Project description:Recent advances in molecular subtyping of Pancreatic Ductal Adenocarcinoma (PDAC) support individualization of therapeutic strategies in this most aggressive disease. With the emergence of various novel therapeutic strategies and neoadjuvant approaches in this quickly deteriorating disease, robust approaches for fast evaluation of therapy response are urgently needed. To this aim, we designed a preclinical imaging-guided therapy trial where genetically engineered mice harboring endogenous aggressive PDAC were treated with the MEK targeting drug refametinib, which induces rapid and profound tumor regression in this model system. Multi-parametric non-invasive imaging was used for therapy response monitoring. A significant increase in the Diffusion-Weighted Magnetic Resonance Imaging derived Apparent Diffusion Coefficient (ADC) was noted already 24 hours after treatment onset. Histopathological analyses showed increased apoptosis and matrix remodeling at this time point. Our findings suggest the ADC parameter as an early predictor of therapy response in PDAC.
Project description:Quantitative correlations between T2 and ADC values were explored on cancerous breast lesions using spatiotemporally encoded (SPEN) MRI. To this end, T2 maps of patients were measured at more than one b-value, and ADC maps at several echo time values were recorded. SPEN delivered quality, artifact-free, TE-weighted DW images, from which T2-ADC correlations could be obtained despite the signal losses brought about by diffusion and relaxation. Data confirmed known aspects of breast cancer lesions, including their reduced ADC values vs. healthy tissue. Data also revealed an anticorrelation between the T2 and ADC values, when comparing regions with healthy and diseased tissues. This is contrary to expectations based on simple water restriction considerations. It is also contrary to what has been observed in a majority of porous materials and tissues. Differences between the healthy tissue of the lesion-affected breast and healthy tissue in the contralateral breast were also noticed. The potential significance of these trends is discussed, as is the potential of combining T2- and ADC-weightings to achieve an enhanced endogenous MRI contrast about the location of breast cancer lesions.
Project description:BackgroundBrain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC.MethodsWe enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere.ResultsOut of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988.ConclusionsIn patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer.Trial registrationThe study was registered in the German Clinical Trials Register (DRKS00023016).
Project description:ObjectiveWe elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers.Materials and methodsForty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor.ResultsInter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k = 0.775). The pre-treatment ADC values of different T stage tumors were not equal (p = 0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p = 0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p = 0.006). There was a negative correlation between Ki-67 LI and the ADC value (r = -0.318, p = 0.026) and between the AgNOR count and the ADC value (r = -0.310, p = 0.030).ConclusionSignificant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers.
Project description:ObjectivesTo develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria.MethodsThis was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant.ResultsA total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached.ConclusionsThe breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI.Clinical relevance statementThe ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes.Key points• The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.