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Background: In vivo proton magnetic resonance spectroscopy ((1)H-MRS) studies on brain in HIV infected patients have shown significant alteration in neuro-biochemicals.Methods: In this study, we measured the neuro-biochemical metabolites from the left frontal white matter (FWM) and left basal ganglia (BG) caudate head nucleus in 71 subjects that include 30 healthy controls, 20 asymptomatic HIV and 21 HIV patients with CNS lesion. Proton MR spectra were acquired at 3 T MRI system and the concentration (institutional units) of tNAA (N-acetylaspartate, NAA + N-acetylaspartylglutamate, NAAG), tCr (Creatine, Cr + phosphocreatine, PCr), choline containing compounds (tCho), glutamate + glutamine (Glx) and lipid and macromolecules at 0.9 ppm were determined using LC Model.Results: In BG, the concentration of tNAA (6.71 ± 0.64) was decreased and in FWM, the concentration of Glx (20.4 ± 7.8), tCr (9.14 ± 3.04) and lipid and macromolecules at 0.9 ppm (8.69 ± 2.96) were increased in HIV patients with CNS lesion. In healthy controls, the concentration of tNAA in BG was 7.31 ± 0.47 and concentration of Glx, tCr and lipid and macromolecules in FWM were 15.0 ± 6.06, 6.95 ± 2.56, 5.59 ± 1.56, respectively.Conclusion: Reduced tNAA in BG suggests neuronal loss in HIV patients with CNS lesion while increased Glx in FWM may suggest excito-toxicity. In addition, increased levels of tCr in FWM of HIV patients were observed. The study indicates region specific metabolic changes in tNAA, tCr and Glx in brain of HIV infected patients. [ABSTRACT FROM AUTHOR]

Abstract: “Radiomics” refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene–protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer. [Copyright &y& Elsevier]

Maximum diameter and volume of the tumour provide important clinical information and are decision-making parameters for patients suspected with prostate cancer (PCa). The objectives of this study were to develop an automated method for 3D tumour measurement and compare it with the radiologist's manual assessment, as well as to investigate the impact of 3D tumour measurement on Prostate Imaging-Reporting and Data System version-2.1 (PI-RADS v2.1) scoring of prostate cancer. Tumour maximum diameter and volume were calculated using automated ellipsoid-fit method. For all PI-RADS scores, mean ± standard deviation range of tumour maximum diameter and volume measured using ellipsoid-fit method were 1.36 ± 0.28 to 1.97 ± 0.67 cm and 0.49 ± 0.31 to 1.05 ± 0.78 cc and manual assessment were in range of 0.73 ± 0.12 to 1.14 ± 0.25 cm and 0.36 ± 0.21 to 0.93 ± 0.39 cc, respectively. Ellipsoid-fit method showed significantly (p < 0.05) higher values for maximum diameter and volume than manual assessment. 3D measurement of tumour using ellipsoid-fit method was found to have higher maximum diameter and volume values (in 40–61% patients) compared to conventional assessment by radiologist, which may have an impact on PI-RADS v2.1 scoring system. [ABSTRACT FROM AUTHOR]

Introduction: Existing screening investigations for the diagnosis of early prostate cancer lack specificity, resulting in a high negative biopsy rate. There is increasing interest in the use of various magnetic resonance methods for improving the yield of transrectal ultrasound-guided biopsies of the prostate in men suspected to have prostate cancer. We review the existing status of such investigations. Methods: A literature search was carried out using the Pubmed database to identify articles related to magnetic resonance methods for diagnosing prostate cancer. References from these articles were also extracted and reviewed. Results: Recent studies have focused on prebiopsy magnetic resonance investigations using conventional magnetic resonance imaging, dynamic contrast enhanced magnetic resonance imaging, diffusion weighted magnetic resonance imaging, magnetization transfer imaging and magnetic resonance spectroscopy of the prostate. This marks a shift from the earlier strategy of carrying out postbiopsy magnetic resonance investigations. Prebiopsy magnetic resonance investigations has been useful in identifying patients who are more likely to have a biopsy positive for malignancy. Conclusions: Prebiopsy magnetic resonance investigations has a potential role in increasing specificity of screening for early prostate cancer. It has a role in the targeting of biopsy sites, avoiding unnecessary biopsies and predicting the outcome of biopsies. [ABSTRACT FROM AUTHOR]

• A robust probabilistic atlas-based approach is presented for semi-automatic segmentation of prostate using diffusion-weighted imaging. • Partial volume correction algorithm is developed for precise estimation of the same probability level pixels between both zones. • The segmentation framework aids the radiologists for accurate extraction of peripheral zone and transition zone, which can be combined in the computer-aided diagnostic system for prostate cancer. Accurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI. In this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm. For our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation. The proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning. [ABSTRACT FROM AUTHOR]

In the past decade several advances have been made in the field of nuclear magnetic resonance (NMR) imaging. MR spectroscopic imaging (MRSI) is one such advance which holds promise for detecting biochemical change on imaging of the prostate, and that can be used in several ways for improving the management of patients with prostate cancer. We review the literature, technique and basics of MRSI, with its current status in various situations as applied to the management of prostate cancer. [ABSTRACT FROM AUTHOR]

Objectives: Screening for prostate cancer using serum prostate-specific antigen (PSA) determination has a positive predictive value of only 30% to 42% for a PSA level between 4 and 10 ng/mL. Magnetic resonance spectroscopic imaging (MRSI), which identifies cancer on the basis of changes in cellular metabolite levels, might be able to identify patients with noncancerous PSA elevation and help avoid unnecessary biopsies. We tested this hypothesis by evaluating the incidence of prostate cancer in men with a PSA level of 4 to 10 ng/mL and a negative MRSI study. Methods: A total of 155 men underwent a three-dimensional proton MRSI of the prostate before transrectal ultrasound-guided biopsy for clinical indications. MRSI was performed using an endorectal coil on a 1.5-T magnetic resonance scanner. Patients with no voxels positive for malignancy underwent standard sextant biopsy, and additional MRSI-targeted biopsies were obtained in men with suspicious or malignant voxels. Patients with a biopsy negative for cancer underwent repeat serum PSA estimation every 6 months for a minimum of 18 months. Results: Of the 155 men, 36 (mean PSA level of 6.47 ng/mL, range 4.25 to 9.9) had no malignant voxels on MRSI. None of them were positive for cancer on biopsy. Of these 36 men, 26 completed at least 18 months (mean 26.9, range 18 to 44) of follow-up. Four patients required repeat biopsies and one, with a persistently elevated PSA level was diagnosed with prostate cancer 29 months after the initial MRSI. Conclusions: The results of our study have shown that prostate biopsy can be deferred in patients with an increased serum PSA of 4 to 10 ng/mL if their MRSI does not show any malignant voxels. [Copyright &y& Elsevier]

Abstract: Quantitative imaging using computed tomography, magnetic resonance imaging and positron emission tomography modalities will play an increasingly important role in the design of oncology trials addressing molecularly targeted, personalized therapies. The advent of molecularly targeted therapies, exemplified by antiangiogenic drugs, creates new complexities in the assessment of response. The Quantitative Imaging Network addresses the need for imaging modalities which can accurately and reproducibly measure not just change in tumor size but changes in relevant metabolic parameters, modulation of relevant signaling pathways, drug delivery to tumor and differentiation of apoptotic cell death from other changes in tumor volume. This article provides an overview of the applications of quantitative imaging to phase 0 through phase 3 oncology trials. We describe the use of a range of quantitative imaging modalities in specific tumor types including malignant gliomas, lung cancer, head and neck cancer, lymphoma, breast cancer, prostate cancer and sarcoma. In the concluding section, we discuss potential constraints on clinical trials using quantitative imaging, including complexity of trial conduct, impact on subject recruitment, incremental costs and institutional barriers. Strategies for overcoming these constraints are presented. [Copyright &y& Elsevier]