Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: The purpose of this study was to explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Purpose: The purpose of this study was to explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Purpose: The purpose of this study was to explore and assess the experiences of cancer patients and their caregivers who had been admitted to comprehensive nursing care service wards. Methods: Data were collected from 10 patients and 10 caregivers by in-depth interviews. The data were analyzed using content analysis of Downe-Wamboldt. Results: Three categories and seven subcategories were extracted. 1) Realizing institutional limitations of comprehensive nursing care service: ‘Wishing for precise operating systems based on patient severity,’ ‘Anticipating active caregiver participation in treatment process,’ ‘Requiring a countermeasure for safety accidents,’ 2) Professional nursing service which provides relief: ‘Patient-centered professional nursing service,’ ‘Inpatient service that provides relief for patients and caregivers,’ 3) Anticipating continuous use of the service: ‘Inpatient service which users are willing to reuse,’ ‘Wishing for expansion and reinforcement of the service.’ Conclusion Cancer patients and their caregivers experienced institutional limitations while satisfied with professional nursing service and willing to reuse the service. To improve this situation, institutional support such as separate wards for severe patients, measures for active caregiver participation and prevention of safety accidents, and adequate staffing would be helpful for relatively severe level cancer patients and their caregivers.

Background/Aims: We evaluated the feasibility and long-term efficacy of the combination of cytarabine, idarubicin, and all-trans retinoic acid (ATRA) for treating patients with newly diagnosed acute promyelocytic leukemia (APL). Methods: We included 87 patients with newly diagnosed acute myeloid leukemia and a t(15;17) or promyelocytic leukemia/retinoic acid receptor alpha (PML-RARα) mutation. Patients received 12 mg/m2/day idarubicin intravenously for 3 days and 100 mg/m2/day cytarabine for 7 days, plus 45 mg/m2/day ATRA. Clinical outcomes included complete remission (CR), relapse-free survival (RFS), overall survival (OS), and the secondary malignancy incidence during a 20-year follow-up. Results: The CR, 10-year RFS, and 10-year OS rates were 89.7%, 94.1%, and 73.8%, respectively, for all patients. The 10-year OS rate was 100% for patients that achieved CR. Subjects were classified according to the white blood cell (WBC) count in peripheral blood at diagnosis (low-risk, WBC < 10,000/mm3; high-risk, WBC ≥ 10,000/mm3). The low-risk group had significantly higher RFS and OS rates than the high-risk group, but the outcomes were not superior to the current standard treatment (arsenic trioxide plus ATRA). Toxicities were similar to those observed with anthracycline plus ATRA, and higher than those observed with arsenic trioxide plus ATRA. The secondary malignancy incidence after APL treatment was 2.7%, among the 75 patients that achieved CR, and 5.0% among the 40 patients that survived more than 5 years after the APL diagnosis. Conclusions Adding cytarabine to anthracycline plus ATRA was not inferior to anthracycline plus ATRA alone, but it was not comparable to arsenic trioxide plus ATRA. The probability of secondary malignancy was low.

PURPOSE: Prenatal maternal stress affects offspring's atopic dermatitis (AD) development, which is thought to be mediated by the oxidative stress. We aimed to evaluate the difference in leukocyte telomere length (LTL), a marker for exposure to oxidative stress, according to the prenatal stress exposure and the later AD development. METHODS: From a birth cohort (the COhort for Childhood Origin of Asthma and allergic diseases) that had displayed a good epidemiologic association between the exposure to prenatal stress and AD development in the offspring, we selected 68 pairs of samples from 4 subject groups based on the level of prenatal maternal stress and later AD development. The LTL was measured from both cord blood and 1-year peripheral blood, and their LTLs were compared between subject groups. Finally, the proportion of AD development was examined in the subject groups that are reclassified based on subjects' exposure to prenatal stress and there LTL. RESULTS: Cord-blood LTL was shorter in prenatally stressed infants than in unstressed ones (P = 0.026), which difference was still significant when subjects became 1 year old (P = 0.008). LTL of cord blood, as well as one of the 1-year peripheral blood, was not different according to later AD development at 1 year (P = 0.915 and 0.174, respectively). Shorter LTL made no increase in the proportion of later AD development in either prenatally high-stressed or low-stressed groups (P = 1.000 and 0.473, respectively). CONCLUSIONS: Cord-blood LTL may reflect subjects' exposure to maternal prenatal stress. However, the LTL shortening is not a risk factor of increasing AD development until the age of 1, and a longer investigation may be necessary for validation. Currently, the results doubt the role of LTL shortening as a marker for risk assessment tool for the prenatal stress associated with AD development in the offspring.
Asthma ; Child ; Cohort Studies ; Dermatitis, Atopic ; Fetal Blood ; Humans ; Infant ; Leukocytes ; Oxidative Stress ; Parturition ; Risk Assessment ; Risk Factors ; Stress, Psychological ; Telomere Shortening ; Telomere

BACKGROUND: Ovarian epithelial cancer (OEC) is the second-most common gynecologic malignancy. CD109 expression is elevated in human tumor cell lines and carcinomas. A previous study showed that CD109 expression is elevated in human tumor cell lines and CD109 plays a role in cancer progression. Therefore, this study aimed to determine whether CD109 is expressed in OEC and can be useful in predicting the prognosis. METHODS: Immunohistochemical staining for CD109 and reverse transcription-quantitative polymerase chain reaction was performed. Then we compared CD109 expression and chemoresistance, overall survival, and recurrence-free survival of OEC patients. Chemoresistance was evaluated by dividing into good-response group and poor-response group by the time to recurrence after chemotherapy. RESULTS: CD109 expression was associated with overall survival (p = .020), but not recurrence-free survival (p = .290). CD109 expression was not an independent risk factor for overall survival due to its reliability (hazard ratio, 1.58; p = .160; 95% confidence interval, 0.82 to 3.05), although we found that CD109 positivity was related to chemoresistance. The poor-response group showed higher rates of CD109 expression than the good-response group (93.8% vs 66.7%, p = .047). Also, the CD109 mRNA expression level was 2.88 times higher in the poor-response group as compared to the good-response group (p = .001). CONCLUSIONS: Examining the CD109 expression in patients with OEC may be helpful in predicting survival and chemotherapeutic effect.
Cell Line, Tumor ; Drug Therapy ; Humans ; Polymerase Chain Reaction ; Prognosis ; Recurrence ; Risk Factors ; RNA, Messenger