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Background: Speckle tracking-derived global longitudinal strain (GLS) of left ventricle is a potent prognostic marker for patients with ST-segment elevation myocardial infarction (STEMI). The purpose of this study was to investigate the difference of prognostic impact of GLS between anterior and nonanterior myocardial infarction. Methods: This study included 686 patients who underwent primary percutaneous coronary intervention for their first STEMI between November 2007 and April 2012. Differences in the prognostic impact of GLS between anterior MI group and nonanterior MI group were evaluated. The composite of all-cause mortality and hospitalization for heart failure in 2 years was investigated for outcome. Results: During the follow-up period, 77 (11.2%) adverse events occurred. The anterior and nonanterior MI groups included 339 and 347 patients, respectively. Among patients with anterior MI, GLS significantly predicted 2-year outcome in an adjusted model (adjusted hazard ratio [HR] 1.186; 95% confidence interval [CI] 1.071-1.314, P = 0.001), whereas the association between GLS and mortality was weaker in the nonanterior MI group (adjusted HR 0.977; 95% CI 0.884-1.081, P = 0.657). The interaction between the infarction territory and GLS was significant (P for interaction = 0.018), indicating that GLS was a more sensitive predictor of mortality in patients with anterior MI than that in those with nonanterior MI. Conclusions: Speckle tracking-derived GLS of left ventricle more sensitively predicted clinical outcome in patients with anterior MI than in those with nonanterior MI. [ABSTRACT FROM AUTHOR]

Background: An anastomotic stricture after colorectal surgery is principally managed by endoscopic balloon dilation (EBD). Although this intervention is effective, however, subsequent procedures or surgical interventions are often required. This study aimed to assess the long-term outcomes of EBD for anastomotic stricture arising from colorectal cancer surgery. Materials and methods: We analyzed 173 patients who received curative surgery for colorectal cancer at our hospital between January 2000 and December 2022 and had undergone EBD to manage anastomotic stricture. The medical records of these cases were retrospectively reviewed to assess the outcomes and risk factors for restenosis and permanent stoma. Results: Of the 173 study patients, 41 (23.7%) presented with restenosis with a median time to recurrence of 49 [37–150] days. The restenosis group was significantly younger (55.6 years versus 60.8 years), with a more prominent rectal location (80.5% versus 57.6%), a higher incidence of hand-sewn anastomosis (24.4% versus 5.3%), and a higher percentage of neoadjuvant radiotherapy (34.1% versus 5.3%, P < 0.001). Multivariable analysis indicated neoadjuvant radiotherapy (adjusted HR 2.48; 95% CI 1.03–5.95) and cerebral vascular disease (adjusted HR 6.97; 95% CI 2.15–22.54) as independent prognostic factors for restenosis. Fourteen patients (8.1%) required a permanent stoma due to treatment failure. All cases needing a permanent stoma were male (14 patients, 100%, P = 0.007) and this group had a higher rate of neoadjuvant radiotherapy, adjuvant chemotherapy, and hand-sewn anastomosis. Conclusion: Patients receiving neoadjuvant radiotherapy are most prone to restenosis after an EBD intervention to manage an anastomotic stricture. Neoadjuvant radiotherapy is also a strong risk factor for requiring a permanent stomas due to treatment failure. [ABSTRACT FROM AUTHOR]

Purpose: To develop two deep learning-based systems for diagnosing and localizing pneumothorax on portable supine chest X-rays (SCXRs). Methods: For this retrospective study, images meeting the following inclusion criteria were included: (1) patient age ≥ 20 years; (2) portable SCXR; (3) imaging obtained in the emergency department or intensive care unit. Included images were temporally split into training (1571 images, between January 2015 and December 2019) and testing (1071 images, between January 2020 to December 2020) datasets. All images were annotated using pixel-level labels. Object detection and image segmentation were adopted to develop separate systems. For the detection-based system, EfficientNet-B2, DneseNet-121, and Inception-v3 were the architecture for the classification model; Deformable DETR, TOOD, and VFNet were the architecture for the localization model. Both classification and localization models of the segmentation-based system shared the UNet architecture. Results: In diagnosing pneumothorax, performance was excellent for both detection-based (Area under receiver operating characteristics curve [AUC]: 0.940, 95% confidence interval [CI]: 0.907–0.967) and segmentation-based (AUC: 0.979, 95% CI: 0.963–0.991) systems. For images with both predicted and ground-truth pneumothorax, lesion localization was highly accurate (detection-based Dice coefficient: 0.758, 95% CI: 0.707–0.806; segmentation-based Dice coefficient: 0.681, 95% CI: 0.642–0.721). The performance of the two deep learning-based systems declined as pneumothorax size diminished. Nonetheless, both systems were similar or better than human readers in diagnosis or localization performance across all sizes of pneumothorax. Conclusions: Both deep learning-based systems excelled when tested in a temporally different dataset with differing patient or image characteristics, showing favourable potential for external generalizability. [ABSTRACT FROM AUTHOR]