Whole Tumor Histogram Analysis Using DW MRI in Primary Central Nervous System Lymphoma Correlates with Tumor Biomarkers and Outcome

Abstract

The ability to non-invasively predict outcomes and monitor treatment response in primary central nervous system lymphoma (PCNSL) is important as treatment regimens are constantly being trialed. The aim of this study was to assess the validity of using apparent diffusion coefficient (ADC) histogram values to predict Ki-67 expression, a tumor proliferation marker, and patient outcomes in PCNSL in both immunocompetent patients and patients living with HIV (PLWH). Qualitative PCNSL magnetic resonance imaging (MRI) characteristics from 93 patients (23 PLWH and 70 immunocompetent) were analyzed, and whole tumor segmentation was performed on the ADC maps. Quantitative histogram analyses of the segmentations were calculated. These measures were compared to PCNSL Ki-67 expression. Progression-free survival (PFS) and overall survival (OS) were analyzed via comparison to the International Primary Central Nervous System Lymphoma Collaboration Group Response Criteria. Associations between ADC measures and clinical outcomes were assessed using univariate and multivariate Cox proportional hazards models. Normalized ADC (nADC)Min, nADCMean, nADC1, nADC5, and nADC15 values were significantly associated with a poorer OS. nADCMax, nADCMean, nADC5, nADC15, nADC75, nADC95, nADC99 inversely correlated with Ki-67 expression. OS was also significantly associated with lesion hemorrhage. PFS was not significantly associated with ADC values but with lesion hemorrhage. ADC histogram values and related parameters can predict the degree of tumor proliferation and patient outcomes for primary central nervous system lymphoma patients and in both immunocompetent patients and patients living with HIV.

Diffusion-weighted MR imaging histogram analysis in HIV positive and negative patients with primary central nervous system lymphoma as a predictor of outcome and tumor proliferation

“…Hemorrhage is also known to alter the tumor microenvironment, leading to changes in ADC values based on the age of the hemorrhage [32][33][34]. Our similar recently published work demonstrated significant associations between multiple ADC parameters and poorer OS, in addition to significant inverse correlations with Ki-67 expression, even in PLWH [28]. However, a major limitation was the lack of co-registration of the ADC and T1 post-contrast sequences during segmentation.…”

“…We reviewed the electronic health records (EHR) and collected relevant socio-demographic characteristics, ECOG performance status, HIV status, pathology results, expression of Ki-67, MR imaging characteristics, treatment and clinical outcomes. The patient population selected for this study is identical to the population used in our prior study evaluating tumor histogram analysis in PCNSL [28].…”

“…Prior studies also did not incorporate whole tumor volume segmentation with T1 contrast co-registration, which is the most reliable and reproducible method of ADC histogram acquisition [27]. In a recent similar study of whole tumor histogram analysis in PCNSL performed by the authors of this study [28], multiple ADC parameters were inversely correlated with Ki-67 expression and associated with poorer OS. However, tumor segmentation was performed using only the ADC sequence with the potential inclusion of intra-tumoral necrosis, hemorrhage or regions outside of the actual solid tumor that would otherwise have been excluded with contrast co-registration, ultimately providing a suboptimal representation of true tumor parenchyma.…”

An Overview of the Role of Multiparametric MRI in the Investigation of Testicular Tumors

“…Most histogram analyses use descriptive parameters—including mean ADC; standard deviation; mode, maximum, and minimum ADC; percentiles of ADC; kurtosis; skewness; and entropy—representing the first-order statistical properties of the image. This technique uses a volume of interest (VOI) covering the entire lesion, which assesses diffusion properties and heterogeneity [ 72 , 73 ]. Volumetric ADC histogram analysis performs better than mean ADC, which is based on an ROI placed on the most representative part of the lesion and, therefore, not reflecting histologic heterogeneity, and possibly resulting in measurement sampling errors and subjective bias [ 72 , 73 ].…”

“…This technique uses a volume of interest (VOI) covering the entire lesion, which assesses diffusion properties and heterogeneity [ 72 , 73 ]. Volumetric ADC histogram analysis performs better than mean ADC, which is based on an ROI placed on the most representative part of the lesion and, therefore, not reflecting histologic heterogeneity, and possibly resulting in measurement sampling errors and subjective bias [ 72 , 73 ]. Changes in histogram shape and asymmetry reflect microstructural and functional differences in tumor composition, and are correlated with the histological grade, TNM, tumor aggressiveness, and therapeutic response [ 72 , 73 ].…”