Can apparent diffusion coefficient values help distinguish between different types of pediatric brain tumors?

Abstract

Rationale and objectivesClassifying brain tumors is challenging, but recently developed imaging techniques offer the opportunity for neuroradiologists and neurosurgeons to diagnose, differentiate, and manage different types of brain tumors. Such advances will be reflected in improvements in patients’ life expectancy and quality of life. Among the newest techniques, the apparent diffusion coefficient (ADC), which tracks the rate of microscopic water diffusion within tissues, has become a focus of investigation. Recently, ADC has been used as a preoperative diffusion-weighted magnetic resonance imaging (MRI) parameter that facilitates tumor diagnosis and grading. Here, we aimed to determine the ADC cutoff values for pediatric brain tumors (PBTs) categorized according to the World Health Organization (WHO) classification of brain tumors.Materials and methodsWe retrospectively reviewed 80 cases, and assessed them based on their MRI-derived ADC. These results were compared with those of WHO classification-based histopathology.ResultsWhole-lesion ADC values ranged 0.225–1.240 × 10−3 mm2/s for ependymal tumors, 0.107–1.571 × 10−3 mm2/s for embryonal tumors, 0.1065–2.37801 × 10−3 mm2/s for diffuse astrocytic and oligodendroglial tumors, 0.5220–0.7840 × 10−3 mm2/s for other astrocytic tumors, and 0.1530–0.8160 × 10−3 mm2/s for meningiomas. These findings revealed the usefulness of ADC in the differential diagnosis of PBT, as it was able to discriminate between five types of PBTs.ConclusionThe application of an ADC diagnostic criterion would reduce the need for spectroscopic analysis. However, further research is needed to implement ADC in the differential diagnosis of PBT.

Grading of Pediatric Intracranial Tumors: Are Intravoxel Incoherent Motion and Diffusional Kurtosis Imaging Superior to Conventional DWI?

“…4 Conventional DWI with ADC values has been reported to be useful in characterizing tumor cellularity and predicting the PIT grade. [4][5][6][7] However, the ADC value calculated using the monoexponential model may not accurately reflect the water molecular diffusion behavior due to the influence of capillary microcirculation and complex cellular microstructural barriers within the tumor.…”

Values of the apparent diffusion coefficient in the differential diagnosis of tumors of the posterior fossa in the pediatric population of Colombia

“…Estos puntos de corte son concordantes con lo descrito previamente en la literatura, donde diferentes autores han intentado estandarizar valores diagnósticos de ADC cuantitativo en tumores de fosa posterior pediátricos en otras latitudes, identificando una relación inversamente proporcional entre el valor de ADC cuantitativo y el grado tumoral en fosa posterior, con menores valores para los tumores más agresivos -como en el caso del meduloblastoma- (11)(12)(13)(14)(15)(16). Por otra parte, en población pediátrica de Egipto, Mohamed et.al, reportan que valores de ADC cuantitativo de 1.43 ± 0.28×10−3 y 0.71 ± 0.21×10−3 tienen una buena capacidad discriminatoria para el diagnóstico de astrocitoma pilocítico y meduloblastomas, respectivamente (16).…”

Posterior fossa extra-axial variations of medulloblastoma: a pictorial review as a primer for radiologists

“…These studies have revealed that the maximum relative tumor blood flow of high-grade tumors (grades III and IV) is significantly higher than that of low-grade tumors (grades I and II) [ 27 , 41 ]. Quantitative Apparent Diffusion Coefficient (ADC) value analysis can facilitate preoperative identification of medulloblastoma from its differentials, as well as grading of pediatric medulloblastoma [ 42 , 43 ]. Further, it can facilitate optimal surgical treatment planning, with reduction of surgery-induced morbidity [ 43 , 44 ].…”

“…Quantitative Apparent Diffusion Coefficient (ADC) value analysis can facilitate preoperative identification of medulloblastoma from its differentials, as well as grading of pediatric medulloblastoma [ 42 , 43 ]. Further, it can facilitate optimal surgical treatment planning, with reduction of surgery-induced morbidity [ 43 , 44 ].…”