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Marazuela M, Martínez-Hernandez R, Marques-Pamies M, Biagetti B, Araujo-Castro M, Puig-Domingo M. Predictors of biochemical response to somatostatin receptor ligands in acromegaly. Best Pract Res Clin Endocrinol Metab 2024; 38:101893. [PMID: 38575404 DOI: 10.1016/j.beem.2024.101893] [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] [Indexed: 04/06/2024]
Abstract
Although predictors of response to first-generation somatostatin receptor ligands (fg-SRLs), and to a lesser extent to pasireotide, have been studied in acromegaly for many years, their use is still not recommended in clinical guidelines. Is there insufficient evidence to use them? Numerous biomarkers including various clinical, functional, radiological and molecular markers have been identified. The first ones are applicable pre-surgery, while the molecular predictors are utilized for patients not cured after surgery. In this regard, factors predicting a good response to fg-SRLs are specifically: low basal GH, a low GH nadir in the acute octreotide test, T2 MRI hypointensity, a densely granulated pattern, high immunohistochemistry staining for somatostatin receptor 2 (SSTR2), and E-cadherin. However, there is still a lack of consensus regarding which of these biomarkers is more useful or how to integrate them into clinical practice. With classical statistical methods, it is complex to define reliable and generalizable cut-off values for a single biomarker. The potential solution to the limitations of traditional methods involves combining systems biology with artificial intelligence, which is currently providing answers to such long-standing questions that may eventually be finally included into the clinical guidelines and make personalized medicine a reality. The aim of this review is to describe the current knowledge of the main fg-SRLs and pasireotide response predictors, discuss their current usefulness, and point to future directions in the research of this field.
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Affiliation(s)
- Mónica Marazuela
- Department of Endocrinology and Nutrition Hospital Universitario La Princesa, Universidad Autónoma de Madrid,Instituto de Investigación Princesa, and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER GCV14/ER/12), Madrid, Spain.
| | | | | | - Betina Biagetti
- Endocrinology & Nutrition Service, Vall d'Hebron University Hospital and Vall d'Hebron Research Institute (VHIR), Department of Medicine, Autonomous University of Barcelona, Reference Networks (ERN), 08035 Barcelona, Spain
| | - Marta Araujo-Castro
- Endocrinology & Nutrition Department. Hospital Universitario Ramón y Cajal, Spain & Instituto de Investigación Biomédica Ramón y Cajal (IRYCIS), Madrid, Spain
| | - Manel Puig-Domingo
- Department of Endocrinology and Nutrition, Department of Medicine, Germans Trias i Pujol Research Institute and Hospital, Universitat Autònoma de Barcelona, Spain and Centro de Investigación Biomédica en Red de Enfermedades Raras CIBERER G747, Badalona, Spain
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Sathya A, Goyal-Honavar A, Chacko AG, Jasper A, Chacko G, Devakumar D, Seelam JA, Sasidharan BK, Pavamani SP, Thomas HMT. Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs? Acta Neurochir (Wien) 2024; 166:91. [PMID: 38376544 DOI: 10.1007/s00701-024-05977-4] [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] [Received: 10/26/2023] [Accepted: 01/18/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making. METHODS Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. RESULTS A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66. CONCLUSIONS Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.
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Affiliation(s)
- Sathya A
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | | | - Ari G Chacko
- Department of Neurosurgery, Christian Medical College, Vellore, India
| | - Anitha Jasper
- Department of Radiodiagnosis, Christian Medical College, Vellore, India
| | - Geeta Chacko
- Department of General Pathology, Christian Medical College, Vellore, India
| | - Devadhas Devakumar
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | | | - Balu Krishna Sasidharan
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | - Simon P Pavamani
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | - Hannah Mary T Thomas
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India.
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Marques-Pamies M, Gil J, Jordà M, Puig-Domingo M. Predictors of Response to Treatment with First-Generation Somatostatin Receptor Ligands in Patients with Acromegaly. Arch Med Res 2023; 54:102924. [PMID: 38042683 DOI: 10.1016/j.arcmed.2023.102924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/27/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023]
Abstract
BACKGROUND AND AIMS Predictors of first-generation somatostatin receptor ligands (fgSRLs) response in acromegaly have been studied for over 30 years, but they are still not recommended in clinical guidelines. Is there not enough evidence to support their use? This systematic review aims to describe the current knowledge of the main predictors of fgSRLs response and discuss their current usefulness, as well as future research directions. METHODS A systematic search was performed in the Scopus and PubMed databases for functional, imaging, and molecular predictive factors. RESULTS A total of 282 articles were detected, of which 64 were included. Most of them are retrospective studies performed between 1990 and 2023 focused on the predictive response to fgSRLs in acromegaly. The usefulness of the predictive factors is confirmed, with good response identified by the most replicated factors, specifically low GH nadir in the acute octreotide test, T2 MRI hypointensity, high Somatostatin receptor 2 (SSTR2) and E-cadherin expression, and a densely granulated pattern. Even if these biomarkers are interrelated, the association is quite heterogeneous. With classical statistical methods, it is complex to define reliable and generalizable cut-off values worth recommending in clinical guidelines. Machine-learning models involving omics are a promising approach to achieve the highest accuracy values to date. CONCLUSIONS This survey confirms a sufficiently robust level of evidence to apply knowledge of predictive factors for greater efficiency in the treatment decision process. The irruption of artificial intelligence in this field is providing definitive answers to such long-standing questions that may change clinical guidelines and make personalized medicine a reality.
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Affiliation(s)
| | - Joan Gil
- Endocrine Research Unit, Germans Trias i Pujol Research Institute, Badalona, Spain; Network Research Center for Rare Diseases, CIBERER, Unit 747, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology, Research Center for Pituitary Diseases, Hospital Sant Pau, IIB-SPau, Barcelona, Spain
| | - Mireia Jordà
- Endocrine Research Unit, Germans Trias i Pujol Research Institute, Badalona, Spain
| | - Manel Puig-Domingo
- Endocrine Research Unit, Germans Trias i Pujol Research Institute, Badalona, Spain; Network Research Center for Rare Diseases, CIBERER, Unit 747, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Germans Trias i Pujol University Hospital, Badalona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.
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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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