Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors

Gonçalves, Fabrício Guimarães; Zandifar, Alireza; Kim, Jorge Du Ub; Tierradentro-García, Luis Octavio; Ghosh, Adarsh; Khrichenko, Dmitry; Andronikou, Savvas; Vossough, Arastoo

doi:10.1007/s00062-022-01179-6

Cited by 12 publications

(5 citation statements)

References 54 publications

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“…Among the 9 different tested machine-learning algorithms the NB algorithm showed the most balanced ratio of sensitivity and specificity and simultaneously a good AUC for all different approaches and subclasses ( Supplementary Tables 2 and 3 ). NB reached a macro-average AUC for the multi-class detection of molecular characteristics of 0.82 (95% confidence interval 12 0.75–0.88) using anatomical N4/WS data. The scores remained consistent including radiomics from ADC naive or ADC N4 (AUC 0.82; 95% CI: 0.73–88 and 95% CI: 0.73–0.87, respectively).…”

Section: Resultsmentioning

confidence: 95%

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Foltyn-Dumitru,

Schell,

Sahm

et al. 2024

Neuro-Oncology Advances

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Background This study investigates the influence of diffusion-weighted MRI (DWI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Methods Radiomic features, compliant with IBSI standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wildtype). Four approaches were compared: anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The UCSF-glioma dataset (n=409) was used for external validation. Results Naïve-Bayes algorithms yielded overall the best performance on the internal test-set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (p= .011) for the IDH-wildtype subgroup, but not for the other two glioma subgroups (p>0.05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wildtype subgroup (p≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4) and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (p< .012 each). Conclusions ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wildtype glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.

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Section: Resultsmentioning

confidence: 95%

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Foltyn-Dumitru,

Schell,

Sahm

et al. 2024

Neuro-Oncology Advances

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“…Diffusion MRI has been used to characterize brain tumors 22 and is implemented in routine clinical protocols 23 . ADC is inversely associated with cell density and several studies have demonstrated lower ADC in glioma compared with surrounding brain tissue or therapy-associated tissue changes 5 .…”

Section: Discussionmentioning

confidence: 99%

Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model

Streibel,

Breckwoldt,

Hunger

et al. 2024

Sci Rep

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Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.

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“…In contrast to those in adults, the pathological types of CNS tumors in young patients are highly heterogeneous [ 2 , 3 , 4 , 5 , 6 , 7 ]. In addition, typical extra-axial tumors, including meningiomas, occur less frequently in younger patients [ 8 , 9 ]. Nevertheless, differentiating between intracranial malignant tumors (MT) and benign tumors (BT) is essential because the tumor type influences the treatment strategy and ultimately the prognosis.…”

Section: Introductionmentioning

confidence: 99%

“…First, the apparent diffusion coefficient (ADC) value obtained through diffusion-weighted imaging (DWI) is a common parameter that reflects the cell density of tumors [ 15 ]. ADC values are reportedly useful for predicting the grade of intracranial tumors in children and for distinguishing between different pediatric brain tumor types [ 9 , 16 , 17 , 18 ]. In addition, arterial spin labeling (ASL) techniques provide tumor blood flow (TBF) values that can predict either low- or high-grade pediatric brain tumors [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study

Tanaka,

Maeda,

Nakayama

et al. 2024

Diagnostics

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Purpose: To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. Methods: Fifteen patients with intracranial MTs and 10 patients with BTs aged 0–30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann–Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. Conclusions: The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

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Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors

Cited by 12 publications

References 54 publications

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization

Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study

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