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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Steele JD, Kernohan N, Nabi G. Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging. Int J Mol Sci 2024; 25:5379. [PMID: 38791417 PMCID: PMC11121591 DOI: 10.3390/ijms25105379] [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: 04/13/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.
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Affiliation(s)
- Chidozie N. Ogbonnaya
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | | | - Abeer J. Alhussaini
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK; (R.H.); (N.P.)
| | - J. Douglas Steele
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
| | - Neil Kernohan
- Department of Pathology, NHS Tayside, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK;
| | - Ghulam Nabi
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK; (C.N.O.); (A.J.A.); (J.D.S.)
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Tsukamoto T, Miki Y. Imaging of pituitary tumors: an update with the 5th WHO Classifications-part 2. Neoplasms other than PitNET and tumor-mimicking lesions. Jpn J Radiol 2023:10.1007/s11604-023-01407-0. [PMID: 36913010 PMCID: PMC10366287 DOI: 10.1007/s11604-023-01407-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 02/24/2023] [Indexed: 03/14/2023]
Abstract
Many types of tumors can develop in the pituitary gland. In the recently revised 5th editions of the World Health Organization (WHO) classifications (2021 WHO Classification of Central Nervous System Tumors and the 2022 WHO Classification of Endocrine and Neuroendocrine Tumors), various changes have been made to the tumors other than pituitary neuroendocrine tumor (PitNET)/pituitary adenoma, as well as PitNET. Adamantinomatous craniopharyngioma and papillary craniopharyngioma are now considered separate tumors in the 5th edition of the WHO classification. Tumors positive for thyroid transcription factor 1, a marker of posterior pituitary cells, are now grouped together in the pituicyte tumor family in the 5th edition of the WHO classification of Endocrine and Neuroendocrine Tumors. Poorly differentiated chordoma is newly listed in the 5th edition of the WHO Classification of Endocrine and Neuroendocrine Tumors. In this paper, we present the latest WHO classification of pituitary tumors (adamantinomatous craniopharyngioma, papillary craniopharyngioma, pituitary blastoma, pituicyte tumor family, tumors of pituitary origin other than those of the pituicyte tumor family, germinoma, meningioma, chordoma, metastatic tumors, lymphoma, and pituitary incidentaloma), review diseases requiring differentiation from tumors (pituitary abscess, hypophysitis, pituitary hyperplasia, Rathke's cleft cyst, arachnoid cyst, and aneurysm), and discuss diagnoses based on imaging findings.
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Affiliation(s)
- Taro Tsukamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan.
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Finger G, Wu KC, Godil SS, Carrau RL, Hardesty D, Prevedello DM. Ultrasound-guided endoscopic endonasal resection of sellar and suprasellar craniopharyngiomas. Front Surg 2023; 10:1073736. [PMID: 36896257 PMCID: PMC9990524 DOI: 10.3389/fsurg.2023.1073736] [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: 10/18/2022] [Accepted: 01/31/2023] [Indexed: 02/19/2023] Open
Abstract
Introduction Optimal planning and minimally invasive surgical approach are essential to complete craniopharyngiomas (CP) resection with limited postoperative morbidity. Given the nature of craniopharyngioma recurrence, complete resection of the neoplasm is crucial. Since CP arise from the pituitary stalk and may grow anteriorly or laterally, some cases require an extended endonasal craniotomy. The extension of the craniotomy is crucial to expose the whole tumor and to make its dissection from the surrounding structures feasible. In order to guide the extension of the approach, the intraoperative use of ultrasound is helpful for the surgeons. The objective of this paper is to describe and to demonstrate the applicability of the utilization of intraoperative ultrasound (US) guidance for planning and confirmation of craniopharyngioma resection in EES. Method The authors selected one operative video of a sellar-suprassellar craniopharyngioma gross-totally resected by EES. The authors demonstrate the extended sellar craniotomy, the anatomic landmarks that guide bone drilling and dural opening, the aspect of the intraoperative real time US, tumor resection and dissection from the surrounding structures. Results The solid component of the tumor was mostly isoechogenic in texture compared to the anterior pituitary gland, with several wide spread hyperechogenic images corresponding to calcifications and hypoechogenic vesicles corresponding to cysts inside the CF ("salt-and-pepper" pattern). Discussion The intraoperative endonasal US is a new surgical tool that allows for real-time active imaging for skull base procedures, such as sellar region tumors. Besides tumor evaluation, the intraoperative US helps the neurosurgeon to determine the size of craniotomy, to anticipate the relation between the tumor and vascular structures and to guide the best strategy for gross-total resection of the tumor. Conclusion The EES allows a straight access to the craniopharyngiomas located in the sellar region or that grow anteriorly or superiorly. This approach allows the surgeon to dissect the tumor with minimal manipulation of the surrounding structures, when compared to craniotomy approaches. In order to accomplish that, the use of intraoperative endonasal ultrasound helps the neurosurgeon to perform the most suitable strategy, optimizing the rate of success.
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Affiliation(s)
- Guilherme Finger
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Kyle C Wu
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Sanyia S Godil
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Ricardo L Carrau
- Department of Otolaryngology and Skull Base Surgery, The Ohio State University Wexner Medical Center, OH, United States
| | - Douglas Hardesty
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, United States
| | - Daniel M Prevedello
- Department of Neurosurgery, The Ohio State University College of Medicine, Columbus, OH, United States
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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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Lee T, Song K, Sohn B, Eom J, Ahn SS, Kim HS, Lee SK. A Radiomics-Based Model with the Potential to Differentiate Growth Hormone Deficiency and Idiopathic Short Stature on Sella MRI. Yonsei Med J 2022; 63:856-863. [PMID: 36031786 PMCID: PMC9424774 DOI: 10.3349/ymj.2022.63.9.856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/21/2022] [Accepted: 06/07/2022] [Indexed: 11/27/2022] Open
Abstract
PURPOSE We hypothesized that a radiomics approach could be employed to classify children with growth hormone deficiency (GHD) and idiopathic short stature (ISS) on sella magnetic resonance imaging (MRI). Accordingly, we aimed to develop a radiomics prediction model for differentiating GHD from ISS and to evaluate the diagnostic performance thereof. MATERIALS AND METHODS Short stature pediatric patients diagnosed with GHD or ISS from March 2011 to July 2020 at our institution were recruited. We enrolled 312 patients (GHD 210, ISS 102) with normal sella MRI and temporally split them into training and test sets (7:3). Pituitary glands were semi-automatically segmented, and 110 radiomic features were extracted from the coronal T2-weighted images. Feature selection and model development were conducted by applying mutual information (MI) and a light gradient boosting machine, respectively. After training, the model's performance was validated in the test set. We calculated mean absolute Shapley values for each of the selected input features using the Shapley additive explanations (SHAP) algorithm. Volumetric comparison was performed for GHD and ISS groups. RESULTS Ten radiomic features were selected by MI. The receiver operating characteristics curve of the developed model in the test set was 0.705, with an accuracy of 70.6%. When analyzing SHAP plots, root mean squared values had the highest impact in the model, followed by various texture features. In volumetric analysis, sagittal height showed a significant difference between GHD and ISS groups. CONCLUSION Radiomic analysis of sella MRI may be able to differentiate between GHD and ISS in clinical practice for short-statured children.
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Affiliation(s)
- Taeyoun Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Kyungchul Song
- Department of Pediatrics, Severance Children's Hospital, Endocrine Research Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Jihwan Eom
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Department of Computer Science, 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
| | - Ho-Seong Kim
- Department of Pediatrics, Severance Children's Hospital, Endocrine Research Institute, 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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Machine learning in neuro-oncology: toward novel development fields. J Neurooncol 2022; 159:333-346. [PMID: 35761160 DOI: 10.1007/s11060-022-04068-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/11/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE Artificial Intelligence (AI) involves several and different techniques able to elaborate a large amount of data responding to a specific planned outcome. There are several possible applications of this technology in neuro-oncology. METHODS We reviewed, according to PRISMA guidelines, available studies adopting AI in different fields of neuro-oncology including neuro-radiology, pathology, surgery, radiation therapy, and systemic treatments. RESULTS Neuro-radiology presented the major number of studies assessing AI. However, this technology is being successfully tested also in other operative settings including surgery and radiation therapy. In this context, AI shows to significantly reduce resources and costs maintaining an elevated qualitative standard. Pathological diagnosis and development of novel systemic treatments are other two fields in which AI showed promising preliminary data. CONCLUSION It is likely that AI will be quickly included in some aspects of daily clinical practice. Possible applications of these techniques are impressive and cover all aspects of neuro-oncology.
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Xanthogranuloma of the Sellar Region: A Comprehensive Review of Neuroimaging in a Rare Inflammatory Entity. J Pers Med 2022; 12:jpm12060943. [PMID: 35743728 PMCID: PMC9225214 DOI: 10.3390/jpm12060943] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/03/2022] [Accepted: 06/05/2022] [Indexed: 11/17/2022] Open
Abstract
Xanthogranuloma of the sellar region is a rare chronic inflammatory lesion resulting from secondary hemorrhage, inflammation, infarction, and necrosis of an existing Rathke’s cleft cyst, craniopharyngioma, or pituitary adenoma. Sellar xanthogranulomas are challenging to differentiate from other cystic lesions preoperatively due to the lack of characteristic imaging features. We performed a literature overview of the clinical and paraclinical features, treatment options, and long-term outcomes of patients with sellar xanthogranuloma, focusing on the preoperative radiological diagnosis. The hyperintense signal in both T1- and T2-weighted sequences, cystic or partially cystic morphology, ovoid shape, sellar epicenter, intra- and suprasellar location, intratumoral calcifications, linear rim contrast enhancement, and the absence of cavernous sinus invasion suggest xanthogranuloma in the preoperative differential diagnosis. An endoscopic endonasal gross total resection without radiotherapy is the preferred first-line treatment. Given the low rate of recurrence rate and low chance of endocrinological recovery, a mass reduction with decompression of the optic apparatus may represent an appropriate surgical goal. Identifying the xanthogranulomas’ mutational profile could complement histopathological diagnosis and give insight into their histo-pathogenesis. A better preoperative neuroimagistic diagnosis of sellar xanthogranulomas and differentiation from lesions with a poorer prognosis, such as craniopharyngioma, would result in an optimal personalized surgical approach.
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Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2022; 12:752119. [PMID: 35069406 PMCID: PMC8770750 DOI: 10.3389/fneur.2021.752119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.
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Affiliation(s)
- Caijie Qin
- Institute of Information Engineering, Sanming University, Sanming, China
| | - Wenxing Hu
- University of New South Wales, Sydney, NSW, Australia
| | - Xinsheng Wang
- School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, China
| | - Xibo Ma
- CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021; 12:152. [PMID: 34676470 PMCID: PMC8531173 DOI: 10.1186/s13244-021-01102-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/26/2021] [Indexed: 12/15/2022] Open
Abstract
This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.
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Affiliation(s)
| | - Ahmed Alksas
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Amr AbdelKhalek
- Internship at Mansoura University Hospital, Mansoura Faculty of Medicine, Mansoura, Egypt
| | - Khaled Abdel Baky
- Department of Diagnostic Radiology, Faculty of Medicine, Port Said University, Port Said, Egypt
| | - Ayman El-Baz
- Biomaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura, 3512, Egypt.
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Zhao Z, Xiao D, Nie C, Zhang H, Jiang X, Jecha AR, Yan P, Zhao H. Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma. Front Oncol 2021; 11:709321. [PMID: 34307178 PMCID: PMC8300562 DOI: 10.3389/fonc.2021.709321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/18/2021] [Indexed: 11/21/2022] Open
Abstract
Background Given the similarities in clinical manifestations of cystic-solid pituitary adenomas (CS-PAs) and craniopharyngiomas (CPs), this study aims to establish and validate a nomogram based on preoperative imaging features and blood indices to differentiate between CS-PAs and CPs. Methods A departmental database was searched to identify patients who had undergone tumor resection between January 2012 and December 2020, and those diagnosed with CS-PAs or CPs by histopathology were included. Preoperative magnetic resonance imaging (MRI) features as well as blood indices were retrieved and analyzed. Radiological features were extracted from the tumor on contrast-enhanced T1 (CE-T1) weighted and T2 weighted sequences. The two independent samples t-test and principal component analysis (PCA) were used for feature selection, data dimension reduction, and radiomics signature building. Next, the radiomics signature was put in five classification models for exploring the best classifier with superior identification performance. Multivariate logistic regression analysis was then used to establish a radiomic-clinical model containing radiomics and hematological features, and the model was presented as a nomogram. The performance of the radiomics-clinical model was assessed by calibration curve, clinical effectiveness as well as internal validation. Results A total of 272 patients were included in this study: 201 with CS-PAs and 71 with CPs. These patients were randomized into training set (n=182) and test set (n=90). The radiomics signature, which consisted of 18 features after dimensionality reduction, showed superior discrimination performance in 5 different classification models. The area under the curve (AUC) values of the training set and the test set obtained by the radiomics signature are 0.92 and 0.88 in the logistic regression model, 0.90 and 0.85 in the Ridge classifier, 0.88 and 0.82 in the stochastic gradient descent (SGD) classifier, 0.78 and 0.85 in the linear support vector classification (Linear SVC), 0.93 and 0.86 in the multilayers perceptron (MLP) classifier, respectively. The predictive factors of the nomogram included radiomic signature, age, WBC count, and FIB. The nomogram showed good discrimination performance (with an AUC of 0.93 in the training set and 0.90 in the test set) and good calibration. Moreover, decision curve analysis (DCA) demonstrated satisfactory clinical effectiveness of the proposed radiomic-clinical nomogram. Conclusions A personalized nomogram containing radiomics signature and blood indices was proposed in this study. This nomogram is simple yet effective in differentiating between CS-PAs and CPs and thus can be used in routine clinical practice.
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Affiliation(s)
- Zhen Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dongdong Xiao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuansheng Nie
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Zhang
- Department of Geriatric Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaobing Jiang
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ali Rajab Jecha
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Yan
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongyang Zhao
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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11
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Liu CX, Heng LJ, Han Y, Wang SZ, Yan LF, Yu Y, Ren JL, Wang W, Hu YC, Cui GB. Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes. Front Oncol 2021; 11:640375. [PMID: 34307124 PMCID: PMC8294058 DOI: 10.3389/fonc.2021.640375] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/16/2021] [Indexed: 01/14/2023] Open
Abstract
Objective To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA). Methods Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. Results Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1. Conclusion Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.
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Affiliation(s)
- Chen-Xi Liu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Li-Jun Heng
- Department of Neurosurgery, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China
| | - Yu Han
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China
| | - Sheng-Zhong Wang
- Faculty of Medical Technology, Shaanxi University of Traditional Chinese Medicine, Xianyang, China
| | - Lin-Feng Yan
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Ying Yu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | | | - Wen Wang
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Yu-Chuan Hu
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
| | - Guang-Bin Cui
- Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, China.,Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, China
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Kunimatsu A, Yasaka K, Akai H, Sugawara H, Kunimatsu N, Abe O. Texture Analysis in Brain Tumor MR Imaging. Magn Reson Med Sci 2021; 21:95-109. [PMID: 33692222 PMCID: PMC9199980 DOI: 10.2463/mrms.rev.2020-0159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.
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Affiliation(s)
- Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Koichiro Yasaka
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Haruto Sugawara
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Natsuko Kunimatsu
- Department of Radiology, International University of Health and Welfare, Mita Hospital
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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