Radiomic Features on Multiparametric <scp>MRI</scp> for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings

Wan, Tao; Wu, Chunxue; Meng, Ming; Liu, Tao; Li, Chuzhong; Ma, Jun; Qin, Zengchang

doi:10.1002/jmri.27930

Cited by 10 publications

(7 citation statements)

References 40 publications

(102 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The preoperative consistency prediction of PITnet is controversial, and different imaging has its unique value. For example, Wan et al made consistent predictions based on a radiomic model of multi-parameter magnetic resonance imaging (mpMRI), while Cohen-Cohen et al argued that MRE was a reliable tool compared to other sequences ( 20 , 21 ). As far as we know, currently, studies on preoperative prediction of tumor consistency have focused on imaging findings on T2-WI ( 11 , 22 ).…”

Section: Discussionmentioning

confidence: 99%

Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors

Nie

Zhao

et al. 2022

Front. Surg.

Self Cite

View full text Add to dashboard Cite

PurposeTumor consistency is important for pituitary neuroendocrine tumors (PitNETs) resection to improve surgical outcomes. In this study, we evaluated the T2-WI of PitNETs and defined a specific T2-WI signaling manifestation, the “Mosaic sign,” to predict tumor consistency and resection of PitNETs.DesignA retrospective review of MRI and tumor histology of 137 consecutive patients who underwent endoscopic endonasal resection for PitNETs was performed.MethodsThe “Mosaic sign” was defined by the ratio of the tumor itself T2-WI signals, and characterized by multiple intratumor hyperintense dots. The degree of tumor resection was an assessment by postoperative MRI examination. The presence of the “Mosaic sign” was compared with patients' basic information, tumor consistency, tumor pathological staining, and surgical result. To determine whether the presence or absence of “Mosaic sign” could predict tumor consistency and guide surgical resection of tumors.ResultsStatistical analysis showed that the consistency of the tumor and the degree of resection were correlated with the “Mosaic sign”. In the 137 cases of T2-WI, 43 had “Mosaic sign”, 39 cases had soft tumor consistency, and 4 were classified as fibrous, of which 42 were completely resected and 1 was subtotal resected. Of the 94 patients without “Mosaic sign”, the consistency of tumor of 54 cases were classified as soft, the remaining 40 cases were fibrous, 80 cases were completely resected, and 14 cases were subtotal resected. Postoperative cerebrospinal fluid leakage occurred in 1 patient. The number of corticotroph adenomas in the group of “Mosaic sign” was higher, with the statistical difference between the two groups (P = 0.0343).ConclusionsThe presence of the “Mosaic sign” in T2-WI may provide preoperative information for pituitary adenomas consistency and effectively guide surgical approaches.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors

Nie

Zhao

et al. 2022

Front. Surg.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The distinction between firm and soft PA was also a goal of Tao Wan et. al [36]. They proved that the model trained on 108 patients which implemented 388 radiomic features calculated from T1, T2, and T1CE coregistered and automatically segmented MRI scans had the best performance (AUC 0.9).…”

Section: Prediction Of Hormonal Secretion and Pa Functionalitymentioning

confidence: 97%

Radiomics of pituitary adenoma using computer vision: a review

Zilka,

Benesova

2024

Med Biol Eng Comput

View full text Add to dashboard Cite

Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of “Radiomics” involves the extraction of high-dimensional features, often referred to as “Radiomic features,” from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects. Graphical abstract

show abstract

“…Fibrous PitNETs are relatively less common than softer ones, with a prevalence that has been estimated as approximately 18% (range of 6-73%) [10]. The fibrous consistency of a pituitary tumor is influenced by several factors, including higher collagen content and poor cellularity [7,11]; furthermore, previous surgical and non-surgical interventions could induce fibrous alterations and tissue deformations [7,11]. Tumor consistency is an important parameter to consider during surgical planning; soft adenomas, in fact, can be properly removed through aspiration and curettage, while those with In the context of pituitary tumors, radiomic analysis holds promising potential to enhance diagnostic accuracy, predict treatment response, and facilitate personalized medicine.…”

Section: Prediction Of Tumor Consistencymentioning

confidence: 99%

“…Therefore, some subsequent studies have attempted to apply radiomic analysis simultaneously to a broader range of MRI sequences, including T1WIs, CE-T1WIs, and T2WIs. Among these, a study published in 2022 by Wan et al [11] analyzed radiomic features from the volume of interest on both individual T1WI/CE-T1WI/T2WI sequences and their combinations. The images were delineated via an automated 3D segmentation method.…”

Section: Prediction Of Tumor Consistencymentioning

confidence: 99%

Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives

Bioletto,

Prencipe,

Berton

et al. 2024

JCM

View full text Add to dashboard Cite

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.

show abstract

Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings

Cited by 10 publications

References 40 publications

Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors

Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors

Radiomics of pituitary adenoma using computer vision: a review

Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives

Contact Info

Product

Resources

About