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Ayoub NF, Glicksman JT. Artificial Intelligence in Rhinology. Otolaryngol Clin North Am 2024; 57:831-842. [PMID: 38821734 DOI: 10.1016/j.otc.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.
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Affiliation(s)
- Noel F Ayoub
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA.
| | - Jordan T Glicksman
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA
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Zheng B, Zhao Z, Zheng P, Liu Q, Li S, Jiang X, Huang X, Ye Y, Wang H. The current state of MRI-based radiomics in pituitary adenoma: promising but challenging. Front Endocrinol (Lausanne) 2024; 15:1426781. [PMID: 39371931 PMCID: PMC11449739 DOI: 10.3389/fendo.2024.1426781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 08/30/2024] [Indexed: 10/08/2024] Open
Abstract
In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.
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Affiliation(s)
- Baoping Zheng
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pingping Zheng
- Department of Neurosurgery, People’s Hospital of Biyang County, Zhumadian, China
| | - Qiang Liu
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuang Li
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xing Huang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youfan Ye
- Department of Ophthalmology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haijun Wang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Zilka T, Benesova W. Radiomics of pituitary adenoma using computer vision: a review. Med Biol Eng Comput 2024:10.1007/s11517-024-03163-3. [PMID: 39012416 DOI: 10.1007/s11517-024-03163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 07/01/2024] [Indexed: 07/17/2024]
Abstract
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.
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Affiliation(s)
- Tomas Zilka
- Saint Michal's Hospital, Bratislava, Slovakia
- Masaryk University, Brno, Czech Republic
| | - Wanda Benesova
- Slovak University of Technology in Bratislava, Bratislava, Slovakia.
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Maroufi SF, Doğruel Y, Pour-Rashidi A, Kohli GS, Parker CT, Uchida T, Asfour MZ, Martin C, Nizzola M, De Bonis A, Tawfik-Helika M, Tavallai A, Cohen-Gadol AA, Palmisciano P. Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary 2024; 27:91-128. [PMID: 38183582 DOI: 10.1007/s11102-023-01369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/08/2024]
Abstract
PURPOSE Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations. METHODS PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies. RESULTS Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately. CONCLUSION AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
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Affiliation(s)
- Seyed Farzad Maroufi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Yücel Doğruel
- Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey
| | - Ahmad Pour-Rashidi
- Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Gurkirat S Kohli
- Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Tatsuya Uchida
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Mohamed Z Asfour
- Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt
| | - Clara Martin
- Department of Neurosurgery, Hospital de Alta Complejidad en Red "El Cruce", Florencio Varela, Buenos Aires, Argentina
| | | | - Alessandro De Bonis
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Amin Tavallai
- Department of Pediatric Neurosurgery, Children's Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Paolo Palmisciano
- Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, CA, 95817, USA.
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Bioletto F, Prencipe N, Berton AM, Aversa LS, Cuboni D, Varaldo E, Gasco V, Ghigo E, Grottoli S. Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives. J Clin Med 2024; 13:336. [PMID: 38256471 PMCID: PMC10816809 DOI: 10.3390/jcm13020336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
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.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Silvia Grottoli
- Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (F.B.); (N.P.); (A.M.B.); (L.S.A.); (D.C.); (E.V.); (V.G.); (E.G.)
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Bertolini G, Romano A, Fusella C, Froio E, Serra S, La Corte E, Mazzatenta D, Ghadirpour R. Role of magnetic resonance imaging in differentiating intrasellar cavernous hemangioma and pituitary adenoma: A case report-Decipit frons prima multos. Neuroradiol J 2023; 36:610-613. [PMID: 36598406 PMCID: PMC10569201 DOI: 10.1177/19714009221150854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Cavernous hemangioma represents a rare vascular malformation usually located in the cavernous sinus that could be exceptionally found purely in the intrasellar region. The clinical presentation of intrasellar cavernous hemangioma (ICH), frequently variable and unspecific, poses the patient at risk for misdiagnosis and the clinical consequences of suboptimal treatment. We present a case of ICH and describe the advanced magnetic resonance imaging (MRI) features that should direct toward the clinical suspicion of ICH. CASE PRESENTATION An illustrative case of a 61-year-old man complaining of recurrent headaches and diagnosed with a sellar and parasellar lesion was reported and used as a cue to discuss MRI imaging sequences that may aid in the distinction of ICH from pituitary adenoma and other skull base lesions. Heterogeneous enhancement followed by intense homogeneous enhancement at the dynamic contrast-enhanced sequences ("fill-in" phenomenon), absence of blooming signs at the gradient recalled echo (GRE) T2*-weighted and/or susceptibility-weighted imaging (SWI) MRI sequences, and elevated apparent diffusion coefficient (ADC) values usually characterize ICH instead of pituitary adenoma. CONCLUSION Advanced MRI imaging plays an invaluable role in the pre-operative characterization of skull base lesions. Although rare, skull base surgeons should be aware of the ICH in the differential diagnosis process in case of the intrasellar lesion, and a tailored MRI examination should be performed to direct the patient toward the safest and optimal treatment.
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Affiliation(s)
- Giacomo Bertolini
- Department of Neurologic Surgery, Azienda Ospedaliero-Universitaria, Italy and IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Antonio Romano
- Department of Neurologic Surgery, Azienda Ospedaliero-Universitaria, Italy and IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
| | - Claudio Fusella
- Department of Neurologic Surgery, Azienda Ospedaliero-Universitaria, Italy and IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
| | - Elisabetta Froio
- Pathological Anatomy Service, Oncology Department and Advanced Technologies, AUSL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Silvia Serra
- Pathological Anatomy Service, Oncology Department and Advanced Technologies, AUSL-IRCCS of Reggio Emilia, Reggio Emilia, Italy
| | - Emanuele La Corte
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, Pituitary Unit,IRCCS Istituto Delle Scienze Neurologiche di Bologna, Italy
| | - Reza Ghadirpour
- Department of Neurologic Surgery, Azienda Ospedaliero-Universitaria, Italy and IRCCS Arcispedale Santa Maria Nuova, Reggio Emilia, Italy
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Shen C, Liu X, Jin J, Han C, Wu L, Wu Z, Su Z, Chen X. A Novel Magnetic Resonance Imaging-Based Radiomics and Clinical Predictive Model for the Regrowth of Postoperative Residual Tumor in Non-Functioning Pituitary Neuroendocrine Tumor. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1525. [PMID: 37763643 PMCID: PMC10535289 DOI: 10.3390/medicina59091525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: To develop a novel magnetic resonance imaging (MRI)-based radiomics-clinical risk stratification model to predict the regrowth of postoperative residual tumors in patients with non-functioning pituitary neuroendocrine tumors (NF-PitNETs). Materials and Methods: We retrospectively enrolled 114 patients diagnosed as NF-PitNET with postoperative residual tumors after the first operation, and the diameter of the tumors was greater than 10 mm. Univariate and multivariate analyses were conducted to identify independent clinical risk factors. We identified the optimal sequence to generate an appropriate radiomic score (Rscore) that combined pre- and postoperative radiomic features. Three models were established by logistic regression analysis that combined clinical risk factors and radiomic features (Model 1), single clinical risk factors (Model 2) and single radiomic features (Model 3). The models' predictive performances were evaluated using receiver operator characteristic (ROC) curve analysis and area under curve (AUC) values. A nomogram was developed and evaluated using decision curve analysis. Results: Knosp classification and preoperative tumor volume doubling time (TVDT) were high-risk factors (p < 0.05) with odds ratios (ORs) of 2.255 and 0.173. T1WI&T1CE had a higher AUC value (0.954) and generated an Rscore. Ultimately, the AUC of Model 1 {0.929 [95% Confidence interval (CI), 0.865-0.993]} was superior to Model 2 [0.811 (95% CI, 0.704-0.918)] and Model 3 [0.844 (95% CI, 0.748-0.941)] in the training set, which were 0.882 (95% CI, 0.735-1.000), 0.834 (95% CI, 0.676-0.992) and 0.763 (95% CI, 0.569-0.958) in the test set, respectively. Conclusions: We trained a novel radiomics-clinical predictive model for identifying patients with NF-PitNETs at increased risk of postoperative residual tumor regrowth. This model may help optimize individualized and stratified clinical treatment decisions.
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Affiliation(s)
- Chaodong Shen
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaoyan Liu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Jinghao Jin
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Cheng Han
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Lihao Wu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Zerui Wu
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Zhipeng Su
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiaofang Chen
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Mendi BAR, Batur H, Çay N, Çakır BT. Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency. Acta Radiol 2023; 64:2470-2478. [PMID: 37170546 DOI: 10.1177/02841851231174462] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
BACKGROUND The consistency of pituitary adenomas affects the course of surgical treatment. PURPOSE To evaluate the diagnostic capabilities of radiomics based on T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) in conjunction with two machine-learning (ML) techniques (support vector machine [SVM] and random forest classifier [RFC]) for assessing the consistency of pituitary adenomas. MATERIAL AND METHODS The institutional database was retrospectively scanned for patients who underwent surgical excision of pituitary adenomas. Surgical notes were accepted as a reference for the adenoma consistency. Radiomics analysis was performed on preoperative coronal 3.0T T1W and T2W images. First- and second-order parameters were calculated. Inter-observer reproducibility was assessed with Spearman's Correlation (ρ) and intra-observer reproducibility was evaluated with the intraclass correlation coefficient (ICC). Least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. SVM and RFC were used as ML methods. RESULTS A total of 52 patients who produced 206 regions of interest (ROIs) were included. Twenty adenomas that produced 88 ROIs had firm consistency. There was both inter-observer and intra-observer reproducibility. Ten parameters that were based on T2W images with high discriminative power and without correlation were chosen by LASSO. The diagnostic performance of SVM and RFC was as follows: sensitivity = 95.580% and 92.950%, specificity = 83.670% and 88.420%, area under the curve = 0.956 and 0.904, respectively. CONCLUSION Radiomics analysis based on T2W MRI combined with various ML techniques, such as SVM and RFC, can provide preoperative information regarding pituitary adenoma consistency with high diagnostic accuracy.
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Affiliation(s)
| | - Halitcan Batur
- Department of Radiology, Nigde Omer Halisdemir University Training and Research Hospital, Nigde, Turkey
| | - Nurdan Çay
- Department of Radiology, Faculty of Medicine, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Banu Topçu Çakır
- Department of Radiology, Faculty of Medicine, Health Sciences University, Gülhane Training and Research Hospital, Ankara, Turkey
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Koechli C, Zwahlen DR, Schucht P, Windisch P. Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review. Eur J Radiol 2023; 164:110866. [PMID: 37207398 DOI: 10.1016/j.ejrad.2023.110866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/21/2023]
Abstract
PURPOSE Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction. METHOD The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability. CONCLUSIONS The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.
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Affiliation(s)
- Carole Koechli
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland; Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland.
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
| | - Philippe Schucht
- Universitätsklinik für Neurochirurgie, Bern University Hospital, 3010 Bern, Switzerland
| | - Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, 8401 Winterthur, Switzerland
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Luzzi S, Giotta Lucifero A, Rabski J, Kadri PAS, Al-Mefty O. The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era. Cancers (Basel) 2023; 15:cancers15082235. [PMID: 37190164 DOI: 10.3390/cancers15082235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/14/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023] Open
Abstract
The evolution of endoscopic trans-sphenoidal surgery raises the question of the role of transcranial surgery for pituitary tumors, particularly with the effectiveness of adjunct irradiation. This narrative review aims to redefine the current indications for the transcranial approaches for giant pituitary adenomas in the endoscopic era. A critical appraisal of the personal series of the senior author (O.A.-M.) was performed to characterize the patient factors and the tumor's pathological anatomy features that endorse a cranial approach. Traditional indications for transcranial approaches include the absent pneumatization of the sphenoid sinus; kissing/ectatic internal carotid arteries; reduced dimensions of the sella; lateral invasion of the cavernous sinus lateral to the carotid artery; dumbbell-shaped tumors caused by severe diaphragm constriction; fibrous/calcified tumor consistency; wide supra-, para-, and retrosellar extension; arterial encasement; brain invasion; coexisting cerebral aneurysms; and separate coexisting pathologies of the sphenoid sinus, especially infections. Residual/recurrent tumors and postoperative pituitary apoplexy after trans-sphenoidal surgery require individualized considerations. Transcranial approaches still have a critical role in giant and complex pituitary adenomas with wide intracranial extension, brain parenchymal involvement, and the encasement of neurovascular structures.
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Affiliation(s)
- Sabino Luzzi
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
- Neurosurgery Unit, Department of Surgical Sciences, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Alice Giotta Lucifero
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Jessica Rabski
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Paulo A S Kadri
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Medical School, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Brazil
| | - Ossama Al-Mefty
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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11
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Wang H, Chang J, Zhang W, Fang Y, Li S, Fan Y, Jiang S, Yao Y, Deng K, Lu L, Bao X, Feng F, Wang R, Feng M. Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas. J Endocrinol Invest 2023:10.1007/s40618-023-02042-2. [PMID: 37020103 DOI: 10.1007/s40618-023-02042-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/12/2023] [Indexed: 04/07/2023]
Abstract
OBJECTIVE Silent corticotroph adenomas (SCAs) are a subtype of nonfunctioning pituitary adenomas that exhibit more aggressive behavior. However, rapid and accurate preoperative diagnostic methods are currently lacking. DESIGN The purpose of this study was to examine the differences between SCA and non-SCA features and to establish radiomics models and a clinical scale for rapid and accurate prediction. METHODS A total of 260 patients (72 SCAs vs. 188 NSCAs) with nonfunctioning adenomas from Peking Union Medical College Hospital were enrolled in the study as the internal dataset. Thirty-five patients (6 SCAs vs. 29 NSCAs) from Fuzhou General Hospital were enrolled as the external dataset. Radiomics models and an SCA scale to preoperatively diagnose SCAs were established based on MR images and clinical features. RESULTS There were more female patients (internal dataset: p < 0.001; external dataset: p = 0.028) and more multiple microcystic changes (internal dataset: p < 0.001; external dataset: p = 0.012) in the SCA group. MRI showed more invasiveness (higher Knosp grades, p ≤ 0.001). The radiomics model achieved AUCs of 0.931 and 0.937 in the internal and external datasets, respectively. The clinical scale achieved an AUC of 0.877 and a sensitivity of 0.952 in the internal dataset and an AUC of 0.899 and a sensitivity of 1.0 in the external dataset. CONCLUSIONS Based on clinical information and imaging characteristics, the constructed radiomics model achieved high preoperative diagnostic ability. The SCA scale achieved the purpose of rapidity and practicality while ensuring sensitivity, which is conducive to simplifying clinical work.
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Affiliation(s)
- H Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Neurospine center, China International Neuroscience Institute, Beijing, China
| | - J Chang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - W Zhang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Department of Thoracic Surgery, Peking University First Hospital, Beijing, China
| | - Y Fang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - S Li
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Y Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - S Jiang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - Y Yao
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - K Deng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - L Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - X Bao
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - F Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - R Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
| | - M Feng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
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Tsukamoto T, Miki Y. Imaging of pituitary tumors: an update with the 5th WHO Classifications-part 1. Pituitary neuroendocrine tumor (PitNET)/pituitary adenoma. Jpn J Radiol 2023:10.1007/s11604-023-01400-7. [PMID: 36826759 PMCID: PMC10366012 DOI: 10.1007/s11604-023-01400-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/04/2023] [Indexed: 02/25/2023]
Abstract
The pituitary gland is the body's master gland of the endocrine glands. Although it is a small organ, many types of tumors can develop within it. The recently revised fifth edition of the World Health Organization (WHO) classifications (2021 World Health Organization Classification of Central Nervous System Tumors and 2022 World Health Organization Classification of Endocrine and Neuroendocrine Tumors) revealed significant changes to the classification of pituitary adenomas, the most common type of pituitary gland tumor. This change categorized pituitary adenomas as neuroendocrine tumors and proposed the name to be revised to pituitary neuroendocrine tumor (PitNET). The International Classification of Diseases for Oncology behavior code for this tumor was previously "0" for benign tumor. In contrast, the fifth edition WHO classification has changed this code to "3" for primary malignant tumors as same to neuroendocrine tumor in other organs. Because the WHO classification made an important and significant change in the fundamental concept of the disease, in this paper, we will discuss the imaging diagnosis (magnetic resonance imaging, computed tomography, and positron emission tomography) of PitNET/pituitary adenoma in detail, considering these revisions as per the latest version of the WHO classification.
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Affiliation(s)
- Taro Tsukamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan.
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13
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Fiore G, Bertani GA, Conte G, Ferrante E, Tariciotti L, Kuhn E, Runza L, Pluderi M, Borsa S, Caroli M, Sala E, Platania G, Kremenova K, Ferrero S, Triulzi FM, Mantovani G, Locatelli M. Predicting tumor consistency and extent of resection in non-functioning pituitary tumors. Pituitary 2023:10.1007/s11102-023-01302-x. [PMID: 36808379 DOI: 10.1007/s11102-023-01302-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE To (1) identify a radiological parameter to predict non-functioning pituitary tumor (NFPT) consistency, (2) examine the relationship between NFPT consistency and extent of resection (EOR), (3) investigate if tumor consistency predictors can anticipate EOR. METHODS The ratio (T2SIR) between the T2 min signal intensity (SI) of the tumor and the T2 mean SI of the CSF was the main radiological parameter, being determined through a radiomic-voxel analysis and calculated using the following formula: T2SIR = [(T2 tumor mean SI - SD)/T2 CSF SI]. The tumor consistency was pathologically estimated as collagen percentage (CP). EOR of NFPTs was evaluated by exploiting a volumetric technique and its relationship with the following explanatory variables was explored: CP, Knosp-grade, tumor volume, inter-carotid distance, sphenoidal sinus morphology, Hardy-grade, suprasellar tumor extension. RESULTS A statistically significant inverse correlation between T2SIR and CP was demonstrated (p = 0.0001), with high diagnostic power of T2SIR in predicting NFPT consistency (ROC curve analysis' AUC = 0.88; p = 0.0001). The following predictors of EOR were identified in the univariate analysis: CP (p = 0.007), preoperative volume (p = 0.045), Knosp grade (p = 0.0001), tumor suprasellar extension (p = 0.044). The multivariate analysis demonstrated two variables as unique predictors of EOR: CP (p = 0.002) and Knosp grade (p = 0.001). The T2SIR was a significant predictor of EOR both in the univariate (p = 0.01) and multivariate model (p = 0.003). CONCLUSION This study offers the potential to improve NFPT preoperative surgical planning and patient counseling by employing the T2SIR as a preoperative predictor of tumor consistency and EOR. Meanwhile, tumor consistency and Knosp grade were found to play an important role in predicting EOR.
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Affiliation(s)
- Giorgio Fiore
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy.
- Unit of Oncology and Onco-Hematology, University of Milan, Milan, Italy.
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK.
| | - Giulio Andrea Bertani
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Giorgio Conte
- Unit of Neuroradiology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Unit of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Emanuele Ferrante
- Unit of Endocrinology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Elisabetta Kuhn
- Unit of Pathology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy
| | - Letterio Runza
- Unit of Pathology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Mauro Pluderi
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Stefano Borsa
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Manuela Caroli
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
| | - Elisa Sala
- Unit of Endocrinology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giulia Platania
- Unit of Neuroradiology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Karin Kremenova
- Unit of Neuroradiology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Stefano Ferrero
- Unit of Pathology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Biomedical, Surgical, and Dental Sciences, University of Milan, Milan, Italy
| | - Fabio Maria Triulzi
- Unit of Neuroradiology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Unit of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giovanna Mantovani
- Unit of Endocrinology, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Marco Locatelli
- Unit of Neurosurgery, Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy
- Unit of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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14
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de Divitiis O, d'Avella E, Fabozzi GL, Cavallo LM, Solari D. Surgeon's Eyes on the Relevant Surgical Target. ACTA NEUROCHIRURGICA. SUPPLEMENT 2023; 135:5-11. [PMID: 38153441 DOI: 10.1007/978-3-031-36084-8_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
The resolution of the naked eye has been a challenge for the neurosurgical endeavor since the very first attempts of cranial surgery, and advances have been achieved over the centuries, driven by a synergism between the application of emerging technology into the surgical environment and the expansion of the capabilities of neurosurgery. The understanding of the principles of the optical properties of lenses by Abbè (1840-1905) led to the introduction of loupes in the surgical practice, increasing the visual performance during macroscopic procedures. Modern neurosurgery began with the possibility of illumination and magnification of the surgical field as provided by the microscope. Pioneering contributions from Donaghy and Yasargil opened the way to the era of minimalism with reduction of operative corridors and surgical trauma through the adoption of the microsurgical technique. Almost at the same time, engineering mirabilia of Hopkins in terms of optics and lenses allowed for introduction of rigid and flexible endoscopes as a viable tool in neurosurgery. Nowadays, neurosurgeons are aware of and confident using effective and modern tools of visualization in their armamentarium. Herein we present a cogent review of the evolution of visualization tools in neurosurgery, with a special glimpse into the current development and future achievements.
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Affiliation(s)
- Oreste de Divitiis
- Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli "Federico II", Naples, Italy.
| | - Elena d'Avella
- Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Gianluca Lorenzo Fabozzi
- Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luigi Maria Cavallo
- Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Solari
- Division of Neurosurgery, Department of Neurosciences and Reproductive and Odontostomatological Sciences, Università degli Studi di Napoli "Federico II", Naples, Italy
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15
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Černý M, Sedlák V, Lesáková V, Francůz P, Netuka D. Methods of preoperative prediction of pituitary adenoma consistency: a systematic review. Neurosurg Rev 2022; 46:11. [PMID: 36482215 DOI: 10.1007/s10143-022-01909-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This study aims to review the current literature on methods of preoperative prediction of pituitary adenoma consistency. Pituitary adenoma consistency may be a limiting factor for successful surgical removal of tumors. Efforts have been made to investigate the possibility of an accurate assessment of the preoperative consistency to allow for safer and more effective surgery planning. We searched major scientific databases and systematically analyzed the results. A total of 54 relevant articles were identified and selected for inclusion. These studies evaluated methods based on either MRI intensity, enhancement, radiomics, MR elastometry, or CT evaluation. The results of these studies varied widely. Most studies used the average intensity of either T2WI or ADC maps. Firm tumors appeared hyperintense on T2WI, although only 55% of the studies reported statistically significant results. There are mixed reports on ADC values in firm tumors with findings of increased values (28%), decreased values (22%), or no correlation (50%). Multiple contrast enhancement-based methods showed good results in distinguishing between soft and firm tumors. There were mixed reports on the utility of MR elastography. Attempts to develop radiomics and machine learning-based models have achieved high accuracy and AUC values; however, they are prone to overfitting and need further validation. Multiple methods of preoperative consistency assessment have been studied. None demonstrated sufficient accuracy and reliability in clinical use. Further efforts are needed to enable reliable surgical planning.
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Affiliation(s)
- Martin Černý
- Department of Neurosurgery, Central Military Hospital Prague, Prague, Czech Republic.
- 1st Faculty of Medicine, Charles University Prague, Prague, Czech Republic.
| | - Vojtěch Sedlák
- Department of Radiodiagnostics, Central Military Hospital Prague, Prague, Czech Republic
| | - Veronika Lesáková
- Department of Chemical Engineering, University of Chemistry and Technology Prague, Prague, Czech Republic
| | - Peter Francůz
- 2nd Faculty of Medicine, Charles University Prague, Prague, Czech Republic
| | - David Netuka
- Department of Neurosurgery, Central Military Hospital Prague, Prague, Czech Republic
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16
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Li N, Desiderio DM, Zhan X. The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas. MASS SPECTROMETRY REVIEWS 2022; 41:964-1013. [PMID: 34109661 DOI: 10.1002/mas.21710] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.
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Affiliation(s)
- Na Li
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
| | - Dominic M Desiderio
- The Charles B. Stout Neuroscience Mass Spectrometry Laboratory, Department of Neurology, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Xianquan Zhan
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
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17
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Won SY, Lee N, Park YW, Ahn SS, Ku CR, Kim EH, Lee SK. Quality reporting of radiomics analysis in pituitary adenomas: promoting clinical translation. Br J Radiol 2022; 95:20220401. [PMID: 36018049 PMCID: PMC9793472 DOI: 10.1259/bjr.20220401] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/15/2022] [Accepted: 07/27/2022] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To evaluate the quality of radiomics studies on pituitary adenoma according to the radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). METHODS PubMed MEDLINE and EMBASE were searched to identify radiomics studies on pituitary adenomas. From 138 articles, 20 relevant original research articles were included. Studies were scored based on RQS and TRIPOD guidelines. RESULTS Most included studies did not perform pre-processing; isovoxel resampling, signal intensity normalization, and N4 bias field correction were performed in only five (25%), eight (40%), and four (20%) studies, respectively. Only two (10%) studies performed external validation. The mean RQS and basic adherence rate were 2.8 (7.6%) and 26.6%, respectively. There was a low adherence rate for conducting comparison to "gold-standard" (20%), multiple segmentation (25%), and stating potential clinical utility (25%). No study stated the biological correlation, conducted a test-retest or phantom study, was a prospective study, conducted cost-effectiveness analysis, or provided open-source code and data, which resulted in low-level evidence. The overall adherence rate for TRIPOD was 54.6%, and it was low for reporting the title (5%), abstract (0%), explaining the sample size (10%), and suggesting a full prediction model (5%). CONCLUSION The radiomics reporting quality for pituitary adenoma is insufficient. Pre-processing is required for feature reproducibility and external validation is necessary. Feature reproducibility, clinical utility demonstration, higher evidence levels, and open science are required. Titles, abstracts, and full prediction model suggestions should be improved for transparent reporting. ADVANCES IN KNOWLEDGE Despite the rapidly increasing number of radiomics researches on pituitary adenoma, the quality of science in these researches is unknown. Our study indicates that the overall quality needs to be significantly improved in radiomics studies on pituitary adenoma, and since the concept of RQS and IBSI is still unfamiliar to clinicians and radiologist researchers, our study may help to reach higher technical and clinical impact in the future study.
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Affiliation(s)
| | - Narae Lee
- Department of Nuclear Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yae Won Park
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Cheol Ryong Ku
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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18
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Nie D, Zhao P, Li C, Liu C, Zhu H, Gui S, Zhang Y, Cao L. Application of “mosiac sign” on T2-WI in predicting the consistency of pituitary neuroendocrine tumors. Front Surg 2022; 9:922626. [PMID: 35959133 PMCID: PMC9360528 DOI: 10.3389/fsurg.2022.922626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose Tumor 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. Design A retrospective review of MRI and tumor histology of 137 consecutive patients who underwent endoscopic endonasal resection for PitNETs was performed. Methods The “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. Results Statistical 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). Conclusions The presence of the “Mosaic sign” in T2-WI may provide preoperative information for pituitary adenomas consistency and effectively guide surgical approaches.
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Affiliation(s)
- Ding Nie
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Peng Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chuzhong Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chunhui Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haibo Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yazhuo Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Correspondence: Yazhuo Zhang Lei Cao
| | - Lei Cao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Correspondence: Yazhuo Zhang Lei Cao
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Sahin S, Yildiz G, Oguz SH, Civan O, Cicek E, Durcan E, Comunoglu N, Ozkaya HM, Oz AB, Soylemezoglu F, Oguz KK, Dagdelen S, Erbas T, Kizilkilic O, Kadioglu P. Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging‑derived texture features. Pituitary 2022; 25:474-479. [PMID: 35334029 DOI: 10.1007/s11102-022-01213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/27/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas. METHODS The radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy. RESULTS A total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types. CONCLUSIONS Our analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions.
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Affiliation(s)
- Serdar Sahin
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gokcen Yildiz
- Department of Radiology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Seda Hanife Oguz
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Orkun Civan
- Department of Radiology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ebru Cicek
- Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University- Cerrahpasa, Istanbul, Turkey
| | - Emre Durcan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Nil Comunoglu
- Department of Pathology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Hande Mefkure Ozkaya
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Aysim Buge Oz
- Department of Pathology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Figen Soylemezoglu
- Department of Pathology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Kader Karli Oguz
- Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Selçuk Dagdelen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Tomris Erbas
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey
| | - Osman Kizilkilic
- Department of Radiology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Pinar Kadioglu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.
- Cerrahpasa Medical Faculty, Department of Internal Medicine, Division of Endocrinology and Metabolism, Istanbul University-Cerrahpasa, Kocamustafapasa Street No: 53, 34098, Fatih, Istanbul, Turkey.
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20
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Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review. Cancers (Basel) 2022; 14:cancers14112676. [PMID: 35681655 PMCID: PMC9179850 DOI: 10.3390/cancers14112676] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/18/2022] [Accepted: 05/26/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary Machine learning in radiology of the central nervous system has seen many interesting publications in the past few years. Since the focus has largely been on malignant tumors such as brain metastases and high-grade gliomas, we conducted a systematic review on benign tumors to summarize what has been published and where there might be gaps in the research. We found several studies that report good results, but the descriptions of methodologies could be improved to enable better comparisons and assessment of biases. Abstract Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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22
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Araujo-Castro M, Acitores Cancela A, Vior C, Pascual-Corrales E, Rodríguez Berrocal V. Radiological Knosp, Revised-Knosp, and Hardy–Wilson Classifications for the Prediction of Surgical Outcomes in the Endoscopic Endonasal Surgery of Pituitary Adenomas: Study of 228 Cases. Front Oncol 2022; 11:807040. [PMID: 35127519 PMCID: PMC8810816 DOI: 10.3389/fonc.2021.807040] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/08/2021] [Indexed: 01/26/2023] Open
Abstract
Purpose To evaluate which radiological classification, Knosp, revised-Knosp, or Hardy–Wilson classification, is better for the prediction of surgical outcomes in the endoscopic endonasal transsphenoidal (EET) surgery of pituitary adenomas (PAs). Methods This is a retrospective study of patients with PAs who underwent EET PA resection for the first time between January 2009 and December 2020. Radiological cavernous sinus invasiveness was defined as a Knosp or revised-Knosp grade >2 or a grade E in the Hardy–Wilson classification. Results A total of 228 patients with PAs were included. Cavernous sinus invasion was evident in 35.1% and suprasellar extension was evident in 74.6%. Overall, surgical cure was achieved in 64.3% of patients. Surgical cure was lower in invasive PAs than in non-invasive PAs (28.8% vs. 83.1%, p < 0.0001), and the risk of major complications was higher (13.8% vs. 3.4%, p = 0.003). The rate of surgical cure decreased as the grade of Knosp increased (p < 0.001), whereas the risk of complications increased (p < 0.001). Patients with Knosp 3B PAs tended to achieve surgical cure less commonly than Knosp 3A PAs (30.0% vs. 56.0%, p = 0.164). Similar results were observed based on the invasion and extension of Hardy–Wilson classification (stage A–C 83.1% vs. E 28.8% p < 0.0001, grade 0–II 81.1% vs. III–IV 59.7% p = 0.008). The Knosp classification offered the greatest diagnostic accuracy for the prediction of surgical cure (AUC 0.820), whereas the invasion Hardy–Wilson classification lacked utility for this purpose (AUC 0.654). Conclusion The Knosp classifications offer a good orientation for the estimation of surgical cure and the risk of complications in patients with PAs submitted to EET surgery. However, the invasion Hardy–Wilson scale lacks utility for this purpose.
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Affiliation(s)
- Marta Araujo-Castro
- Neuroendocrinology Unit, Department of Endocrinology & Nutrition, Hospital Universitario Ramón y Cajal & Instituto de Investigación Biomédica Ramón y Cajal (IRYCIS), Madrid, Spain
- Department of Medicine, Universidad de Alcalá de Henares, Madrid, Spain
- *Correspondence: Marta Araujo-Castro, ; orcid.org/0000-0002-0519-0072
| | | | - Carlos Vior
- Department of Neurosurgery, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Eider Pascual-Corrales
- Neuroendocrinology Unit, Department of Endocrinology & Nutrition, Hospital Universitario Ramón y Cajal & Instituto de Investigación Biomédica Ramón y Cajal (IRYCIS), Madrid, Spain
| | - Víctor Rodríguez Berrocal
- Department of Neurosurgery, Hospital Universitario Ramón y Cajal, Madrid, Spain
- Department of Neurosurgery, Hospital HM Puerta del Sur, Madrid, Spain
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23
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Stumpo V, Staartjes VE, Regli L, Serra C. Machine Learning in Pituitary Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:291-301. [PMID: 34862553 DOI: 10.1007/978-3-030-85292-4_33] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.
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Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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24
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Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021; 64:647-668. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.
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25
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Rui W, Qiao N, Wu Y, Zhang Y, Aili A, Zhang Z, Ye H, Wang Y, Zhao Y, Yao Z. Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas. Eur Radiol 2021; 32:1570-1578. [PMID: 34837512 DOI: 10.1007/s00330-021-08361-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 09/01/2021] [Accepted: 09/25/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics. METHODS A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC. RESULTS Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction. CONCLUSION Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies. KEY POINTS • Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.
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Affiliation(s)
- Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Nidan Qiao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China.,Neurosurgical Institute of Fudan University, Shanghai, People's Republic of China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, People's Republic of China.,National Center for Neurological Disorders, Shanghai, People's Republic of China
| | - Yue Wu
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Yong Zhang
- GE Healthcare, MR Research, Huatuo Road, Shanghai, 201203, People's Republic of China
| | - Ababikere Aili
- Department of Radiology, Kuqa County People's Hospital, Aksu, 842000, Xinjiang, People's Republic of China
| | - Zhaoyun Zhang
- Department of Endocrinology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Hongying Ye
- Department of Endocrinology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China.,Neurosurgical Institute of Fudan University, Shanghai, People's Republic of China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, People's Republic of China.,Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, People's Republic of China.,National Center for Neurological Disorders, Shanghai, People's Republic of China
| | - Yao Zhao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China. .,Neurosurgical Institute of Fudan University, Shanghai, People's Republic of China. .,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, People's Republic of China. .,Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, People's Republic of China. .,National Center for Neurological Disorders, Shanghai, People's Republic of China.
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China.
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26
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Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z. Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings. J Magn Reson Imaging 2021; 55:1491-1503. [PMID: 34549842 DOI: 10.1002/jmri.27930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/05/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning. PURPOSE To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA. STUDY TYPE Retrospective. POPULATION One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings. FIELD STRENGTH/SEQUENCE T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner. ASSESSMENT An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency. STATISTICAL TESTS Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant. RESULTS Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort. DATA CONCLUSION Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tao Wan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chunxue Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ming Meng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chuzhong Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zengchang Qin
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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27
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Tariciotti L, Palmisciano P, Giordano M, Remoli G, Lacorte E, Bertani G, Locatelli M, Dimeco F, Caccavella VM, Prada F. Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives. J Neurosurg Sci 2021; 66:139-150. [PMID: 34545735 DOI: 10.23736/s0390-5616.21.05483-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties. METHODS A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool. RESULTS 41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified. CONCLUSIONS In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.
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Affiliation(s)
- Leonardo Tariciotti
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.,NEVRALIS, Milan, Italy
| | - Paolo Palmisciano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Trauma, Gamma Knife Center Cannizzaro Hospital, Catania, Italy
| | - Martina Giordano
- NEVRALIS, Milan, Italy.,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giulia Remoli
- NEVRALIS, Milan, Italy.,National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Eleonora Lacorte
- National Center for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Giulio Bertani
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Marco Locatelli
- Unit of Neurosurgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Aldo Ravelli Research Center for Neurotechnology and Experimental Brain Therapeutics, University of Milan, Milan, Italy
| | - Francesco Dimeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy
| | - Valerio M Caccavella
- NEVRALIS, Milan, Italy - .,Department of Neurosurgery, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Prada
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy.,Department of Neurological Surgery, University of Virginia Health Science Center, Charlottesville, VA, USA
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28
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Wang H, Zhang W, Li S, Fan Y, Feng M, Wang R. Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task. J Clin Endocrinol Metab 2021; 106:2535-2546. [PMID: 34060609 DOI: 10.1210/clinem/dgab371] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability. OBJECTIVE This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency. METHODS Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency. RESULTS A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively. CONCLUSION The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period.
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Affiliation(s)
- He Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wentai Zhang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuo Li
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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29
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Chibbaro S, Signorelli F, Milani D, Cebula H, Scibilia A, Bozzi MT, Messina R, Zaed I, Todeschi J, Ollivier I, Mallereau CH, Dannhoff G, Romano A, Cammarota F, Servadei F, Pop R, Baloglu S, Lasio GB, Luca F, Goichot B, Proust F, Ganau M. Primary Endoscopic Endonasal Management of Giant Pituitary Adenomas: Outcome and Pitfalls from a Large Prospective Multicenter Experience. Cancers (Basel) 2021; 13:cancers13143603. [PMID: 34298816 PMCID: PMC8304085 DOI: 10.3390/cancers13143603] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 06/22/2021] [Accepted: 07/10/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Giant pituitary adenomas are highly invasive tumors whose treatment is challenging. Surgery is their management mainstay. However, there is no consensus about the type of approach. Open transcranial, microscopic, and endoscopic trans-sphenoidal approaches have all been employed, alone or in combination. Extended endoscopic endonasal techniques may represent a versatile and safe one-stage approach. Our research aimed at evaluating prospectively their applicability, effectiveness, and safety in a multicenter series, to acquire further evidence toward its use in the treatment of those challenging lesions. Ninety-six patients were recruited and followed-up for 52.4 months on average. Most of them (81.2%) presented with visual deficits and >50% had various degrees of adenohypophysis insufficiency. Resection of at least 75% of initial volume was achieved in all cases, with 98.7% visual improvement, >50% endocrine deficit recovery and a permanent complication rate of 4.2%, indicating extended endoscopic endonasal approaches as a valuable treatment option. Abstract Purpose: To evaluate factors influencing clinical and radiological outcome of extended endoscopic endonasal transtuberculum/transplanum approach (EEA-TTP) for giant pituitary adenomas (GPAs). Methods: We recruited prospectively all consecutive GPAs patients undergoing EEA-TTP between 2015 and 2019 in 5 neurosurgical centers. Preoperative clinical and radiologic features, visual and hormonal outcomes, extent of resection (EoR), complications and recurrence rates were recorded and analyzed. Results: Of 1169 patients treated for pituitary adenoma, 96 (8.2%) had GPAs. Seventy-eight (81.2%) patients had visual impairment, 12 (12.5%) had headaches, 3 (3.1%) had drowsiness due to hydrocephalus, and 53 (55.2%) had anterior pituitary insufficiency. EoR was gross or near-total in 46 (47.9%) and subtotal in 50 (52.1%) patients. Incomplete resection was associated with lateral suprasellar, intraventricular and/or cavernous sinus extension and with firm/fibrous consistence. At the last follow-up, all but one patient (77, 98.7%) with visual deficits improved. Headache improved in 8 (88.9%) and anterior pituitary function recovered in 27 (50.9%) patients. Recurrence rate was 16.7%, with 32 months mean recurrence-free survival. Conclusions: EEA-TTP is a valid option for GPAs and seems to provide better outcomes, lower rate of complications and higher EoR compared to one- or multi-stage microscopic, non-extended endoscopic transsphenoidal, and transcranial resections.
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Affiliation(s)
- Salvatore Chibbaro
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Francesco Signorelli
- Neurosurgery Unit, Department of Basic Medical Sciences, Neurosciences, Sense Organs, University “Aldo Moro”, 70124 Bari, Italy;
- Correspondence: ; Tel.: +39-0805592900
| | - Davide Milani
- Neurosurgery Unit, Humanitas Research Hospital, 20089 Milano, Italy; (D.M.); (F.S.); (G.B.L.)
| | - Helene Cebula
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Antonino Scibilia
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Maria Teresa Bozzi
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Raffaella Messina
- Neurosurgery Unit, Department of Basic Medical Sciences, Neurosciences, Sense Organs, University “Aldo Moro”, 70124 Bari, Italy;
| | - Ismail Zaed
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
- Neurosurgery Unit, Humanitas Research Hospital, 20089 Milano, Italy; (D.M.); (F.S.); (G.B.L.)
| | - Julien Todeschi
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Irene Ollivier
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Charles Henry Mallereau
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Guillaume Dannhoff
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Antonio Romano
- Neurosurgery Department, Parma and Reggio Emilia Hospital, University of Parma, 43126 Parma, Italy;
| | - Francesco Cammarota
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Franco Servadei
- Neurosurgery Unit, Humanitas Research Hospital, 20089 Milano, Italy; (D.M.); (F.S.); (G.B.L.)
| | - Raoul Pop
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Seyyid Baloglu
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Giovanni Battista Lasio
- Neurosurgery Unit, Humanitas Research Hospital, 20089 Milano, Italy; (D.M.); (F.S.); (G.B.L.)
| | - Florina Luca
- Endocrinology Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (F.L.); (B.G.)
| | - Bernard Goichot
- Endocrinology Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (F.L.); (B.G.)
| | - Francois Proust
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
| | - Mario Ganau
- Neurosurgery Unit, Hautepierre Regional Hospital, Strasbourg University, 67200 Strasbourg, France; (S.C.); (H.C.); (A.S.); (M.T.B.); (I.Z.); (J.T.); (I.O.); (C.H.M.); (G.D.); (F.C.); (R.P.); (S.B.); (F.P.); (M.G.)
- Neurosurgery Department, Oxford University Hospital, Oxford OX3 9DU, UK
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Liu CX, Heng LJ, Han Y, Wang SZ, Yan LF, Yu Y, Ren JL, Wang W, Hu YC, Cui GB. Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes. Front Oncol 2021; 11:640375. [PMID: 34307124 PMCID: PMC8294058 DOI: 10.3389/fonc.2021.640375] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/16/2021] [Indexed: 01/14/2023] Open
Abstract
Objective To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA). Methods Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. Results Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1. Conclusion Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.
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Affiliation(s)
- Chen-Xi Liu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Li-Jun Heng
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China
| | - Yu Han
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China
| | - Sheng-Zhong Wang
- Faculty of Medical Technology, Shaanxi University of Traditional Chinese Medicine, Xianyang, China
| | - Lin-Feng Yan
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Ying Yu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | | | - Wen Wang
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Yu-Chuan Hu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Guang-Bin Cui
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 2021; 63:1293-1304. [PMID: 33649882 PMCID: PMC8295153 DOI: 10.1007/s00234-021-02668-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/03/2021] [Indexed: 02/07/2023]
Abstract
Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02668-0.
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Zhang Y, Ko CC, Chen JH, Chang KT, Chen TY, Lim SW, Tsui YK, Su MY. Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas. Front Oncol 2020; 10:590083. [PMID: 33392084 PMCID: PMC7775655 DOI: 10.3389/fonc.2020.590083] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/19/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs. Methods Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features. Results Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001). Conclusions Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Yu-Kun Tsui
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
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Park YW, Kang Y, Ahn SS, Ku CR, Kim EH, Kim SH, Lee EJ, Kim SH, Lee SK. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary 2020; 23:691-700. [PMID: 32851505 DOI: 10.1007/s11102-020-01077-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients. METHODS Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC). RESULTS Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037). CONCLUSION Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Yunjun Kang
- Integrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Cheol Ryong Ku
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea.
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Ho Kim
- Department of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
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