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Findlay MC, Tenhoeve S, Alt J, Rennert RC, Couldwell WT, Evans J, Collopy S, Kim W, Delery W, Pacione D, Kim A, Silverstein JM, Chicoine MR, Gardner P, Rotman L, Yuen KCJ, Barkhoudarian G, Fernandez-Miranda J, Benjamin C, Kshettry VR, Zada G, Van Gompel J, Catalino MHS, Little AS, Karsy M. Predictors of Durable Remission After Successful Surgery for Cushing Disease: Results From the Multicenter RAPID Registry. Neurosurgery 2024:00006123-990000000-01223. [PMID: 38905223 DOI: 10.1227/neu.0000000000003042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/14/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND AND OBJECTIVE Cushing disease (CD) affects mortality and quality of life along with limited long-term remission, underscoring the need to better identify recurrence risk. The identification of surgical or imaging predictors for CD remission after transsphenoidal surgery has yielded some inconsistent results and has been limited by single-center, single-surgeon, or meta-analyses studies. We sought to evaluate the multicenter Registry of Adenomas of the Pituitary and Related Disorders (RAPID) database of academic US pituitary centers to assess whether robust nonhormonal recurrence predictors could be elucidated. METHODS Patients with treated CD from 2011 to 2023 were included. The perioperative and long-term characteristics of CD patients with and without recurrence were assessed using univariable and multivariable analyses. RESULTS Of 383 patients with CD from 26 surgeons achieving postoperative remission, 288 (75.2%) maintained remission at last follow-up while 95 (24.8%) showed recurrence (median time to recurrence 9.99 ± 1.34 years). Patients with recurrence required longer postoperative hospital stays (5 ± 3 vs 4 ± 2 days, P = .002), had larger average tumor volumes (1.76 ± 2.53 cm3 vs 0.49 ± 1.17 cm3, P = .0001), and more often previously failed prior treatment (31.1% vs 14.9%, P = .001) mostly being prior surgery. Multivariable hazard prediction models for tumor recurrence found younger age (odds ratio [OR] = 0.95, P = .002) and Knosp grade of 0 (OR = 0.09, reference Knosp grade 4, P = .03) to be protective against recurrence. Comparison of Knosp grade 0 to 2 vs 3 to 4 showed that lower grades had reduced risk of recurrence (OR = 0.27, P = .04). Other factors such as length of stay, surgeon experience, prior tumor treatment, and Knosp grades 1, 2, or 3 failed to reach levels of statistical significance in multivariable analysis. CONCLUSION This multicenter study centers suggests that the strongest predictors of recurrence include tumor size/invasion and age. This insight can help with patient counseling and prognostication. Long-term follow-up is necessary for patients, and early treatment of small tumors may improve outcomes.
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Affiliation(s)
- Matthew C Findlay
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
- School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Sam Tenhoeve
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
- School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Jeremiah Alt
- Department of Otolaryngology-Head and Neck Surgery, University of Utah, Salt Lake City, Utah, USA
| | - Robert C Rennert
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - William T Couldwell
- Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah, USA
| | - James Evans
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Sarah Collopy
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Won Kim
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, California, USA
| | - William Delery
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, California, USA
| | - Donato Pacione
- Department of Neurosurgery, New York University, Lagone Medical Center, New York, New York, USA
| | - Albert Kim
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Julie M Silverstein
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, Missouri, USA
- Division of Endocrinology, Metabolism, & Lipid Research, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Michael R Chicoine
- Department of Neurosurgery, University of Missouri, Columbia, Missouri, USA
| | - Paul Gardner
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburg, Pennsylvania, USA
| | - Lauren Rotman
- Department of Neurosurgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Kevin C J Yuen
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Garni Barkhoudarian
- Department of Neurosurgery, Providence Medical Center, Los Angeles, California, USA
| | | | - Carolina Benjamin
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Varun R Kshettry
- Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Gabriel Zada
- Department of Neurosurgery, University of Southern California, Los Angeles, California, USA
| | - Jamie Van Gompel
- Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael H S Catalino
- Department of Neurosurgery, University of Virginia, Charlottesville, Virginia, USA
| | - Andrew S Little
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Michael Karsy
- Global Neurosciences Institute, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
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Yang DB, Smith AD, Smith EJ, Naik A, Janbahan M, Thompson CM, Varshney LR, Hassaneen W. The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review. J Neurol Surg B Skull Base 2023; 84:548-559. [PMID: 37854535 PMCID: PMC10581827 DOI: 10.1055/a-1941-3618] [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: 12/30/2021] [Accepted: 03/03/2022] [Indexed: 10/14/2022] Open
Abstract
The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model-agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.
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Affiliation(s)
- Darrion B. Yang
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Alexander D. Smith
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Emily J. Smith
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Anant Naik
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Mika Janbahan
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Charee M. Thompson
- Department of Communication, University of Illinois Urbana Champaign, Champaign, Illinois, United States
| | - Lav R. Varshney
- Department of Electrical and Computer Engineering, University of Illinois Urbana Champaign, Urbana, Illinois, United States
| | - Wael Hassaneen
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, United States
- Department of Neurosurgery, Carle Foundation Hospital, Urbana, Illinois, United States
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3
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Zanier O, Zoli M, Staartjes VE, Alalfi MO, Guaraldi F, Asioli S, Rustici A, Pasquini E, Faustini-Fustini M, Erlic Z, Hugelshofer M, Voglis S, Regli L, Mazzatenta D, Serra C. Development and external validation of clinical prediction models for pituitary surgery. BRAIN & SPINE 2023; 3:102668. [PMID: 38020983 PMCID: PMC10668061 DOI: 10.1016/j.bas.2023.102668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/14/2023] [Accepted: 08/25/2023] [Indexed: 12/01/2023]
Abstract
Introduction Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63-0.80) for GTR, 0.69 (0.52-0.83) for BR, as well as 0.82 (0.76-0.89) for IMP. Discussion and conclusion All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient.
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Affiliation(s)
- Olivier Zanier
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matteo Zoli
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
| | - Victor E. Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Federica Guaraldi
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
| | - Sofia Asioli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
- Azienda USL di Bologna, Anatomic Pathology Unit, Bologna, Italy
| | - Arianna Rustici
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Italy
| | - Ernesto Pasquini
- Azienda USL di Bologna, Bellaria Hospital, ENT Unit, Bologna, Italy
| | - Marco Faustini-Fustini
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
| | - Zoran Erlic
- Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich (USZ) and University of Zurich (UZH), Zurich, Switzerland
| | - Michael Hugelshofer
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stefanos Voglis
- 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
| | - Diego Mazzatenta
- IRCCS Istituto Delle Scienze Neurologiche di Bologna. Programma Neurochirurgia Ipofisi - Pituitary Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Italy
| | - 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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Dumitriu-Stan RI, Burcea IF, Salmen T, Poiana C. Prognostic Models in Growth-Hormone- and Prolactin-Secreting Pituitary Neuroendocrine Tumors: A Systematic Review. Diagnostics (Basel) 2023; 13:2118. [PMID: 37371013 DOI: 10.3390/diagnostics13122118] [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: 04/26/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Growth-hormone (GH)- and prolactin (PRL)-secreting PitNETs (pituitary neuroendocrine tumors) are divided into multiple histological subtypes, which determine their clinical and biological variable behavior. Proliferation markers alone have a questionable degree of prediction, so we try to identify validated prognostic models as accurately as possible. (1) Background: The data available so far show that the use of staging and clinical-pathological classification of PitNETs, along with imaging, are useful in predicting the evolution of these tumors. So far, there is no consensus for certain markers that could predict tumor evolution. The application of the WHO (World Health Organisation) classification in practice needs to be further evaluated and validated. (2) Methods: We performed the CRD42023401959 protocol in Prospero with a systematic literature search in PubMed and Web of Science databases and included original full-text articles (randomized control trials and clinical trials) from the last 10 years, published in English, and the search used the following keywords: (i) pituitary adenoma AND (prognosis OR outcome OR prediction), (ii) growth hormone pituitary adenoma AND (prognosis OR outcome OR prediction), (iii) prolactin pituitary adenoma AND (prognosis OR outcome OR prediction); (iv) mammosomatotroph adenoma AND (prognosis OR outcome OR prediction). (3) Results: Two researchers extracted the articles of interest and if any disagreements occurred in the selection process, these were settled by a third reviewer. The articles were then assessed using the ROBIS bias assessment and 75 articles were included. (4) Conclusions: the clinical-pathological classification along with factors such as GH, IGF-1, prolactin levels both preoperatively and postoperatively offer valuable information.
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Affiliation(s)
- Roxana-Ioana Dumitriu-Stan
- Department of Endocrinology, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Doctoral School of 'Carol Davila' University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Iulia-Florentina Burcea
- Department of Endocrinology, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania
- 'C. I. Parhon' National Institute of Endocrinology, 011863 Bucharest, Romania
| | - Teodor Salmen
- Doctoral School of 'Carol Davila' University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Catalina Poiana
- Department of Endocrinology, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania
- 'C. I. Parhon' National Institute of Endocrinology, 011863 Bucharest, Romania
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Taheri MS, Ghomi Z, Mirshahi R, Moradpour M, Niroomand M, Yarmohamadi P, Zeidabadi H. Usefulness of subtraction images for accurate diagnosis of pituitary microadenomas in dynamic contrast-enhanced magnetic resonance imaging. Acta Radiol 2023; 64:1148-1154. [PMID: 35731731 DOI: 10.1177/02841851221107344] [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: 11/15/2022]
Abstract
BACKGROUND Dynamic contrast-enhanced magnetic resonance imaging (MRI) is the modality of choice for the diagnosis of pituitary microadenomas; however, it may be associated with a relatively high false-negative rate, especially in small lesions. PURPOSE To evaluate the usefulness of subtraction images for enhancing the visual detection of pituitary microadenomas. MATERIAL AND METHODS In total, 50 patients with clinically established diagnosis hyperprolactinemia, acromegaly, and Cushing's disease were enrolled. Ten patients referred for brain MRI for reasons other than pituitary abnormality were selected as control group. Routine dynamic MRI of the pituitary gland and obtained subtraction MRI scans were scrutinized separately on different sessions by an experienced radiologist blinded to the study design and patient's data. The investigator's opinion on the presence or absence of a lesion and lesion size were collected. RESULTS In patients with pituitary microadenoma, dynamic MRI images were reported positive in 42 (84%) patients and negative in 8 (16%). Subtraction images were described as positive in all patients (100%)-including all patients with negative dynamic MRI-and the difference was statistically significant (P=0.016). Undetected lesions on dynamic MRI had a mean size of 2.84 ± 1.79 mm (median= 2.20 mm, interquartile range=1.62-4.62 mm) and a significant inverse correlation was noted between lesion size and negative report of dynamic MRI (P=0.018). Brain MRI scans in the control group were reported negative for pituitary microadenoma in both dynamic contrast-enhanced and subtraction images. CONCLUSION Subtraction images can successfully identify all lesions detectable with conventional dynamic MRI as well as improving visualization of lesions undetected on dynamic MRI, especially in small lesions.
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Affiliation(s)
- Morteza Sanei Taheri
- Department of Radiology, Shohade-E-Tajrish hospital, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Ghomi
- Department of Radiology, School of Medicine, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Mirshahi
- Eye Research Center, The Five Senses Health Institute, 48492Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Moein Moradpour
- Department of Radiology, Shohade-E-Tajrish hospital, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mahtab Niroomand
- Internal Medicine Department, Endocrinology Division, 556492Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parsa Yarmohamadi
- Young Researchers and Elite Club, Tehran Medical Sciences, 68106Islamic Azad University, Tehran, Iran
| | - Hamideh Zeidabadi
- Gynecology and Obstetrics Department,Tehran University of Medical Science, 213474Imam Khomeini Hospital Complex Imam Hospital, Tehran, Iran
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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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7
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Micko A, Hosmann A, Marik W, Bartsch S, Weber M, Knosp E, Wolfsberger S. Optimizing MR imaging for intraoperative image guidance in sellar pathologies. Pituitary 2020; 23:266-272. [PMID: 32170516 PMCID: PMC7181417 DOI: 10.1007/s11102-020-01035-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE With the advancement of extended endonasal approaches, the ability to surgically reach parasellar tumor extensions increase. The aim of the study was to propose an optimized imaging protocol for surgical guidance in the cavernous sinus (CS) for proper visualization structures at risk. METHODS Prospective case control analysis of 20 consecutive pituitary adenoma patients scheduled for endoscopic transnasal surgery. Assessment of the capability of three different MRI sequences (MPRAGE, VIBE, CISS) by 4 investigators to correctly visualize sellar and parasellar structures. Invasiveness and position of the normal pituitary gland were compared with the intraoperative findings. RESULTS The consensus between the 4 examiners to achieve the same results for all modalities was 40% for MPRAGE, 70% for VIBE and 60% for CISS sequences (p = 0.155). A consensus of Knosp Grade per patient was 80% for MPRAGE, 100% for VIBE and 90% for CISS (overall kappa 0.60). A higher Knosp Grade was found in MPRAGE sequences compared to the other sequences. Intraoperative status of invasiveness was correctly identified in 12/20 (60%) with MPRAGE, 19/20 (95%) with VIBE and 11/20 (55%) with CISS sequences. The position of the normal pituitary gland was most frequent evaluable in 15/20 (75%) and correctly identified in 12/15 (80%) cases. CONCLUSION Our data showed that VIBE sequences obtain the highest degree of consensus with intraoperative findings of invasiveness and position of the normal pituitary gland. VIBE sequences, due to their high spatial resolution and at the same time fast image acquisition could provide improved imaging for neuronavigation.
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Affiliation(s)
- Alexander Micko
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria
| | - Arthur Hosmann
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria
| | - Wolfgang Marik
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sophie Bartsch
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Michael Weber
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Engelbert Knosp
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria
| | - Stefan Wolfsberger
- Department of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
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Galm BP, Buckless C, Swearingen B, Torriani M, Klibanski A, Bredella MA, Tritos NA. MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands. Pituitary 2020; 23:212-222. [PMID: 31897778 DOI: 10.1007/s11102-019-01023-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs). METHODS We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes. RESULTS Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33-26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression. CONCLUSION Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy.
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Affiliation(s)
- Brandon P Galm
- Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Suite 140, Boston, MA, 02114, USA.
| | - Colleen Buckless
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brooke Swearingen
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Martin Torriani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Anne Klibanski
- Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Suite 140, Boston, MA, 02114, USA
| | - Miriam A Bredella
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas A Tritos
- Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Suite 140, Boston, MA, 02114, USA
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Pappy AL, Savinkina A, Bicknese C, Neill S, Oyesiku NM, Ioachimescu AG. Predictive modeling for pituitary adenomas: single center experience in 501 consecutive patients. Pituitary 2019; 22:520-531. [PMID: 31432313 DOI: 10.1007/s11102-019-00982-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Personalized postoperative management of patients with pituitary adenomas requires an early risk stratification system. METHODS We reviewed 501 cases operated between 10/27/2011 and 5/5/2016 by a single neurosurgeon. We determined biochemical remission and tumor resection at 3 months, and biochemical recurrence, tumor recurrence, radiation and reoperation during follow-up. We considered age, gender, tumor diameter, cavernous sinus invasion (CSI) by MRI, diagnostic category (clinical, biochemical and immunohistochemical), and proliferation markers in a Cox proportional hazards model. We built predictive models with the significant parameters and used Kaplan-Meier survival curves for time-dependent analyses. RESULTS The 501 cases comprised 141 functional and 360 nonfunctional adenomas. Tumor diameter, CSI, and ki-67 index predicted long-term events. Model 1 (CSI, diameter ≥ 2.9 cm and ki-67 > 3%) identified 18 (3.6%) adenomas and predicted persistent hypersecretory syndrome and residual tumor with 98.7% specificity (OR 8.6; CI 3.0-24.7). Model 2 (ki-67 > 3% and CSI) identified 48 (9.6%) adenomas and had 93.1% specificity (OR 3.3; CI 1.8-6.0). Model 3 (ki-67 > 3%, mitoses and p53, former "atypical" adenoma) identified 26 (5.2%) adenomas and had 96.0% specificity (OR 2.3; CI 1.0-5.0). Model 1 best predicted the long-term event-free survival and was strengthened when Knosp 3-4 CSI grades were used. Model 2 better identified the smaller adenomas at risk. Among the WHO 2017 special PA subtypes, patients with silent corticotroph adenoma had a lower event-free survival than ACTH-negative nonfunctional adenomas. CONCLUSION Use of CSI, ki-67 and tumor diameter in prediction models facilitates tailored surveillance and management of patients with pituitary adenomas.
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Affiliation(s)
- A L Pappy
- Emory University School of Medicine, Atlanta, GA, USA
| | - A Savinkina
- Emory Rollins School of Public Health, Atlanta, GA, USA
| | - C Bicknese
- Emory University School of Medicine, Atlanta, GA, USA
| | - S Neill
- Department of Pathology, Emory University School of Medicine, Atlanta, GA, USA
| | - N M Oyesiku
- Division of Endocrinology, Metabolism and Lipids, Department of Medicine, Emory University School of Medicine, 1364 Clifton Road NE, Atlanta, GA, 30322, USA
- Department of Neurosurgery, Emory University School of Medicine, 1365 B Clifton Rd NE, Atlanta, GA, USA
| | - A G Ioachimescu
- Division of Endocrinology, Metabolism and Lipids, Department of Medicine, Emory University School of Medicine, 1364 Clifton Road NE, Atlanta, GA, 30322, USA.
- Department of Neurosurgery, Emory University School of Medicine, 1365 B Clifton Rd NE, Atlanta, GA, USA.
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