RESUMO
Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract extensive visual characteristics for correct categorization. To address this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of several deep-learning architectures and use aggregate knowledge of many models to improve classification performance, allowing for more accurate and efficient gastric cancer detection. To determine how well these proposed models performed, this study compared them with other works, all of which were based on the Gastric Histopathology Sub-Size Images Database, a publicly available dataset for gastric cancer. This research demonstrates that the ensemble models achieved a high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80-pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120-pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160-pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, highlighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings show that ensemble models may successfully detect critical characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.
RESUMO
BACKGROUND: Labor and delivery can entail complications and severe maternal morbidities that threaten a woman's life or cause her to believe that her life is in danger. Women with these experiences are at risk for developing posttraumatic stress disorder. Postpartum posttraumatic stress disorder, or childbirth-related posttraumatic stress disorder, can become an enduring and debilitating condition. At present, validated tools for a rapid and efficient screen for childbirth-related posttraumatic stress disorder are lacking. OBJECTIVE: We examined the diagnostic validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, for detecting posttraumatic stress disorder among women who have had a traumatic childbirth. This Checklist assesses the 20 Diagnostic and Statistical Manual of Mental Disorders, posttraumatic stress disorder symptoms and is a commonly used patient-administrated screening instrument. Its diagnostic accuracy for detecting childbirth-related posttraumatic stress disorder is unknown. STUDY DESIGN: The sample included 59 patients who reported a traumatic childbirth experience determined in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, posttraumatic stress disorder criterion A for exposure involving a threat or potential threat to the life of the mother or infant, experienced or perceived, or physical injury. The majority (66%) of the participants were less than 1 year postpartum (for full sample: median, 4.67 months; mean, 1.5 years) and were recruited via the Mass General Brigham's online platform, during the postpartum unit hospitalization or after discharge. Patients were instructed to complete the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, concerning posttraumatic stress disorder symptoms related to childbirth. Other comorbid conditions (ie, depression and anxiety) were also assessed. They also underwent a clinician interview for posttraumatic stress disorder using the gold-standard Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A second administration of the checklist was performed in a subgroup (n=43), altogether allowing an assessment of internal consistency, test-retest reliability, and convergent and diagnostic validity of the Checklist. The diagnostic accuracy of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, in reference to the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, was determined using the area under the receiver operating characteristic curve; an optimal cutoff score was identified using the Youden's J index. RESULTS: One-third of the sample (35.59%) met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria for a posttraumatic stress disorder diagnosis stemming from childbirth. The Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, symptom severity score was strongly correlated with the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, total score (ρ=0.82; P<.001). The area under the receiver operating characteristic curve was 0.93 (95% confidence interval, 0.87-0.99), indicating excellent diagnostic performance of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. A cutoff value of 28 maximized the sensitivity (0.81) and specificity (0.90) and correctly diagnosed 86% of women. A higher value (32) identified individuals with more severe posttraumatic stress disorder symptoms (specificity, 0.95), but with lower sensitivity (0.62). Checklist scores were also stable over time (intraclass correlation coefficient, 0.73), indicating good test-retest reliability. Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, scores were moderately correlated with the depression and anxiety symptom scores (Edinburgh Postnatal Depression Scale: ρ=0.58; P<.001 and the Brief Symptom Inventory, anxiety subscale: ρ=0.51; P<.001). CONCLUSION: This study demonstrates the validity of the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, as a screening tool for posttraumatic stress disorder among women who had a traumatic childbirth experience. The instrument may facilitate screening for childbirth-related posttraumatic stress disorder on a large scale and help identify women who might benefit from further diagnostics and services. Replication of the findings in larger, postpartum samples is needed.