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Chukwujindu E, Faiz H, Ai-Douri S, Faiz K, De Sequeira A. Role of artificial intelligence in brain tumour imaging. Eur J Radiol 2024; 176:111509. [PMID: 38788610 DOI: 10.1016/j.ejrad.2024.111509] [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: 01/24/2024] [Revised: 04/29/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.
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Affiliation(s)
| | | | | | - Khunsa Faiz
- McMaster University, Department of Radiology, L8S 4L8, Canada.
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Sathya A, Goyal-Honavar A, Chacko AG, Jasper A, Chacko G, Devakumar D, Seelam JA, Sasidharan BK, Pavamani SP, Thomas HMT. Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs? Acta Neurochir (Wien) 2024; 166:91. [PMID: 38376544 DOI: 10.1007/s00701-024-05977-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/18/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making. METHODS Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. RESULTS A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66. CONCLUSIONS Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.
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Affiliation(s)
- Sathya A
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | | | - Ari G Chacko
- Department of Neurosurgery, Christian Medical College, Vellore, India
| | - Anitha Jasper
- Department of Radiodiagnosis, Christian Medical College, Vellore, India
| | - Geeta Chacko
- Department of General Pathology, Christian Medical College, Vellore, India
| | - Devadhas Devakumar
- Department of Nuclear Medicine, Christian Medical College, Vellore, India
| | | | - Balu Krishna Sasidharan
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | - Simon P Pavamani
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India
| | - Hannah Mary T Thomas
- Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India.
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Zhang W, Zhang D, Liu S, Wang H, Liu X, Dai C, Fang Y, Fan Y, Wei Z, Feng M, Wang R. Predicting delayed remission in Cushing's disease using radiomics models: a multi-center study. Front Oncol 2024; 13:1218897. [PMID: 38264759 PMCID: PMC10803608 DOI: 10.3389/fonc.2023.1218897] [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: 05/08/2023] [Accepted: 11/28/2023] [Indexed: 01/25/2024] Open
Abstract
Purpose No multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing's disease (CD). The present study aims to build clinical and radiomics models based on data from three centers to predict DR after TSS in CD. Methods A total of 122 CD patients from Peking Union Medical College Hospital, Xuanwu Hospital, and Fuzhou General Hospital were enrolled between January 2000 and January 2019. The T1-weighted gadolinium-enhanced MRI images and clinical data were used as inputs to build clinical and radiomics models. The regions of interest (ROI) of MRI images were automatically defined by a deep learning algorithm developed by our team. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. In total, 10 machine learning algorithms were used to construct models. Results The overall DR rate is 44.3% (54/122). According to multivariate Logistic regression analysis, patients with higher BMI and lower postoperative cortisol levels are more likely to achieve a higher rate of delayed remission. Among the 10 models, XGBoost achieved the best performance among all models in both clinical and radiomics models with AUC values of 0.767 and 0.819 respectively. The results from SHAP value and LIME algorithms revealed that postoperative cortisol level (PoC) and BMI were the most important features associated with DR. Conclusion Radiomics models can be built as an effective noninvasive method to predict DR and might be useful in assisting neurosurgeons in making therapeutic plans after TSS for CD patients. These results are preliminary and further validation in a larger patient sample is needed.
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Affiliation(s)
- Wentai Zhang
- Department of Thoracic Surgery, Peking University First Hospital, Beijing, China
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China
| | - Dewei Zhang
- Department of Neurosurgery, Jing'an District Center Hospital of Shanghai, Fudan University, Shanghai, China
| | - Shaocheng Liu
- Intensive Care Unit, Beijing Mentougou District Hospital, Beijing, China
| | - He Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China
| | - Xiaohai Liu
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Congxin Dai
- Department of Neurosurgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yi Fang
- Department of Neurosurgery, The Fuzhou General Hospital, Fuzhou, China
| | - Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhenqing Wei
- Department of Neurosurgery, The First Hospital Affiliated to Dalian Medical University, Dalian, China
| | - Ming Feng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, Beijing, 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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Bioletto F, Prencipe N, Berton AM, Aversa LS, Cuboni D, Varaldo E, Gasco V, Ghigo E, Grottoli S. Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives. J Clin Med 2024; 13:336. [PMID: 38256471 PMCID: PMC10816809 DOI: 10.3390/jcm13020336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/29/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024] Open
Abstract
Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Silvia Grottoli
- Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, 10126 Turin, Italy; (F.B.); (N.P.); (A.M.B.); (L.S.A.); (D.C.); (E.V.); (V.G.); (E.G.)
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Khan DZ, Hanrahan JG, Baldeweg SE, Dorward NL, Stoyanov D, Marcus HJ. Current and Future Advances in Surgical Therapy for Pituitary Adenoma. Endocr Rev 2023; 44:947-959. [PMID: 37207359 PMCID: PMC10502574 DOI: 10.1210/endrev/bnad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/14/2023] [Accepted: 05/17/2023] [Indexed: 05/21/2023]
Abstract
The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.
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Affiliation(s)
- Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
| | - Stephanie E Baldeweg
- Department of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London WC1E 6BT, UK
| | - Neil L Dorward
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
- Digital Surgery Ltd, Medtronic, London WD18 8WW, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK
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Wijethilake N, MacCormac O, Vercauteren T, Shapey J. Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review. Front Oncol 2023; 13:1131013. [PMID: 37182138 PMCID: PMC10167010 DOI: 10.3389/fonc.2023.1131013] [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: 12/24/2022] [Accepted: 03/27/2023] [Indexed: 05/16/2023] Open
Abstract
Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity. Systematic Review Registration: PROSPERO, CRD42022306922.
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Affiliation(s)
- Navodini Wijethilake
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Oscar MacCormac
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan Shapey
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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Wang H, Chang J, Zhang W, Fang Y, Li S, Fan Y, Jiang S, Yao Y, Deng K, Lu L, Bao X, Feng F, Wang R, Feng M. Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas. J Endocrinol Invest 2023:10.1007/s40618-023-02042-2. [PMID: 37020103 DOI: 10.1007/s40618-023-02042-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/12/2023] [Indexed: 04/07/2023]
Abstract
OBJECTIVE Silent corticotroph adenomas (SCAs) are a subtype of nonfunctioning pituitary adenomas that exhibit more aggressive behavior. However, rapid and accurate preoperative diagnostic methods are currently lacking. DESIGN The purpose of this study was to examine the differences between SCA and non-SCA features and to establish radiomics models and a clinical scale for rapid and accurate prediction. METHODS A total of 260 patients (72 SCAs vs. 188 NSCAs) with nonfunctioning adenomas from Peking Union Medical College Hospital were enrolled in the study as the internal dataset. Thirty-five patients (6 SCAs vs. 29 NSCAs) from Fuzhou General Hospital were enrolled as the external dataset. Radiomics models and an SCA scale to preoperatively diagnose SCAs were established based on MR images and clinical features. RESULTS There were more female patients (internal dataset: p < 0.001; external dataset: p = 0.028) and more multiple microcystic changes (internal dataset: p < 0.001; external dataset: p = 0.012) in the SCA group. MRI showed more invasiveness (higher Knosp grades, p ≤ 0.001). The radiomics model achieved AUCs of 0.931 and 0.937 in the internal and external datasets, respectively. The clinical scale achieved an AUC of 0.877 and a sensitivity of 0.952 in the internal dataset and an AUC of 0.899 and a sensitivity of 1.0 in the external dataset. CONCLUSIONS Based on clinical information and imaging characteristics, the constructed radiomics model achieved high preoperative diagnostic ability. The SCA scale achieved the purpose of rapidity and practicality while ensuring sensitivity, which is conducive to simplifying clinical work.
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Affiliation(s)
- H Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Neurospine center, China International Neuroscience Institute, Beijing, China
| | - J Chang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - W Zhang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
- Department of Thoracic Surgery, Peking University First Hospital, Beijing, China
| | - Y Fang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - S Li
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - Y Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - S Jiang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - Y Yao
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - K Deng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - L Lu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - X Bao
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China
| | - F Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China
| | - R Wang
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
| | - M Feng
- Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
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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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The Prognostic-Based Approach in Growth Hormone-Secreting Pituitary Neuroendocrine Tumors (PitNET): Tertiary Reference Center, Single Senior Surgeon, and Long-Term Follow-Up. Cancers (Basel) 2022; 15:cancers15010267. [PMID: 36612263 PMCID: PMC9818833 DOI: 10.3390/cancers15010267] [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: 11/23/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
Postoperative deserved outcomes in acromegalic patients are to normalize serum insulin-like growth factor (IGF-1), reduce the tumoral mass effect, improve systemic comorbidities, and reverse metabolic alterations. Pituitary neuroendocrine tumors (PitNET) are characterized to present a heterogeneous behavior, and growth hormone (GH)-secreting PitNET is not an exception. Promptly determining which patients are affected by more aggressive tumors is essential to guide the optimal postoperative decision-making process [prognostic-based approach]. From 2006 to 2019, 394 patients affected by PitNET were intervened via endoscopic endonasal transsphenoidal approach by the same senior surgeon. A total of 44 patients that met the criteria to be diagnosed as acromegalic and were followed up at least for 24 months (median of 66 months (26-156) were included in the present study. Multiple predictive variables [age, gender, preoperative GH and IGF-1 levels, maximal tumor diameter, Hardy's and Knosp's grade, MRI. T2-weighted tumor intensity, cytokeratin expression pattern, and clinicopathological classification] were evaluated through uni- and multivariate statistical analysis. Sparse probability of long-term remission was related to younger age, higher preoperative GH and- or IGF-1, group 2b of the clinicopathological classification, and sparsely granulated cytokeratin expression pattern. Augmented recurrence risk was related to elevated preoperative GH levels, tumor MRI T2-weighted hyperintensity, and sparsely granulated cytokeratin expression pattern. Finally, elevated risk for reintervention was related to group 2b of the clinicopathological classification, Knosp's grade IV, and tumor MRI T2-weighted hyperintensity. In this study, the authors determined younger age, higher preoperative GH and- or IGF-1 levels, group 2b of the clinicopathological classification, Knosp's grade IV, MRI T2-weighted tumor hyperintensity and sparsely granulated cytokeratin expression pattern are related to worse postoperative outcomes in long-term follow-up patients affected with GH-secreting PitNET.
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Gologorsky R, Harake E, von Oiste G, Nasir-Moin M, Couldwell W, Oermann E, Hollon T. Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation. Pituitary 2022; 25:842-853. [PMID: 35943676 DOI: 10.1007/s11102-022-01255-7] [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: 07/08/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE The estimated incidence of pituitary adenomas in the general population is 10-30%, yet radiographic diagnosis remains a challenge. Diagnosis is complicated by the heterogeneity of radiographic features in both normal (e.g. complex anatomy, pregnancy) and pathologic states (e.g. primary endocrinopathy, hypophysitis). Clinical symptoms and laboratory testing are often equivocal, which can result in misdiagnosis or unnecessary specialist referrals. Computer vision models can aid in pituitary adenoma diagnosis; however, a major challenge to model development is the lack of dedicated pituitary imaging datasets. We hypothesized that deep volumetric segmentation models trained to extract the sellar and parasellar region from existing whole-brain MRI scans could be used to generate a novel dataset of pituitary imaging. METHODS Six open-source whole-brain MRI datasets, created for research purposes, were included for model development. Deep learning-based volumetric segmentation models were trained using 318 manually annotated MRI scans from a single open-source MRI dataset. Out-of-distribution volumetric segmentation performance was then tested on 418 MRIs from five held-out research datasets. RESULTS On our annotated images, agreement between manual and model volumetric segmentations was high. Dice scores (a measure of overlap) ranged 0.76-0.82 for both in-distribution and out-of-distribution model testing. In total, 6,755 MRIs from six data sources were included in the final generated pituitary dataset. CONCLUSIONS We present the first and largest dataset of pituitary imaging constructed using existing MRI data and deep volumetric segmentation models trained to identify sellar and parasellar anatomy. The model generalizes well across patient populations and MRI scanner types. We hope our pituitary dataset will be an integral part of future machine learning research on pituitary pathologies.
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Affiliation(s)
- Rachel Gologorsky
- Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, 10029, New York, NY, USA
| | - Edward Harake
- Department of Medicine, University of Michigan Medical School, 1500 E Medical Center Dr, 48109, Ann Arbor, MI, USA
| | - Grace von Oiste
- Department of Neurosurgery, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA
| | - Mustafa Nasir-Moin
- Department of Neurosurgery, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA
| | - William Couldwell
- Department of Neurosurgery, University of Utah, 201 Presidents' Cir, 84132, Salt Lake City, UT, USA
| | - Eric Oermann
- Department of Neurosurgery, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA
- Department of Radiology, NYU Langone Health System, 530 First Ave, 10016, New York, NY, USA
- Center for Data Science, New York University, 60 5th Ave, 10011, New York, NY, USA
| | - Todd Hollon
- Machine Learning in Neurosurgery Laboratory, Department of Neurosurgery, University of Michigan, 1500 E Medical Center Dr, 48109, Ann Arbor, MI, USA.
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Li N, Desiderio DM, Zhan X. The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas. MASS SPECTROMETRY REVIEWS 2022; 41:964-1013. [PMID: 34109661 DOI: 10.1002/mas.21710] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.
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Affiliation(s)
- Na Li
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
| | - Dominic M Desiderio
- The Charles B. Stout Neuroscience Mass Spectrometry Laboratory, Department of Neurology, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Xianquan Zhan
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
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12
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Won SY, Lee N, Park YW, Ahn SS, Ku CR, Kim EH, Lee SK. Quality reporting of radiomics analysis in pituitary adenomas: promoting clinical translation. Br J Radiol 2022; 95:20220401. [PMID: 36018049 PMCID: PMC9793472 DOI: 10.1259/bjr.20220401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/15/2022] [Accepted: 07/27/2022] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To evaluate the quality of radiomics studies on pituitary adenoma according to the radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). METHODS PubMed MEDLINE and EMBASE were searched to identify radiomics studies on pituitary adenomas. From 138 articles, 20 relevant original research articles were included. Studies were scored based on RQS and TRIPOD guidelines. RESULTS Most included studies did not perform pre-processing; isovoxel resampling, signal intensity normalization, and N4 bias field correction were performed in only five (25%), eight (40%), and four (20%) studies, respectively. Only two (10%) studies performed external validation. The mean RQS and basic adherence rate were 2.8 (7.6%) and 26.6%, respectively. There was a low adherence rate for conducting comparison to "gold-standard" (20%), multiple segmentation (25%), and stating potential clinical utility (25%). No study stated the biological correlation, conducted a test-retest or phantom study, was a prospective study, conducted cost-effectiveness analysis, or provided open-source code and data, which resulted in low-level evidence. The overall adherence rate for TRIPOD was 54.6%, and it was low for reporting the title (5%), abstract (0%), explaining the sample size (10%), and suggesting a full prediction model (5%). CONCLUSION The radiomics reporting quality for pituitary adenoma is insufficient. Pre-processing is required for feature reproducibility and external validation is necessary. Feature reproducibility, clinical utility demonstration, higher evidence levels, and open science are required. Titles, abstracts, and full prediction model suggestions should be improved for transparent reporting. ADVANCES IN KNOWLEDGE Despite the rapidly increasing number of radiomics researches on pituitary adenoma, the quality of science in these researches is unknown. Our study indicates that the overall quality needs to be significantly improved in radiomics studies on pituitary adenoma, and since the concept of RQS and IBSI is still unfamiliar to clinicians and radiologist researchers, our study may help to reach higher technical and clinical impact in the future study.
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Affiliation(s)
| | - Narae Lee
- Department of Nuclear Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Yae Won Park
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Cheol Ryong Ku
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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13
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Buchlak QD, Esmaili N, Bennett C, Wang YY, King J, Goldschlager T. Predictors of improvement in quality of life at 12-month follow-up in patients undergoing anterior endoscopic skull base surgery. PLoS One 2022; 17:e0272147. [PMID: 35895728 PMCID: PMC9328523 DOI: 10.1371/journal.pone.0272147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
Background Patients with pituitary lesions experience decrements in quality of life (QoL) and treatment aims to arrest or improve QoL decline. Objective To detect associations with QoL in trans-nasal endoscopic skull base surgery patients and train supervised learning classifiers to predict QoL improvement at 12 months. Methods A supervised learning analysis of a prospective multi-institutional dataset (451 patients) was conducted. QoL was measured using the anterior skull base surgery questionnaire (ASBS). Factors associated with QoL at baseline and at 12-month follow-up were identified using multivariate logistic regression. Multiple supervised learning models were trained to predict postoperative QoL improvement with five-fold cross-validation. Results ASBS at 12-month follow-up was significantly higher (132.19,SD = 24.87) than preoperative ASBS (121.87,SD = 25.72,p<0.05). High preoperative scores were significantly associated with institution, diabetes and lesions at the planum sphenoidale / tuberculum sella site. Patients with diabetes were five times less likely to report high preoperative QoL. Low preoperative QoL was significantly associated with female gender, a vision-related presentation, diabetes, secreting adenoma and the cavernous sinus site. Top quartile change in postoperative QoL at 12-month follow-up was negatively associated with baseline hypercholesterolemia, acromegaly and intraoperative CSF leak. Positive associations were detected for lesions at the sphenoid sinus site and deficient preoperative endocrine function. AdaBoost, logistic regression and neural network classifiers yielded the strongest predictive performance. Conclusion It was possible to predict postoperative positive change in QoL at 12-month follow-up using perioperative data. Further development and implementation of these models may facilitate improvements in informed consent, treatment decision-making and patient QoL.
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Affiliation(s)
- Quinlan D. Buchlak
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
- Department of Neurosurgery, Monash Health, Melbourne, VIC, Australia
- * E-mail:
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - Christine Bennett
- School of Medicine, The University of Notre Dame Australia, Sydney, NSW, Australia
| | - Yi Yuen Wang
- St Vincent’s Hospital, Melbourne, VIC, Australia
| | - James King
- Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Tony Goldschlager
- Department of Neurosurgery, Monash Health, Melbourne, VIC, Australia
- Department of Surgery, Monash University, Melbourne, VIC, Australia
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14
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Qiao N, Yu D, Wu G, Zhang Q, Yao B, He M, Ye H, Zhang Z, Wang Y, Wu H, Zhao Y, Yu J. Low-rank fusion convolutional neural network for prediction of remission after stereotactic radiosurgery in patients with acromegaly: a proof-of-concept study. J Pathol 2022; 258:49-57. [PMID: 35657600 DOI: 10.1002/path.5974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 11/10/2022]
Abstract
Artificial intelligence approaches to analyze pathological images (pathomic) for outcome prediction have not been sufficiently considered in the field of pituitary research. A total of 5,504 Hematoxylin & Eosin-stained pathology image tiles from 58 acromegalic patients with a good or poor outcome were integrated with other clinical and genetic information to train a low-rank fusion convolutional neural network (LFCNN). The model was externally validated in 1,536 patches from an external cohort. The primary outcome was the time to the first endocrine remission after SRS. The median time of initial endocrine remission was 43 months [IQR: 13-60 months] after SRS, and the 24-month initial cumulative remission rate was 57.9% [IQR: 46.4-72.3%]. The patient-wise accuracy of the LFCNN model in predicting the primary outcome was 92.9% in the internal test dataset, and the sensitivity and specificity were 87.5% and 100.0%, respectively. The LFCNN model was a strong predictor of initial cumulative remission in the training cohort (HR 9.58, 95% CI 3.89-23.59; p < 0.001) and was higher than that of established prognostic markers. The predictive value of LFCNN model was further validated in an external cohort (HR 9.06, 95% CI 1.14-72.25; p = 0.012). In this proof-of-concept study, clinically and genetically useful prognostic markers were integrated with digital images to predict endocrine outcomes after SRS in patients with active acromegaly. The model considerably outperforms established prognostic markers and can potentially be used by clinicians to improve decision-making regarding adjuvant treatment choices. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nidan Qiao
- Department of Neurosurgery, Huashan Hospital, Shanghai, PR China.,Neurosurgical Institute of Fudan University, Shanghai, PR China.,National Center for Neurological Disorders, Shanghai, PR China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, PR China.,Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai, PR China
| | - Damin Yu
- School of Information Science and Technology, Fudan University, Shanghai, PR China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai, PR China
| | - Qilin Zhang
- Department of Neurosurgery, Huashan Hospital, Shanghai, PR China.,Neurosurgical Institute of Fudan University, Shanghai, PR China.,National Center for Neurological Disorders, Shanghai, PR China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, PR China.,Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai, PR China
| | - Boyuan Yao
- Fudan University Graduate School, Shanghai, PR China
| | - Min He
- Department of Endocrinology, Huashan Hospital, Shanghai, PR China
| | - Hongying Ye
- Department of Endocrinology, Huashan Hospital, Shanghai, PR China
| | - Zhaoyun Zhang
- Department of Endocrinology, Huashan Hospital, Shanghai, PR China
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai, PR China.,Neurosurgical Institute of Fudan University, Shanghai, PR China.,National Center for Neurological Disorders, Shanghai, PR China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, PR China.,Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai, PR China
| | - Hanfeng Wu
- Shanghai Gamma Hospital, Shanghai, PR China
| | - Yao Zhao
- Department of Neurosurgery, Huashan Hospital, Shanghai, PR China.,Neurosurgical Institute of Fudan University, Shanghai, PR China.,National Center for Neurological Disorders, Shanghai, PR China.,Shanghai Clinical Medical Center of Neurosurgery, Shanghai, PR China.,Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai, PR China
| | - Jinhua Yu
- Neurosurgical Institute of Fudan University, Shanghai, PR China.,School of Information Science and Technology, Fudan University, Shanghai, PR China
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15
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Hsieh HP, Wu DY, Hung KC, Lim SW, Chen TY, Fan-Chiang Y, Ko CC. Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features. J Pers Med 2022; 12:jpm12040522. [PMID: 35455638 PMCID: PMC9032338 DOI: 10.3390/jpm12040522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 01/04/2023] Open
Abstract
A subset of parasagittal and parafalcine (PSPF) meningiomas may show early progression/recurrence (P/R) after surgery. This study applied machine learning using combined clinical and texture features to predict P/R in PSPF meningiomas. A total of 57 consecutive patients with pathologically confirmed (WHO grade I) PSPF meningiomas treated in our institution between January 2007 to January 2019 were included. All included patients had complete preoperative magnetic resonance imaging (MRI) and more than one year MRI follow-up after surgery. Preoperative contrast-enhanced T1WI, T2WI, T1WI, and T2 fluid-attenuated inversion recovery (FLAIR) were analyzed retrospectively. The most significant 12 clinical features (extracted by LightGBM) and 73 texture features (extracted by SVM) were combined in random forest to predict P/R, and personalized radiomic scores were calculated. Thirteen patients (13/57, 22.8%) had P/R after surgery. The radiomic score was a high-risk factor for P/R with hazard ratio of 15.73 (p < 0.05) in multivariate hazards analysis. In receiver operating characteristic (ROC) analysis, an AUC of 0.91 with cut-off value of 0.269 was observed in radiomic scores for predicting P/R. Subtotal resection, low apparent diffusion coefficient (ADC) values, and high radiomic scores were associated with shorter progression-free survival (p < 0.05). Among different data input, machine learning using combined clinical and texture features showed the best predictive performance, with an accuracy of 91%, precision of 85%, and AUC of 0.88. Machine learning using combined clinical and texture features may have the potential to predict recurrence in PSPF meningiomas.
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Affiliation(s)
- Hsun-Ping Hsieh
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ding-You Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Kuo-Chuan Hung
- Department of Anesthesiology, Chi Mei Medical Center, Tainan City 71004, Taiwan;
- Department of Hospital and Health Care Administration, College of Recreation and Health Management, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi Mei Medical Center, Chiali, Tainan 722, Taiwan;
- Department of Nursing, Min-Hwei College of Health Care Management, Tainan 73658, Taiwan
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 71101, Taiwan
| | - Yang Fan-Chiang
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; (H.-P.H.); (D.-Y.W.); (Y.F.-C.)
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan;
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
- Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Correspondence:
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16
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Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
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17
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Qiao N, Ma Y, Chen X, Ye Z, Ye H, Zhang Z, Wang Y, Lu Z, Wang Z, Xiao Y, Zhao Y. Machine Learning Prediction of Visual Outcome after Surgical Decompression of Sellar Region Tumors. J Pers Med 2022; 12:jpm12020152. [PMID: 35207641 PMCID: PMC8879436 DOI: 10.3390/jpm12020152] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/09/2022] [Accepted: 01/14/2022] [Indexed: 02/05/2023] Open
Abstract
Introduction: This study aims to develop a machine learning-based model integrating clinical and ophthalmic features to predict visual outcomes after transsphenoidal resection of sellar region tumors. Methods: Adult patients with optic chiasm compression by a sellar region tumor were examined to develop a model, and an independent retrospective cohort and a prospective cohort were used to validate our model. Predictors included demographic information, and ophthalmic and laboratory test results. We defined “recovery” as more than 5% for a p-value in mean deviation compared with the general population in the follow-up. Seven machine learning classifiers were employed, and the best-performing algorithm was selected. A decision curve analysis was used to assess the clinical usefulness of our model by estimating net benefit. We developed a nomogram based on essential features ranked by the SHAP score. Results: We included 159 patients (57.2% male), and the mean age was 42.3 years old. Among them, 96 patients were craniopharyngiomas and 63 patients were pituitary adenomas. Larger tumors (3.3 cm vs. 2.8 cm in tumor height) and craniopharyngiomas (73.6%) were associated with a worse prognosis (p < 0.001). Eyes with better outcomes were those with better visual field and thicker ganglion cell layer before operation. The ensemble model yielded the highest AUC of 0.911 [95% CI, 0.885–0.938], and the corresponding accuracy was 84.3%, with 0.863 in sensitivity and 0.820 in specificity. The model yielded AUCs of 0.861 and 0.843 in the two validation cohorts. Our model provided greater net benefit than the competing extremes of intervening in all or no patients in the decision curve analysis. A model explanation using SHAP score demonstrated that visual field, ganglion cell layer, tumor height, total thyroxine, and diagnosis were the most important features in predicting visual outcome. Conclusion: SHAP score can be a valuable resource for healthcare professionals in identifying patients with a higher risk of persistent visual deficit. The large-scale and prospective application of the proposed model would strengthen its clinical utility and universal applicability in practice.
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Affiliation(s)
- Nidan Qiao
- Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China; (N.Q.); (Z.Y.); (Y.W.)
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, 985 Jinguang Road, Shanghai 201107, China
| | - Yichen Ma
- Fudan University Graduate School, Fudan University, Shanghai 200043, China;
| | - Xiaochen Chen
- Surgical Theatre, Huashan Hospital Hongqiao Campus, Shanghai 201107, China;
| | - Zhao Ye
- Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China; (N.Q.); (Z.Y.); (Y.W.)
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, 985 Jinguang Road, Shanghai 201107, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
- Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai 200040, China
| | - Hongying Ye
- Department of Endocrinology, Huashan Hospital, Shanghai 200040, China; (H.Y.); (Z.Z.)
| | - Zhaoyun Zhang
- Department of Endocrinology, Huashan Hospital, Shanghai 200040, China; (H.Y.); (Z.Z.)
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China; (N.Q.); (Z.Y.); (Y.W.)
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, 985 Jinguang Road, Shanghai 201107, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
- Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai 200040, China
| | - Zhaozeng Lu
- Department of Ophthalmology, Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai 200040, China; (Z.L.); (Z.W.)
| | - Zhiliang Wang
- Department of Ophthalmology, Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai 200040, China; (Z.L.); (Z.W.)
| | - Yiqin Xiao
- Department of Ophthalmology, Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai 200040, China; (Z.L.); (Z.W.)
- Correspondence: (Y.X.); (Y.Z.)
| | - Yao Zhao
- Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China; (N.Q.); (Z.Y.); (Y.W.)
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, 985 Jinguang Road, Shanghai 201107, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
- Shanghai Key Laboratory of Medical Brain Function and Restoration and Neural Regeneration, Fudan University, Shanghai 200040, China
- Correspondence: (Y.X.); (Y.Z.)
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18
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A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment. Aging (Albany NY) 2022; 14:907-922. [PMID: 35073519 PMCID: PMC8833127 DOI: 10.18632/aging.203850] [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: 09/24/2021] [Accepted: 01/11/2022] [Indexed: 12/24/2022]
Abstract
Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell’s c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732–0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.
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19
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Fan Y, Liu P, Li Y, Liu F, He Y, Wang L, Zhang J, Wu Z. Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model. Front Oncol 2022; 11:792521. [PMID: 35059316 PMCID: PMC8763962 DOI: 10.3389/fonc.2021.792521] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/29/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Accurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively. METHODS A total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS Six significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful. CONCLUSIONS Our clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.
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Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Panpan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yiping Li
- Department of Gastroenterology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Feng Liu
- Department of Neurosurgery, Jiangxi Provincial Children's Hospital, The Affiliated Children's Hospital of Nanchang University, Nanchang, China
| | - Yu He
- Department of Craniomaxillofacial Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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20
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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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21
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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22
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Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021; 64:647-668. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/21/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To systematically review the literature regarding the application of machine learning (ML) of magnetic resonance imaging (MRI) radiomics in common sellar tumors. To identify future directions for application of ML in sellar tumor MRI. METHODS PubMed, Medline, Embase, Google Scholar, Scopus, ArxIV, and bioRxiv were searched to identify relevant studies published between 2010 and September 2021. Studies were included if they specifically involved ML of MRI radiomics in the analysis of sellar masses. Risk of bias assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) Tool. RESULTS Fifty-eight articles were identified for review. All papers utilized retrospective data, and a quantitative systematic review was performed for thirty-one studies utilizing a public dataset which compared pituitary adenomas, meningiomas, and gliomas. One of the analyzed architectures yielded the highest classification accuracy of 0.996. The remaining twenty-seven articles were qualitatively reviewed and showed promising findings in predicting specific tumor characteristics such as tumor consistency, Ki-67 proliferative index, and post-surgical recurrence. CONCLUSION This review highlights the potential clinical application of ML using MRI radiomic data of the sellar region in diagnosis and predicting treatment outcomes. We describe future directions for practical application in the clinical care of patients with pituitary neuroendocrine and other sellar tumors.
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23
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Wang H, Zhang W, Li S, Fan Y, Feng M, Wang R. Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task. J Clin Endocrinol Metab 2021; 106:2535-2546. [PMID: 34060609 DOI: 10.1210/clinem/dgab371] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT The resection plan of pituitary adenoma (PA) needs preoperative observation of the sellar region. Radiomics prediction requires high-quality segmentations. Manual delineation is time-consuming and subject to rater variability. OBJECTIVE This work aims to create an automated segmentation method for the sellar region, several tools to extract invasiveness-related features, and evaluate their clinical usefulness by predicting the tumor consistency. METHODS Patients included were diagnosed with pituitary adenoma at Peking Union Medical College Hospital. A deep convolutional neural network, called gated-shaped U-net (GSU-Net), was created to automatically segment the sellar region into 8 classes. Five magnetic resonance imaging (MRI) features were extracted from the segmentation results, including tumor diameters, volume, optic chiasma height, Knosp grading system, and degree of internal carotid artery contact. The clinical usefulness of the proposed methods was evaluated by the diagnostic accuracy of the tumor consistency. RESULTS A total of 163 patients with confirmed pituitary adenoma were included as the first group and were randomly divided into a training data set and test data set (131 and 32 patients, respectively). Fifty patients with confirmed acromegaly were included as the second group. The Dice coefficient of pituitary adenoma in important image slices was 0.940. The proposed methods achieved accuracies of more than 80% for the prediction of 5 invasive-related MRI features. Methods derived from the automatic segmentation showed better performance than original methods and achieved areas under the curve of 0.840 and 0.920 for clinical models and radiomics models, respectively. CONCLUSION The proposed methods could automatically segment the sellar region and extract features with high accuracy. The outstanding performance of the prediction of the tumor consistency indicates the methods' clinical usefulness for supporting neurosurgeons in judging patients' conditions, predicting prognosis, and other downstream tasks during the preoperative period.
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Affiliation(s)
- He Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wentai Zhang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuo Li
- Department of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanghua Fan
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Feng
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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24
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Park YW, Eom J, Kim S, Kim H, Ahn SS, Ku CR, Kim EH, Lee EJ, Kim SH, Lee SK. Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma. J Clin Endocrinol Metab 2021; 106:e3069-e3077. [PMID: 33713414 DOI: 10.1210/clinem/dgab159] [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] [Received: 12/22/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning. OBJECTIVE To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients. DESIGN Retrospective study. SETTING Severance Hospital, Seoul, Korea. PATIENTS A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set. RESULTS The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74-0.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set. CONCLUSIONS Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Jihwan Eom
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Sooyon Kim
- Department of Statistics and Data Science, Yonsei University, Seoul, Korea
| | - Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Cheol Ryong Ku
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Ho Kim
- Department of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
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25
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Wildemberg LE, da Silva Camacho AH, Miranda RL, Elias PCL, de Castro Musolino NR, Nazato D, Jallad R, Huayllas MKP, Mota JIS, Almeida T, Portes E, Ribeiro-Oliveira A, Vilar L, Boguszewski CL, Winter Tavares AB, Nunes-Nogueira VS, Mazzuco TL, Rech CGSL, Marques NV, Chimelli L, Czepielewski M, Bronstein MD, Abucham J, de Castro M, Kasuki L, Gadelha M. Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands. J Clin Endocrinol Metab 2021; 106:2047-2056. [PMID: 33686418 DOI: 10.1210/clinem/dgab125] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Indexed: 01/12/2023]
Abstract
CONTEXT Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. OBJECTIVE To develop a prediction model of therapeutic response of acromegaly to fg-SRL. METHODS Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). RESULTS A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. CONCLUSION We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.
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Affiliation(s)
- Luiz Eduardo Wildemberg
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
| | - Aline Helen da Silva Camacho
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Renan Lyra Miranda
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Paula C L Elias
- Division of Endocrinology-Department of Internal Medicine, Ribeirao Preto Medical School-University of Sao Paulo, São Paulo, SP, Brazil
| | - Nina R de Castro Musolino
- Neuroendocrine Unit, Division of Functional Neurosurgery, Hospital das Clinicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Debora Nazato
- Neuroendocrine Unit-Division of Endocrinology and Metabolism-Escola Paulista de Medicina-Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil
| | - Raquel Jallad
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, SP, Brazil
- Cellular and Molecular Endocrinology Laboratory/LIM25, Discipline of Endocrinology, Hospital das Clinicas HCFMUSP, Faculty of Medicine, University of Sao Paulo, São Paulo, SP, Brazil
| | - Martha K P Huayllas
- Neuroendocrinology and Neurosurgery unit Hospital Brigadeiro, São Paulo, SP, Brazil
| | - Jose Italo S Mota
- Endocrinology and Metabolism Unit, Hospital Geral de Fortaleza, Secretaria Estadual de Saúde, Fortaleza, CE, Brazil
| | - Tobias Almeida
- Division of Endocrinology, Hospital de Clinicas de Porto Alegre (UFRGS), Porto Alegre, RS, Brazil
| | - Evandro Portes
- Institute of Medical Assistance to the State Public Hospital, São Paulo, SP, Brazil
| | | | - Lucio Vilar
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, Federal University of Pernambuco Medical School, Recife, PE, Brazil
| | - Cesar Luiz Boguszewski
- Endocrine Division (SEMPR), Department of Internal Medicine, Universidade Federal do Parana, Curitiba, PR, Brazil
| | - Ana Beatriz Winter Tavares
- Endocrine Unit-Department of Internal Medicine, Faculty of Medical Sciences, Universidade do Estado do Rio de Janeiro, RJ, Brazil
| | - Vania S Nunes-Nogueira
- Department of Internal Medicine, São Paulo State University/UNESP, Medical School, Botucatu, SP, Brazil
| | - Tânia Longo Mazzuco
- Division of Endocrinology of Medical Clinical Department, Universidade Estadual de Londrina (UEL), Londrina, PR, Brazil
| | | | - Nelma Veronica Marques
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Leila Chimelli
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
| | - Mauro Czepielewski
- Division of Endocrinology, Hospital de Clinicas de Porto Alegre (UFRGS), Porto Alegre, RS, Brazil
| | - Marcello D Bronstein
- Neuroendocrine Unit, Division of Endocrinology and Metabolism, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, SP, Brazil
- Cellular and Molecular Endocrinology Laboratory/LIM25, Discipline of Endocrinology, Hospital das Clinicas HCFMUSP, Faculty of Medicine, University of Sao Paulo, São Paulo, SP, Brazil
| | - Julio Abucham
- Neuroendocrine Unit-Division of Endocrinology and Metabolism-Escola Paulista de Medicina-Universidade Federal de São Paulo (Unifesp), São Paulo, SP, Brazil
| | - Margaret de Castro
- Division of Endocrinology-Department of Internal Medicine, Ribeirao Preto Medical School-University of Sao Paulo, São Paulo, SP, Brazil
| | - Leandro Kasuki
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
| | - Mônica Gadelha
- Endocrine Unit and Neuroendocrinology Research Center, Medical School and Hospital Universitário Clementino Fraga Filho-Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Neuroendocrine Unit-Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde , Rio de Janeiro, RJ, Brazil
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, RJ, Brazil
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26
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Ko CC, Zhang Y, Chen JH, Chang KT, Chen TY, Lim SW, Wu TC, Su MY. Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas. Front Neurol 2021; 12:636235. [PMID: 34054688 PMCID: PMC8160291 DOI: 10.3389/fneur.2021.636235] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/29/2021] [Indexed: 02/06/2023] Open
Abstract
Objectives: A subset of meningiomas may show progression/recurrence (P/R) after surgical resection. This study applied pre-operative MR radiomics based on support vector machine (SVM) to predict P/R in meningiomas. Methods: From January 2007 to January 2018, 128 patients with pathologically confirmed WHO grade I meningiomas were included. Only patients who had undergone pre-operative MRIs and post-operative follow-up MRIs for more than 1 year were studied. Pre-operative T2WI and contrast-enhanced T1WI were analyzed. On each set of images, 32 first-order features and 75 textural features were extracted. The SVM classifier was utilized to evaluate the significance of extracted features, and the most significant four features were selected to calculate SVM score for each patient. Results: Gross total resection (Simpson grades I–III) was performed in 93 (93/128, 72.7%) patients, and 19 (19/128, 14.8%) patients had P/R after surgery. Subtotal tumor resection, bone invasion, low apparent diffusion coefficient (ADC) value, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, bone invasion, ADC value, and SVM score were high-risk factors for P/R (p < 0.05) with hazard ratios of 7.31, 4.67, and 8.13, respectively. Using the SVM score, an AUC of 0.80 with optimal cutoff value of 0.224 was obtained for predicting P/R. Patients with higher SVM scores were associated with shorter progression-free survival (p = 0.003). Conclusions: Our preliminary results showed that pre-operative MR radiomic features may have the potential to offer valuable information in treatment planning for meningiomas.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
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27
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Xiao B, Fan Y, Zhang Z, Tan Z, Yang H, Tu W, Wu L, Shen X, Guo H, Wu Z, Zhu X. Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma. Front Oncol 2021; 11:625220. [PMID: 33937027 PMCID: PMC8082417 DOI: 10.3389/fonc.2021.625220] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background Postoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma. Objective To evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma. Methods A total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction. Results Three significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction. Conclusions By integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma.
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Affiliation(s)
- Bing Xiao
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhe Zhang
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zilong Tan
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Huan Yang
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei Tu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Wu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoli Shen
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hua Guo
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xingen Zhu
- Department of Neurosurgery, Second Affiliated Hospital of Nanchang University, Nanchang, China
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28
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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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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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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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Zhang Y, Ko CC, Chen JH, Chang KT, Chen TY, Lim SW, Tsui YK, Su MY. Radiomics Approach for Prediction of Recurrence in Non-Functioning Pituitary Macroadenomas. Front Oncol 2020; 10:590083. [PMID: 33392084 PMCID: PMC7775655 DOI: 10.3389/fonc.2020.590083] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/19/2020] [Indexed: 02/06/2023] Open
Abstract
Objectives A subset of non-functioning pituitary macroadenomas (NFPAs) may exhibit early progression/recurrence (P/R) after surgical resection. The purpose of this study was to apply radiomics in predicting P/R in NFPAs. Methods Only patients who had undergone preoperative MRI and postoperative MRI follow-ups for more than 1 year were included in this study. From September 2010 to December 2017, 50 eligible patients diagnosed with pathologically confirmed NFPAs were identified. Preoperative coronal T2WI and contrast-enhanced (CE) T1WI imaging were analyzed by computer algorithms. For each imaging sequence, 32 first-order features and 75 texture features were extracted. Support vector machine (SVM) classifier was utilized to evaluate the importance of extracted parameters, and the most significant three parameters were used to build the prediction model. The SVM score was calculated based on the three selected features. Results Twenty-eight patients exhibited P/R (28/50, 56%) after surgery. The median follow-up time was 38 months, and the median time to P/R was 20 months. Visual disturbance, hypopituitarism, extrasellar extension, compression of the third ventricle, large tumor height and volume, failed optic chiasmatic decompression, and high SVM score were more frequently encountered in the P/R group (p < 0.05). In multivariate Cox hazards analysis, symptoms of sex hormones, hypopituitarism, and SVM score were high risk factors for P/R (p < 0.05) with hazard ratios of 10.71, 2.68, and 6.88. The three selected radiomics features were T1 surface-to-volume radio, T1 GLCM-informational measure of correlation, and T2 NGTDM-coarseness. The radiomics predictive model shows 25 true positive, 16 true negative, 6 false positive, and 3 false negative cases, with an accuracy of 82% and AUC of 0.78 in differentiating P/R from non-P/R NFPAs. For SVM score, optimal cut-off value of 0.537 and AUC of 0.87 were obtained for differentiation of P/R. Higher SVM scores were associated with shorter progression-free survival (p < 0.001). Conclusions Our preliminary results showed that objective and quantitative MR radiomic features can be extracted from NFPAs. Pending more studies and evidence to support the findings, radiomics analysis of preoperative MRI may have the potential to offer valuable information in treatment planning for NFPAs.
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Affiliation(s)
- Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Kai-Ting Chang
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.,Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan
| | - Sher-Wei Lim
- Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.,Department of Nursing, Min-Hwei College of Health Care Management, Tainan, Taiwan
| | - Yu-Kun Tsui
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, Irvine, CA, United States
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Park YW, Kang Y, Ahn SS, Ku CR, Kim EH, Kim SH, Lee EJ, Kim SH, Lee SK. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary 2020; 23:691-700. [PMID: 32851505 DOI: 10.1007/s11102-020-01077-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients. METHODS Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC). RESULTS Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037). CONCLUSION Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Yunjun Kang
- Integrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
| | - Cheol Ryong Ku
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Eui Hyun Kim
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea.
- Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
- Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Ho Kim
- Department of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Pituitary Tumor Center, Severance Hospital, Seoul, Korea
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Agrawal N, Ioachimescu AG. Prognostic factors of biochemical remission after transsphenoidal surgery for acromegaly: a structured review. Pituitary 2020; 23:582-594. [PMID: 32602066 DOI: 10.1007/s11102-020-01063-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
PURPOSE Biochemical control is the main determinant of survival, clinical manifestations and comorbidities in acromegaly. Transsphenoidal selective adenomectomy (TSA) is the initial treatment of choice with reported biochemical remission rates varying between 32 and 85%. Understanding the limiting factors is essential for identification of patients who require medical treatment. METHODS We reviewed the English literature published in Medline/Pubmed until Dec 31, 2019 to identify eligible studies that described outcomes of TSA as primary therapy and performed analyses to determine the main predictors of remission. RESULTS Most publications reported single-institution, retrospective studies. The following preoperative parameters were consistently associated with lower remission rates: cavernous sinus invasion by imaging, larger tumor size and higher GH levels. Young age and preoperative IGF-1 levels were predictive in some studies. When controlled for covariates, the best single preoperative predictor was cavernous sinus invasion, followed by preoperative GH levels. Conversely, low GH level in the first few days postoperatively was a robust predictor of durable remission. The influence of tumor histology (sparsely granular pattern, co-expression of prolactin and proliferation markers) on surgical remission remains to be established. Few studies developed predictive models that yielded much higher predictive values than individual parameters. CONCLUSION Surgical outcome prognostication systems could be further generated by machine learning algorithms in order to support development and implementation of personalized care in patients with acromegaly.
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Affiliation(s)
- Nidhi Agrawal
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, NYU School of Medicine, 550 First Avenue, New York City, NY, 10016, USA
| | - Adriana G Ioachimescu
- Department of Medicine and Neurosurgery, Emory University School of Medicine, 1365 B Clifton Road B-2200, Northeast, B6209, Atlanta, GA, 30322, USA.
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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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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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Bing MMD, Shaobo DMD, Ruiqing LMD, Na LP, Yaqiong LP, Lianzhong ZMD. The Roles of Ultrasound-Based Radiomics In Precision Diagnosis and Treatment of Different Cancers: A Literature Review. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2020. [DOI: 10.37015/audt.2020.200051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
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37
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Kessel KA, Diehl CD, Oechsner M, Meyer B, Gempt J, Zimmer C, Schmidt-Graf F, Combs SE. Patient-Reported Outcome (PRO) as an Addition to Long-Term Results after High-Precision Stereotactic Radiotherapy in Patients with Secreting and Non-Secreting Pituitary Adenomas: A Retrospective Cohort Study up to 17-Years Follow-Up. Cancers (Basel) 2019; 11:cancers11121884. [PMID: 31783579 PMCID: PMC6966568 DOI: 10.3390/cancers11121884] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/10/2019] [Accepted: 11/18/2019] [Indexed: 12/11/2022] Open
Abstract
High-precision radiotherapy has been established as a valid and effective treatment option in patients with pituitary adenomas. We report on outcome after fractionated stereotactic radiotherapy (FSRT) in correlation with patient-reported outcomes (PROs). We analyzed 69 patients treated between 2000 and 2019. FSRT was delivered with a median total dose of 54 Gy (single fraction: 1.8 Gy). PRO questionnaires were sent to 28 patients. Median overall survival was 17.2 years; mean local control was 15.6 years (median not reached). Median follow-up was 5.8 years. Twenty (71%) patients participated in the PRO assessment. Physicians reported symptoms grade ≥3 in 6 cases (9%). Of all, 35 (51%) patients suffered from hypopituitarism at baseline, and during follow-up, new or progressive hypopituitarism was observed in 11 cases (16%). Patients reported 10 cases of severe side effects. Most of these symptoms were already graded as CTCAE (Common Terminology Criteria for Adverse Events) grade 2 by a physician in a previous follow-up exam. PROs are an essential measure and only correlate to a certain extent with the physician-reported outcomes. For high-precision radiotherapy of pituitary adenomas, they confirm excellent overall outcomes and low toxicity. In the future, the integration of PROs paired with high-end treatment will further improve outcomes.
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Affiliation(s)
- Kerstin A. Kessel
- Department of Radiation Oncology, Technical University of Munich (TUM), 81675 Munich, Germany; (C.D.D.); (M.O.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), DKTK Partner Site, 81675 Munich, Germany; (B.M.); (J.G.); (C.Z.)
- Correspondence: ; Tel.: +49-089-4140-4502
| | - Christian D. Diehl
- Department of Radiation Oncology, Technical University of Munich (TUM), 81675 Munich, Germany; (C.D.D.); (M.O.); (S.E.C.)
| | - Markus Oechsner
- Department of Radiation Oncology, Technical University of Munich (TUM), 81675 Munich, Germany; (C.D.D.); (M.O.); (S.E.C.)
| | - Bernhard Meyer
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), DKTK Partner Site, 81675 Munich, Germany; (B.M.); (J.G.); (C.Z.)
- Department of Neurosurgery, Technical University of Munich (TUM), 81675 Munich, Germany
| | - Jens Gempt
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), DKTK Partner Site, 81675 Munich, Germany; (B.M.); (J.G.); (C.Z.)
- Department of Neurosurgery, Technical University of Munich (TUM), 81675 Munich, Germany
| | - Claus Zimmer
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), DKTK Partner Site, 81675 Munich, Germany; (B.M.); (J.G.); (C.Z.)
- Department of Neuroradiology, Technical University of Munich (TUM), 81675 Munich, Germany
| | | | - Stephanie E. Combs
- Department of Radiation Oncology, Technical University of Munich (TUM), 81675 Munich, Germany; (C.D.D.); (M.O.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), DKTK Partner Site, 81675 Munich, Germany; (B.M.); (J.G.); (C.Z.)
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