Although usually benign, anterior pituitary tumours occasionally exhibit aggressive behaviour, with invasion of surrounding tissues, rapid growth, resistance to conventional treatments and multiple recurrences. In very rare cases, they metastasize and are termed pituitary carcinomas. The time between a 'classical' pituitary tumour and a pituitary carcinoma can be years, which means that monitoring should be performed regularly in patients with clinical (invasion and/or tumour growth) or pathological (Ki67 index, mitotic count and/or p53 detection) markers suggesting aggressiveness. However, although both invasion and proliferation have prognostic value, such parameters cannot predict outcome or malignancy without metastasis. Future research should focus on the biology of both tumour cells and their microenvironment, hopefully with improved therapeutic outcomes. Currently, the initial therapeutic approach for aggressive pituitary tumours is generally to repeat surgery or radiotherapy in expert centres. Standard medical treatments usually have no effect on tumour progression but they can be maintained on a long-term basis to, at least partly, control hypersecretion. In cases where standard treatments prove ineffective, temozolomide, the sole formally recommended treatment, is effective in only one-third of patients. Personalized use of emerging therapies, including peptide receptor radionuclide therapy, angiogenesis-targeted therapy and immunotherapy, will hopefully improve the outcomes of patients with this severe condition.
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http://dx.doi.org/10.1038/s41574-021-00550-w | DOI Listing |
Bioengineering (Basel)
December 2024
Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.
Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes.
View Article and Find Full Text PDFBrain Sci
November 2024
Department of Computer Science, Yobe State University, Damaturu 600213, Nigeria.
Background/objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.
View Article and Find Full Text PDFZhonghua Bing Li Xue Za Zhi
January 2025
Department of Pathology, the First Affiliated Hospital of University of Science and Technology of China/Anhui Provincial Hospital, Hefei230036, China.
Clin Nucl Med
January 2025
From the Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India.
Purpose: This study aimed to assess the biodistribution and radiation dosimetry of 68Ga-DATA5m LM4 in patients with gastroenteropancreatic neuroendocrine tumors.
Patients And Methods: Eight patients (5 females and 3 males) with various gastroenteropancreatic neuroendocrine tumors were included in the study. Each patient underwent 3 whole-body PET scans at 10, 60, and 120 minutes after receiving an IV injection of approximately 162.
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