) were enrolled; 755 (64.1%) patients chose AS and 422 (35.9%) underwent surgery. Among 755 patients under AS, 706 (female 537, 76.1%) underwent at least two ultrasound examinations and were analyzed. Over a follow-up period of 41.4 months (standard deviation, 16.0), 163 AS patients (23.1%) underwent surgery. Progression defined by the composite outcome was observed in 9.6% (68/706) of patients, and the 2- and 5-year progression estimates were 5.3% and 14.2%, respectively. The observed progression rates were 5.8% (41/706) and 5.4% (38/706) as defined by tumor size enlargement by 3 mm and 2 × 2 mm, respectively, and 1.3% (9/706) and 0.4% (3/706) for new LNM and ETE, respectively. No distant metastases developed during AS. In multivariate logistic regression analysis examining variables associated with progression under AS, age at diagnosis <30 years (odds ratio [OR], 2.86; 95% confidence interval [CI], 1.10 - 7.45), male sex (OR, 2.48; 95% CI, 1.47 - 4.20), and tumor size ≥6 mm (OR, 1.89; 95% CI, 1.09 - 3.27) were independently significant. Conclusions: The progression of low-risk PTMC during AS in the Korean population was low, but slightly higher than previously reported in other populations. Risk factors for disease progression under AS include younger age, male sex, and larger tumor size. Clinical trial registration: Clinicaltrials.gov NCT02938702.
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Carcinoma Papilar , Neoplasias de la Tiroides , Humanos , Masculino , Femenino , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Estudios Prospectivos , Espera Vigilante , Carcinoma Papilar/patología , Neoplasias de la Tiroides/diagnóstico por imagen , Neoplasias de la Tiroides/epidemiología , Neoplasias de la Tiroides/terapia , Tiroidectomía , Metástasis Linfática , Factores de Riesgo , Progresión de la Enfermedad , Estudios RetrospectivosRESUMEN
While wireless vital sign monitoring is expected to reduce the vital sign measurement time (thus reducing the nursing workload), its impact on the rapid response system is unclear. This study compared the time from vital sign measurement to recording and rapid response system activation between wireless and conventional vital sign monitoring in the general ward, to investigate the impact of wireless vital sign monitoring system on the rapid response system. The study divided 249 patients (age > 18 years; female: 47, male: 202) admitted to the general ward into non-wireless (n = 101) and wireless (n = 148) groups. Intervals from vital sign measurement to recording and from vital sign measurement to rapid response system activation were recorded. Effects of wireless system implementation for vital sign measurement on the nursing workload were surveyed in 30 nurses. The interval from vital sign measurement to recording was significantly shorter in the wireless group than in the non-wireless group (4.3 ± 2.9 vs. 44.7 ± 14.4 min, P < 0.001). The interval from vital sign measurement to rapid response system activation was also significantly lesser in the wireless group than in the non-wireless group (27.5 ± 12.9 vs. 41.8 ± 19.6 min, P = 0.029). The nursing workload related to vital sign measurement significantly decreased from 3 ± 0.87 to 2.4 ± 9.7 (P = 0.021) with wireless system implementation. Wireless vital sign monitoring significantly reduced the time to rapid response system activation by shortening the time required to measure the vital signs. It also significantly reduced the nursing workload.
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Habitaciones de Pacientes , Signos Vitales , Adulto , Femenino , Humanos , Masculino , Persona de Mediana Edad , Monitoreo Fisiológico , Carga de TrabajoRESUMEN
Background: Active surveillance (AS) is offered as a choice to patients with low-risk papillary thyroid microcarcinoma (PTMC). This study aimed to identify patient and physician factors associated with the choice of AS. Methods: We conducted a cross-sectional survey of patients with low-risk PTMC who were enrolled in a prospective study comparing outcomes following AS and surgery. Patients completed a questionnaire to assess their prior knowledge of the disease, considerations in the decision-making process, and reasons for choosing the treatment. We also surveyed 19 physician investigators about their disease management preferences. Variables affecting the patients' choice of AS, including patients' characteristics and their decision-making process, were analyzed in a multivariable analysis. Results: The response rate of the patient survey was 72.8% (857/1177). Among the patients who responded to the survey, 554 patients (128 male; mean age 49.4 ± 11.6 years; response rate 73.4%) with low-risk PTMC chose AS (AS group), whereas 303 patients (55 male; 46.6 ± 10.7 years; 71.8%) chose immediate surgery (iOP group). In the AS group, 424 patients (76.5%) used a decision aid, and 144 (47.5%) used it in the iOP group. The choice of AS was associated with the following variables: patient age >50 years (odds ratio 1.713 [confidence interval, CI 1.090-2.690], p = 0.020), primary tumor size ≤5 mm (odds ratio 1.960 [CI 1.137-3.379], p = 0.015), and consulting an endocrinologist (odds ratio 114.960 [CI 48.756-271.057], p < 0.001), and use of a decision aid (odds ratio 2.469 [CI 1.320-4.616], p = 0.005). The proportion of patients who were aware of AS before their initial consultation for treatment decision was higher in the AS group than in the iOP group (64.6% vs. 56.8%). Family members were reported to have influenced the treatment decisions more in the iOP group (p = 0.025), whereas the AS group was more influenced by information from the media (p = 0.017). Physicians' attitudes regarding AS of low-risk PTMC tended to be more favorable among endocrinologists than surgeons and all became more favorable as the study progressed. Conclusions: Emerging evidence suggests that physicians' attitudes and communication tools influence the treatment decision of low-risk PTMC patients. Support is needed for patient-centered decision making. (Clinical trial No: NCT02938702).
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Neoplasias de la Tiroides , Tiroidectomía , Adulto , Carcinoma Papilar , Estudios Transversales , Humanos , Masculino , Persona de Mediana Edad , Estudios Prospectivos , Neoplasias de la Tiroides/patología , Neoplasias de la Tiroides/cirugía , Espera VigilanteRESUMEN
AIM: To compare treatment patterns and clinical outcomes of single-pill fixed-dose combination (FDC) and two-pill combination (TPC) therapies using real-world data. METHODS: We conducted a nationwide retrospective cohort study using South Korea's healthcare database (2002-2015). We identified two cohorts of incident patients with type 2 diabetes who initiated FDC or TPC therapy within 4 months of their first prescription for metformin or sulphonylurea. We examined persistence and adherence patterns and the clinical outcome of a composite endpoint of death or hospitalization for acute myocardial infarction, heart failure or stroke and compared the differences in treatment patterns and clinical outcomes using Cox models. RESULTS: Of 5143 and 10 973 patients who initiated FDC and TPC therapy, respectively, we identified 5143 patient pairs after propensity score matching. The FDC group exhibited greater median time to treatment discontinuation (163 vs. 146 days), and proportion of days covered at 12 months (mean 0.60 vs. 0.57, P < .0001) and at 24 months (0.53 vs. 0.51, P = .014) than the TPC group. The FDC group, compared with the TPC group, had reduced risks of the composite clinical outcome (hazard ratio 0.86, 95% confidence intervals 0.77-0.97) and hospitalization for stroke (0.80, 0.67-0.96). CONCLUSION: FDC therapy may provide favourable cardiovascular benefits, especially reducing the risk of hospitalization for stroke, and has better medication adherence among patients with type 2 diabetes.
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Diabetes Mellitus Tipo 2 , Accidente Cerebrovascular , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Combinación de Medicamentos , Quimioterapia Combinada , Humanos , Hipoglucemiantes/uso terapéutico , Cumplimiento de la Medicación , Estudios Retrospectivos , Accidente Cerebrovascular/tratamiento farmacológico , Accidente Cerebrovascular/epidemiología , Accidente Cerebrovascular/prevención & controlRESUMEN
BACKGRUOUND: This study aims to elucidate the associations among dietary seaweed (gim and miyeok/dashima) and iodine intakes, the rs77277498 polymorphism of the SLC5A5 gene codifying the sodium/iodine symporter, and thyroid cancer risk in a Korean population. METHODS: We conducted a case-control study of 117 thyroid cancer cases and 173 controls who participated in the Cancer Screenee Cohort between 2002 and 2014 at the National Cancer Center, Korea. The amount of seaweed and iodine consumption (g/day) was estimated using the residual energy adjustment method. We calculated odds ratios (ORs) and their 95% confidence intervals (CIs) using a multivariable logistic regression model for the separate and combined effect of dietary iodine-based intake and SLC5A5 polymorphism (rs77277498, C>G) on thyroid cancer. RESULTS: Dietary gim and iodine intakes were inversely associated with thyroid cancer, with ORs of 0.50 (95% CI, 0.30 to 0.83) and 0.57 (95% CI, 0.35 to 0.95), respectively, whereas the associations for dietary miyeok/dashima and total seaweed intakes were not significant. However, compared with individuals carrying the C/C genotype of the rs77277498 polymorphism with a low intake of all dietary factors, those carrying the G allele with a high intake had a lower risk of thyroid cancer, with ORs of 0.25 (95% CI, 0.10 to 0.56), 0.31 (95% CI, 0.12 to 0.77), 0.26 (95% CI, 0.10 to 0.62), and 0.30 (95% CI, 0.12 to 0.73) for the consumption of gim, miyeok/dashima, total seaweed, and iodine, respectively. CONCLUSION: In summary, our results supported the evidence of the protective effects of dietary gim and iodine intake against thyroid cancer risk, and this association can be strengthened by SLC5A5 rs77277498 genotypes.
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Yodo , Algas Marinas , Simportadores , Neoplasias de la Tiroides , Estudios de Casos y Controles , Dieta , Humanos , Simportadores/genética , Simportadores/metabolismo , Neoplasias de la Tiroides/epidemiología , Neoplasias de la Tiroides/genéticaRESUMEN
OBJECTIVE: To evaluate the association between computed tomography (CT) scanning and newly diagnosed thyroid cancer cases in relation to the confounding effect of the healthcare utilization rate. METHODS: This nested case-control study used the Korean National Health Insurance Service-National Sample Cohort 2002-2015: 3557 adult thyroid cancer cases were matched to 17,785 controls by age, sex, and diagnosis date. Odds ratios (ORs) were estimated for thyroid cancer associated with cumulative exposure to CT scanning > 3 years before cancer diagnosis. Changes in estimated ORs with and without adjustment for outpatient visit frequency were investigated. RESULTS: ORs for newly diagnosed thyroid cancer increased according to the higher number of total CT scans and thyroid-exposing CT scans (CT scans of the head, neck, or chest compartment; OR and 95% confidence interval [CI], 1.09 [1.03-1.16] and 1.28 [1.05-1.57], respectively). ORs for thyroid cancer increased according to higher outpatient visit frequency. The association between thyroid cancer incidence and CT scans became insignificant when outpatient visit frequency was adjusted in the models (OR [95% CI], 1.03 [0.97-1.10]: total CT scans, 1.14 [0.93-1.41]: thyroid-exposing CT scans). Subgroup analyses stratified by age, sex, and history of other malignancies did not reveal independent associations between CT scanning and thyroid cancer. CONCLUSIONS: The high incidence of thyroid cancer in adults exposed to ionizing radiation during CT scanning can be largely explained by the confounding effect of the healthcare utilization rate. These effects should be considered to avoid overestimation of the CT scanning-associated risk of thyroid cancer. KEY POINTS: ⢠Studies indicate that diagnostic imaging using low-ionizing radiation may increase risks for thyroid cancer in adults. ⢠Our findings suggest that the risk for radiation-induced thyroid cancer following CT scanning in adults may have been overestimated in observational studies due to medical surveillance-related biases.
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Neoplasias Inducidas por Radiación , Neoplasias de la Tiroides , Adulto , Estudios de Casos y Controles , Humanos , Neoplasias Inducidas por Radiación/epidemiología , Medición de Riesgo , Neoplasias de la Tiroides/diagnóstico por imagen , Neoplasias de la Tiroides/epidemiología , Tomografía Computarizada por Rayos XRESUMEN
We evaluated the metabolic effects of gastrectomies and endoscopic submucosal dissections (ESDs) in early gastric cancer (EGC) patients with type 2 diabetes mellitus (T2DM). Forty-one EGC patients with T2DM undergoing gastrectomy or ESD were prospectively evaluated. Metabolic parameters in the patients who underwent gastrectomy with and without a duodenal bypass (groups 1 and 2, n = 24 and n = 5, respectively) were compared with those in patients who underwent ESD (control, n = 12). After 1 year, the proportions of improved/equivocal/worsened glycemic control were 62.5%/29.2%/8.3% in group 1, 40.0%/60.0%/0.0% in group 2, and 16.7%/50.0%/33.3% in the controls, respectively (p = 0.046). The multivariable ordered logistic regression analysis results showed that both groups had better 1-year glycemic control. Groups 1 and 2 showed a significant reduction in postprandial glucose (-97.9 and -67.8 mg/dL), body mass index (-2.1 and -2.3 kg/m2), and glycosylated hemoglobin (group 1 only, -0.5% point) (all p < 0.05). Furthermore, improvements in group 1 were more prominent when preoperative leptin levels were high (p for interaction < 0.05). Metabolic improvements in both groups were also observed for insulin resistance, leptin, plasminogen activator inhibitor-1, and resistin. Gastrectomy improved glycemic control and various metabolic parameters in EGC patients with T2DM. Patients with high leptin levels may experience greater metabolic benefits from gastrectomy with duodenal bypass.
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OBJECTIVE: Along with the exponentially-growing data produced and accumulated every day through mobile platforms, social networking services, the Internet, and other media, information is becoming increasingly important as a strategic resource. This report presents specific and clear directions and suggests empirical project plans regarding innovations in regional health information systems to promote the utilization of medical information. METHODS: We reviewed and examined documents about global trends and examples of regional health information systems. The problems and solutions of health information utilization and regional health information systems in Korea were analyzed. RESULTS: This study presented examples of the establishment of health information systems, problems in the use of local healthcare information, and an empirical project for improvement. CONCLUSIONS: The results of this study imply the need for long-term and systematic approaches for the use of medical information and the establishment of a local healthcare information system, along with implementation plans. As a first step, it is imperative to clarify the goal of building a medical information system, the information that must be provided to build the system, and the data that should be collected to provide such information, while moving away from the mentality of focusing on technology-oriented medical information services. In addition, it is necessary to consider information governance, data-based service development, and the medical innovation framework, which are ways to efficiently manage, utilize, and systemize the data to be collected.
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Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; it is generally recommended in men ≥ 70 and women ≥ 65 years old. Therefore, assessment of clinical risk factors for osteoporosis is very important in individuals under the recommended age for DXA. Here, we examine the diagnostic performance of machine learning-based prediction models for osteoporosis in individuals under the recommended age for DXA examination. Data of 2210 men aged 50-69 and 1099 women aged 50-64 obtained from the Korea National Health and Nutrition Examination Survey IV-V were analyzed. Extreme gradient boosting (XGBoost) was used to find relevant clinical features and applied to three machine learning models: XGBoost, logistic regression, and a multilayer perceptron. For the prediction of osteoporosis, the XGBoost model using the top 20 features extracted from XGBoost showed the most reliable performance with area under the receiver operating characteristic curve (AUROC) of 0.73 and 0.79 in men and women, respectively. We compared the diagnostic accuracy of the Shapley additive explanation values based on a risk-score model obtained from XGBoost and conventional osteoporosis risk assessment tools for prediction of osteoporosis using optimal cut-off values for each model. We observed that a cut-off risk score of ≥ 28 in men and ≥ 47 in women was optimal to classify a positive screening for osteoporosis (an AUROC of 0.86 in men and 0.91 in women). The XGBoost-based osteoporosis-prediction model outperformed conventional risk assessment tools. Therefore, machine learning-based prediction models are a more suitable option than conventional risk assessment methods for screening osteoporosis in individuals under the recommended age for DXA examination.
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Densidad Ósea , Osteoporosis , Absorciometría de Fotón , Anciano , Femenino , Humanos , Aprendizaje Automático , Masculino , Encuestas Nutricionales , Osteoporosis/diagnóstico por imagen , Factores de RiesgoRESUMEN
The cumulative effect of single-nucleotide polymorphisms (SNPs) on thyroid cancer has been adequately defined in individuals of European ancestry; however, similar evidence in the Korean population is limited. This study aimed to investigate the influence of modifiable factors and the polygenic risk score (PRS) and their interactive and combined effects on thyroid cancer. Using data from the cancer screenee cohort, this study included 759 thyroid cancer cases and 759 age- and sex-matched controls. We examined the effects of tobacco smoking, alcohol consumption, and regular exercise habits, BMI, and the PRS of six SNPs on thyroid cancer. Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations were obtained using a conditional logistic regression model. The results indicated that family history, obesity, and the unweighted and weighted PRS were independently associated with susceptibility to thyroid cancer, with ORs (95% CIs) of 2.96 (1.63-5.36), 1.72 (1.20-2.48), 1.46 (1.10-1.93), and 1.56 (1.19-2.03), respectively, whereas the effect of smoking, drinking, and regular exercise was not significant. The contribution of the PRS remained after stratifying participants with healthy behaviors, such as nonsmokers/nondrinkers, and regular exercise. Although the PRS did not significantly contribute to the risk for thyroid cancer when participants were stratified according to BMI, BMI and the PRS had a cumulative effect on thyroid cancer risk. The combined effect of genetic polymorphisms on predisposition to thyroid cancer may differ based on tobacco smoking, alcohol consumption, regular exercise behaviors and cumulative BMI. Larger population-based studies are needed to validate these findings.
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Fumar , Neoplasias de la Tiroides , Predisposición Genética a la Enfermedad , Estudio de Asociación del Genoma Completo , Humanos , Polimorfismo de Nucleótido Simple , Factores de Riesgo , Fumar/efectos adversos , Neoplasias de la Tiroides/epidemiología , Neoplasias de la Tiroides/genéticaRESUMEN
Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms as "malignant"-cases that lead to a cancer diagnosis and treatment-or "normal" and "benign," non-malignant cases that do not require immediate medical intervention. In this retrospective study, 9111 unique mammograms-5672 normal, 1686 benign, and 1754 malignant cases were collected from the National Cancer Center in the Republic of Korea. Image compression was applied to mammograms with compression ratios (CRs) ranging from 15 to 11 K. Convolutional neural networks (CNNs) with three convolutional layers and three fully-connected layers were trained using these images to classify a mammogram as malignant or not malignant across a range of CRs using five-fold cross-validation. Models trained on images with maximum CRs of 5 K had an average area under the receiver operating characteristic curve (AUROC) of 0.87 and area under the precision-recall curve (AUPRC) of 0.75 across the five folds and compression ratios. For images compressed with CRs of 10 K and 11 K, model performance decreased (average 0.79 in AUROC and 0.49 in AUPRC). Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR < = 5 K) images had maps encapsulating a radiologist's label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. This paper finds that while training models on images with increased the robustness of the models when tested on compressed data, moderate image compression did not substantially impact the classification performance of DL-based models.
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Compresión de Datos , Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador , Mamografía/clasificación , Adulto , Anciano , Anciano de 80 o más Años , Humanos , Persona de Mediana Edad , Modelos Teóricos , Redes Neurales de la Computación , Curva ROCRESUMEN
AIMS: To investigate the incidence of and risk factors for new-onset type 2 diabetes mellitus (DM) developed during chemotherapy that included steroids in cancer patients without DM. METHODS: This multicenter, prospective, and observational cohort study enrolled 299 cancer patients without DM (aged > 18 years), planning 4-8 cycles of adjuvant chemotherapy. The endpoints were the incidence, remission rate, and independent determinants of new-onset DM during chemotherapy. RESULTS: Between April 2015 and March 2018, 270 subjects with colorectal cancer or breast cancer (mean age, 51.0 years) completed the follow up (mean 39 months). Of whom, 17 subjects (6.3%) developed DM within a median time of 90 days (range, 17-359 days). Male sex (hazard ratio [HR], 15.839; 95% confidence interval [CI], 2.004-125.20) and impaired fasting glucose (IFG) at baseline (HR, 8.307; CI, 1.826-37.786) were independent risk factors. Six months after chemotherapy completion, 11/17 subjects (64.7%) experienced DM remission, associated with a significantly higher C-peptide level at baseline (C-peptide levels, 1.3 ng/mL in subjects with remission and 0.9 ng/mL in subjects without remission, age- and sex-adjusted P = 0.007). CONCLUSIONS: DM incidence was 6.3% in patients who received chemotherapy with dexamethasone. Close monitoring for hyperglycemia is recommended, especially for men with IFG. TRIAL REGISTRATION: ClinicalTrials.gov (NCT03062072).
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Antineoplásicos/efectos adversos , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias Colorrectales/tratamiento farmacológico , Diabetes Mellitus Tipo 2/epidemiología , Hiperglucemia/epidemiología , Estado Prediabético/epidemiología , Glucemia/análisis , Neoplasias de la Mama/patología , Neoplasias Colorrectales/patología , Diabetes Mellitus Tipo 2/inducido químicamente , Diabetes Mellitus Tipo 2/patología , Progresión de la Enfermedad , Femenino , Humanos , Hiperglucemia/inducido químicamente , Hiperglucemia/patología , Incidencia , Masculino , Persona de Mediana Edad , Estado Prediabético/inducido químicamente , Estado Prediabético/patología , Pronóstico , Estudios Prospectivos , República de Corea/epidemiología , Factores de Riesgo , Tasa de SupervivenciaRESUMEN
In this cross-sectional study, we investigated the baseline risk factors of diabetes mellitus (DM) in patients with undiagnosed DM (UDM). We utilized the Korean National Health and Nutrition Examination Survey (KNHANES) 2010-2017 data. Data regarding the participants' demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory tests were gathered to explore the differences between the DM, UDM, and without-DM groups. Among the 64,759 individuals who participated in the KNHANES 2010-2017, 32,611 individuals aged ≥20 years with fasting plasma glucose levels of <100 or ≥126 mg/dL were selected. The odds ratios (ORs) regarding family history of diabetes and the performance of national health and cancer screening tests were lower in the UDM group than in the DM group (adjusted OR: 0.54; 95% confidence interval (CI): 0.43, 0.66; adjusted OR: 0.74; 95% CI: 0.62, 0.89; adjusted OR: 0.71; 95% CI: 0.60, 0.85). The ORs of hypertension and obesity were higher in the UDM group than in the DM group (adjusted OR: 1.32; 95% CI: 1.06, 1.64; adjusted OR: 1.80; 95% CI: 1.37, 2.36, respectively). Patients with UDM were more likely to be exposed to DM-related risk factors than those with and without DM. Public health interventions to prevent UDM development are necessary.