Metastatic Pheochromocytoma: In Search of a Cure

CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study

OBJECTIVES

The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma.

METHODS

A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n = 105; test cohort: n = 50); the risk stratification scoring system classified a PASS score of < 4 as low risk and a PASS score of ≥ 4 as high risk. From CT images captured during the non-enhanced, arterial, and portal venous phase, radiomic features were extracted. After reducing dimensions and selecting features, Logistic Regression (LR), Extra Trees, and K-Nearest Neighbor (KNN) were utilized to construct the radiomics models. By adopting ROC curve analysis, the optimal radiomics model was selected. Univariate and multivariate logistic regression analyses of clinical radiological features were used to determine the variables and establish a clinical model. The integration of radiomics and clinical features resulted in the creation of a combined model. ROC curve analysis was used to evaluate the performance of the model, while decision curve analysis (DCA) was employed to assess its clinical value.

RESULTS

3591 radiomics features were extracted from the region of interest in unenhanced and dual-phase (arterial and portal venous phase) CT images. 13 radiomics features were deemed to be valuable. The LR model demonstrated the highest prediction efficiency and robustness among the tested radiomics models, with an AUC of 0.877 in the training cohort and 0.857 in the test cohort. Ultimately, the composite of clinical features was utilized to formulate the clinical model. The combined model demonstrated the best discriminative ability (AUC, training cohort: 0.887; test cohort: 0.874). The DCA of the combined model showed the best clinical efficacy.

CONCLUSION

The combined model integrating radiomics and clinical features had an outstanding performance in differentiating the risk of pheochromocytoma and could offer a non-intrusive and effective approach for making clinical decisions.

Paraganglioma of the Neck: A Rare Case With Spinal Metastasis

Paragangliomas are rare neuroendocrine tumors that arise from the paraganglia, which are clusters of neuroendocrine cells associated with the autonomic nervous system. These tumors are commonly found in the adrenal medulla but can also occur in other locations outside the adrenal gland. Here, we present a case report of a slow-growing paraganglioma in the left neck with spinal metastasis in a 60-year-old man. This case highlights the importance of considering paraganglion tumors in the differential diagnosis of neck masses and the need for early diagnosis and management to prevent potential complications. Importantly, both the clinical picture and anatomical location of these tumors is important when determining treatment plans.

Metastatic paraganglioma

A man in his 70s, with a history of a glomus jugulare paraganglioma diagnosed 18 years ago, presented with an unprovoked deep vein thrombosis (DVT). The paraganglioma had been treated by radiotherapy, and yearly scans had not shown any progression since treatment. A sclerotic focus in L4 vertebral body was reported on a CT scan of the neck and trunk which was done to exclude a neoplastic process being the precipitating factor for the DVT. Nuclear imaging showed multiple areas of bony uptake, suggestive of metastases. A bone biopsy of the left femur resulted positive for metastatic paraganglioma. A monthly intramuscular injection of octreotide 30 mg was prescribed.

Endocrine Causes of Hypertension

PURPOSE OF REVIEW

To discuss selected endocrine causes of hypertension and to provide practical clinical recommendations regarding the evaluation and treatment of these disorders.

RECENT FINDINGS

More than 15 endocrine disorders with varying rates of prevalence can cause hypertension. For example, primary aldosteronism may exist in up to 20% of patients with resistant hypertension. Recognition of these important secondary causes of hypertension is essential to provide contemporary targeted therapies in order to improve long-term disease outcomes. Clinicians must have a broad understanding of the prevalence, clinical presentation, and current diagnostic modalities for endocrine causes of hypertension to facilitate prompt referral, identification, and optimal management of these disorders. Endocrine causes of hypertension are multifactorial and, in some cases, widely prevalent. It is important for clinicians considering secondary causes of hypertension to be knowledgeable about the underlying pathophysiology of these disorders and to understand when additional evaluation and treatment may be needed.