The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation

Yan, Pingkun; Ling, Yan To; Hu, Tingting; Xiao, Dongdong; Zhang, Zhen; Zhao, Hongyang; Feng, Jun

doi:10.1016/j.tranon.2017.04.006

Cited by 82 publications

(45 citation statements)

References 34 publications

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“…In breast cancer, texture analysis could discriminate between lobular and ductal breast carcinomas [20] . In another recent study, it could discriminate between benign and malignant meningiomas [21] .…”

Section: Discussionmentioning

confidence: 95%

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer – A First Preliminary Study

Meyer

Schob

Höhn

et al. 2017

Translational Oncology

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OBJECT: Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer. METHODS: The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated. RESULTS: Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r = 0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r = −0.69, −0.66, −0.69 and −0.63, respectively) was also identified. For T2-weighted images, Variance with r = 0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r = −0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r = −0.80, S(0;5)SumVarnc was positively with r = 0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r = −0.77). S(1;0)Correlat was with r = 0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r = 0.65 with p53 count. For T2-weighted images S(1;−1)SumEntrp was the inverse correlation with r = −0.72, whereas S(0;4)AngScMom correlated positively with r = 0.63. CONCLUSIONS: MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology.

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Section: Discussionmentioning

confidence: 95%

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer – A First Preliminary Study

Meyer

Schob

Höhn

et al. 2017

Translational Oncology

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“…Previous studies have used classifiers for the detection and grading of meningiomas and other CNS tumors, but these have almost exclusively focused on MRI 28,29 or histopathological 30 imaging characteristics to drive their predictions. We also extend classical survival analysis methods to the machine learning framework and demonstrate how proportional hazard ratios can be used to create individualized patient-specific survival curves (also illustrated in Fig.…”

Section: Discussionmentioning

confidence: 99%

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

et al. 2020

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Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedure. We demonstrate that our models are capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across 16 SEER registries. A free smartphone and web application is provided for readers to access and test the predictive models (www.meningioma.app). Future model improvements and prospective replication will be necessary to demonstrate true clinical utility. Rather than being used in isolation, we expect that the proposed models will be integrated into larger and more comprehensive models that integrate imaging and molecular biomarkers. Whether for meningiomas or other tumors of the CNS, the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.npj Digital Medicine (2020) 3:12 ; https://doi.

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“…Thus, our results and conclusions should be interpreted with potential selection and information biases in mind. It should also be noted that the accuracy of the predictive models we develop are not perfect, and would be improved by increasing the number of patients in the study and the addition of radiomic or biologic features, as has been demonstrated by other investigators for prediction of meningioma grade [ 10 , 23 ]. Indeed, the magnetic resonance characteristics of meningiomas have high sensitivity and specificity for meningioma grade and histopathologic subtype [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Preoperative and postoperative prediction of long-term meningioma outcomes

Gennatas

Braunstein

et al. 2018

PLoS ONE

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BackgroundMeningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.Methods and findingsWe developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms’ accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.ConclusionsClinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.

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The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation

Cited by 82 publications

References 34 publications

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer – A First Preliminary Study

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer – A First Preliminary Study

Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database

Preoperative and postoperative prediction of long-term meningioma outcomes

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