Atypical type A thymoma component identified by pulmonary metastasectomy 11 years after surgery of type AB thymoma.

Atypical type A thymomas exhibit more aggressive features than conventional type A thymomas. Type AB thymomas rarely have atypical type A components. We report a rare case of type AB thymoma with an atypical type A component, that was identified after pulmonary metastasectomy 11 years after the primary surgery and long-term follow-up after recurrence. A 61-year-old female underwent extended thymectomy for an anterior mediastinal tumor 11 years prior and was diagnosed with type AB thymoma (Masaoka stage II). Five years ago, follow-up computed tomography showed well-circumscribed pulmonary nodules up to 1.0 cm in both lungs. All the pulmonary nodules grew slowly; however, one of the nodules grew to 1.6 cm, and thoracoscopic wedge resection was performed for diagnosis. Pathologically, the pulmonary nodule was consisted of type A thymoma component. Conventional type AB thymomas are usually locally aggressive neoplasms; thus, we reviewed the tissue slides of primary thymomas. Histologically, cytological atypia, hypercellularity, and increased mitosis are observed in the type A component. Consequently, the diagnosis was revised to a type AB thymoma with an atypical type A component. The pulmonary nodule exhibited the same atypical type A features. Pulmonary metastasectomy was performed two more times as volume-reduction surgery. The residual metastasis was located only in the lung with slow growth, 4 years after the first pulmonary resection; therefore, we followed up as an outpatient without treatment.

Machine Learning-based Model for Predicting Postoperative Complications among Patients with Colonic Perforation: A Retrospective study.

OBJECTIVES: Surgery for colonic perforation has high morbidity and mortality rates. Predicting complications preoperatively would help improve short-term outcomes; however, no predictive risk stratification model exists to date. Therefore, the current study aimed to determine risk factors for complications after colonic perforation surgery and use machine learning to construct a predictive model. METHODS: This retrospective study included 51 patients who underwent emergency surgery for colorectal perforation. We investigated the connection between overall complications and several preoperative indicators, such as lactate and the Glasgow Prognostic Score. Moreover, we used the classification and regression tree (CART), a machine-learning method, to establish an optimal prediction model for complications. RESULTS: Overall complications occurred in 32 patients (62.7%). Multivariate logistic regression analysis identified high lactate levels [odds ratio (OR), 1.86; 95% confidence interval (CI), 1.07-3.22; p = 0.027] and hypoalbuminemia (OR, 2.56; 95% CI, 1.06-6.25; p = 0.036) as predictors of overall complications. According to the CART analysis, the albumin level was the most important parameter, followed by the lactate level. This prediction model had an area under the curve (AUC) of 0.830. CONCLUSIONS: Our results determined that both preoperative albumin and lactate levels were valuable predictors of postoperative complications among patients who underwent colonic perforation surgery. The CART analysis determined optimal cutoff levels with high AUC values to predict complications, making both indicators clinically easier to use for decision making.