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Background SUV Max is a glycolytic index obtained from PET imaging, relates to tumour cell proliferation. FDG uptake (i.e. SUV max) is found to be high in aggressive tumours and is used to identify malignant from benign pathologies. Meningiomas are intracranial tumours which display varying grades of FDG avidity based on its biological aggressiveness. Benign grade I meningiomas are FDG non-avid, while the rest of the typical and atypical meningiomas show varying degrees of FDG avidity. Uptake of FDG can be high in certain infectious and inflammatory brain etiologies and pose a diagnostic challenge in differentiating benign brain lesions from neoplasms. MRI is the preferred modality for accurately identifying meningiomas, providing superior contrast differentiation and its ability to differentiate extra-axial from intra-axial brain lesions. CT is said to be superior in specific types of meningioma where there is calcification and adjacent changes in calvarium. Although typical meningiomas have characteristic MRI features, care must be taken to avoid misleading diagnosis between brain tumours and atypical meningiomas. Case presentation We are presenting a recently diagnosed case of invasive breast carcinoma (Ca) referred for staging by PET/MR imaging. Based on atypical DWI and ADC map findings, MRI falsely reported an atypical meningioma as a brain metastasis. Abnormal intense FDG uptake was noted in a well-defined homogeneously enhancing mass lesion in posterior fossa in left paramedian aspect and broad base to left transverse sinus protruding into left cerebellar hemisphere. Atypical meningioma Grade III, i.e. papillary meningioma was later histologically proven. Conclusions We wish to highlight the inconsistency of DWI and ADC map MR findings in papillary meningioma masquerading as solitary brain metastases in a Ca breast patient on 18F FDG PET/MR imaging. From an imaging standpoint, it is important to recognize the variable and pleomorphic features exhibited by meningiomas in MR based on atypical location, histological subtypes, and biologic behaviours. Further FDG PET was incremental in displaying a high SUV max indicating biologic aggressiveness of lesion and correlating with the CT diagnosis of papillary meningioma.

The purpose of this study was to determine diffusion coefficient (ADC) in plaque, peri-plaque and normal-appearing white matter (NAWM) in multiple sclerosis (MS), compare them with the control and correlate findings with cognitive state. Subjects and methods: Sixty-five participants were included and categorized into MS patients with normal cognition (n = 25); MS with mild cognitive impairment (n = 20) and control group (no MS and normal cognition; n = 20). The Montreal Cognitive Assessment was used to determine cognitive state. Mean ADC was measured in plaque, peri-plaques and NAWM, compared with ADC from corresponding white matter in control and correlated with cognitive scores. Chi Square and Pearson correlation coefficient were used. Results: The mean ADC of peri-plaque and NAWM in MS group with cognitive impairment was significantly higher than MS group with normal cognition (p

Simple Summary Artificial Intelligence methods using machine learning and radiomics is an emerging area of research for radiological and oncological applications for patient management. Recent evidence from breast cancer suggests that different breast cancer subtypes may respond differently to adjuvant therapies. The use of a 21-gene array assay called OncotypeDX can predict potential recurrence of cancer in patients with estrogen positive breast cancer after treatment, however, there are potential cost disadvantages that hamper its widespread use. Multiparametric magnetic resonance imaging can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast cancer, which is used in radiomic analysis. Radiomics provide quantitative information of different tissue types. We have developed a new machine learning radiomic informatics tool that integrates clinical and imaging variables, single, and multiparametric radiomics to compare with the OncotypeDX test to stratify patients into three risk groups: low, medium, and high risk of breast cancer recurrence. Abstract Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

Background and purpose: Apparent diffusion coefficient (ADC) reflects micro-enviromental changes and therefore might be useful in predicting recurrence prior to brachytherapy. The purpose of this study is to evaluate change in ADC of the primary tumour and pathologic lymph nodes during treatment and to correlate this with clinical outcome. Material and methods: Twenty patients were included who received chemoradiation for locally advanced cervical cancer between July 2016 and November 2017. All patients underwent magnetic resonance imaging (MRI) prior to treatment, and three MRIs in weeks 1/2, 3 and 4 of treatment, including T2 and diffusion weighted imaging (b-values 0, 200, 800 s/mm2) for determining an ADC-map. Primary tumour was delineated on T2 and ADC-map and pathologic lymph nodes were delineated only on ADC-map. Results: At time of analysis median follow-up was 15 (range 7–22) months. From MRI one to four, primary tumour on ADC-map showed a significant signal increase of 0.94 (range 0.74–1.46) × 10−3 mm2/s to 1.13 (0.98–1.49) × 10−3 mm2/s (p

Introduction: Leukoaraiosis is a common cerebral white matter disease identified radiologically as discrete focal or diffuse patchy bilateral hypodense on computed tomography and hyperintense on T2 weighted Resonance Imaging. Underlying pathogenesis and clinical essence of this radiological finding is yet to be defined clearly. Some patients present with impairment of cognition and dementia. Aim: To assess the usefulness of ADC values in grading the leukoaraiosis and defining its complete extent, identification of the normal appearing white matter with early demyelination changes which is not detectable on conventional MRI and to evaluate the association of leukoaraiosis with impaired cognition. Materials and Methods: This prospective study was done at MRI unit of Department of Radiodiagnosis, C.S.M. Medical University, Lucknow, India during a period of one year from August 2007 to July 2008. Study comprised of Group 1 (cases, n-60) with leukoaraiosis and Group 2 (controls, n-20), healthy subjects with no leukoaraiosis both above 50 years of age. All subjects were studied with 1.5T MRI machine, ADCav values of leukoaraiosis lesions and normal white matter (WM) were estimated. Results: The ADCav values of the leukoaraiotic regions were higher than the normal white matter and the values proportionately increased with the grade of the lesions (p-value ≤ 0.001). The ADCav values of normal white matter were higher in cases than controls and the values increased with grade of leukoaraiosis (p-value < 0.001). Significant association found between reduced Mini Mental Status Examination (MMSE) and presence of leukoaraiosis (p-value ≤ 0.001) and proportional reduction of MMSE score with increasing grade of leukoaraiosis (p-value ≤ 0.001). Conclusions: Increasing ADC values of leukoaraiosis lesions with increasing grade could be used as a grading scale of leukoaraiosis. Higher ADCav values of normal white matter in patients with leukoaraiosis which were increasing with higher grades of leukoaraiosis, is probably representing those areas that are prone to develop leukoaraiosis over time and hence help in assessment of complete extent of the lesion. The significant association of leukoaraiosis with impaired cognition, proposes leukoaraiosis as one of the cause for cognitive function.

Introduction: With widespread use of imaging modalities including USG, triple Phase CT-scan and MRI, there is increase in rate of detecting focal liver lesions in otherwise asymptomatic patient. This poses a diagnostic challenge in an Oncology patient as their accurate diagnosis is must for proper staging in a patient with known oncological disease. The accurate diagnosis of these focal liver lesions requires either FNAC or biopsies. Even though all these modalities help in characterizing liver lesions, MRI is most accurate modality in characterizing these lesions, especially DWI sequence, which provides information at the molecular level of the tissue giving structural and functional information and also helps in assessing the treatment response in tumor cells. Aim: To study the role of DWI/ADC in detecting focal liver lesions and its further characterization. Also, assessing its role in differentiating benign from malignant lesions using DWI/ADC map and providing a quantitative cut off ADC to differentiate benign from malignant lesions. Materials and Methods: Total 50 patients with 71 liver lesions were evaluated with diffusion-weighted MR imaging for over a period of 2 years. All these lesions were assessed by experienced radiologist in the field of Onco-imaging. Necessary clinical history and laboratory data were considered and all lesions either biopsied or underwent FNAC for final Diagnosis. All cases underwent DWI at two different b values of b 600 and b 1000 and corresponding ADC were calculated for each lesion. Results: For the current study total of 50 patients (71 liver lesions) were investigated, majority of patients were in age group of 41-60 years (48%). Out of 71 liver lesions, 40(56.3%) were malignant lesions and 31(43.7%) were benign lesions, with most common diagnosis being metastases 17 (34%) followed by HCC 12(24%) and simple cyst 10(20%). Conclusion: Based on qualitative and quantitative assessment of liver lesions on DWI and ADC map, we could characterize liver lesions and differentiate malignant and benign lesions. DWI is a useful diagnostic tool in patients where contrast is contraindicated like in patients with renal impairment.

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) method and is considered potentially useful for detecting prostate cancer. We evaluated the clinical value of DWI with apparent diffusion coefficient (ADC) maps in addition to T2-weighted imaging (T2WI) using 3 tesla (3 T) MRI. Thirty-three patients with elevated prostate specific antigen were evaluated by MRI with T2WI and DWI prior to transperineal template-guided mapping biopsy. The MRI findings were compared with the pathology of biopsy specimens in six parts of prostate : both sides of outer peripheral zones, inner peripheral zones, and transition zones. The sensitivities, specificities and accuracies were 42.1, 84.4 and 76.3％ in T2WI, 57.1, 84.7 and 80.8% in T2WI/DWI, and 87.5, 85.2 and 85.4% in DWI/ADC using 0.951×10 −3 mm2/s as cutoff ADC value. The hazard ratio of patients whose ADC values were under the cutoff was 25.86 by multivariate analysis. Mean ADC values were significantly different between cancer positive and negative cores (p＜0.001). The ADC value showed a negative correlation with increasing tumor length (p＝0.0047). Although further study with a large number of patients is necessary, DWI/ADC using 3 T MRI is a useful tool for detecting prostate cancer.

Objetivos – Com este estudo pretendeu-se i) avaliar o contributo da aplicação da sequência de difusão na caracterização das lesões mamárias malignas; ii) considerar se a sequência de difusão deve incorporar o protocolo standard em RM mamária e iii) correlacionar os resultados dos valores de coeficiente aparente de difusão (ADC) e os resultados histológicos. Metodologia – A amostra incluiu 18 pacientes do sexo feminino, com idades compreendidas entre 38 e 71 anos, que apresentavam lesões mamárias malignas confirmadas histologicamente. Foi adicionado ao protocolo de RM mamária a sequência de difusão, de modo a calcular os valores de ADC das lesões observadas. Resultados – Verificou-se que a range de valores de ADC para lesões malignas em ROI’s calculados no centro da lesão apresentavam uma média e desvio-padrão de (0,89 ± 0,14x10-3mm2/s). O método da utilização dos valores de ADC na caracterização de lesões mamárias malignas demonstrou uma sensibilidade de 100%. Conclusões – Neste estudo, com uma sensibilidade de 100%, a ponderação em difusão demonstrou ser uma técnica vantajosa na caracterização de lesões mamárias malignas pelo que se sugere a sua introdução no protocolo standard da RM mamária. ABSTRACT - Aims – The aim of this study was i) to evaluate the potential of the DWI sequence in the characterization of malignant breast lesions; ii) to verify if this sequence should incorporate the breast MRI protocol and iii) to correlate the apparent diffusion coefficients (ADC) values and histological results. Methodology – The sample includes 18 female patients between the ages of 38 and 71 years, who presented with malignant breast lesion confirmed by histology. The DWI sequence was added to the MRI standard protocol to calculate the ADC values. Results – In the results obtained we observed that the range of the ADC values calculated in the center of the malignant lesions, showed a mean and standard deviation of 0.89 ± 0.14 x10-3 mm2 / s. This method of using the ADC values for the detection of malignant lesions showed a sensitivity of 100%. Conclusion – The DWI technique proved to be a useful method in the characterization of malignant breast lesions, as it showed a sensitivity of 100%, so we suggest its inclusion in the Breast MR standard protocol.

A new MRI parameter representative of active tumor burden is proposed: diffusion volume (DV), defined as the sum of all the voxels within a tumor with apparent diffusion coefficient (ADC) values within a specific range. The aims of the study were: (a) to calculate DV on ADC maps in patients with cervical/endometrial cancer; (b) to correlate DV with histological grade (G) and risk classification; (c) to evaluate intra/inter-observer agreement of DV calculation.Fifty-three patients with endometrial (n=28) and cervical (n=25) cancers underwent pelvic MRI with DWI sequences. Both endometrial and cervical tumors were classified on the basis of G (G1/G2/G3) and FIGO staging (low/medium/high-risk). A semi-automated segmentation procedure was used to calculate the DV. A freehand closed ROI outlined the whole visible tumor on the most representative slice of ADC maps defined as the slice with the maximum diameter of the solid neoplastic component. Successively, two thresholds were generated on the basis of the mean and standard deviation (SD) of the ADC values: lower threshold (LT="mean minus three SD") and higher threshold (HT="mean plus one SD"). The closed ROI was expanded in 3D, including all the contiguous voxels with ADC values in the range LT-HT × 10-3mm(2)/s. A Kruskal-Wallis test was used to assess the differences in DV among G and risk groups. Intra-/inter-observer variability for DV measurement was analyzed according to the method of Bland and Altman and the intraclass-correlation-coefficient (ICC).DV values were significantly different among G and risk groups in both endometrial (p0.05) and cervical cancers (p ≤ 0.01). For endometrial cancer, DV of G1 (mean ± sd: 2.81 ± 3.21 cc) neoplasms were significantly lower than G2 (9.44 ± 9.58 cc) and G3 (11.96 ± 8.0 cc) ones; moreover, DV of low risk cancers (5.23 ± 8.0 cc) were significantly lower than medium (7.28 ± 4.3 cc) and high risk (14.7 ± 9.9 cc) ones. For cervical cancer, DV of G1 (0.31 ± 0.13 cc) neoplasms was significantly lower than G3 (40.68 ± 45.65 cc) ones; moreover, DV of low risk neoplasms (6.98 ± 8.08 cc) was significantly lower than medium (21.7 ± 17.13 cc) and high risk (62.9 ± 51.12 cc) ones and DV of medium risk neoplasms was significantly lower than high risk ones. The intra-/inter-observer variability for DV measurement showed an excellent correlation for both cancers (ICC ≥ 0.86).DV is an accurate index for the assessment of G and risk classification of cervical/endometrial cancers with low intra-/inter-observer variability.

Breast cancer is a major global health problem and the most common form of cancer among women. Major advances in the technologies of imaging provide improved detection and sensitivity with fewer unnecessary biopsies. Commonly used imaging modalities include mammography, ultrasonography, magnetic resonance imaging (MRI), scintimammography, single photon emission computed tomography (SPECT) and positron emission tomography (PET). The current study is focused on breast MRI imaging, especially one of the most promising recent techniques, i.e. the Diffusion Weighted Imaging breast MRI (DWI). DWI is an unenhanced MRI technique, based on volume sequences on various b values (the b value identifies the measurement's sensitivity to diffusion and determines the strength and duration of the diffusion gradients) measuring the mobility of water molecules (Brownian motion) in vivo (in tissues) and provides different and potentially complementary information to Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technique. As DWI based on the diffusive properties of water molecules, reflects their random motion resulting from thermal agitation. Water diffusion on breast can be quantified by measuring the mean diffusivity, which is the average of Apparent Diffusion Coefficient (ADC). The ADC can be calculated by making measurements at a low b factor, b1, and a higher b factor, b2. DWI allows the mapping of the diffusion process of molecules by the ADC map. ADC maps are calculated by collecting images with at least 2 different values, b1 and b2, of the b factor. The ADC map is a parametric image whose color scale or gray scale represents the ADC values of the voxels and is usually generated by proprietary or in house software. DWI apart from the 3D anatomical information, provides a noninvasive investigation of tissue vascularity, a novel contrast mechanism in MRI and has a high sensitivity in the detection of changes in the local biologic environment due to a pathologic process. Therefore, in addition to contrast enhancement-based characterization (DCE-MRI), measurement of the motion of water molecules in DWI provides an additional feature for lesion characterization that may further increase the specificity of MRI for classifying breast lesions. The diagnostic task that the current study deals with, accounts for the diagnosis of mass-like lesions in Diffusion Weighed Magnetic Resonance Imaging, based on low ADC values compared to high once in case of benign versus normal tissue. The hypothesis is that diffusivity of water molecules is restricted in environments of high cellularity, intracellular and extracellular edema, high viscosity, and fibrosis, such as malignant tumors, because these conditions become barriers to the movement of water molecules. Therefore, most of breast cancers show low ADC values compared with benign and normal tissue. Many studies have revealed the usefulness of ADC values in the differential diagnosis of breast lesions; however, the clinical effect remains limited because of the substantial overlap between benign and malignant lesions, which presents challenges for implementing a useful diagnostic ADC threshold. The majority of studies, similar to the current study, determined optimal cutoff levels of the ADC value between malignant and benign lesions by using ROC analysis, and ranged from 0.90 to 1.76 × 10-3 mm2/s while the sensitivity and specificity ranged from 63% to 100% and 46% to 97%, respectively. In addition, the methods for measuring ADC differ among reported studies, with the most representative method being the mean value of ADC (mean ± standard deviation) over a Region Of Interest representative of the breast lesion. The purpose of this study was to investigate the ability of histogram characteristics of Apparent Diffusion Coefficient (Apparent Diffusion Coefficient-ADC) to differentiate malignant from benign breast lesions in breast DWI. To this end the ADC maps of representative lesion ROIs were subjected to first order statistics analysis by calculating five first order textural features: Mean value, Standard Deviation, Kurtosis, Skewness and Entropy. This approach is intended to offer a more complete assessment of tumor texture and heterogeneity. The dataset analyzed is comprised of 92 histologically verified breast lesions, originating from 69 women with mammographically and/or ultrasonographically detected or palpable findings. Histology revealed 53 malignant lesions originating from 45 women and 39 benign lesions originating from 26 women. All of the breast MR examinations were performed with a 3T MR scanner, for b= 0, 900 s/mm2. Diagnostic performances of these parameters were compared by receiver operating characteristic (ROC) curve analysis. The mean of ADC of benign lesions [(1.470 ± 0.342) × 10-3 mm2/s] was found to be significantly higher than that of malignant tumours, [(0.965 ± 0.268) × 10-3 mm2/s, (p

Objetivos – Com este estudo pretendeu-se i) avaliar o contributo da aplicação da sequência de difusão na caracterização das lesões mamárias malignas; ii) considerar se a sequência de difusão deve incorporar o protocolo standard em RM mamária e iii) correlacionar os resultados dos valores de coeficiente aparente de difusão (ADC) e os resultados histológicos. Metodologia – A amostra incluiu 18 pacientes do sexo feminino, com idades compreendidas entre 38 e 71 anos, que apresentavam lesões mamárias malignas confirmadas histologicamente. Foi adicionado ao protocolo de RM mamária a sequência de difusão, de modo a calcular os valores de ADC das lesões observadas. Resultados – Verificou-se que a range de valores de ADC para lesões malignas em ROI’s calculados no centro da lesão apresentavam uma média e desvio-padrão de (0,89±0,14x10-3mm2/s). O método da utilização dos valores de ADC na caracterização de lesões mamárias malignas demonstrou uma sensibilidade de 100%. Conclusões – Neste estudo, com uma sensibilidade de 100%, a ponderação em difusão demonstrou ser uma técnica vantajosa na caracterização de lesões mamárias malignas pelo que se sugere a sua introdução no protocolo standard da RM mamária., Saúde & Tecnologia, 2012: Temático 1: Cancro da mama - Uma abordagem multidisciplinar

To establish if a correlation exists between apparent diffusion coefficient (ADC) values, obtained by diffusion-weighted imaging (DWI), and placental aging.The study is divided into a retrospective phase and a prospective one.In the first phase, 145 pregnant women underwent fetal magnetic resonance imaging (MRI) for suspected disorders in several organs. We performed DWI (b value 0, 200 and 700 s/mm(2)) in all the fetuses, evaluating the patients in whom the whole placenta was visible.In the prospective phase, 50 women (52 fetuses) underwent MRI. We performed, in the same patient, two echo-planar sequences with b values of 0, 200 and 700, and 50, 200 and 700 s/mm(2), including the whole placenta.The ADC maps were calculated for all fetuses, divided into three groups based on gestational age (GA): group I: 20-26 weeks' gestation, II: 27-33, III: 34-40.In the retrospective phase, ADC values had a range from 1 to 2.4 mm(2)/s, showing a significant correlation between ADC values and GA.ADC values obtained by DWI with b value 0, 200 and 700 s/mm(2) had a range from 0.8 to 2.5 mm(2)/s, with an inverse correlation between ADC values and GA, whereas the ADC values with b value 50, 200 and 700 s/mm(2) did not show any statistical correlation (range: 1.5-1.7 mm(2)/s).DWI with ADC maps can not be considered markers for placental aging because they are affected by perfusional and circulatory motion.