Overview of the 2022 WHO Classification of Pituitary Adenomas/Pituitary Neuroendocrine Tumors:Clinical Practices,Controversies,and Perspectives

The latest edition of the WHO classification of the central nervous system was published in 2021.This review summarizes the major revisions to the classification of anterior pituitary tumors.The most important revision involves preferring the terminology of pituitary neuroendocrine tumor(PitNET),even though the terminology of pituitary adenoma(PA)still can be used according to this WHO classification compared to the previous one.Moreover,immunohistochemistry(IHC)examination of pituitary-specific transcription factors(TFs),including PIT1,TPIT,SF-1,GATA2/3,and ERα,is endorsed to determine the tumor cell lineage and to facilitate the classification of PitNET/PA subgroups.However,TF-negative IHC staining indicates PitNET/PA with no distinct cell lineages,which includes unclassified plurihormonal(PH)tumors and null cell(NC)tumors in this edition.The new WHO classification of PitNET/PA has incorporated tremendous advances in the understanding of the cytogenesis and pathogenesis of pituitary tumors.However,due to the shortcomings of the technology used in the diagnosis of PitNET/PA and the limited understanding of the tumorigenesis of PitNET/PA,the application of this new classification system in practice should be further evaluated and validated.Besides providing information for deciding the follow-up plans and adjunctive treatment after surgery,this classification system offers no additional help for neurosurgeons in clinical practice,especially in determining the treatment strategies.Therefore,it is necessary for neurosurgeons to establish a comprehensive pituitary classification system for PitNET/PA that incorporates neuroimaging grading data or direct observation of invasiveness during operation or the predictor of prognosis,as well as pathological diagnosis,thereby distinguishing the invasiveness of the tumor and facilitating neurosurgeons to decide on the treatment strategies and follow-up plans as well as adjunctive treatment after surgery.

Numerical and experimental analysis of AC loss for CFETR CS model coil

The central solenoid(CS) is an important component of China Fusion Engineering Test Reactor, for producing, forming and stabilizing plasma in the superconducting tokamak. It is a complicated work to design and manufacture the large superconducting CS magnet, so it is meaningful to design a central solenoid model coil(CSMC) and analyze its electromagnetic properties in advance. In this paper, the structure, design parameters and magnetic field distribution of the CS model coil are discussed. The peak power of radial and axial turn conductors and time bucket loss are analyzed by using piecewise-linear method. The CSMC AC loss with different Nb3 Sn CICCs and AC loss of ITER CS coil are compared. The special electrometric method to measure AC loss of the CS model coil for future reference is presented.

Surgical Technique and Efficacy Analysis of Extra-pseudocapsular Transnasal Transsphenoidal Surgery for Pituitary Microprolactinoma

Objective To elucidate the role of transsphenoidal surgery in the treatment of pituitary microprolactinoma.Methods The clinical data of 107 prolactinoma cases treated by extra-pseudocapsular transnasal transsphenoidal surgery(ETTS)for different indications in our department since 2011 was retrospectively analyzed.Results The most common indication was the ineffectiveness of oral medication(41.1%),followed by the personal willingness of the patient(35.5%),and 20.6%of the patients were young women with clear tumor boundaries.The pseudocapsule was not observed in 63 cases(58.9%),incomplete pseudocapsule was observed in 26 cases(24.3%),and complete pseudocapsule in 18 cases(16.8%).A total of 97 patients(90.7%)obtained 1-year post-operation remission.According to the relative location of the adenoma and pituitary gland on the MRI scan,46 patients were classified into a central type,59 a lateral type,and 2 a supra-pituitary type.Two patients developed hypogonadism,one patient developed hypocortisolism,and one patient developed post-operative hypothyroidism.Two patients were administrated with hormone replacement treatment,and the treatment was stopped within one week.There was no permanent hypopituitarism.Further investigation demonstrated that the adenoma types could affect the remission rates of hyperprolactinemia and gross total resection rate in microprolactinoma.Conclusion ETTS was an effective treatment for pituitary microprolactinomas.This could be the first choice for patients who presented enclosed adenoma on the MRI and were potentially curable in a preoperative evaluation.Maximal safe removal of the adenoma by ETTS with the aim to increase the sensitivity of the drugs was also recommended for patients with invasive dopamine agonist resistant prolactinomas and patients with difficulty in childbirth.

PuzzleNet:Boundary-Aware Feature Matching for Non-Overlapping 3D Point Clouds Assembly

We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and DublinPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.