Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?

Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights into their imaging characteristics, revealing frequent presentation with headaches, seizures, and mass effect symptoms, predominantly located in the supratentorial region near the lateral ventricles. Lesions often appeared as mixed cystic and solid masses with septations or as cystic masses with mural nodules. Given these complexities, artificial intelligence (AI) and machine learning (ML) offer promising advancements for PGNT diagnosis. Previous studies have demonstrated AI's efficacy in diagnosing various brain tumors, utilizing deep learning and advanced imaging techniques for rapid and accurate identification. Implementing AI in PGNT diagnosis involves assembling comprehensive datasets, preprocessing data, extracting relevant features, and iteratively training models for optimal performance. Despite AI's potential, medical professionals must validate AI predictions, ensuring they complement rather than replace clinical expertise. This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.

Post Renal Transplant Malignancies; A Basic Concept.

BACKGROUND: Kidney transplantation remains the best possible solution for patients with chronic kidney disease, providing better long-term outcomes and drastically improving quality of life. However, it comes with its own set of risks. The use of immunosuppressives following renal transplants has been shown to increase the development of malignancies and infections, and the occurrence of post-transplant malignancies is now the third most common cause of death in transplant patients. This involves multiple mechanisms, including the carcinogenic tendency of some immunosuppressive drugs, along with the induction and promotion of post-transplant malignancies by certain viruses. The quantification of Cancer risk must be made an integral part of the overall management of transplant patients, and appropriate follow-up screening needs to be adopted. Kaposi's sarcoma, lymphoma, and non-melanoma skin cancers have a greater incidence. If a malignancy develops immediately after transplantation, it may have been transmitted from the donor; donor-transmitted and donor-derived tumours may be differentiated based on a two-year time limit. Immunosuppressive medications with carcinogenic tendencies, reduced immunological control of oncogenic viruses, and poor immunosurveillance remain the most important risk factors. The gravity of this situation is further exacerbated by the fact that not only is there an increased risk of developing these malignancies in the post-transplant period, but the prognosis is also worsened when compared to non-transplant patients. All transplant centers should therefore adopt a multidisciplinary approach including early detection and prompt treatment, to improve outcomes in transplanted patients.