Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma

Meyer, Hans Jonas; Leifels, Leonard; Hamerla, Gordian; Höhn, Anne Kathrin

doi:10.1007/s11307-018-1283-y

Cited by 21 publications

(43 citation statements)

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“…Increasing body of evidence suggests the superior value of additionally using the second order histogram dimensions skewness, kurtosis and entropy of the ADC-continuum, for better reflection of tumor heterogeneity and associated tumor-biology (16,17,20,21,(25)(26)(27)(28)(29)(30)(31)(32). In this regard, our results show the significant differences of ADC-entropy in grade I vs. grade II LGG, with higher values being associated with higher tumor grade.…”

Section: Discussionmentioning

confidence: 50%

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

et al. 2020

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Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as "benign" neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status. Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed. Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status. Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.

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Section: Discussionmentioning

confidence: 50%

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

et al. 2020

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“…Histogram parameters can reflect the microstructure of tissues by issuing every voxel of the MRI into a histogram, and thus, statistical information of the images can be obtained 6‐12 . Thus, in oncologic imaging, it was extensively researched that histogram parameters can reflect microstructure in tumors, which was shown for several tumor entities 6‐12 . There are several different parameters, which can be calculated.…”

Section: Discussionmentioning

confidence: 99%

“…All measures were performed by one radiologist blinded to the histopathology results (AS, 16 years of general radiological experience). The following histogram parameters were calculated: mean, maximum (max), minimum (min), median, mode, percentiles: 10th, 25th, 75th, and 90th, kurtosis, skewness, and entropy, as described in previous studies 7‐9 . These parameters were each derived from T2‐weighted and native T1‐weighted images.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, some preliminary studies, predominantly in the field of oncologic imaging, showed that histogram parameters derived from MRI are capable to reflect different microstructure characteristics of tumors, such as cellularity and proliferation potential 7‐10 . Furthermore, histogram analysis parameters were able to predict treatment response in longitudinal analyses in several malignancies 6,11,12 .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, histogram analysis parameters were able to predict treatment response in longitudinal analyses in several malignancies 6,11,12 . So, it was discussed that even morphological sequences can reflect tissue features, comprising proliferation potential, cellularity, and nucleic sizes in several malignant tumors 7‐10 …”

Section: Introductionmentioning

confidence: 99%

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Associations between magnetic resonance imaging and EMG findings in myopathies

Meyer¹,

Schneider

Emmer

et al. 2020

Acta Neurol Scand

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An MRI‐Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors

Wang

Hao

Gao

et al. 2022

Magnetic Resonance Imaging

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BackgroundSinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem.PurposeTo establish an MRI‐based radiomics nomogram for assessing relapse risk in patients with SNMT.Study TypeRetrospective.PopulationA total of 143 patients with 68.5% females (development/validation set, 98/45 patients).Field Strength/SequenceA 1.5‐T and 3‐T, fat‐suppressed fast spin echo (FSE) T2‐weighted imaging (FS‐T2WI), FSE T1‐weighted imaging (T1WI), and FSE contrast‐enhanced T1WI (T1WI + C).AssessmentThree MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS‐T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high‐dimensional features. The clinical model was built based on clinical and MRI features. The Ki‐67‐based and tumor‐node‐metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse‐free survival analysis was used among 143 patients.Statistical TestsThe intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan–Meier curve, log‐rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant.ResultsThe T1 + C‐based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS‐T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki‐67‐based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated.Data ConclusionA radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki‐67‐based model, and TNM model.Evidence Level3.Technical EfficacyStage 5.

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Histogram Analysis Parameters Derived from Conventional T1- and T2-Weighted Images Can Predict Different Histopathological Features Including Expression of Ki67, EGFR, VEGF, HIF-1α, and p53 and Cell Count in Head and Neck Squamous Cell Carcinoma

Cited by 21 publications

References 36 publications

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology

Associations between magnetic resonance imaging and EMG findings in myopathies

An MRI‐Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors

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