1
|
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.
Collapse
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.
| |
Collapse
|
2
|
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.
Collapse
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.)
| |
Collapse
|
3
|
Scavuzzo A, Pasini G, Crescio E, Jimenez-Rios MA, Figueroa-Rodriguez P, Comelli A, Russo G, Vazquez IC, Araiza SM, Ortiz DG, Perez Montiel D, Lopez Saavedra A, Stefano A. Radiomics Analyses to Predict Histopathology in Patients with Metastatic Testicular Germ Cell Tumors before Post-Chemotherapy Retroperitoneal Lymph Node Dissection. J Imaging 2023; 9:213. [PMID: 37888320 PMCID: PMC10607637 DOI: 10.3390/jimaging9100213] [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: 08/30/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. AIM To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. METHODS Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models' performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. RESULT Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. CONCLUSIONS The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.
Collapse
Affiliation(s)
- Anna Scavuzzo
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Giovanni Pasini
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy
| | - Elisabetta Crescio
- Science Department, Tecnológico de Monterrey, Mexico City 14080, Mexico;
| | - Miguel Angel Jimenez-Rios
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Pavel Figueroa-Rodriguez
- Department of Biomedical Engineering, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico
| | - Albert Comelli
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
| | - Ivan Calvo Vazquez
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Sebastian Muruato Araiza
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - David Gomez Ortiz
- Department of Uro-Oncology, Instituto Nacional de Cancerologia, Universidad Autonoma de Mexico-UNAM, Mexico City 14080, Mexico; (A.S.)
| | - Delia Perez Montiel
- Department of Pathology, Instituto Nacional de Cancerología, Mexico City 14080, Mexico
| | - Alejandro Lopez Saavedra
- Advanced Microscopy Applications Unit (ADMiRA), Instituto Nacional de Cancerología, Mexico City 14080, Mexico
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (G.P.); (G.R.)
| |
Collapse
|
4
|
Kim K, Ku CR, Lee EJ. Multiomics Approach to Acromegaly: Unveiling Translational Insights for Precision Medicine. Endocrinol Metab (Seoul) 2023; 38:463-471. [PMID: 37828709 PMCID: PMC10613768 DOI: 10.3803/enm.2023.1820] [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] [Received: 09/06/2023] [Revised: 09/24/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
The clinical characteristics and prognoses of acromegaly vary among patients. Assessment of current and novel predictors can lead to multilevel categorization of patients, allowing integration into new clinical guidelines and a reduction in the increased morbidity and mortality associated with acromegaly. Despite advances in the diagnosis and treatment of acromegaly, its pathophysiology remains unclear. Recent advancements in multiomics technologies, including genomics, transcriptomics, proteomics, metabolomics, and radiomics, have offered new opportunities to unravel the complex pathophysiology of acromegaly. This review comprehensively explores the emerging role of multiomics approaches in elucidating the molecular landscape of acromegaly. We discuss the potential implications of multiomics data integration in the development of novel diagnostic tools, identification of therapeutic targets, and the prospects of precision medicine in acromegaly management. By integrating diverse omics datasets, these approaches can provide valuable insights into disease mechanisms, facilitate the identification of diagnostic biomarkers, and identify potential therapeutic targets for precision medicine in the management of acromegaly.
Collapse
Affiliation(s)
- Kyungwon Kim
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Cheol Ryong Ku
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
5
|
Khan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ. Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 2023; 44:947-959. [PMID: 37207359 PMCID: PMC10502574 DOI: 10.1210/endrev/bnad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/14/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.
Collapse
Affiliation(s)
- Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Stephanie E Baldeweg
- Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK
| | - Neil L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Digital Surgery Ltd, Medtronic, London WD18 8WW, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Kim EH, Kim J, Ku CR, Lee EJ, Kim SH. Surgical Treatment of Prolactinomas: Potential Role as a First-Line Treatment Modality. Yonsei Med J 2023; 64:489-496. [PMID: 37488700 PMCID: PMC10375245 DOI: 10.3349/ymj.2022.0406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 05/01/2023] [Accepted: 06/01/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Treatment with dopamine agonists (DAs) has been the first-line standard treatment for prolactinoma, and surgery has been reserved for drug intolerance and resistance for several decades. We evaluated whether surgery plays a primary role in prolactinoma management. MATERIALS AND METHODS We conducted a retrospective study of 210 prolactinoma patients who had received surgical treatment at our institution. We analyzed the treatment outcomes according to tumor extent, sex, and preoperative DA medication. RESULTS Overall hormonal remission was achieved in 164 patients (78.1%), and complete removal was achieved in 194 patients (92.4%). When the tumors were completely removed, the remission rate increased to 84.5%. Anterior pituitary function was normalized or improved in 94.6% of patients, whereas only 4.1% of patients showed worsening of hormone control. Hormonal remission was higher in patients who had not received DA preoperatively than in those who had received preoperative DA treatment. Smaller tumor size (<1 cm), no invasion into the cavernous sinus, and female sex were predictors of good surgical outcomes. CONCLUSION Although DAs remain the first-line standard treatment for prolactinomas, surgery can be an excellent option and should be considered as an alternative primary treatment modality when patients are predicted to achieve a good surgical outcome.
Collapse
Affiliation(s)
- Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Yonsei Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Junhyung Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Cheol Ryong Ku
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Yonsei Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Yonsei Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Ho Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.
| |
Collapse
|
8
|
Wang L, Song D, Wang W, Li C, Zhou Y, Zheng J, Rao S, Wang X, Shao G, Cai J, Yang S, Dong J. Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models. Cancers (Basel) 2023; 15:cancers15061784. [PMID: 36980670 PMCID: PMC10046511 DOI: 10.3390/cancers15061784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/02/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.
Collapse
Affiliation(s)
- Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Danjun Song
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Wentao Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chengquan Li
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
| | - Yiming Zhou
- Department of Hepatobiliary and Pancreatic Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Jiaping Zheng
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xiaoying Wang
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guoliang Shao
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Jiabin Cai
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Correspondence: (J.C.); (S.Y.)
| | - Shizhong Yang
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
- Correspondence: (J.C.); (S.Y.)
| | - Jiahong Dong
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| |
Collapse
|
9
|
Padovan M, Cerretti G, Caccese M, Barbot M, Bergo E, Occhi G, Scaroni C, Lombardi G, Ceccato F. Knowing when to discontinue Temozolomide therapy in responding aggressive pituitary tumors and carcinomas: a systematic review and Padua (Italy) case series. Expert Rev Endocrinol Metab 2023; 18:181-198. [PMID: 36876325 DOI: 10.1080/17446651.2023.2185221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023]
Abstract
INTRODUCTION Pituitary adenomas can show a tendency to grow, despite multimodal treatment. Temozolomide (TMZ) has been used in the last 15 years in patients with aggressive pituitary tumors. TMZ requires a careful balance of different expertise, especially for selection criteria. AREAS COVERED We conducted: (1) a systematic review of the published literature from 2006 to 2022, collecting only cases with a complete description of patient follow-up after TMZ discontinuation; (2) a description of all patients with aggressive pituitary adenoma or carcinoma treated in Padua (Italy). EXPERT OPINION There is considerable heterogeneity in the literature: TMZ cycles duration ranged from 3 to 47 months; the follow-up time after TMZ discontinuation ranged from 4 to 91 months (mean 24 months, median 18 months), at least a stable disease has been reported in 75% of patients after a mean 13 months (range 3-47 months, median 10 months). The Padua (Italy) cohort reflects the literature. Future directions to explore are to understand the pathophysiological mechanism of TMZ resistance escape, to develop predicting factors to TMZ treatment (especially through the delineation of the underlying transformation processes), and to further expand the therapeutic applications of TMZ (as neoadjuvant, combined with radiotherapy).
Collapse
Affiliation(s)
- Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Mattia Barbot
- Department of Medicine DIMED, University of Padua, Padua, Italy
- Endocrine Disease Unit, University-Hospital of Padua, Padua, Italy
| | - Eleonora Bergo
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Gianluca Occhi
- Department of Biology DIBIO, University of Padua, Padua, Italy
| | - Carla Scaroni
- Department of Medicine DIMED, University of Padua, Padua, Italy
- Endocrine Disease Unit, University-Hospital of Padua, Padua, Italy
| | - Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Filippo Ceccato
- Department of Medicine DIMED, University of Padua, Padua, Italy
- Endocrine Disease Unit, University-Hospital of Padua, Padua, Italy
| |
Collapse
|
10
|
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.
Collapse
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:
| |
Collapse
|
11
|
Editorial Comment: Radiomics analysis allows for precise prediction of silent corticotroph adenoma among non-functioning pituitary adenomas. Eur Radiol 2022; 32:1475-1476. [DOI: 10.1007/s00330-021-08509-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 10/25/2021] [Accepted: 11/10/2021] [Indexed: 11/04/2022]
|
12
|
Dai C, Sun B, Wang R, Kang J. The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas. Front Oncol 2022; 11:784819. [PMID: 35004306 PMCID: PMC8733587 DOI: 10.3389/fonc.2021.784819] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/02/2021] [Indexed: 12/28/2022] Open
Abstract
Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.
Collapse
Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Bowen Sun
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Kang
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
13
|
Huber M, Luedi MM, Schubert GA, Musahl C, Tortora A, Frey J, Beck J, Mariani L, Christ E, Andereggen L. Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas. Front Endocrinol (Lausanne) 2022; 13:810219. [PMID: 35250868 PMCID: PMC8888454 DOI: 10.3389/fendo.2022.810219] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/17/2022] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making. OBJECTIVE To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia. METHODS By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers. RESULTS We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term. CONCLUSIONS This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers.
Collapse
Affiliation(s)
- Markus Huber
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus M. Luedi
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | | | - Christian Musahl
- Department of Neurosurgery, Kantonsspital Aarau, Aarau, Switzerland
| | - Angelo Tortora
- Department of Neurosurgery, Kantonsspital Aarau, Aarau, Switzerland
| | - Janine Frey
- Department of Gynecology and Obstetrics, Kantonsspital Lucerne, Lucerne, Switzerland
| | - Jürgen Beck
- Department of Neurosurgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Luigi Mariani
- Department of Neurosurgery, University Hospital of Basel, Basel, Switzerland
| | - Emanuel Christ
- Department of Endocrinology, Diabetes and Metabolism, University Hospital of Basel, Basel, Switzerland
| | - Lukas Andereggen
- Department of Neurosurgery, Kantonsspital Aarau, Aarau, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
- *Correspondence: Lukas Andereggen, ; orcid.org/0000-0003-1764-688X
| |
Collapse
|
14
|
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: 12] [Impact Index Per Article: 4.0] [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.
Collapse
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.
| |
Collapse
|
15
|
Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
Collapse
Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,XiangYa School of Medicine, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
16
|
Kim HK, Hong JW, Moon JH, Ahn SS, Kim EH, Lee SK, Lee EJ, Park YW, Ku CR. Efficacy and Cerebrospinal Fluid Rhinorrhea after Cabergoline Treatment in Patients with Bioactive Macroprolactinoma. Cancers (Basel) 2021; 13:cancers13215374. [PMID: 34771538 PMCID: PMC8582509 DOI: 10.3390/cancers13215374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Predicting dopamine agonist resistance in patients with macroprolactinoma is essential for clinicians to prevent treatment failure and subsequent complications such as medication-induced cerebrospinal fluid (CSF) rhinorrhea. We evaluated the features of patients with cabergoline resistance and CSF rhinorrhea in patients with prolactinomas with prolactin levels ≥1000 ng/mL. A total of 140 patients who were newly diagnosed with prolactinoma secreting only prolactin ≥1000 ng/mL and treated with cabergoline for the first time were included in this study. Based on the hormonal and radiologic response of the prolactinoma, the patients were divided into responders and non-responders. Non-responders (36/140, 25.8%) included a higher number of patients receiving hormone replacement than responders (responders, n (%) = 12(11.5) vs. non-responders = 13(36.1), p = 0.001). In propensity score matching analysis, patients who developed CSF rhinorrhea presented more frequent hormone deficiency than responders regardless of initial cabergoline dose. Hormone deficiency was associated with a greater odds ratio for the risk of non-responders (adjusted odds ratio = 5.13, 95% CI 1.96-13.46, p = 0.001). Cabergoline was effective in bioactive macroprolactinoma. Furthermore, initial cabergoline dose was not significantly associated with long-term responsiveness and development of CSF rhinorrhea but the hypopituitarism was independently associated with an increased risk of cabergoline resistance and CSF rhinorrhea.
Collapse
Affiliation(s)
- Hae-Kyung Kim
- Department of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul 03722, Korea; (H.-K.K.); (E.-J.L.)
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
| | - Jae-Won Hong
- Department of Internal Medicine, Division of Endocrinology, Ilsan-Paik Hospital, Inje University College of Medicine, 170 Juhawro, Ilsanseo-gu, Goyang 10380, Korea;
| | - Ju-Hyung Moon
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Sung-Soo Ahn
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Center for Clinical Imaging Data Science, Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Eui-Hyun Kim
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Seung-Koo Lee
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Center for Clinical Imaging Data Science, Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, Korea
| | - Eun-Jig Lee
- Department of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul 03722, Korea; (H.-K.K.); (E.-J.L.)
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
| | - Yae-Won Park
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Center for Clinical Imaging Data Science, Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.-W.P.); (C.-R.K.); Tel.: +82-2-2228-7400 (Y.-W.P.); +82-2-2228-0833 (C.R.K.); Fax: +82-2-393-3035 (Y.-W.P.); +82-2-312-0578 (C.-R.K.)
| | - Cheol-Ryong Ku
- Department of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul 03722, Korea; (H.-K.K.); (E.-J.L.)
- Pituitary Tumor Center, Severance Hospital, Seoul 03722, Korea; (J.-H.M.); (S.-S.A.); (E.-H.K.); (S.-K.L.)
- Correspondence: (Y.-W.P.); (C.-R.K.); Tel.: +82-2-2228-7400 (Y.-W.P.); +82-2-2228-0833 (C.R.K.); Fax: +82-2-393-3035 (Y.-W.P.); +82-2-312-0578 (C.-R.K.)
| |
Collapse
|