Non-adrenal tumors of the adrenal area; what are the pitfalls?

What may an adrenal mass conceal? Adrenal gastrointestinal stromal tumor: a case report

Extraintestinal gastrointestinal stromal tumors (GISTs) are extremely rare, and adrenal GISTs are even exceptional. Only three cases have been reported in the literature thus far, the current case being the fourth. This case demonstrates the need of including extraintestinal GIST in the differential diagnosis when investigating adrenal tumors. Herein, we present a case of adrenal GIST diagnosed in a 60-year-old female patient who had a left adrenal GIST surgically removed as an adrenal tumor.

Case report: Incidental MALT lymphoma of the left adrenal gland mimicking a metastatic spread within durvalumab maintenance treatment in inoperable stage III non-small cell lung cancer

Durvalumab after chemotherapy in non-operable stage III non-small cell lung cancer (NSCLC) is the standard of care worldwide. We present a patient with the incidental discovery of a unilateral MALT lymphoma of the adrenal gland and adrenalitis during durvalumab maintenance treatment detected by 18F-FDG-PET/CT. We assessed the clinical and histopathological findings, radiological examinations and overall treatment. Our work emphasizes the significance of considering other differential diagnoses and the importance of multidisciplinary treatment of the findings, especially within clinical trials.

Construction and validation of a prognostic model for predicting overall survival of primary adrenal malignant tumor patients: A population-based study with 1,080 patients

Objective

Primary adrenal malignant tumor is rare. The factors affecting the prognosis remain poorly defined. This study targeted to construct and corroborate a model for predicting the overall survival of adrenal malignant tumor patients.

Methods

We investigated the SEER database for patients with primary adrenal malignant tumor. 1,080 patients were divided into a construction cohort (n = 756) and a validation cohort (n = 324), randomly. The prognostic factors for overall survival were evaluated using univariate and multivariate Cox analyses. The nomogram was constructed and then validated with C-index, calibration curve, time-dependent ROC curve, and decision curve analysis in both cohorts. Then we divided the patients into 3 different risk groups according to the total points of the nomogram and analyzed their survival status by Kaplan-Meier curve with log-rank test.

Results

The baseline characteristics of these two cohorts were not statistically different (P > 0.05). Using univariate and multivariate Cox analyses, 5 variables, including age, tumor size, histological type, tumor stage, and surgery of primary site, were distinguished as prognostic factors (P < 0.05). Based on these variables, we constructed a nomogram to predict the 3- year, 5- year, and 10-year overall survival. The C-indexes were 0.780 (0.760–0.800) in the construction cohort and 0.780 (0.751–0.809) in the validation cohort. In both cohorts, the AUC reached a fairly high level at all time points. The internal and external calibration curves and ROC analysis showed outstanding accuracy and discrimination. The decision curves indicated excellent clinical usefulness. The best cut-off values for the total points of the nomogram were 165.4 and 243.1, and the prognosis was significantly different for the three different risk groups (P < 0.001).

Conclusion

We successfully constructed a model to predict the overall survival of primary adrenal malignant tumor patients. This model was validated to perform brilliantly internally and externally, which can assist us in individualized clinical management.

Binary Particle Swarm Optimization Intelligent Feature Optimization Algorithm-Based Magnetic Resonance Image in the Diagnosis of Adrenal Tumor

This research was aimed to explore the application value of magnetic resonance imaging (MRI) based on binary particle swarm optimization algorithm (BPSO) in the diagnosis of adrenal tumors. 120 patients with adrenal tumors admitted to the hospital were selected and randomly divided into the control group (conventional MRI examination) and the observation group (MRI examination based on the BPSO intelligent feature optimization algorithm), with 60 cases in each group. The sensitivity, specificity, accuracy, and Kappa of the diagnostic methods were compared between the two groups. The results showed that the calculation rate of the BPSO algorithm was the best under the same processing effect (P < 0.05). Optimization algorithm-based MRI is used in the diagnosis of adrenal tumors, and the results showed that the sensitivity, specificity, accuracy, and Kappa (83.33%, 79.17%, 81.67%, and 0.69) of the observation group were higher than those of the control group (50%, 75%, 58.33%, and 0.45). The similarity of tumor location results in the observation group (89.24%) was significantly higher than that in the control group (65.9%) (P < 0.05). In conclusion, compared with SFFS and other algorithms, the BPSO algorithm has more advantages in calculation speed. MRI based on the BPSO intelligent feature optimization algorithm has a good diagnostic effect and higher accuracy in adrenal tumors, showing the good development prospects of computer intelligence technology in the field of medicine.