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Autophagy is a highly regulated, energy dependent cellular process where proteins, organelles and cytoplasm are sequestered in autophagosomes and digested to sustain cellular homeostasis. We hypothesized that during autophagy induced in cancer cells by i) starvation through serum and amino acid deprivation or ii) treatment with PI-103, a class I PI3K/mTOR inhibitor, glycolytic metabolism would be affected, reducing flux to lactate, and that this effect may be reversible. We probed metabolism during autophagy in colorectal HT29 and HCT116 Bax knock-out cells using hyperpolarized 13C-magnetic resonance spectroscopy (MRS) and steady-state 1H-MRS. 24 hr PI103-treatment or starvation caused significant reduction in the apparent forward rate constant (kPL) for pyruvate to lactate exchange compared with controls in HT29 (100 μM PI-103: 82%, p = 0.05) and HCT116 Bax-ko cells (10 μM PI-103: 53%, p = 0.05; 20 μM PI-103: 42%, p<0.0001; starvation: 52%, p<0.001), associated with reduced lactate excretion and intracellular lactate in all cases, and unchanged lactate dehydrogenase (LDH) activity and increased NAD+/NADH ratio following PI103 treatment or decreased LDH activity and unchanged NAD+/NADH ratio following starvation. After 48 hr recovery from PI103 treatment, kPL remained below control levels in HT29 cells (74%, p = 0.02), and increased above treated values, but remained below 24 hr vehicle-treated control levels in HCT116 Bax-ko cells (65%, p = 0.004) both were accompanied by sustained reduction in lactate excretion, recovery of NAD+/NADH ratio and intracellular lactate. Following recovery from starvation, kPL was significantly higher than 24 hr vehicle-treated controls (140%, p = 0.05), associated with increased LDH activity and total cellular NAD(H). Changes in kPL and cellular and excreted lactate provided measureable indicators of the major metabolic processes accompanying starvation- and drug-induced autophagy. The changes are reversible, returning towards and exceeding control values on cellular recovery, which potentially identifies resistance. kPL (hyperpolarized 13C-MRS) and lactate (1H-MRS) provide useful biomarkers for the autophagic process, enabling non-invasive monitoring of the Warburg effect. [ABSTRACT FROM AUTHOR]

Pyruvate-lactate exchange is mediated by the enzyme lactate dehydrogenase (LDH) and is central to the altered energy metabolism in cancer cells. The measurement of exchange kinetics using hyperpolarized 13C NMR has provided a biomarker of response to novel therapeutics. However, the observable signal is restricted to the exchanging hyperpolarized 13C pools and the endogenous pools of 12C-labelled metabolites are invisible in these measurements. In this study, we investigated an alternative in vitro 1H NMR assay, using [3-13C]pyruvate, and compared the measured kinetics with a hyperpolarized 13C NMR assay, using [1-13C]pyruvate, under the same conditions in human colorectal carcinoma SW1222 cells. The apparent forward reaction rate constants ( kPL) derived from the two assays showed no significant difference, and both assays had similar reproducibility ( kPL = 0.506 ± 0.054 and kPL = 0.441 ± 0.090 nmol/s/106 cells; mean ± standard deviation; n = 3); 1H, 13C assays, respectively). The apparent backward reaction rate constant ( kLP) could only be measured with good reproducibility using the 1H NMR assay ( kLP = 0.376 ± 0.091 nmol/s/106 cells; mean ± standard deviation; n = 3). The 1H NMR assay has adequate sensitivity to measure real-time pyruvate-lactate exchange kinetics in vitro, offering a complementary and accessible assay of apparent LDH activity. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Real-time detection of the rates of metabolic flux, or exchange rates of endogenous enzymatic reactions, is now feasible in biological systems using Dynamic Nuclear Polarization Magnetic Resonance. Derivation of reaction rate kinetics from this technique typically requires multi-compartmental modeling of dynamic data, and results are therefore model-dependent and prone to misinterpretation. We present a model-free formulism based on the ratio of total areas under the curve (AUC) of the injected and product metabolite, for example pyruvate and lactate. A theoretical framework to support this novel analysis approach is described, and demonstrates that the AUC ratio is proportional to the forward rate constant k. We show that the model-free approach strongly correlates with k for whole cell in vitro experiments across a range of cancer cell lines, and detects response in cells treated with the pan-class I PI3K inhibitor GDC-0941 with comparable or greater sensitivity. The same result is seen in vivo with tumor xenograft-bearing mice, in control tumors and following drug treatment with dichloroacetate. An important finding is that the area under the curve is independent of both the input function and of any other metabolic pathways arising from the injected metabolite. This model-free approach provides a robust and clinically relevant alternative to kinetic model-based rate measurements in the clinical translation of hyperpolarized 13C metabolic imaging in humans, where measurement of the input function can be problematic. [ABSTRACT FROM AUTHOR]

Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets. [ABSTRACT FROM PUBLISHER]

To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo. A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo. The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown. The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity. This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo ., Methods: A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T 1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo ., Results: The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown., Conclusions: The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity., Advances in Knowledge: This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.

Purpose To assess the repeatability of apparent diffusion coefficient (ADC) estimates in extracranial soft-tissue diffusion-weighted magnetic resonance imaging across a wide range of imaging protocols and patient populations. Materials and Methods Nine prospective patient studies and one prospective volunteer study, performed between 2006 and 2016 with research ethics committee approval and written informed consent from each subject, were included in this single-institution study. A total of 141 tumors and healthy organs were imaged twice (interval between repeated examinations, 45 minutes to 10 days, depending the on study) to assess the repeatability of median and mean ADC estimates. The Levene test was used to determine whether ADC repeatability differed between studies. The Pearson linear correlation coefficient was used to assess correlation between coefficient of variation (CoV) and the year the study started, study size, and volumes of tumors and healthy organs. The repeatability of ADC estimates from small, medium, and large tumors and healthy organs was assessed irrespective of study, and the Levene test was used to determine whether ADC repeatability differed between these groups. Results CoV aggregated across all studies was 4.1% (range for each study, 1.7%-6.5%). No correlation was observed between CoV and the year the study started or study size. CoV was weakly correlated with volume (r = -0.5, P = .1). Repeatability was significantly different between small, medium, and large tumors (P < .05), with the lowest CoV (2.6%) for large tumors. There was a significant difference in repeatability between studies-a difference that did not persist after the study with the largest tumors was excluded. Conclusion ADC is a robust imaging metric with excellent repeatability in extracranial soft tissues across a wide range of tumor sites, sizes, patient populations, and imaging protocol variations. Online supplemental material is available for this article.

Pyruvate-lactate exchange is mediated by the enzyme lactate dehydrogenase (LDH) and is central to the altered energy metabolism in cancer cells. The measurement of exchange kinetics using hyperpolarized (13) C NMR has provided a biomarker of response to novel therapeutics. However, the observable signal is restricted to the exchanging hyperpolarized (13) C pools and the endogenous pools of (12) C-labelled metabolites are invisible in these measurements. In this study, we investigated an alternative in vitro (1) H NMR assay, using [3-(13) C]pyruvate, and compared the measured kinetics with a hyperpolarized (13) C NMR assay, using [1-(13) C]pyruvate, under the same conditions in human colorectal carcinoma SW1222 cells. The apparent forward reaction rate constants (kPL ) derived from the two assays showed no significant difference, and both assays had similar reproducibility (kPL  = 0.506 ± 0.054 and kPL  = 0.441 ± 0.090 nmol/s/10(6) cells; mean ± standard deviation; n = 3); (1) H, (13) C assays, respectively). The apparent backward reaction rate constant (kLP ) could only be measured with good reproducibility using the (1) H NMR assay (kLP  = 0.376 ± 0.091 nmol/s/10(6) cells; mean ± standard deviation; n = 3). The (1) H NMR assay has adequate sensitivity to measure real-time pyruvate-lactate exchange kinetics in vitro, offering a complementary and accessible assay of apparent LDH activity., (Copyright © 2013 John Wiley & Sons, Ltd.)

Objective: Diffusion-weighted MRI is increasingly applied in the body. It has been recognized for some time, on the basis of scientific experiments and studies in the brain, that the calculation of apparent diffusion coefficient by simple monoexponential relationship between MRI signal and b value does not fully account for tissue behavior. However, appreciation of this fact in body diffusion MRI is relatively new, because technologic advancements have only recently enabled high-quality body diffusion-weighted images to be acquired using multiple b values. There is now increasing interest in the radiologic community to apply more sophisticated analytic approaches, such as those based on the principles of intravoxel incoherent motion, which allows quantitative parameters that reflect tissue microcapillary perfusion and tissue diffusivity to be derived., Conclusion: In this review, we discuss the principles of intravoxel incoherent motion as applied to body diffusion-weighted MRI. The evidence for the technique in measuring tissue perfusion is presented and the emerging clinical utility surveyed. The requisites and challenges of quantitative evaluation beyond simple monoexponential relationships are highlighted.