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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 MP, Little AS, Karsy M. Predictors of Durable Remission After Successful Surgery for Cushing Disease: Results From the Multicenter RAPID Registry. Neurosurgery 2024; 95:761-769. [PMID: 39293794 DOI: 10.1227/neu.0000000000003042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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 cm 3 vs 0.49 ± 1.17 cm 3 , 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 P 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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Gupta N, Konsam BD, Walia R, Bhadada SK, Chhabra R, Dhandapani S, Singh A, Ahuja CK, Sachdeva N, Saikia UN. An objective way to predict remission and relapse in Cushing disease using Bayes' theorem of probability. J Endocrinol Invest 2024; 47:2461-2468. [PMID: 38619729 DOI: 10.1007/s40618-024-02336-z] [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: 07/02/2023] [Accepted: 02/12/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE In this study on patients with Cushing disease, post-transsphenoidal surgery (TSS), we attempt to predict the probability of remaining in remission, at least for a year and relapse after that, using Bayes' theorem and the equation of conditional probability. The number of parameters, as well as the weightage of each, is incorporated in this equation. DESIGN AND METHODS The study design was a single-centre ambispective study. Ten clinical, biochemical, radiological and histopathological parameters capable of predicting Cushing disease remission were identified. The presence or absence of each parameter was entered as binary numbers. Bayes' theorem was applied, and each patient's probability of remission and relapse was calculated. RESULTS A total of 145 patients were included in the study. ROC plot showed a cut-off value of the probability of 0.68, with a sensitivity of 82% (range 73-89%) and a specificity of 94% (range 83-99%) to predict the probability of remission. Eighty-one patients who were in remission at 1 year were followed up for relapse and 23 patients developed relapse of the disease. The Bayes' equation was able to predict relapse in only 3 out of 23 patients. CONCLUSIONS Using various parameters, remission of Cushing disease can be predicted by applying Bayes' theorem of conditional probability with a sensitivity and a specificity of 82% and 94%, respectively. This study provided an objective way of predicting remission after TSS and relapse in patients with Cushing disease giving a weightage advantage to every parameter.
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Affiliation(s)
- N Gupta
- Department of Endocrinology, Post Graduate Institute of Medical Education and Research (PGIMER), 1010, Nehru Extension Block, Chandigarh, 160012, India
| | - B D Konsam
- Department of Endocrinology, Post Graduate Institute of Medical Education and Research (PGIMER), 1010, Nehru Extension Block, Chandigarh, 160012, India
| | - R Walia
- Department of Endocrinology, Post Graduate Institute of Medical Education and Research (PGIMER), 1010, Nehru Extension Block, Chandigarh, 160012, India.
| | - S K Bhadada
- Department of Endocrinology, Post Graduate Institute of Medical Education and Research (PGIMER), 1010, Nehru Extension Block, Chandigarh, 160012, India
| | - R Chhabra
- Department of Neurosurgery, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - S Dhandapani
- Department of Neurosurgery, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - A Singh
- Department of Neurosurgery, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - C K Ahuja
- Department of Radiodiagnosis, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - N Sachdeva
- Department of Endocrinology, Post Graduate Institute of Medical Education and Research (PGIMER), 1010, Nehru Extension Block, Chandigarh, 160012, India
| | - U N Saikia
- Department of Histopathology, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
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Zdrojowy-Wełna A, Valassi E. Cushing's Syndrome in the Elderly. Exp Clin Endocrinol Diabetes 2024. [PMID: 38698635 DOI: 10.1055/a-2317-8821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Management of Cushing's syndrome (CS) can be particularly challenging in older patients, compared with younger individuals, due to the lack of several clinical features associated with cortisol excess along with a greater burden of associated comorbidities. Moreover, the interpretation of diagnostic tests could be influenced by age-related physiological changes in cortisol secretion. While mortality is higher and quality of life is more impaired in the elderly with CS as compared with the younger, there is currently no agreement on the most effective therapeutic options in aged individuals, and safety data concerning medical treatment are scanty. In this review, we summarize the current knowledge about age-related differences in CS etiology, clinical presentation, treatment, and outcomes and describe the potential underlying mechanisms.
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Affiliation(s)
- Aleksandra Zdrojowy-Wełna
- Department of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wroclaw, Poland
- Endocrinology Department, Wroclaw University Hospital, Wroclaw, Poland
| | - Elena Valassi
- Endocrinology and Nutrition Department, Germans Trias i Pujol Hospital and Research Institute, Badalona, Spain
- School of Medicine, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
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4
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Maroufi SF, Doğruel Y, Pour-Rashidi A, Kohli GS, Parker CT, Uchida T, Asfour MZ, Martin C, Nizzola M, De Bonis A, Tawfik-Helika M, Tavallai A, Cohen-Gadol AA, Palmisciano P. Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary 2024; 27:91-128. [PMID: 38183582 DOI: 10.1007/s11102-023-01369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/08/2024]
Abstract
PURPOSE Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations. METHODS PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies. RESULTS Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately. CONCLUSION AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
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Affiliation(s)
- Seyed Farzad Maroufi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Yücel Doğruel
- Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey
| | - Ahmad Pour-Rashidi
- Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Gurkirat S Kohli
- Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Tatsuya Uchida
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Mohamed Z Asfour
- Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt
| | - Clara Martin
- Department of Neurosurgery, Hospital de Alta Complejidad en Red "El Cruce", Florencio Varela, Buenos Aires, Argentina
| | | | - Alessandro De Bonis
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Amin Tavallai
- Department of Pediatric Neurosurgery, Children's Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Paolo Palmisciano
- Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, CA, 95817, USA.
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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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Ünal M, Selek A, Sözen M, Gezer E, Köksalan D, Canturk Z, Cetinarslan B, Çabuk B, Anık I, Ceylan S. Recurrent Cushing's Disease in Adults: Predictors and Long-Term Follow-Up. Horm Metab Res 2023; 55:520-527. [PMID: 37015254 DOI: 10.1055/a-2047-6017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Cushing's disease (CD) is characterized by endogenous hypercortisolism that is associated with increased mortality and morbidity. Due to high recurrence rates in CD, the determination of high-risk patients is of paramount importance. In this study, we aimed to determine recurrence rates and clinical, laboratory, and histological predictors of recurrence in a high volume single-center. This retrospective study included 273 CD patients operated in a single pituitary center between 1997 and 2020. The patients with early postoperative remission were further grouped according to recurrence status (recurrent and sustained remission groups). Demographic, radiologic, laboratory, pathologic, and follow-up clinical data of the patients were analyzed and compared between groups. The recurrence rate was 9.6% in the first 5 years; however, the overall recurrence rate was 14.2% in this study. Higher preoperative basal ACTH levels were significantly correlated with CD recurrence even with ACTH levels adjusted for tumor size, Ki-67 levels, and tumoral invasion. Recurrence rates were significantly higher in patients with ACTH levels higher than 55 pg/ml, tumor diameter>9.5 mm, and if adrenal axis recovery was before 6 months. The severity of hypercortisolism, morbidities, and demographic factors except age were not predictive factors of recurrence. Based on our study data, younger age at diagnosis, a diagnosis of osteoporosis, higher preoperative ACTH levels, larger tumor size, invasive behavior, higher Ki 67 index, and early recovery of the adrenal axis during the postoperative period attracted attention as potential predictors of recurrent disease.
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Affiliation(s)
- Mustafa Ünal
- Department of Internal Medicine, Kocaeli University, Kocaeli, Turkey
| | - Alev Selek
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Mehmet Sözen
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Emre Gezer
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Damla Köksalan
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Zeynep Canturk
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Berrin Cetinarslan
- Department of Endocrinology and Metabolism, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Burak Çabuk
- Department of Neurosurgery, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Ihsan Anık
- Department of Neurosurgery, Pituitary Center, Kocaeli University, Kocaeli, Turkey
| | - Savaş Ceylan
- Department of Neurosurgery, Pituitary Center, Kocaeli University, Kocaeli, Turkey
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7
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Hou S, Li X, Meng F, Liu S, Wang Z. A Machine Learning-Based Prediction of Diabetes Insipidus in Patients Undergoing Endoscopic Transsphenoidal Surgery for Pituitary Adenoma. World Neurosurg 2023; 175:e55-e63. [PMID: 36907270 DOI: 10.1016/j.wneu.2023.03.027] [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: 12/03/2022] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND Diabetes insipidus (DI) is a common complication after endoscopic transsphenoidal surgery (TSS) for pituitary adenoma (PA), which affects the quality of life in patients. Therefore, there is a need to develop prediction models of postoperative DI specifically for patients who undergo endoscopic TSS. This study establishes and validates prediction models of DI after endoscopic TSS for patients with PA using machine learning algorithms. METHODS We retrospectively collected information about patients with PA who underwent endoscopic TSS in otorhinolaryngology and neurosurgery departments between January 2018 and December 2020. The patients were randomly split into a training set (70%) and a test set (30%). The 4 machine learning algorithms (logistic regression, random forest, support vector machine, and decision tree) were used to establish the prediction models. Area under the receiver operating characteristic curves were calculated to compare the performance of the models. RESULTS A total of 232 patients were included, and 78 patients (33.6%) developed transient DI after surgery. Data were randomly divided into a training set (n = 162) and a test set (n = 70) for development and validation of the model, respectively. The area under the receiver operating characteristic curve was highest in the random forest model (0.815) and lowest in the logistic regression model (0.601). Invasion of pituitary stalk was the most important feature for model performance, closely followed by macroadenomas, size classification of PA, tumor texture, and Hardy-Wilson suprasellar grade. CONCLUSIONS Machine learning algorithms identify preoperative features of importance and reliably predict DI after endoscopic TSS for patients with PA. Such a prediction model may enable clinicians to develop individualized treatment strategy and follow-up management.
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Affiliation(s)
- Siyuan Hou
- Department of Otolaryngology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaomin Li
- Department of Otolaryngology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Fanyue Meng
- Department of Otolaryngology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shaokun Liu
- Department of Otolaryngology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhenlin Wang
- Department of Otolaryngology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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Fuse Y, Takeuchi K, Nishiwaki H, Imaizumi T, Nagata Y, Ohno K, Saito R. Machine learning models predict delayed hyponatremia post-transsphenoidal surgery using clinically available features. Pituitary 2023:10.1007/s11102-023-01311-w. [PMID: 36995457 DOI: 10.1007/s11102-023-01311-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/14/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE Delayed hyponatremia (DHN), a unique complication, is the leading cause of unexpected readmission after pituitary surgery. Therefore, this study aimed to develop tools for predicting postoperative DHN in patients undergoing endoscopic transsphenoidal surgery (eTSS) for pituitary neuroendocrine tumors (PitNETs). METHODS This was a single-center, retrospective study involving 193 patients with PitNETs who underwent eTSS. The objective variable was DHN, defined as serum sodium levels < 135 mmol/L at ≥ 1 time between post operative days 3 and 9. We trained four machine learning models to predict this objective variable using the clinical variables available preoperatively and on the first postoperative day. The clinical variables included patient characteristics, pituitary-related hormone levels, blood test results, radiological findings, and postoperative complications. RESULTS The random forest (RF) model demonstrated the highest (0.759 ± 0.039) area under the curve of the receiver operating characteristic curve (ROC-AUC), followed by the support vector machine (0.747 ± 0.034), the light gradient boosting machine (LGBM: 0.738 ± 0.026), and the logistic regression (0.710 ± 0.028). The highest accuracy (0.746 ± 0.029) was observed in the LGBM model. The best-performing RF model was based on 24 features, nine of which were clinically available preoperatively. CONCLUSIONS The proposed machine learning models with pre- and post-resection features predicted DHN after the resection of PitNETs.
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Affiliation(s)
- Yutaro Fuse
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Kazuhito Takeuchi
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Hiroshi Nishiwaki
- Division of Neurogenetics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takahiro Imaizumi
- Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan
| | - Yuichi Nagata
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Kinji Ohno
- Division of Neurogenetics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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Lyu X, Zhang D, Pan H, Zhu H, Chen S, Lu L. Machine learning models for differential diagnosis of Cushing's disease and ectopic ACTH secretion syndrome. Endocrine 2023; 80:639-646. [PMID: 36933156 DOI: 10.1007/s12020-023-03341-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/25/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND Using machine learning (ML) to explore the noninvasive differential diagnosis of Cushing's disease (CD) and ectopic corticotropin (ACTH) secretion (EAS) model is the next hot research topic. This study was to develop and evaluate ML models for differentially diagnosing CD and EAS in ACTH-dependent Cushing's syndrome (CS). METHODS Two hundred sixty-four CD and forty-seven EAS were randomly divided into training and validation and test datasets. We applied 8 ML algorithms to select the most suitable model. The diagnostic performance of the optimal model and bilateral petrosal sinus sampling (BIPSS) were compared in the same cohort. RESULTS Eleven adopted variables included age, gender, BMI, duration of disease, morning cortisol, serum ACTH, 24-h UFC, serum potassium, HDDST, LDDST, and MRI. After model selection, the Random Forest (RF) model had the most extraordinary diagnostic performance, with a ROC AUC of 0.976 ± 0.03, a sensitivity of 98.9% ± 4.4%, and a specificity of 87.9% ± 3.0%. The serum potassium, MRI, and serum ACTH were the top three most important features in the RF model. In the validation dataset, the RF model had an AUC of 0.932, a sensitivity of 95.0%, and a specificity of 71.4%. In the complete dataset, the ROC AUC of the RF model was 0.984 (95% CI 0.950-0.993), which was significantly higher than HDDST and LDDST (both p < 0.001). There was no significant statistical difference in the comparison of ROC AUC between the RF model and BIPSS (baseline ROC AUC 0.988 95% CI 0.983-1.000, after stimulation ROC AUC 0.992 95% CI 0.983-1.000). This diagnostic model was shared as an open-access website. CONCLUSIONS A machine learning-based model could be a practical noninvasive approach to distinguishing CD and EAS. The diagnostic performance might be close to BIPSS.
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Affiliation(s)
- Xiaohong Lyu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China
- Eight-Year Program of Clinical Medicine, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China
| | - Dingyue Zhang
- Eight-Year Program of Clinical Medicine, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China
| | - Hui Pan
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China
| | - Huijuan Zhu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China
| | - Shi Chen
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China.
| | - Lin Lu
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, 100730, Beijing, China.
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Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence. Brain Sci 2023; 13:brainsci13030495. [PMID: 36979305 PMCID: PMC10046799 DOI: 10.3390/brainsci13030495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Background: The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. Methods: We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms “artificial intelligence”, “machine learning”, and “pituitary”. Results: 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60–72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80–0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Conclusion: Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI’s real-world utility and improve clinical practice.
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11
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Li X, Yan L, Wang X, Ouyang C, Wang C, Chao J, Zhang J, Lian G. Predictive models for endoscopic disease activity in patients with ulcerative colitis: Practical machine learning-based modeling and interpretation. Front Med (Lausanne) 2022; 9:1043412. [PMID: 36619650 PMCID: PMC9810755 DOI: 10.3389/fmed.2022.1043412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
Background Endoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs. Methods The patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models. Results A total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values. Conclusion This proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.
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Affiliation(s)
- Xiaojun Li
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Lamei Yan
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,Department of Gastroenterology, The First Affiliated Hospital of Shaoyang College, Shaoyang, Hunan, China
| | - Xuehong Wang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Chunhui Ouyang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Chunlian Wang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China
| | - Jun Chao
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,Hunan Aicortech Intelligent Research Institute Co., Changsha, Hunan, China
| | - Jie Zhang
- Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Research Center of Digestive Disease, Central South University, Changsha, China,*Correspondence: Jie Zhang,
| | - Guanghui Lian
- Department of Gastroenterology, Xiangya Hospital of Central South University, Changsha, Hunan, China,Guanghui Lian,
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Thirunavukkarasu MK, Karuppasamy R. Forecasting determinants of recurrence in lung cancer patients exploiting various machine learning models. J Biopharm Stat 2022; 33:257-271. [PMID: 36397284 DOI: 10.1080/10543406.2022.2148162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Lung cancer recurrence seems to be the most leading cause of death as well as deterioration of lifespan. Proper assessment of the probability of recurrence in early-stage lung cancer is necessary to push up the treatment progress. We therefore employed machine-learning technologies to forecast post-operative recurrence risks using 174 lung cancer patient records. Six classification algorithms logistic regression, SVM, decision tree classification, random forest classification, XGBoost and lightGBM were used to predict the cancer recurrence. The patient samples were divided into training and test group with the split ratio of 3:1 for model generation and the accuracy were validated using k-fold cross-validation method. It is worth noting that the logistic regression model outperformed all the models in both training (Accuracy = 0.82) and test set (Accuracy = 0.79) on k-fold validation. Further, the optimal features (n = 7) identified using the RFE method is certainly helpful to improve the model in a high precision. The imperative risk factors associated with recurrence were identified using three feature selection methods. Importantly, our research showed that age is an important prognostic factor to be considered during the recurrence prediction. Indeed, severe concern on the identified risk factors combined with predictive models assists the physician to reduce the cancer recurrence rate in patients with lung cancer.
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Affiliation(s)
- Muthu Kumar Thirunavukkarasu
- Department of Biotechnology, School of Bio Sciences and Technology Vellore Institute of Technology, Vellore Tamil Nadu, India
| | - Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology Vellore Institute of Technology, Vellore Tamil Nadu, India
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13
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Liu YF, Pan L, Feng M. Structural and functional brain alterations in Cushing's disease: A narrative review. Front Neuroendocrinol 2022; 67:101033. [PMID: 36126747 DOI: 10.1016/j.yfrne.2022.101033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/21/2022] [Accepted: 08/26/2022] [Indexed: 11/26/2022]
Abstract
Neurocognitive and psychiatric symptoms are non-negligible in Cushing's disease and are accompanied by structural and functional alterations of the brain. In this review, we have summarized multimodal neuroimaging and neurophysiological studies to highlight the current and historical understandings of the structural and functional brain alterations in Cushing's disease. Specifically, structural studies showed atrophy of the gray matter, loss of white matter integrity, and demyelination in widespread brain regions. Functional imaging studies have identified three major functional brain connectome networks influenced by hypercortisolemia: the limbic network, the default mode network, and the executive control network. After endocrinological remission, atrophy of gray matter regions and the compromised functional network activities were partially reversible, and the widespread white matter integrity alterations cannot recover in years. In conclusion, Cushing's disease patients display structural and functional brain connectomic alterations, which provides insights into the neurocognitive and psychiatric symptoms observed in this disease.
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Affiliation(s)
- Yi-Fan Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Lei Pan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; School of Medicine, Tsinghua University, Beijing 100083, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
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Wu M, Zhao Y, Dong X, Jin Y, Cheng S, Zhang N, Xu S, Gu S, Wu Y, Yang J, Yao L, Wang Y. Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study. Front Oncol 2022; 12:975703. [PMID: 36212430 PMCID: PMC9532858 DOI: 10.3389/fonc.2022.975703] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis. Purpose We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers. Methods A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis. Results ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001). Conclusions Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.
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Affiliation(s)
- Meixuan Wu
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yaqian Zhao
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Xuhui Dong
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Yue Jin
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shanshan Cheng
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Nan Zhang
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Shilin Xu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Sijia Gu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Yongsong Wu
- Department of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China
| | - Jiani Yang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
| | - Liangqing Yao
- Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
| | - Yu Wang
- Department of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yu Wang, ; Liangqing Yao, ; Jiani Yang,
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15
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Guignat L, Bertherat J. Long-term follow-up and predictors of recurrence of Cushing's disease. J Neuroendocrinol 2022; 34:e13186. [PMID: 35979714 DOI: 10.1111/jne.13186] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/27/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Transsphenoidal surgery is the first-line treatment for Cushing's disease to selectively remove the tumor. The rate of postoperative remission is estimated around 70%-80% in expert centers. However, the long-term remission rate is lower because of recurrence during follow-up that can be observed in 15% to 25% of the patients depending on the studies and duration of follow-up. There is no significant predictive factor of recurrence before surgery, but postoperative corticotroph insufficiency and its duration has been found to be a protective factor for recurrence in many studies. The persistence of a positive response to desmopressin after surgery is associated with a higher rate of recurrence. Long term monitoring for recurrence with annual clinical and hormonal investigations after the hypothalamic-pituitary-adrenal axis postoperative recovery is advised. The biological tests used for the diagnosis of Cushing's syndrome (24 h-urinary-free cortisol [UFC], late-night salivary or serum cortisol, 1 mg dexamethasone suppression test) can be used to screen for recurrence. Several studies report that increased late night cortisol and alterations of dynamic testing can be observed before the increased 24 h-UFC. For this reason it is suggested that late-night salivary cortisol would be a very sensitive tool to diagnose recurrence, pending the realization of several assays in case of borderline or discrepant result. This review will summarize the knowledge about recurrence of Cushing's disease after pituitary surgery and the current recommendations for its monitoring and diagnosis.
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Affiliation(s)
- Laurence Guignat
- Department of Endocrinology and National Reference Center for Rare Adrenal Disorders, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Jérôme Bertherat
- Department of Endocrinology and National Reference Center for Rare Adrenal Disorders, Hôpital Cochin, Assistance Publique Hôpitaux de Paris, Paris, France
- Université de Paris Cité, Institut Cochin, Inserm U1016, CNRS UMR8104, Paris, France
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16
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Moreno-Moreno P, Ibáñez-Costa A, Venegas-Moreno E, Fuentes-Fayos AC, Alhambra-Expósito MR, Fajardo-Montañana C, García-Martínez A, Dios E, Vázquez-Borrego MC, Remón-Ruiz P, Cámara R, Lamas C, Carlos Padillo-Cuenca J, Solivera J, Cano DA, Gahete MD, Herrera-Martínez AD, Picó A, Soto-Moreno A, Gálvez-Moreno MÁ, Castaño JP, Luque RM. Integrative Clinical, Radiological, and Molecular Analysis for Predicting Remission and Recurrence of Cushing Disease. J Clin Endocrinol Metab 2022; 107:e2938-e2951. [PMID: 35312002 DOI: 10.1210/clinem/dgac172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Adrenocorticotropin (ACTH)-secreting pituitary tumors (ACTHomas) are associated with severe comorbidities and increased mortality. Current treatments mainly focus on remission and prevention of persistent disease and recurrence. However, there are still no useful biomarkers to accurately predict the clinical outcome after surgery, long-term remission, or disease relapse. OBJECTIVES This work aimed to identify clinical, biochemical, and molecular markers for predicting long-term clinical outcome and remission in ACTHomas. METHODS A retrospective multicenter study was performed with 60 ACTHomas patients diagnosed between 2004 and 2018 with at least 2 years' follow-up. Clinical/biochemical variables were evaluated yearly. Molecular expression profile of the somatostatin/ghrelin/dopamine regulatory systems components and of key pituitary factors and proliferation markers were evaluated in tumor samples after the first surgery. RESULTS Clinical variables including tumor size, time until diagnosis/first surgery, serum prolactin, and postsurgery cortisol levels were associated with tumor remission and relapsed disease. The molecular markers analyzed were distinctly expressed in ACTHomas, with some components (ie, SSTR1, CRHR1, and MKI67) showing instructive associations with recurrence and/or remission. Notably, an integrative model including selected clinical variables (tumor size/postsurgery serum cortisol), and molecular markers (SSTR1/CRHR1) can accurately predict the clinical evolution and remission of patients with ACTHomas, generating a receiver operating characteristic curve with an area under the curve of 1 (P < .001). CONCLUSION This study demonstrates that the combination of a set of clinical and molecular biomarkers in ACTHomas is able to accurately predict the clinical evolution and remission of patients. Consequently, the postsurgery molecular profile represents a valuable tool for clinical evaluation and follow-up of patients with ACTHomas.
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Affiliation(s)
- Paloma Moreno-Moreno
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Service of Endocrinology and Nutrition, IMIBIC, HURS, 14004 Cordoba, Spain
| | - Alejandro Ibáñez-Costa
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
| | - Eva Venegas-Moreno
- Unidad de Gestión de Endocrinología y Nutrición. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - Antonio C Fuentes-Fayos
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
| | - María R Alhambra-Expósito
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Service of Endocrinology and Nutrition, IMIBIC, HURS, 14004 Cordoba, Spain
| | - Carmen Fajardo-Montañana
- Department of Endocrinology, Hospital Universitario de La Ribera, Alzira, 46600, Valencia, Spain
| | - Araceli García-Martínez
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
| | - Elena Dios
- Unidad de Gestión de Endocrinología y Nutrición. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - Mari C Vázquez-Borrego
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
| | - Pablo Remón-Ruiz
- Unidad de Gestión de Endocrinología y Nutrición. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - Rosa Cámara
- Department of Endocrinology and Nutrition, Polytechnic University Hospital La Fe, 46026, Valencia, Spain
| | - Cristina Lamas
- Department of Endocrinology and Nutrition, Albacete University Hospital, 02006, Albacete, Spain
| | - José Carlos Padillo-Cuenca
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Service of Endocrinology and Nutrition, IMIBIC, HURS, 14004 Cordoba, Spain
| | | | - David A Cano
- Unidad de Gestión de Endocrinología y Nutrición. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - Manuel D Gahete
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
| | - Aura D Herrera-Martínez
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Service of Endocrinology and Nutrition, IMIBIC, HURS, 14004 Cordoba, Spain
| | - Antonio Picó
- Department of Endocrinology and Nutrition, Alicante General University Hospital. Institute for Health and Biomedical Research (ISABIAL). University Miguel Hernandez, CIBER Rare Diseases, 03010, Alicante, Spain
| | - Alfonso Soto-Moreno
- Unidad de Gestión de Endocrinología y Nutrición. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - María Ángeles Gálvez-Moreno
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Service of Endocrinology and Nutrition, IMIBIC, HURS, 14004 Cordoba, Spain
| | - Justo P Castaño
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
| | - Raúl M Luque
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), 14004 Cordoba, Spain
- Reina Sofia University Hospital (HURS), 14004 Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, 14004 Cordoba, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), 14004 Cordoba, Spain
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17
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Sulu C, Bektaş AB, Şahin S, Durcan E, Kara Z, Demir AN, Özkaya HM, Tanrıöver N, Çomunoğlu N, Kızılkılıç O, Gazioğlu N, Gönen M, Kadıoğlu P. Machine learning as a clinical decision support tool for patients with acromegaly. Pituitary 2022; 25:486-495. [PMID: 35435565 DOI: 10.1007/s11102-022-01216-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis. METHODS We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used. RESULTS One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance. CONCLUSIONS ML models may serve as valuable tools in the prediction of remission and SRL resistance.
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Affiliation(s)
- Cem Sulu
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
| | - Ayyüce Begüm Bektaş
- Graduate School of Sciences and Engineering, Koç University, Istanbul, Turkey
| | - Serdar Şahin
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
| | - Emre Durcan
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
| | - Zehra Kara
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
| | - Ahmet Numan Demir
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
| | - Hande Mefkure Özkaya
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey
- Pituitary Center, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Necmettin Tanrıöver
- Pituitary Center, Istanbul University-Cerrahpasa, Istanbul, Turkey
- Department of Neurosurgery, Cerrahpasa Medical School, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Nil Çomunoğlu
- Department of Medical Pathology, Cerrahpasa Medical School, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Osman Kızılkılıç
- Pituitary Center, Istanbul University-Cerrahpasa, Istanbul, Turkey
- Department of Radiology, Cerrahpasa Medical School, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Nurperi Gazioğlu
- Pituitary Center, Istanbul University-Cerrahpasa, Istanbul, Turkey
- Department of Neurosurgery, Istinye University, Istanbul, Turkey
| | - Mehmet Gönen
- Department of Industrial Engineering, College of Engineering, Koç University, Istanbul, Turkey
- School of Medicine, Koç University, Istanbul, Turkey
| | - Pınar Kadıoğlu
- Department of Internal Medicine, Division of Endocrinology, Metabolism, and Diabetes, Cerrahpasa Medical School, Istanbul University-Cerrahpaşa, Kocamustafapaşa Street No:53, 34098 Fatih, Istanbul, Turkey.
- Pituitary Center, Istanbul University-Cerrahpasa, Istanbul, Turkey.
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18
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Lu L, Wan X, Xu Y, Chen J, Shu K, Lei T. Prognostic Factors for Recurrence in Pituitary Adenomas: Recent Progress and Future Directions. Diagnostics (Basel) 2022; 12:diagnostics12040977. [PMID: 35454025 PMCID: PMC9024548 DOI: 10.3390/diagnostics12040977] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/01/2022] [Accepted: 04/11/2022] [Indexed: 02/04/2023] Open
Abstract
Pituitary adenomas (PAs) are benign lesions; nonetheless, some PAs exhibit aggressive behaviors, which lead to recurrence. The impact of pituitary dysfunction, invasion-related risks, and other complications considerably affect the quality of life of patients with recurrent PAs. Reliable prognostic factors are needed for recurrent PAs but require confirmation. This review summarizes research progress on two aspects—namely, the clinical and biological factors (biomarkers) for recurrent PAs. Postoperative residue, age, immunohistological subtypes, invasion, tumor size, hormone levels, and postoperative radiotherapy can predict the risk of recurrence in patients with PAs. Additionally, biomarkers such as Ki-67, p53, cadherin, pituitary tumor transforming gene, matrix metalloproteinase-9, epidermal growth factor receptor, fascin actin-bundling protein 1, cyclooxygenase-2, and some miRNAs and lncRNAs may be utilized as valuable tools for predicting PA recurrence. As no single marker can independently predict PA recurrence, we introduce an array of comprehensive models and grading methods, including multiple prognostic factors, to predict the prognosis of PAs, which have shown good effectiveness and would be beneficial for predicting PA recurrence.
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Affiliation(s)
| | | | | | | | | | - Ting Lei
- Correspondence: ; Tel./Fax: +86-27-8366-5202
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19
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Tabarin A, Assié G, Barat P, Bonnet F, Bonneville JF, Borson-Chazot F, Bouligand J, Boulin A, Brue T, Caron P, Castinetti F, Chabre O, Chanson P, Corcuff JB, Cortet C, Coutant R, Dohan A, Drui D, Espiard S, Gaye D, Grunenwald S, Guignat L, Hindie E, Illouz F, Kamenicky P, Lefebvre H, Linglart A, Martinerie L, North MO, Raffin-Samson ML, Raingeard I, Raverot G, Raverot V, Reznik Y, Taieb D, Vezzosi D, Young J, Bertherat J. Consensus statement by the French Society of Endocrinology (SFE) and French Society of Pediatric Endocrinology & Diabetology (SFEDP) on diagnosis of Cushing's syndrome. ANNALES D'ENDOCRINOLOGIE 2022; 83:119-141. [PMID: 35192845 DOI: 10.1016/j.ando.2022.02.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Cushing's syndrome is defined by prolonged exposure to glucocorticoids, leading to excess morbidity and mortality. Diagnosis of this rare pathology is difficult due to the low specificity of the clinical signs, the variable severity of the clinical presentation, and the difficulties of interpretation associated with the diagnostic methods. The present consensus paper by 38 experts of the French Society of Endocrinology and the French Society of Pediatric Endocrinology and Diabetology aimed firstly to detail the circumstances suggesting diagnosis and the biologic diagnosis tools and their interpretation for positive diagnosis and for etiologic diagnosis according to ACTH-independent and -dependent mechanisms. Secondly, situations making diagnosis complex (pregnancy, intense hypercortisolism, fluctuating Cushing's syndrome, pediatric forms and genetically determined forms) were detailed. Lastly, methods of surveillance and diagnosis of recurrence were dealt with in the final section.
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Affiliation(s)
- Antoine Tabarin
- Service Endocrinologie, Diabète et Nutrition, Université, Hôpital Haut-Leveque CHU de Bordeaux, 33604 Pessac, France.
| | - Guillaume Assié
- Centre de Référence Maladies Rares de la Surrénale (CRMRS), Service d'Endocrinologie, Hôpital Cochin, AP-HP, Université de Paris, Paris, France
| | - Pascal Barat
- Unité d'Endocrinologie-Diabétologie-Gynécologie-Obésité Pédiatrique, Hôpital des Enfants CHU Bordeaux, Bordeaux, France
| | - Fidéline Bonnet
- UF d'Hormonologie Hôpital Cochin, Université de Paris, Institut Cochin Inserm U1016, CNRS UMR8104, Paris, France
| | | | - Françoise Borson-Chazot
- Fédération d'Endocrinologie, Hôpital Louis-Pradel, Hospices Civils de Lyon, INSERM U1290, Université Lyon1, 69002 Lyon, France
| | - Jérôme Bouligand
- Faculté de Médecine Paris-Saclay, Unité Inserm UMRS1185 Physiologie et Physiopathologie Endocriniennes, Paris, France
| | - Anne Boulin
- Service de Neuroradiologie, Hôpital Foch, 92151 Suresnes, France
| | - Thierry Brue
- Aix-Marseille Université, Institut National de la Recherche Scientifique (INSERM) U1251, Marseille Medical Genetics, Marseille, France; Assistance publique-Hôpitaux de Marseille, Service d'Endocrinologie, Hôpital de la Conception, Centre de Référence Maladies Rares HYPO, 13005 Marseille, France
| | - Philippe Caron
- Service d'Endocrinologie et Maladies Métaboliques, Pôle Cardiovasculaire et Métabolique, CHU Larrey, 24, chemin de Pouvourville, TSA 30030, 31059 Toulouse cedex, France
| | - Frédéric Castinetti
- Aix-Marseille Université, Institut National de la Recherche Scientifique (INSERM) U1251, Marseille Medical Genetics, Marseille, France; Assistance publique-Hôpitaux de Marseille, Service d'Endocrinologie, Hôpital de la Conception, Centre de Référence Maladies Rares HYPO, 13005 Marseille, France
| | - Olivier Chabre
- Université Grenoble Alpes, UMR 1292 INSERM-CEA-UGA, Endocrinologie, CHU Grenoble Alpes, 38000 Grenoble, France
| | - Philippe Chanson
- Université Paris-Saclay, Inserm, Physiologie et Physiopathologie Endocriniennes, Assistance publique-Hôpitaux de Paris, Hôpital Bicêtre, Service d'Endocrinologie et des Maladies de la Reproduction, Centre de Référence des Maladies Rares de l'Hypophyse HYPO, Le Kremlin-Bicêtre, France
| | - Jean Benoit Corcuff
- Laboratoire d'Hormonologie, Service de Médecine Nucléaire, CHU Bordeaux, Laboratoire NutriNeuro, UMR 1286 INRAE, Université de Bordeaux, Bordeaux, France
| | - Christine Cortet
- Service d'Endocrinologie, Diabétologie, Métabolisme et Nutrition, CHU de Lille, Lille, France
| | - Régis Coutant
- Service d'Endocrinologie Pédiatrique, CHU Angers, Centre de Référence, Centre Constitutif des Maladies Rares de l'Hypophyse, CHU Angers, Angers, France
| | - Anthony Dohan
- Department of Radiology A, Hôpital Cochin, AP-HP, 75014 Paris, France
| | - Delphine Drui
- Service Endocrinologie-Diabétologie et Nutrition, l'institut du Thorax, CHU Nantes, 44092 Nantes cedex, France
| | - Stéphanie Espiard
- Service d'Endocrinologie, Diabétologie, Métabolisme et Nutrition, INSERM U1190, Laboratoire de Recherche Translationnelle sur le Diabète, 59000 Lille, France
| | - Delphine Gaye
- Service de Radiologie, Hôpital Haut-Lêveque, CHU de Bordeaux, 33604 Pessac, France
| | - Solenge Grunenwald
- Service d'Endocrinologie, Hôpital Larrey, CHU Toulouse, Toulouse, France
| | - Laurence Guignat
- Centre de Référence Maladies Rares de la Surrénale (CRMRS), Service d'Endocrinologie, Hôpital Cochin, AP-HP, Université de Paris, Paris, France
| | - Elif Hindie
- Service de Médecine Nucléaire, CHU de Bordeaux, Université de Bordeaux, Bordeaux, France
| | - Frédéric Illouz
- Centre de Référence Maladies Rares de la Thyroïde et des Récepteurs Hormonaux, Service Endocrinologie-Diabétologie-Nutrition, CHU Angers, 49933 Angers cedex 9, France
| | - Peter Kamenicky
- Assistance publique-Hôpitaux de Paris, Hôpital Bicêtre, Service d'Endocrinologie et des Maladies de la Reproduction, Centre de Référence des Maladies Rares de l'Hypophyse, 94275 Le Kremlin-Bicêtre, France
| | - Hervé Lefebvre
- Service d'Endocrinologie, Diabète et Maladies Métaboliques, CHU de Rouen, Rouen, France
| | - Agnès Linglart
- Paris-Saclay University, AP-HP, Endocrinology and Diabetes for Children, Reference Center for Rare Disorders of Calcium and Phosphate Metabolism, Filière OSCAR, and Platform of Expertise for Rare Disorders, INSERM, Physiologie et Physiopathologie Endocriniennes, Bicêtre Paris-Saclay Hospital, Le Kremlin-Bicêtre, France
| | - Laetitia Martinerie
- Service d'Endocrinologie Pédiatrique, CHU Robert-Debré, AP-HP, Paris, France; Université de Paris, Paris, France
| | - Marie Odile North
- Service de Génétique et Biologie Moléculaire, Hôpital Cochin, AP-HP, Université de Paris, Paris, France
| | - Marie Laure Raffin-Samson
- Service d'Endocrinologie Nutrition, Hôpital Ambroise-Paré, GHU Paris-Saclay, AP-HP Boulogne, EA4340, Université de Versailles-Saint-Quentin, Paris, France
| | - Isabelle Raingeard
- Maladies Endocriniennes, Hôpital Lapeyronie, CHU Montpellier, Montpellier, France
| | - Gérald Raverot
- Fédération d'Endocrinologie, Centre de Référence Maladies Rares Hypophysaires, "Groupement Hospitalier Est", Hospices Civils de Lyon, Lyon, France
| | - Véronique Raverot
- Hospices Civils de Lyon, LBMMS, Centre de Biologie Est, Service de Biochimie et Biologie Moléculaire, 69677 Bron cedex, France
| | - Yves Reznik
- Department of Endocrinology and Diabetology, CHU Côte-de-Nacre, 14033 Caen cedex, France; University of Caen Basse-Normandie, Medical School, 14032 Caen cedex, France
| | - David Taieb
- Aix-Marseille Université, CHU La Timone, AP-HM, Marseille, France
| | - Delphine Vezzosi
- Service d'Endocrinologie, Hôpital Larrey, CHU Toulouse, Toulouse, France
| | - Jacques Young
- Assistance publique-Hôpitaux de Paris, Hôpital Bicêtre, Service d'Endocrinologie et des Maladies de la Reproduction, Centre de Référence des Maladies Rares de l'Hypophyse, 94275 Le Kremlin-Bicêtre, France
| | - Jérôme Bertherat
- Centre de Référence Maladies Rares de la Surrénale (CRMRS), Service d'Endocrinologie, Hôpital Cochin, AP-HP, Université de Paris, Paris, France
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20
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Dai C, Feng M, Lu L, Sun B, Fan Y, Bao X, Yao Y, Deng K, Wang R, Kang J. Transsphenoidal Surgery of Corticotroph Adenomas With Cavernous Sinus Invasion: Results in a Series of 86 Consecutive Patients. Front Oncol 2022; 12:810234. [PMID: 35211404 PMCID: PMC8861297 DOI: 10.3389/fonc.2022.810234] [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: 11/06/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Transsphenoidal surgery (TSS) is the first-line treatment for corticotroph adenomas. Although most corticotroph adenomas are noninvasive microadenomas, a small subset of them invading cavernous sinus (CS) is notoriously difficult to manage. The aim of this study was to evaluate the surgical outcome of corticotroph adenomas with CSI from a single center. Patients and Methods The clinical features and outcomes of CD patients who underwent TSS between January 2000 and September 2019 at Peking Union Medical College Hospital were collected from medical records. The clinical, endocrinological, radiological, histopathological, and surgical outcomes, and a minimum 12-month follow-up of patients with corticotroph adenomas invading CS were retrospectively reviewed. Results Eighty-six patients with corticotroph adenomas invading CS were included in the study. The average age at TSS was 37.7 years (range, 12 to 67 years), with a female-to-male ratio of 3.1:1 (65/21). The median duration of symptoms was 52.6 months (range, 1.0 to 264 months). The average of maximum diameter of tumor was 17.6 mm (range, 4.5–70 mm). All included 86 patients underwent TSS using a microscopic or an endoscopic approach. Gross total resection was achieved in 63 patients (73.3%), subtotal resection was attained in 18 (20.9%), and partial resection was achieved in 5 (5.8%). After surgery, the overall postoperative immediate remission rate was 48.8% (42/86); 51.2% (44/86) of patients maintained persistent hypercortisolism. In 42 patients with initial remission, 16.7% (7/42) experienced a recurrence. In these patients with persistent disease and recurrent CD, data about further treatment were available for 30 patients. Radiotherapy was used for 15 patients, and 4 (26.7%) of them achieved biochemical remission. Repeat TSS was performed in 5 patients, and none achieved remission. Medication was administered in 4 patients, and one of them obtained disease control. Adrenalectomy was performed in 6 patients, and 5 (83.3%) achieved biochemical remission. At the last follow-up, 10 of 30 patients (33.3%) were in remission, and 20 patients still had persistent disease. The remission rate in corticotroph adenomas with cavernous sinus invasion (CSI) that underwent gross total resection and first TSS was significantly higher than that in patients undergoing subtotal resection, partial resection, and a second TSS (all p < 0.05). However, there was no significant difference in the remission rate between patients with different tumor sizes, Knosp grades, and surgical approaches (p > 0.05). Conclusion The management of corticotroph adenomas with CSI remain a therapeutic challenge due to incomplete resection of invasive and/or a large adenoma. With the application of multiple techniques, approximately half of the patients could achieve gross total resection and biochemical remission via TSS by experienced neurosurgeons. The extent of tumor resection and the number of operations were associated with surgical remission rate in corticotroph adenomas with CSI. If the remission was not achieved by surgery, other treatments including radiotherapy, medical therapy, and even bilateral adrenalectomy are required.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bowen Sun
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kan Deng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Kang
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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21
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Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage. J Pers Med 2022; 12:jpm12010112. [PMID: 35055424 PMCID: PMC8778760 DOI: 10.3390/jpm12010112] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/15/2021] [Accepted: 12/23/2021] [Indexed: 12/04/2022] Open
Abstract
Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, radiographic, and laboratory data. A modified Rankin scale (mRS) of 0–2 was defined as a favorable functional outcome, while an mRS of 3–6 was defined as an unfavorable functional outcome. We evaluated 90-day functional outcome and mortality to develop six ML-based predictive models and compared their efficacy with a traditional risk stratification scale, the intracerebral hemorrhage (ICH) score. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). A total of 553 patients (73.6%) reached the functional outcome at the 3rd month, with the 90-day mortality rate of 10.2%. Logistic regression (LR) and logistic regression CV (LRCV) showed the best predictive performance for functional outcome (AUC = 0.890 and 0.887, respectively), and category boosting presented the best predictive performance for the mortality (AUC = 0.841). Therefore, ML might be of potential assistance in the prediction of the prognosis of SICH.
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22
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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.
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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
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23
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Koh CH, Khan DZ, Digpal R, Layard Horsfall H, Ali AMS, Baldeweg SE, Bouloux PM, Dorward NL, Drake WM, Evanson J, Grieve J, Stoyanov D, Korbonits M, Marcus HJ. The clinical outcomes of imaging modalities for surgical management Cushing's disease - A systematic review and meta-analysis. Front Endocrinol (Lausanne) 2022; 13:1090144. [PMID: 36714581 PMCID: PMC9880448 DOI: 10.3389/fendo.2022.1090144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
INTRODUCTION Cushing's disease presents major diagnostic and management challenges. Although numerous preoperative and intraoperative imaging modalities have been deployed, it is unclear whether these investigations have improved surgical outcomes. Our objective was to investigate whether advances in imaging improved outcomes for Cushing's disease. METHODS Searches of PubMed and EMBASE were conducted. Studies reporting on imaging modalities and clinical outcomes after surgical management of Cushing's disease were included. Multilevel multivariable meta-regressions identified predictors of outcomes, adjusting for confounders and heterogeneity prior to investigating the effects of imaging. RESULTS 166 non-controlled single-arm studies were included, comprising 13181 patients over 44 years.The overall remission rate was 77.0% [CI: 74.9%-79.0%]. Cavernous sinus invasion (OR: 0.21 [CI: 0.07-0.66]; p=0.010), radiologically undetectable lesions (OR: 0.50 [CI: 0.37-0.69]; p<0.0001), previous surgery (OR=0.48 [CI: 0.28-0.81]; p=0.008), and lesions ≥10mm (OR: 0.63 [CI: 0.35-1.14]; p=0.12) were associated with lower remission. Less stringent thresholds for remission was associated with higher reported remission (OR: 1.37 [CI: 1.1-1.72]; p=0.007). After adjusting for this heterogeneity, no imaging modality showed significant differences in remission compared to standard preoperative MRI.The overall recurrence rate was 14.5% [CI: 12.1%-17.1%]. Lesion ≥10mm was associated with greater recurrence (OR: 1.83 [CI: 1.13-2.96]; p=0.015), as was greater duration of follow-up (OR: 1.53 (CI: 1.17-2.01); p=0.002). No imaging modality was associated with significant differences in recurrence.Despite significant improvements in detection rates over four decades, there were no significant changes in the reported remission or recurrence rates. CONCLUSION A lack of controlled comparative studies makes it difficult to draw definitive conclusions. Within this limitation, the results suggest that despite improvements in radiological detection rates of Cushing's disease over the last four decades, there were no changes in clinical outcomes. Advances in imaging alone may be insufficient to improve surgical outcomes. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42020187751.
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Affiliation(s)
- Chan Hee Koh
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
- Department of Neurosurgery, Royal Stoke University Hospital, Stoke, United Kingdom
- *Correspondence: Chan Hee Koh,
| | - Danyal Z. Khan
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Ronneil Digpal
- Department of Neurosurgery, University Hospital Southampton, Southampton, United Kingdom
| | - Hugo Layard Horsfall
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Ahmad M. S. Ali
- Department of Neurosurgery, The Walton Centre, Liverpool, United Kingdom
| | - Stephanie E. Baldeweg
- Department of Diabetes and Endocrinology, University College Hospital, London, United Kingdom
- Centre for Obesity & Metabolism, Department of Experimental & Translational Medicine, Division of Medicine, University College London, London, United Kingdom
| | - Pierre-Marc Bouloux
- Centre for Neuroendocrinology University College London Medical School, London, United Kingdom
| | - Neil L. Dorward
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - William M. Drake
- Centre for Endocrinology, Barts and The London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Jane Evanson
- Department of Radiology, Barts Health NHS Trust, London, United Kingdom
| | - Joan Grieve
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Márta Korbonits
- Centre for Endocrinology, Barts and The London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Hani J. Marcus
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
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Dai C, Feng M, Sun B, Bao X, Yao Y, Deng K, Ren Z, Zhao B, Lu L, Wang R, Kang J. Surgical outcome of transsphenoidal surgery in Cushing's disease: a case series of 1106 patients from a single center over 30 years. Endocrine 2022; 75:219-227. [PMID: 34415482 DOI: 10.1007/s12020-021-02848-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/05/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Transsphenoidal surgery (TSS) is the first-line treatment for patients with Cushing's disease (CD). However, the reported remission rates of patients who received TSS vary widely between different studies, and the predictors of surgical outcomes remain controversial. The present study analyzed the early outcome of TSS in a large population of patients with CD at a single center, and identified potential predictors of initial remission of TSS in patients with CD. METHODS The clinical features and surgical outcomes of CD patients who underwent TSS between 1988 and 2018 at Peking Union Medical College Hospital (PUMCH) were collected and analyzed from their medical records. RESULTS Of the 1604 CD patients who underwent TSS at PUMCH between February 1988 and October 2018, 1106 patients had complete medical data and pathological results. After surgery, the overall postoperative initial remission rate was 72.5, and 27.5% of patients maintained persistent hypercortisolism. The initial remission rate of patients with preoperative noninvasive adenoma based on MRI (77.1%), intraoperative noninvasiveness (72.5%), microadenoma (74.3%), pathological confirmation (76.4%), and first TSS (73.9%) was significantly higher than that in patients with preoperative invasive adenoma (53.0%), intraoperative invasiveness (60.7%), macroadenomas (65.9%), pathologically negative (49.7%), and repeat TSS (56.0%), respectively (all P < 0.05). The initial remission rate in patients with pseudocapsule-based extracapsular resection (88.1%), MRI-visible adenoma (74.2%) was higher than that in patients without pseudocapsule-based extracapsular resection (77.1%), and with MRI-negative results (64.5%), respectively, but did not reach statistical significance (All P > 0.05). Striking, there was no significant differences in initial remission rates between patients who underwent selective adenomectomy and enlarged adenomectomy (P > 0.05). Whereas, the initial remission rates in patients who underwent partial hypophysectomy only was 51.0%, which was much lower than that in patients underwent selective adenomectomy and enlarged adenomectomy (P < 0.05). CONCLUSION The TSS is a safe and effective procedure for the treatment of CD. Whereas, preoperative invasiveness based on MRI, intraoperative invasiveness, macroadenomas pathologically negative, and repeat TSS are related to lower initial remission rates.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bowen Sun
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kan Deng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zuyuan Ren
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Binghao Zhao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Jun Kang
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
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Stumpo V, Staartjes VE, Regli L, Serra C. Machine Learning in Pituitary Surgery. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:291-301. [PMID: 34862553 DOI: 10.1007/978-3-030-85292-4_33] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.
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Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Zhang W, Li D, Feng M, Hu B, Fan Y, Chen Q, Wang R. Electronic Medical Records as Input to Predict Postoperative Immediate Remission of Cushing's Disease: Application of Word Embedding. Front Oncol 2021; 11:754882. [PMID: 34722308 PMCID: PMC8548651 DOI: 10.3389/fonc.2021.754882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
Background No existing machine learning (ML)-based models use free text from electronic medical records (EMR) as input to predict immediate remission (IR) of Cushing’s disease (CD) after transsphenoidal surgery. Purpose The aim of the present study is to develop an ML-based model that uses EMR that include both structured features and free text as input to preoperatively predict IR after transsphenoidal surgery. Methods A total of 419 patients with CD from Peking Union Medical College Hospital were enrolled between January 2014 and August 2020. The EMR of the patients were embedded and transformed into low-dimensional dense vectors that can be included in four ML-based models together with structured features. The area under the curve (AUC) of receiver operating characteristic curves was used to evaluate the performance of the models. Results The overall remission rate of the 419 patients was 75.7%. From the results of logistic multivariate analysis, operation (p < 0.001), invasion of cavernous sinus from MRI (p = 0.046), and ACTH (p = 0.024) were strongly correlated with IR. The AUC values for the four ML-based models ranged from 0.686 to 0.793. The highest AUC value (0.793) was for logistic regression when 11 structured features and “individual conclusions of the case by doctor” were included. Conclusion An ML-based model was developed using both structured and unstructured features (after being processed using a word embedding method) as input to preoperatively predict postoperative IR.
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Affiliation(s)
- Wentai Zhang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China
| | - Dongfang Li
- School of Computer Science, and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Ming Feng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China
| | - Baotian Hu
- School of Computer Science, and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Qingcai Chen
- School of Computer Science, and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
| | - Renzhi Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China
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Guo T, Fang Z, Yang G, Zhou Y, Ding N, Peng W, Gong X, He H, Pan X, Chai X. Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients. Front Cardiovasc Med 2021; 8:727773. [PMID: 34604356 PMCID: PMC8484712 DOI: 10.3389/fcvm.2021.727773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/24/2021] [Indexed: 01/01/2023] Open
Abstract
Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.
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Affiliation(s)
- Tuo Guo
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Zhuo Fang
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Guifang Yang
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Yang Zhou
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Ning Ding
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Wen Peng
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xun Gong
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Huaping He
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xiaogao Pan
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
| | - Xiangping Chai
- Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.,Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.,Trauma Center, Changsha, China
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Zoli M, Staartjes VE, Guaraldi F, Friso F, Rustici A, Asioli S, Sollini G, Pasquini E, Regli L, Serra C, Mazzatenta D. Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming? Neurosurg Focus 2021; 48:E5. [PMID: 32480364 DOI: 10.3171/2020.3.focus2060] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). METHODS All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). RESULTS The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. CONCLUSIONS ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.
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Affiliation(s)
- Matteo Zoli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Victor E Staartjes
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland.,4Neurosurgery, Amsterdam Movement Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
| | - Federica Guaraldi
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Filippo Friso
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna
| | - Arianna Rustici
- 5Department of Neuroradiology, IRCCS Istitute of Neurological Sciences of Bologna.,6Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna
| | - Sofia Asioli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy.,7Section of Anatomic Pathology 'M. Malpighi' at Bellaria Hospital, Bologna; and
| | - Giacomo Sollini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Ernesto Pasquini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Luca Regli
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Carlo Serra
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Diego Mazzatenta
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
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Qiao N, Shen M, He W, He M, Zhang Z, Ye H, Li Y, Shou X, Li S, Jiang C, Wang Y, Zhao Y. Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study. Pituitary 2021; 24:53-61. [PMID: 33025547 DOI: 10.1007/s11102-020-01086-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/19/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This study aims to train and validate machine learning prediction models for early endocrine remission of acromegalic patients. METHODS The training cohort included 833 patients with growth hormone (GH) secreting pituitary adenoma from 2010 to 2018. We trained a partial model (only using pre-operative variables) and a full model (using all variables) to predict off-medication endocrine remission at six-month follow-up after surgery using multiple algorithms. The models were validated in 99 prospectively collected patients from a second campus and 52 patients from a third institution. RESULTS C-statistic and the accuracy of the best partial model was 0.803 (95% CI 0.757-0.849) and 72.5% (95% CI 67.6-77.5%), respectively. C-statistic and the accuracy of the best full model was 0.888 (95% CI 0.861-0.914) and 80.3% (95% CI 77.5-83.1%), respectively. The c-statistics (and accuracy) of using only Knosp grade, total resection, or postoperative day 1 GH level as the single predictor were lower than our partial model or full model (p < 0.001). C-statistics remained similar in the prospective cohort (partial model 0.798, and full model 0.903) and in the external cohort (partial model 0.771, and full model 0.871). A web-based application integrated with the trained models was published at https://deepvep.shinyapps.io/Acropred/ . CONCLUSION We developed and validated interpretable and applicable machine learning models to predict early endocrine remission after surgical resection of a GH-secreting pituitary adenoma. Predication accuracy of the trained models were better than those using single variables.
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Affiliation(s)
- Nidan Qiao
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Medical Science in Clinical Investigation, Harvard Medical School, Boston, USA
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Pituitary Tumor Center, Shanghai, China
| | - Ming Shen
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Pituitary Tumor Center, Shanghai, China
| | - Wenqiang He
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Pituitary Tumor Center, Shanghai, China
| | - Min He
- Department of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhaoyun Zhang
- Department of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongying Ye
- Department of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China
| | - Yiming Li
- Department of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China
| | - Xuefei Shou
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Pituitary Tumor Center, Shanghai, China
| | - Shiqi Li
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Pituitary Tumor Center, Shanghai, China
| | - Changzhen Jiang
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fujian Medical University, 20 Chazhong Road, Fujian, China.
| | - Yongfei Wang
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China.
- Neurosurgical Institute of Fudan University, Shanghai, China.
- Shanghai Pituitary Tumor Center, Shanghai, China.
| | - Yao Zhao
- Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China.
- Neurosurgical Institute of Fudan University, Shanghai, China.
- Shanghai Pituitary Tumor Center, Shanghai, China.
- State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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30
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Hou B, Gao L, Shi L, Luo Y, Guo X, Young GS, Qin L, Zhu H, Lu L, Wang Z, Feng M, Bao X, Wang R, Xing B, Feng F. Reversibility of impaired brain structures after transsphenoidal surgery in Cushing's disease: a longitudinal study based on an artificial intelligence-assisted tool. J Neurosurg 2021; 134:512-521. [PMID: 31899871 DOI: 10.3171/2019.10.jns191400] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 10/25/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Cushing's disease (CD) involves brain impairments caused by excessive cortisol. Whether these impairments are reversible in remitted CD after surgery has long been controversial due to a lack of high-quality longitudinal studies. In this study the authors aimed to assess the reversibility of whole-brain changes in remitted CD after transsphenoidal surgery (TSS), and its correlations with clinical and hormonal parameters, in the largest longitudinal study cohort to date for CD patient brain analysis. METHODS Fifty patients with pathologically diagnosed CD and 36 matched healthy controls (HCs) were enrolled in a tertiary comprehensive hospital and national pituitary disease registry center in China. 3-T MRI studies were analyzed using an artificial intelligence-assisted web-based autosegmentation tool to quantify 3D brain volumes. Clinical parameters as well as levels of serum cortisol, adrenocorticotrophic hormone (ACTH), and 24-hour urinary free cortisol were collected for the correlation analysis. All CD patients underwent TSS and 46 patients achieved remission. All clinical, hormonal, and MRI parameters were reevaluated at the 3-month follow-up after surgery. RESULTS Widespread brain volume loss was observed in active CD patients compared with HCs, including total gray matter (p = 0.003, with false discovery rate [FDR] correction) and the frontal, parietal, occipital, and temporal lobes; insula; cingulate lobe; and enlargement of lateral and third ventricles (p < 0.05, corrected with FDR). All affected brain regions improved significantly after TSS (p < 0.05, corrected with FDR). In patients with remitted CD, total gray matter and most brain regions (except the frontal and temporal lobes) showed full recovery of volume, with volumes that did not differ from those of HCs (p > 0.05, corrected with FDR). ACTH and serum cortisol changes were negatively correlated with brain volume changes during recovery (p < 0.05). CONCLUSIONS This study demonstrates the rapid reversal of total gray matter loss in remitted CD. The combination of full recovery areas and partial recovery areas after TSS is consistent with the incomplete recovery of memory and cognitive function observed in CD patients in clinical practice. Correlation analyses suggest that ACTH and serum cortisol levels are reliable serum biomarkers of brain recovery for clinical use after surgery.
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Affiliation(s)
- Bo Hou
- Departments of1Radiology and
| | - Lu Gao
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
- 4Department of Radiology, Harvard Medical School, Boston, Massachusetts
- 5Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lin Shi
- 6Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, China
- 7BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Yishan Luo
- 7BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Xiaopeng Guo
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
| | - Geoffrey S Young
- 4Department of Radiology, Harvard Medical School, Boston, Massachusetts
- 5Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lei Qin
- 4Department of Radiology, Harvard Medical School, Boston, Massachusetts
- 8Department of Imaging, Dana-Farber Cancer Institute, Boston, Massachusetts; and
| | - Huijuan Zhu
- 9Key Laboratory of Endocrinology of the National Health Commission of the People's Republic of China, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lu
- 9Key Laboratory of Endocrinology of the National Health Commission of the People's Republic of China, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Wang
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
| | - Ming Feng
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
| | - Xinjie Bao
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
| | - Renzhi Wang
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
| | - Bing Xing
- 2Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 3China Pituitary Disease Registry Center, China Pituitary Adenoma Specialist Council, Beijing, China
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Zhang W, Sun M, Fan Y, Wang H, Feng M, Zhou S, Wang R. Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease. Front Endocrinol (Lausanne) 2021; 12:635795. [PMID: 33737912 PMCID: PMC7961560 DOI: 10.3389/fendo.2021.635795] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/25/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND There are no established accurate models that use machine learning (ML) methods to preoperatively predict immediate remission after transsphenoidal surgery (TSS) in patients diagnosed with histology-positive Cushing's disease (CD). PURPOSE Our current study aims to devise and assess an ML-based model to preoperatively predict immediate remission after TSS in patients with CD. METHODS A total of 1,045 participants with CD who received TSS at Peking Union Medical College Hospital in a 20-year period (between February 2000 and September 2019) were enrolled in the present study. In total nine ML classifiers were applied to construct models for the preoperative prediction of immediate remission with preoperative factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the models. The performance of each ML-based model was evaluated in terms of AUC. RESULTS The overall immediate remission rate was 73.3% (766/1045). First operation (p<0.001), cavernous sinus invasion on preoperative MRI(p<0.001), tumour size (p<0.001), preoperative ACTH (p=0.008), and disease duration (p=0.010) were significantly related to immediate remission on logistic univariate analysis. The AUCs of the models ranged between 0.664 and 0.743. The highest AUC, i.e., the best performance, was 0.743, which was achieved by stacking ensemble method with four factors: first operation, cavernous sinus invasion on preoperative MRI, tumour size and preoperative ACTH. CONCLUSION We developed a readily available ML-based model for the preoperative prediction of immediate remission in patients with CD.
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Affiliation(s)
- Wentai Zhang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mengke Sun
- Medical Imaging, Robotics, Analytic Computing Laboratory/Engineering (MIRACLE), Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Ming Feng, ; Shaohua Zhou, ; Renzhi Wang,
| | - Shaohua Zhou
- Medical Imaging, Robotics, Analytic Computing Laboratory/Engineering (MIRACLE), Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- *Correspondence: Ming Feng, ; Shaohua Zhou, ; Renzhi Wang,
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Ming Feng, ; Shaohua Zhou, ; Renzhi Wang,
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Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S, Feng M, Wang R. Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease. J Clin Endocrinol Metab 2021; 106:e217-e231. [PMID: 33000120 DOI: 10.1210/clinem/dgaa698] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 09/24/2020] [Indexed: 12/14/2022]
Abstract
CONTEXT Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up. OBJECTIVE We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD. METHODS We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features and applied 5 ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. RESULTS Eighty-eight (43.8%) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had a low body mass index, a Knosp grade of III-IV, and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grading and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC, and age were the most important features, and showed the reliability and clinical practicability of the Adaboost model in DC prediction. CONCLUSIONS Machine learning-based models could serve as an effective noninvasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yichao Li
- DHC Software Co. Ltd, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijuan Zhu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lin Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | | | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shanshan Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Dai C, Fan Y, Liu X, Bao X, Yao Y, Wang R, Feng M. Predictors of Immediate Remission after Surgery in Cushing's Disease Patients: A Large Retrospective Study from a Single Center. Neuroendocrinology 2021; 111:1141-1150. [PMID: 32512562 DOI: 10.1159/000509221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 06/08/2020] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Transsphenoidal surgery (TSS) is the first-line treatment of patients with Cushing's disease (CD). However, biochemical remission rates after TSS for CD vary from 59 to 95%, and the predictors of surgical outcomes remain unclear. The aim of this study was to identify the predictors of early outcomes in patients with CD treated with TSS. METHODS The clinical features and outcomes of CD patients who underwent TSS between February 2000 and September 2019 at the Peking Union Medical College Hospital were collected from medical records and analyzed. Uni- and multivariate odds ratio (OR) analyses were performed to identify the predictors of early outcomes in patients with CD. RESULTS A total of 1,045 patients were included. The median age at TSS was 34.0 years (IQR 26.0-45.0), with a female:male ratio of 4.2:1 (844/201). The median duration of symptoms was 46.0 months (IQR 24.0-72.0). After surgery, the overall postoperative immediate remission rate was 73.3%, and 26.7% of patients had persistent hypercortisolism. Univariate analysis demonstrated that the number of operations was correlated with a lower immediate remission rate (OR 0.393, 95% CI 0.266-0.580, p = 0.000), as was tumor size (OR 0.462, 95% CI 0.334-0.639, p = 0.000), the duration of disease (OR 0.996, 95% CI 0.993-0.999, p = 0.003), and preoperative ACTH concentration (0.998, 95% CI 0.996-0.999, p = 0.003). Cavernous sinus invasion has also been identified as an important factor associated with a lower immediate remission rate (OR 0.275, 95% CI 0.166-0.456, p = 0.000). No correlations were detected between the immediate outcomes and age, gender, BMI, the combination of a low- and high-dose dexamethasone suppression test, preoperative morning serum cortisol level, or 24-h urinary free cortisol level (all p > 0.05). The results of multivariate analysis were similar to those of univariate analysis. Preoperative ACTH ≤67.35 ng/L predicted remission with 60.9% sensitivity and 49.5% specificity (AUC 0.553; p = 0.008). A cutoff of ≤64.5 months for disease duration predicted immediate remission with 40.5% sensitivity and 71.0% specificity (AUC 0.552; p = 0.01). CONCLUSION Early outcomes of TSS in CD patients can be predicted by factors including the number of operations, duration of disease, tumor invasion, tumor size, and preoperative ACTH concentration. These predictors can be used to improve the perioperative management of CD patients.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaohai Liu
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,
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Hinojosa-Amaya JM, Cuevas-Ramos D. The definition of remission and recurrence of Cushing's disease. Best Pract Res Clin Endocrinol Metab 2021; 35:101485. [PMID: 33472761 DOI: 10.1016/j.beem.2021.101485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Accurate classification of postsurgical remission, and early recognition of recurrence are crucial to timely treat and prevent excess mortality in Cushing's Disease, yet the criteria used to define remission are variable and there is no consensus to define recurrence. Remission is defined as postsurgical hypocortisolemia, but delayed remission may occur. Recurrence is the return of clinical manifestations with biochemical evidence of hypercortisolism. The proper combination of tests and their timing are controversial. Reliable predicting tools may lead to earlier diagnosis upon recurrence. Many factors have been studied independently for prediction with variable performance. Novel artificial intelligence approaches seek to integrate these variables into risk calculators and machine-learning algorithms with an acceptable short-term predictive performance but lack longer-term accuracy. Prospective studies using these approaches are needed. This review summarizes the evidence behind the definitions of remission and recurrence and provide an overview of the available tools to predict and/or diagnose them.
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Affiliation(s)
- José Miguel Hinojosa-Amaya
- Pituitary Clinic, Endocrinology Division and Department of Medicine, Hospital Universitario "Dr. José E. González", Universidad Autónoma de Nuevo León, Monterrey, Mexico.
| | - Daniel Cuevas-Ramos
- Neuroendocrinology Clinic, Department of Endocrinology and Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
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Braun LT, Rubinstein G, Zopp S, Vogel F, Schmid-Tannwald C, Escudero MP, Honegger J, Ladurner R, Reincke M. Recurrence after pituitary surgery in adult Cushing's disease: a systematic review on diagnosis and treatment. Endocrine 2020; 70:218-231. [PMID: 32743767 PMCID: PMC7396205 DOI: 10.1007/s12020-020-02432-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/20/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE Recurrence after pituitary surgery in Cushing's disease (CD) is a common problem ranging from 5% (minimum) to 50% (maximum) after initially successful surgery, respectively. In this review, we give an overview of the current literature regarding prevalence, diagnosis, and therapeutic options of recurrent CD. METHODS We systematically screened the literature regarding recurrent and persistent Cushing's disease using the MESH term Cushing's disease and recurrence. Of 717 results in PubMed, all manuscripts in English and German published between 1980 and April 2020 were screened. Case reports, comments, publications focusing on pediatric CD or CD in veterinary disciplines or studies with very small sample size (patient number < 10) were excluded. Also, papers on CD in pregnancy were not included in this review. RESULTS AND CONCLUSIONS Because of the high incidence of recurrence in CD, annual clinical and biochemical follow-up is paramount. 50% of recurrences occur during the first 50 months after first surgery. In case of recurrence, treatment options include second surgery, pituitary radiation, targeted medical therapy to control hypercortisolism, and bilateral adrenalectomy. Success rates of all these treatment options vary between 25 (some of the medical therapy) and 100% (bilateral adrenalectomy). All treatment options have specific advantages, limitations, and side effects. Therefore, treatment decisions have to be individualized according to the specific needs of the patient.
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Affiliation(s)
- Leah T Braun
- Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, München, Germany
| | - German Rubinstein
- Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, München, Germany
| | - Stephanie Zopp
- Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, München, Germany
| | - Frederick Vogel
- Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, München, Germany
| | | | - Montserrat Pazos Escudero
- Klinik und Poliklinik für Strahlentherapie und Radioonkologie, Klinikum der Universität München, München, Germany
| | - Jürgen Honegger
- Department for Neurosurgery, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Roland Ladurner
- Klinik für Allgemeine, Unfall- und Wiederherstellungschirurgie, Campus Innenstadt, Klinikum der Universität München, München, Germany
| | - Martin Reincke
- Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Klinikum der Universität München, München, Germany.
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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Voglis S, van Niftrik CHB, Staartjes VE, Brandi G, Tschopp O, Regli L, Serra C. Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery. Pituitary 2020; 23:543-551. [PMID: 32488759 DOI: 10.1007/s11102-020-01056-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE Hyponatremia after pituitary surgery is a frequent finding with potential severe complications and the most common cause for readmission. Several studies have found parameters associated with postoperative hyponatremia, but no reliable specific predictor was described yet. This pilot study evaluates the feasibility of machine learning (ML) algorithms to predict postoperative hyponatremia after resection of pituitary lesions. METHODS Retrospective screening of a prospective registry of patients who underwent transsphenoidal surgery for pituitary lesions. Hyponatremia within 30 days after surgery was the primary outcome. Several pre- and intraoperative clinical, procedural and laboratory features were selected to train different ML algorithms. Trained models were compared using common performance metrics. Final model was internally validated on the testing dataset. RESULTS From 207 patients included in the study, 44 (22%) showed a hyponatremia within 30 days postoperatively. Hyponatremic measurements peaked directly postoperatively (day 0-1) and around day 7. Bootstrapped performance metrics of different trained ML-models showed largest area under the receiver operating characteristic curve (AUROC) for the boosted generalized linear model (67.1%), followed by the Naïve Bayes classifier (64.6%). The discriminative capability of the final model was assessed by predicting on unseen dataset. Large AUROC (84.3%; 67.0-96.4), sensitivity (81.8%) and specificity (77.5%) with an overall accuracy of 78.4% (66.7-88.2) was reached. CONCLUSION Our trained ML-model was able to learn the complex risk factor interactions and showed a high discriminative capability on unseen patient data. In conclusion, ML-methods can predict postoperative hyponatremia and thus potentially reduce morbidity and improve patient safety.
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Affiliation(s)
- Stefanos Voglis
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland.
| | - Christiaan H B van Niftrik
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Victor E Staartjes
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Giovanna Brandi
- Neurosurgical Intensive Care Unit, Institute for Intensive Care Medicine, University Hospital and University of Zurich, Zurich, Switzerland
| | - Oliver Tschopp
- Department of Endocrinology, Diabetes, and Clinical Nutrition, University Hospital and University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
| | - Carlo Serra
- Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland
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Fan Y, Li D, Liu Y, Feng M, Chen Q, Wang R. Toward better prediction of recurrence for Cushing’s disease: a factorization-machine based neural approach. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01192-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: A review. Artif Intell Med Imaging 2020; 1:70-77. [DOI: 10.35711/aimi.v1.i2.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/15/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
The aim of this review was to provide an overview of the main concepts in machine learning (ML) and to analyze the ML applications in the imaging of pituitary adenomas. After describing the clinical, pathological and imaging features of pituitary tumors, we defined the difference between ML and classical rule-based algorithms, we illustrated the fundamental ML techniques: supervised, unsupervised and reinforcement learning and explained the characteristic of deep learning, a ML approach employing networks inspired by brain’s structure. Pre-treatment assessment and neurosurgical outcome prediction were the potential ML applications using magnetic resonance imaging. Regarding pre-treatment assessment, ML methods were used to have information about tumor consistency, predict cavernous sinus invasion and high proliferative index, discriminate null cell adenomas, which respond to neo-adjuvant radiotherapy from other subtypes, predict somatostatin analogues response and visual pathway injury. Regarding neurosurgical outcome prediction, the following applications were discussed: Gross total resection prediction, evaluation of Cushing disease recurrence after transsphenoidal surgery and prediction of cerebrospinal fluid fistula’s formation after surgery. Although clinical applicability requires more replicability, generalizability and validation, results are promising, and ML software can be a potential power to facilitate better clinical decision making in pituitary tumor patients.
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Affiliation(s)
- Elvira Guerriero
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples 80131, Italy
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Soldozy S, Farzad F, Young S, Yağmurlu K, Norat P, Sokolowski J, Park MS, Jane JA, Syed HR. Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning. World Neurosurg 2020; 146:315-321.e1. [PMID: 32711142 DOI: 10.1016/j.wneu.2020.07.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/15/2020] [Accepted: 07/17/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Machine learning has emerged as a viable asset in the setting of pituitary surgery. In the past decade, the number of machine learning models developed to aid in the diagnosis of pituitary lesions and predict intraoperative and postoperative complications following transsphenoidal surgery has increased exponentially. As computational processing power continues to increase, big data sets continue to expand, and learning algorithms continue to surpass gold standard predictive tools, machine learning will serve to become an important component in improving patient care and outcomes. METHODS Relevant studies were identified based on a literature search in PubMed and MEDLINE databases, as well as from other sources including reference lists of published articles. RESULTS Radiomics and artificial neural networks comprise the majority of machine learning-based applications in pituitary surgery. Radiomics serves to quantify specific imaging features, which can then be used to noninvasively identify tumor characteristics and make definitive diagnoses, circumventing presurgical biopsy altogether. Neural networks can be adapted to predict intraoperative changes in visual evoked potentials or cerebral spinal fluid leak. In addition, these algorithms may be combined with others to predict tumor aggressiveness, gross total resection, recurrence and remission, and even total cost burden. CONCLUSIONS The field of machine learning is broad, with radiomics and artificial neural networks comprising 2 commonly used supervised learning methods in pituitary surgery. Given the large heterogeneity of pituitary and sellar lesions, the promise of machine learning lies in its ability to identify relationships and patterns that are otherwise hidden from standard statistical methods. While machine learning has great potential as a clinical adjunct during the surgical preplanning process and in predicting complications and outcomes, challenges moving forward include standardization and validation of these paradigms.
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Affiliation(s)
- Sauson Soldozy
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Faraz Farzad
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Steven Young
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Kaan Yağmurlu
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Pedro Norat
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Jennifer Sokolowski
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Min S Park
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - John A Jane
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Hasan R Syed
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA.
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Saha A, Tso S, Rabski J, Sadeghian A, Cusimano MD. Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions. Pituitary 2020; 23:273-293. [PMID: 31907710 DOI: 10.1007/s11102-019-01026-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients. METHOD We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations. RESULTS Of the 16 studies included in our analysis, 10 appeared in 2018-2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model. CONCLUSION Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.
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Affiliation(s)
- Ashirbani Saha
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada.
| | - Samantha Tso
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
| | - Jessica Rabski
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
| | | | - Michael D Cusimano
- Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
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Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 2020; 67:412-422. [PMID: 31673954 DOI: 10.1007/s12020-019-02121-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/21/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE Preoperative prediction of transsphenoidal surgical (TSS) response is important for determining individual treatment strategies for acromegaly. There is currently no accurate predictive model for TSS response for acromegaly. The current study sought to develop and validate machine learning (ML)-based models for preoperative prediction of TSS response for acromegaly. METHODS Six hundred sixty-eight patients with acromegaly were enrolled and divided into training (n = 534) and text datasets (n = 134) in this retrospective, data mining and ML study. The forward search algorithm was used to select features, and six ML algorithms were applied to construct TSS response prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. Model calibration, discrimination ability, and clinical usefulness were also assessed. RESULTS Three hundred forty-nine (52.2%) patients achieved postoperative remission criteria and exhibited good TSS response. A univariate analysis was conducted and eight features, including age, hypertension, ophthalmic disorders, GH, IGF-1, nadir GH, maximal tumor diameter, and Knosp grade, were significantly associated with the TSS response in patients with acromegaly. After feature selection, the gradient boosting decision tree (GBDT), which was constructed with the eight significant features showed the best favorable discriminatory ability both the training (AUC = 0.8555) and validation (AUC = 0.8178) cohorts. The GBDT model showed good discrimination ability and calibration, with the highest levels of accuracy and specificity, and provided better estimates of TTS responses of patients with acromegaly compared with using only the Knosp grade. Decision curve analysis confirmed that the model was clinically useful. CONCLUSIONS ML-based models could aid neurosurgeons in the preoperative prediction of TTS response for patients with acromegaly, and could contribute to determining individual treatment strategies.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | | | - Yichao Li
- DHC Software Co. Ltd, Beijing, China
| | - Shanshan Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China.
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuai Fu Yuan, Dongcheng District, 100730, Beijing, China.
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Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F, Feng M, Wang R. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up. Front Endocrinol (Lausanne) 2020; 11:643. [PMID: 33042013 PMCID: PMC7525125 DOI: 10.3389/fendo.2020.00643] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/07/2020] [Indexed: 12/11/2022] Open
Abstract
Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy. Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery. Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction. Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.
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Affiliation(s)
- Congxin Dai
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yichao Li
- DHC Mediway Technology Co., Ltd., Beijing, China
| | - Xinjie Bao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yansheng Li
- DHC Mediway Technology Co., Ltd., Beijing, China
| | - Mingliang Su
- DHC Mediway Technology Co., Ltd., Beijing, China
| | - Yong Yao
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kan Deng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bing Xing
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Ming Feng
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Renzhi Wang
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