Diffusion‐weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors

Meeus, Emma M.; Zarinabad, Niloufar; Manias, Karen; Novák, Jan; Rose, Heather; Dehghani, Hamid; Foster, Katharine; Morland, Bruce; Peet, Andrew C.

doi:10.1002/jmri.25901

Cited by 28 publications

(25 citation statements)

References 40 publications

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“…D mean and D dark measurements of diffusion coefficient D also resulted in high levels of diagnostic accuracy, confirming previous results for the IVIM model in other entities. 21 The diagnostic performance of ADC-derived values was similar to that of IVIM-derived values with high levels of sensitivity (ADC mean 92%; D mean 92%) and specificity (ADC mean 98%; D mean 98%).…”

Section: Well Et Al Diffusion-weighted Imaging In Nf1mentioning

confidence: 60%

“…By contrast, the biexponential intravoxel incoherent motion (IVIM) model enables further separation of tissue perfusion and diffusivity. 19,20 IVIM has shown promise for distinguishing malignant from benign pediatric abdominal tumors 21 and in the differentiation of high-from low-grade hepatocellular carcinoma. 22 Therefore, we aimed to assess the value of DW MRI for characterization of benign and malignant PNSTs in patients with NF1, and to compare the diagnostic performance of biexponential IVIM with that of monoexponential data analysis and established morphological features of malignant transformation.…”

Section: Neuro-oncologymentioning

confidence: 99%

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Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging

et al. 2018

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Background. We sought to determine the value of diffusion-weighted (DW) magnetic resonance imaging (MRI) for characterization of benign and malignant peripheral nerve sheath tumors (PNSTs) in patients with neurofibromatosis type 1 (NF1). Methods. Twenty-six patients with NF1 and suspicion of malignant transformation of PNSTs were prospectively enrolled and underwent DW MRI at 3T. For a set of benign (n = 55) and malignant (n = 12) PNSTs, functional MRI parameters were derived from both biexponential intravoxel incoherent motion (diffusion coefficient D and perfusion fraction f) and monoexponential data analysis (apparent diffusion coefficients [ADCs]). A panel of morphological MRI features was evaluated using T1-and T2-weighted imaging. Mann-Whitney U-test, Fisher's exact test, and receiver operating characteristic (ROC) analyses were applied to assess the diagnostic accuracy of quantitative and qualitative MRI. Cohen's kappa was used to determine interrater reliability. Results. Malignant PNSTs demonstrated significantly lower diffusivity (P < 0.0001) compared with benign PNSTs. The perfusion fraction f was significantly higher in malignant PNSTs (P < 0.001). In ROC analysis, functional MRI parameters showed high diagnostic accuracy for differentiation of PNSTs (eg, ADC mean, 92% sensitivity with 98% specificity, AUC 0.98; D mean, 92% sensitivity with 98% specificity, AUC 0.98). By contrast, morphological imaging features had only limited sensitivity (18-94%) and specificity (18-82%) for identification of malignancy. Interrater reliability was higher for monoexponential data analysis. Conclusion. DW imaging shows better diagnostic performance than morphological features and allows accurate differentiation of benign and malignant peripheral nerve sheath tumors in NF1. Key Points 1. DWI allows sensitive and specific identification of malignancy in NF1. 2. Morphological features have limited accuracy for identifying malignant transformation. Well et al. Diffusion-weighted imaging in NF1 Neuro-Oncology Keywords diffusion-weighted imaging | intravoxel incoherent motion analysis | morphological features | neurofibromatosis type 1 | peripheral nerve sheath tumor

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Section: Well Et Al Diffusion-weighted Imaging In Nf1mentioning

confidence: 60%

Section: Neuro-oncologymentioning

confidence: 99%

Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging

et al. 2018

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“…As noted above, MRI is an attractive option because it offers improved spatial resolution and lack of ionizing radiation exposure ( 25 ). When examining multiparametric MRI data from pediatric patients with abdominal lesions, Meeus et al ( 26 ) were able to use apparent diffusion coefficient (ADC) mapping and the intravoxel incoherent motion (IVIM) model to distinguish between different abdominal tumor histologic types. Not only could MRI discriminate between benign and malignant tumors, it also was able to specifically identify WT apart from neuroblastomas based on diffusion and perfusion characteristics.…”

Section: Wtmentioning

confidence: 99%

Lymph node imaging of pediatric renal and suprarenal malignancies

Gold

Sabarwal

Gordhan

et al. 2018

Transl. Androl. Urol

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Pediatric renal and suprarenal cancers are relatively rare malignancies, but are not without significant consequence to both the patient and caretakers. These tumors are often found incidentally and present as large abdominal masses. Standard of care management involves surgical excision of the mass, but contemporary treatment guidelines advocate for use of neoadjuvant or adjuvant chemotherapy for advanced stage disease, such as those cases with lymph node involvement (LNI). However, LNI detection is based primarily on surgical pathology and performing extended lymph node dissection can add significant morbidity to a surgical case. In this review, we focus on the use and performance of imaging modalities to detect LNI in Wilms’ tumor (WT), neuroblastoma, and pediatric renal cell carcinoma (RCC). We report on how imaging impacts management of these cases and the clinical implications of LNI. A literature search was conducted for studies published on imaging-based detection of LNI in pediatric renal and suprarenal cancers. Further review focused on surgical and medical management of those cases with suspected LNI. Current imaging protocols assisting in diagnosis and staging of pediatric renal and suprarenal cancers are generally limited to abdominal ultrasound and cross-sectional imaging, mainly computed tomography (CT). Recent research has investigated the role of more advance modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), in the management of these malignancies. Special consideration must be made for pediatric patients who are more vulnerable to ionizing radiation and have characteristic imaging features different from adult controls. Management of pediatric renal and suprarenal cancers is influenced by LNI, but the rarity of these conditions has limited the volume of clinical research regarding imaging-based staging. As such, standardized criteria for LNI on imaging are lacking. Nevertheless, advanced imaging modalities are being investigated and potentially represent more accurate and safer options.

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“…MRI-based radiomics has value in the diagnosis and differential diagnosis of posterior fossa tumors in children (15)(16)(17). With regard to radiomics of children's abdominal tumors, at present, there is only one study (18), which found that histogram parameters (90th percentile of D *, mean value of f, etc.) based on MRI with intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can help to distinguish retroperitoneal neuroblastoma from nephroblastoma in children.…”

Section: Introductionmentioning

confidence: 99%

CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma From Yolk Sac Tumors in Children

Chen

Huang

et al. 2020

Front. Oncol.

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BackgroundThe purpose of this study was to investigate the role of CT radiomics features combined with a support vector machine (SVM) model in potentially differentiating pelvic rhabdomyosarcoma (RMS) from yolk sac tumors (YSTs) in children.MethodsA total of 94 patients with RMS (n = 49) and YSTs (n = 45) were enrolled. Non-enhanced phase (NP), arterial phase (AP), and venous phase (VP) images were retrieved for analysis. The volumes of interest (VOIs) were constructed by segmenting tumor regions on CT images to extract radiomics features. Datasets were randomly divided into two sets including a training set and a test set. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen out the optimal radiomics features that could distinguish RMS from YSTs, and the features were combined with the SVM algorithm to build the classifier model. In the testing set, the areas under the receiver operating characteristic (ROC) curves (AUCs), accuracy, specificity, and sensitivity of the model were calculated to evaluate its diagnostic performance. The clinical factors (including age, sex, tumor site, tumor volume, AFP level) were collected.ResultsIn total, 1,321 features were extracted from the NP, AP, and VP images. The LASSO regression algorithm was used to screen out 23, 26, and 17 related features, respectively. Subsequently, to prevent model overfitting, the 10 features with optimal correlation coefficients were retained. The SVM classifier achieved good diagnostic performance. The AUCs of the NP, AP, and VP radiomics models were 0.937 (95% CI: 0.862, 0.978), 0.973 (95% CI: 0.913, 0.996), and 0.855 (95% CI: 0.762, 0.922) in the training set, respectively, which were confirmed in the test set by AUCs of 0.700 (95% CI: 0.328, 0.940), 0.800 (95% CI: 0.422, 0.979), and 0.750 (95% CI: 0.373, 0.962), respectively. The difference in sex, tumor volume, and AFP level were statistically significant (P < 0.05).ConclusionsThe CT-based radiomics model can be used to effectively distinguish RMS and YST, and combined with clinical features, which can improve diagnostic accuracy and increase the confidence of radiologists in the diagnosis of pelvic solid tumors in children.

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Diffusion‐weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors

Cited by 28 publications

References 40 publications

Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging

Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging

Lymph node imaging of pediatric renal and suprarenal malignancies

CT-Based Radiomics to Differentiate Pelvic Rhabdomyosarcoma From Yolk Sac Tumors in Children

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