Sarah J. Nelson

Bio: Sarah J. Nelson is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Magnetic resonance spectroscopic imaging & Magnetic resonance imaging. The author has an hindex of 86, co-authored 291 publications receiving 22507 citations. Previous affiliations of Sarah J. Nelson include University of California & University of California, Berkeley.

Mutational Analysis Reveals the Origin and Therapy-Driven Evolution of Recurrent Glioma

Abstract: Tumor recurrence is a leading cause of cancer mortality. Therapies for recurrent disease may fail, at least in part, because the genomic alterations driving the growth of recurrences are distinct from those in the initial tumor. To explore this hypothesis, we sequenced the exomes of 23 initial low-grade gliomas and recurrent tumors resected from the same patients. In 43% of cases, at least half of the mutations in the initial tumor were undetected at recurrence, including driver mutations in TP53, ATRX, SMARCA4, and BRAF; this suggests that recurrent tumors are often seeded by cells derived from the initial tumor at a very early stage of their evolution. Notably, tumors from 6 of 10 patients treated with the chemotherapeutic drug temozolomide (TMZ) followed an alternative evolutionary path to high-grade glioma. At recurrence, these tumors were hypermutated and harbored driver mutations in the RB (retinoblastoma) and Akt-mTOR (mammalian target of rapamycin) pathways that bore the signature of TMZ-induced mutagenesis.

Metabolic Imaging of Patients with Prostate Cancer Using Hyperpolarized [1-13C]Pyruvate

Abstract: This first-in-man imaging study evaluated the safety and feasibility of hyperpolarized [1-13C]pyruvate as an agent for noninvasively characterizing alterations in tumor metabolism for patients with prostate cancer. Imaging living systems with hyperpolarized agents can result in more than 10,000-fold enhancement in signal relative to conventional magnetic resonance (MR) imaging. When combined with the rapid acquisition of in vivo 13C MR data, it is possible to evaluate the distribution of agents such as [1-13C]pyruvate and its metabolic products lactate, alanine, and bicarbonate in a matter of seconds. Preclinical studies in cancer models have detected elevated levels of hyperpolarized [1-13C]lactate in tumor, with the ratio of [1-13C]lactate/[1-13C]pyruvate being increased in high-grade tumors and decreased after successful treatment. Translation of this technology into humans was achieved by modifying the instrument that generates the hyperpolarized agent, constructing specialized radio frequency coils to detect 13C nuclei, and developing new pulse sequences to efficiently capture the signal. The study population comprised patients with biopsy-proven prostate cancer, with 31 subjects being injected with hyperpolarized [1-13C]pyruvate. The median time to deliver the agent was 66 s, and uptake was observed about 20 s after injection. No dose-limiting toxicities were observed, and the highest dose (0.43 ml/kg of 230 mM agent) gave the best signal-to-noise ratio for hyperpolarized [1-13C]pyruvate. The results were extremely promising in not only confirming the safety of the agent but also showing elevated [1-13C]lactate/[1-13C]pyruvate in regions of biopsy-proven cancer. These findings will be valuable for noninvasive cancer diagnosis and treatment monitoring in future clinical trials.

Three-dimensional H-1 MR spectroscopic imaging of the in situ human prostate with high (0.24-0.7-cm3) spatial resolution.

Abstract: PURPOSE: To evaluate if three-dimensional hydrogen-1 magnetic resonance spectroscopic imaging (3D MRSI) when combined with a clinical MR imaging examination could discriminate prostatic adenocarcinoma from normal prostatic zonal anatomy and benign prostatic hyperplasia (BPH) on the basis of observable metabolite levels. MATERIALS AND METHODS: Combined phased-array, endorectal MR imaging and 3D MRSI was performed in nine young healthy volunteers, five patients with BPH, and 85 patients with prostate cancer and BPH. Volume MR imaging and 3D MRSI data were analytically corrected for the reception profile of the endorectal and pelvic phased-array coils, aligned with the MR imaging data, and compared with postoperative pathologic histology findings. RESULTS: Statistically significant variations in metabolite levels with prostatic zonal anatomy, age, and pathologic condition were detected with a 3D MRSI examination added to a clinical MR imaging examination. Significantly higher choline levels and significantly...

Prostate cancer: localization with three-dimensional proton MR spectroscopic imaging--clinicopathologic study.

Abstract: PURPOSE: To assess the efficacy of combined magnetic resonance (MR) imaging and three-dimensional (3D) proton MR spectroscopic imaging in the detection and localization of prostate cancer. MATERIALS AND METHODS: MR imaging and 3D MR spectroscopic imaging examinations were performed in 53 patients with biopsy-proved prostate cancer and subsequent radical prostatectomy with step-section histopathologic examination. The prostate was divided into sextants. At MR imaging, the presence or absence of cancer in the peripheral zone of each sextant was assessed independently by two readers (readers 1 and 2) unaware of the findings at 3D MR spectroscopic imaging and histopathologic examination. At 3D MR spectroscopic imaging, cancer was diagnosed as possible if the ratio of choline plus creatine to citrate exceeded 2 SD above population norms or as definite if that ratio exceeded 3 SDs above the norm. RESULTS: On the basis of sextants, sensitivity and specificity, respectively, for MR imaging were 77% and 61% (reade...

Hyperpolarized 13C Lactate, Pyruvate, and Alanine: Noninvasive Biomarkers for Prostate Cancer Detection and Grading

Abstract: An extraordinary new technique using hyperpolarized (13)C-labeled pyruvate and taking advantage of increased glycolysis in cancer has the potential to improve the way magnetic resonance imaging is used for detection and characterization of prostate cancer The aim of this study was to quantify, for the first time, differences in hyperpolarized [1-(13)C] pyruvate and its metabolic products between the various histologic grades of prostate cancer using the transgenic adenocarcinoma of mouse prostate (TRAMP) model Fast spectroscopic imaging techniques were used to image lactate, alanine, and total hyperpolarized carbon (THC = lactate + pyruvate + alanine) from the entire abdomen of normal mice and TRAMP mice with low- and high-grade prostate tumors in 14 s Within 1 week, the mice were dissected and the tumors were histologically analyzed Hyperpolarized lactate SNR levels significantly increased (P < 005) with cancer development and progression (41 +/- 11, 74 +/- 17, and 154 +/- 24 in normal prostates, low-grade primary tumors, and high-grade primary tumors, respectively) and had a correlation coefficient of 095 with the histologic grade In addition, there was minimal overlap in the lactate levels between the three groups with only one of the seven normal prostates overlapping with the low-grade primary tumors The amount of THC, a possible measure of substrate uptake, and hyperpolarized alanine also increased with tumor grade but showed more overlap between the groups In summary, elevated hyperpolarized lactate and potentially THC and alanine are noninvasive biomarkers of prostate cancer presence and histologic grade that could be used in future three-dimensional (13)C spectroscopic imaging studies of prostate cancer patients

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria

Abstract: New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

Abstract: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.