Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.

BACKGROUND: There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen. METHODS: We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS: All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS: The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

The Chemical Synthesis and Applications of Tropane Alkaloids.

Tropanes are an important class of alkaloid natural products that are found in plants all over the world. These compounds can exhibit significant biological activity and are among the oldest known medicines. In the early 19th century, tropanes were isolated, characterized, and synthesized by notable chemical researchers. Their significant biological activities have inspired tremendous research efforts toward their synthesis and the elucidation of their pharmacological activity both in academia and in industry. In this chapter, which addresses the developments in this field since 1994, the focus is on the synthesis of these compounds, and several examples of sophisticated synthetic protocols involving both asymmetric and catalytic approaches are described. In addition, the structures of more than 100 new alkaloids are included as well as the applications and pharmacological properties of some tropane alkaloids.

[Treatment of nasopharyngeal carcinoma in children].

OBJECTIVE: To evaluate the clinical efficacy of the ARAR0331 protocol for treatment of childhood nasopharyngeal carcinoma. METHODS: The clinical data of eight children with nasopharyngeal carcinoma between May 2004 and May 2012 were retrospectively studied. The eight patients included six boys and two girls, and the onset age was between 3 and 13 years. Six patients were in AJCC Stage Ⅲ, one was in StageⅡA and one was in Stage ⅣA. One patient had been treated with combined radiotherapy and chemotherapy which mainly included EAP, BEP and EA. The other seven patients had been treated with the ARAR0331 protocol provided by the America Children's Oncology Group (COG). RESULTS: The patient who had been treated with combined radiotherapy and chemotherapy developed multiple bony metastasis during the chemotherapeutic period. Four out of seven patients who had been treated with ARAR0331 protocol achieved complete remission, and two achieved partial remission. The seven patients were followed-up from 8 to 75 months and the survival rate was 100%. The ARAR0331 protocol treatment-related complications included radiodermatitis, mucocitis and nausea. Late toxicity was not found. CONCLUSIONS: Based on the limited cases, ARAR0331 protocol appears to be effective and safe for childhood nasopharyngeal carcinoma.