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Kobets AJ, Alavi SAN, Ahmad SJ, Castillo A, Young D, Minuti A, Altschul DJ, Zhu M, Abbott R. Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review. Neurosurg Rev 2024; 47:170. [PMID: 38637466 PMCID: PMC11026186 DOI: 10.1007/s10143-024-02366-4] [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: 01/28/2024] [Revised: 03/04/2024] [Accepted: 03/16/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Segmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a myriad of posterior fossa-related conditions, such as Chiari malformation, trigeminal neuralgia, post-operative pediatric cerebellar mutism syndrome, and Crouzon syndrome. Herein, we present a summary of the current literature on segmentation of the posterior fossa. The review highlights the various segmentation techniques, and their respective strengths and weaknesses, employed along with objectives and outcomes of the various studies reported in the literature. METHODS A literature search was conducted in PubMed, Embase, Cochrane, and Web of Science up to November 2023 for articles on segmentation techniques of posterior fossa. The two senior authors searched through databases based on the keywords of the article separately and then enrolled joint articles that met the inclusion and exclusion criteria. RESULTS The initial search identified 2205 articles. After applying inclusion and exclusion criteria, 77 articles were selected for full-text review after screening of titles/abstracts. 52 articles were ultimately included in the review. Segmentation techniques included manual, semi-automated, and fully automated (atlas-based, convolutional neural networks). The most common pathology investigated was Chiari malformation. CONCLUSIONS Various forms of segmentation techniques have been used to assess posterior fossa volumes/pathologies and each has its advantages and disadvantages. We discuss these nuances and summarize the current state of literature in the context of posterior fossa-associated pathologies.
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Affiliation(s)
- Andrew J Kobets
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Seyed Ahmad Naseri Alavi
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA.
| | | | | | | | | | - David J Altschul
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
| | - Michael Zhu
- Albert Einstein College of Medicine, New York City, USA
| | - Rick Abbott
- Department of Neurological Surgery, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY, 10467, USA
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2
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Han T, Liu X, Jing M, Zhang Y, Deng L, Zhang B, Zhou J. The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence. J Cancer Res Clin Oncol 2023; 149:17427-17436. [PMID: 37878091 DOI: 10.1007/s00432-023-05463-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023]
Abstract
OBJECTIVE To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. MATERIALS AND METHODS Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively. RESULTS Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model. CONCLUSION This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.
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Affiliation(s)
- Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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3
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Jaju A, Li Y, Dahmoush H, Gottardo NG, Laughlin S, Mirsky D, Panigrahy A, Sabin ND, Shaw D, Storm PB, Poussaint TY, Patay Z, Bhatia A. Imaging of pediatric brain tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee/ASPNR White Paper. Pediatr Blood Cancer 2023; 70 Suppl 4:e30147. [PMID: 36519599 PMCID: PMC10466217 DOI: 10.1002/pbc.30147] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 12/16/2022]
Abstract
Tumors of the central nervous system are the most common solid malignancies in children and the most common cause of pediatric cancer-related mortality. Imaging plays a central role in diagnosis, staging, treatment planning, and response assessment of pediatric brain tumors. However, the substantial variability in brain tumor imaging protocols across institutions leads to variability in patient risk stratification and treatment decisions, and complicates comparisons of clinical trial results. This White Paper provides consensus-based imaging recommendations for evaluating pediatric patients with primary brain tumors. The proposed brain magnetic resonance imaging protocol recommendations balance advancements in imaging techniques with the practicality of deployment across most imaging centers.
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Affiliation(s)
- Alok Jaju
- Department of Medical Imaging, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Yi Li
- UCSF Department of Radiology and Biomedical Imaging, San Francisco, California, USA
| | - Hisham Dahmoush
- Department of Radiology, Lucile Packard Children's Hospital at Stanford, Palo Alto, California, USA
| | - Nicholas G Gottardo
- Department of Paediatric and Adolescent Oncology and Haematology, Perth Children's Hospital, Brain Tumour Research Programme, Telethon Kids Institute, Perth, Western Australia, Australia
| | - Suzanne Laughlin
- Department of Diagnostic Imaging, The Hospital for Sick Children and Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - David Mirsky
- Department of Radiology, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Ashok Panigrahy
- Department of Radiology, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Noah D Sabin
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Dennis Shaw
- Department of Radiology, Seattle Children's Hospital, Seattle, Washington, USA
| | - Phillip B Storm
- Division of Neurosurgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Tina Young Poussaint
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Zoltan Patay
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Aashim Bhatia
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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4
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Gonçalves FG, Tierradentro-Garcia LO, Kim JDU, Zandifar A, Ghosh A, Viaene AN, Khrichenko D, Andronikou S, Vossough A. The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups. Pediatr Radiol 2022; 52:2595-2609. [PMID: 35798974 DOI: 10.1007/s00247-022-05411-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 04/05/2022] [Accepted: 05/31/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce. OBJECTIVE This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features. MATERIALS AND METHODS In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups. RESULTS We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found. CONCLUSION ADC histogram analysis can be implemented as a complementary tool in the preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively.
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Affiliation(s)
- Fabrício Guimarães Gonçalves
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.
| | - Luis Octavio Tierradentro-Garcia
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Jorge Du Ub Kim
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Alireza Zandifar
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Adarsh Ghosh
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Dmitry Khrichenko
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Savvas Andronikou
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arastoo Vossough
- Department of Radiology, Division of Neuroradiology, The Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Luo Y, Zhang S, Tan W, Lin G, Zhuang Y, Zeng H. The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12112796. [PMID: 36428860 PMCID: PMC9689934 DOI: 10.3390/diagnostics12112796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89-0.97) and 0.94 (95% CI: 0.90-0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96-0.99). Heterogeneity was found in the sensitivity (I2 = 62.59%) and the specificity (I2 = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors.
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Affiliation(s)
- Yi Luo
- Shantou University Medical College, 22 Xinling Road, Jinping District, Shantou 515041, China
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Siqi Zhang
- Shantou University Medical College, 22 Xinling Road, Jinping District, Shantou 515041, China
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Weiting Tan
- Shenzhen Children’s Hospital of China Medical University, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Guisen Lin
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Yijiang Zhuang
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children’s Hospital, 7019 Yitian Road, Futian District, Shenzhen 518038, China
- Correspondence:
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Dury RJ, Lourdusamy A, Macarthur DC, Peet AC, Auer DP, Grundy RG, Dineen RA. Meta-Analysis of Apparent Diffusion Coefficient in Pediatric Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma. J Magn Reson Imaging 2021; 56:147-157. [PMID: 34842328 DOI: 10.1002/jmri.28007] [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: 09/06/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Medulloblastoma, ependymoma, and pilocytic astrocytoma are common pediatric posterior fossa tumors. These tumors show overlapping characteristics on conventional MRI scans, making diagnosis difficult. PURPOSE To investigate whether apparent diffusion coefficient (ADC) values differ between tumor types and to identify optimum cut-off values to accurately classify the tumors using different performance metrics. STUDY TYPE Systematic review and meta-analysis. SUBJECTS Seven studies reporting ADC in pediatric posterior fossa tumors (115 medulloblastoma, 68 ependymoma, and 86 pilocytic astrocytoma) were included following PubMed and ScienceDirect searches. SEQUENCE AND FIELD STRENGTH Diffusion weighted imaging (DWI) was performed on 1.5 and 3 T across multiple institution and vendors. ASSESSMENT The combined mean and standard deviation of ADC were calculated for each tumor type using a random-effects model, and the effect size was calculated using Hedge's g. STATISTICAL TESTS Sensitivity/specificity, weighted classification accuracy, balanced classification accuracy. A P value < 0.05 was considered statistically significant, and a Hedge's g value of >1.2 was considered to represent a large difference. RESULTS The mean (± standard deviation) ADCs of medulloblastoma, ependymoma, and pilocytic astrocytoma were 0.76 ± 0.16, 1.10 ± 0.10, and 1.49 ± 0.16 mm2 /sec × 10-3 . To maximize sensitivity and specificity using the mean ADC, the cut-off was found to be 0.96 mm2 /sec × 10-3 for medulloblastoma and ependymoma and 1.26 mm2 /sec × 10-3 for ependymoma and pilocytic astrocytoma. The meta-analysis showed significantly different ADC distributions for the three posterior fossa tumors. The cut-off values changed markedly (up to 7%) based on the performance metric used and the prevalence of the tumor types. DATA CONCLUSION There were significant differences in ADC between tumor types. However, it should be noted that only summary statistics from each study were analyzed and there were differences in how regions of interest were defined between studies. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Richard J Dury
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Anbarasu Lourdusamy
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Donald C Macarthur
- Department of Neurosurgery, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK.,Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Dorothee P Auer
- Radiological Sciences, Mental Health & Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Richard G Grundy
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Robert A Dineen
- Radiological Sciences, Mental Health & Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK.,Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
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7
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Payabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Front Oncol 2020; 10:71. [PMID: 32117728 PMCID: PMC7018938 DOI: 10.3389/fonc.2020.00071] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 01/15/2020] [Indexed: 12/16/2022] Open
Abstract
We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Tarik Tihan
- Department of Pathology, University of California, San Francisco, San Francisco, CA, United States
| | - Soonmee Cha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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8
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Szklaruk J, Son JB, Wei W, Bhosale P, Javadi S, Ma J. Comparison of free breathing and respiratory triggered diffusion-weighted imaging sequences for liver imaging. World J Radiol 2019; 11:134-143. [PMID: 31798795 PMCID: PMC6885723 DOI: 10.4329/wjr.v11.i11.134] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/26/2019] [Accepted: 09/26/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) has become a useful tool in the detection, characterization, and evaluation of response to treatment of many cancers, including malignant liver lesions. DWI offers higher image contrast between lesions and normal liver tissue than other sequences. DWI images acquired at two or more b-values can be used to derive an apparent diffusion coefficient (ADC). DWI in the body has several technical challenges. This include ghosting artifacts, mis-registration and susceptibility artifacts. New DWI sequences have been developed to overcome some of these challenges. Our goal is to evaluate 3 new DWI sequences for liver imaging.
AIM To qualitatively and quantitatively compare 3 DWI sequences for liver imaging: free-breathing (FB), simultaneous multislice (SMS), and prospective acquisition correction (PACE).
METHODS Magnetic resonance imaging (MRI) was performed in 20 patients in this prospective study. The MR study included 3 separate DWI sequences: FB-DWI, SMS-DWI, and PACE-DWI. The image quality, mean ADC, standard deviations (SD) of ADC, and ADC histogram were compared. Wilcoxon signed-rank tests were used to compare qualitative image quality. A linear mixed model was used to compare the mean ADC and the SDs of the ADC values. All tests were 2-sided and P values of < 0.05 were considered statistically significant.
RESULTS There were 56 lesions (50 malignant) evaluated in this study. The mean qualitative image quality score of PACE-DWI was 4.48. This was significantly better than that of SMS-DWI (4.22) and FB-DWI (3.15) (P < 0.05). Quantitatively, the mean ADC values from the 3 different sequences did not significantly differ for each liver lesion. FB-DWI had a markedly higher variation in the SD of the ADC values than did SMS-DWI and PACE-DWI. We found statistically significant differences in the SDs of the ADC values for FB-DWI vs PACE-DWI (P < 0.0001) and for FB-DWI vs SMS-DWI (P = 0.03). The SD of the ADC values was not statistically significant for PACE-DWI and SMS-DWI (P = 0.18). The quality of the PACE-DWI ADC histograms were considered better than the SMS-DWI and FB-DWI.
CONCLUSION Compared to FB-DWI, both PACE-DWI and SMS-DWI provide better image quality and decreased quantitative variability in the measurement of ADC values of liver lesions.
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Affiliation(s)
- Janio Szklaruk
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Wei Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Priya Bhosale
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Sanaz Javadi
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
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9
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Chen C, Ren CP, Zhao RC, Ding JW, Cheng JL. Histogram Analysis Parameters ADC for Distinguishing Ventricular Neoplasms of Ependymoma, Choroid Plexus Papilloma, and Central Neurocytoma. Med Sci Monit 2019; 25:5886-5891. [PMID: 31390342 PMCID: PMC6693364 DOI: 10.12659/msm.915398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Background To determine if histograms of ADC can be used to differentiate ventricular ependymomas, choroid plexus papillomas (CPPs), and central neurocytomas (CNCs). Material/Methods We retrospectively reviewed records from 185 patients from 1 January 2014 to 1 November 2018. We finally included a total of 60 patients: 36 (60.00%) had histologically confirmed ependymomas, 10 (16.67%) had CPPs, and 14 (23.33%) had CNCs, as determined by routine MRI scanning at 3.0T. The ADC histogram features were derived and then compared by Kruskal-Wallis test (they were not normally distributed). Bonferroni test was used to compare the 2 groups and then we determined the ROC. Results Ependymomas had significantly higher mean, perc.01%, perc.10%, perc.50%, perc.90%, and perc.99% than CNCs. Ependymomas had significantly lower skewness than CNCs. Histogram metrics derived from mean, perc.01%, perc.10%, perc.50%, and perc.90% were significantly lower in the CNCs group than in the CPPs group. CPPs showed significantly lower skewness than CNCs. A threshold value of 86.50 for perc.50% to predict ependymomas from CNCs was estimated (AUC=0.97, sensitivity=97.20%, specificity=85.70%). Optimal diagnostic performance to predict CPPs from CNCs (AUC=0.96, sensitivity=100.00%, specificity=85.70%) was obtained when setting Perc.50%=84.00 as the threshold value. Conclusions The ADC histogram analysis may help to discriminate ependymomas, CPPs, and CNCs.
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Affiliation(s)
- Chen Chen
- Department of Magnetic Resonance Imaging (MRI), First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland)
| | - Cui-Ping Ren
- Department of Magnetic Resonance Imaging (MRI), First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland)
| | - Rui-Chen Zhao
- Department of Magnetic Resonance Imaging (MRI), First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland)
| | - Jiang-Wei Ding
- Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland)
| | - Jing-Liang Cheng
- Department of Magnetic Resonance Imaging (MRI), First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China (mainland)
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Luo W, Xu Y, Yang J, Liu Z, Liu H. Fourth Ventricular Meningiomas. World Neurosurg 2019; 127:e1201-e1209. [PMID: 31004862 DOI: 10.1016/j.wneu.2019.04.097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 04/09/2019] [Accepted: 04/10/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Fourth ventricular meningiomas (FVMs) are extremely rare. Here, we report a series of 11 patients at a single institution. A comprehensive literature analysis is conducted. METHODS Information about 11 patient was extracted from the patient data. Reports of cases in English were obtained from the literature. Including our 11 patients, 71 patients were analyzed in this study. RESULTS The most common age of onset was from the third decade to sixth decade of life. The female/male ratio was about 1.16:1. The most frequent subtype of meningioma was fibrous meningioma. FVMs had specific imaging features, such as calcification (20%), peritumoral edema (30.3%), heterogeneous enhancement (22.5%), cystic formation (4.3%), and hydrocephalus (52.8%). The proportion of total tumor resection was about 94.9%, with 15.3% of postoperative complications. During follow-up, the recurrent rate of FVMs was about 6.8%. There was no significant difference in the analysis of correlation between hydrocephalus and the maximum diameter of tumors, correlation between hydrocephalus and the volume of tumor, or correlation between peritumoral edema and the volume of tumor, as well as correlation between heterogeneous enhancement and the grade of meningiomas. CONCLUSIONS FVMs have their own characteristics in age of onset, gender ratio, histologic types, and imaging features. The recommended treatment is surgical treatment via the telovelar approach with suboccipital craniotomy/craniectomy. Adjuvant therapy is needed in some high-grade meningiomas and in patients undergoing partial resection. The prognosis is relatively good, with fewer postoperative complications and a higher rate of total resection.
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Affiliation(s)
- Wei Luo
- Department of Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yulun Xu
- Department of Neurosurgery, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Yang
- Department of Neurosurgery, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhongmin Liu
- Department of Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hai Liu
- Department of Neurosurgery, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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