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Register a new userAutomated Definition of Skeletal Disease Burden in Metastatic Prostate Carcinoma: a 3D analysis of SPECT/CT images
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To meet the current need for skeletal tumor-load estimation in prostate cancer (mCRPC), we developed a novel approach, based on adaptive bone segmentation. In this study, we compared the program output with existing estimates and with the radiological outcome. Seventy-six whole-body 99mTc-DPD-SPECT/CT from mCRPC patients were analyzed. The software identified the whole skeletal volume (SVol) and classified it voxels metastases (MVol) or normal bone (BVol). SVol was compared with the estimation of a commercial software. MVol was compared with manual assessment and with PSA-level. Counts/voxel were extracted from MVol and BVol. After six cycles of 223RaCl2-therapy every patient was re-evaluated as progressing (PD), stabilized (SD) or responsive (PR). SVol correlated with the one of the commercial software (R=0,99, p<0,001). MVol correlated with manually-counted lesions (R=0,61, p<0,001) and PSA (R=0,46, p<0.01). PD had a lower counts/voxel in MVol than PR/SD (715 pm 190 Vs. 975 pm 215 and 1058 pm 255, p<0,05 and p<0,01) and in BVol (PD 275 pm 60, PR 515 pm 188 and SD 528 pm 162 counts/voxel, p<0,001). Segmentation-based tumor load correlated with radiological/laboratory indices. Uptake was linked with the clinical outcome, suggesting that metastases in PD patients have a lower affinity for bone-seeking radionuclides and might benefit less from bone-targeted radioisotope therapies.
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Mathematical modeling in cancer has been growing in popularity and impact since its inception in 1932. The first theoretical mathematical modeling in cancer research was focused on understanding tumor growth laws and has grown to include the competition between healthy and normal tissue, carcinogenesis, therapy and metastasis. It is the latter topic, metastasis, on which we will focus this short review, specifically discussing various computational and mathematical models of different portions of the metastatic process, including: the emergence of the metastatic phenotype, the timing and size distribution of metastases, the factors that influence the dormancy of micrometastases and patterns of spread from a given primary tumor.
Quantitative measures of uptake in caudate, putamen, and globus pallidus in dopamine transporter (DaT) brain SPECT have potential as biomarkers for the severity of Parkinson disease. Reliable quantification of uptake requires accurate segmentation of these regions. However, segmentation is challenging in DaT SPECT due to partial-volume effects, system noise, physiological variability, and the small size of these regions. To address these challenges, we propose an estimation-based approach to segmentation. This approach estimates the posterior mean of the fractional volume occupied by caudate, putamen, and globus pallidus within each voxel of a 3D SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of the true fractional volumes is obtained from magnetic resonance images from clinical populations. The proposed method accounts for both the sources of partial-volume effects in SPECT, namely the limited system resolution and tissue-fraction effects. The method was implemented using an encoder-decoder network and evaluated using realistic clinically guided SPECT simulation studies, where the ground-truth fractional volumes were known. The method significantly outperformed all other considered segmentation methods and yielded accurate segmentation with dice similarity coefficients of ~ 0.80 for all regions. The method was relatively insensitive to changes in voxel size. Further, the method was relatively robust up to +/- 10 degrees of patient head tilt along transaxial, sagittal, and coronal planes. Overall, the results demonstrate the efficacy of the proposed method to yield accurate fully automated segmentation of caudate, putamen, and globus pallidus in 3D DaT-SPECT images.
Prediction and prevention of musculo-skeletal injuries is an important aspect of preventive health science. Using as an example a human knee joint, this paper proposes a new coupled-loading-rate hypothesis, which states that a generic cause of any musculo-skeletal injury is a Euclidean jolt, or SE(3)-jolt, an impulsive loading that hits a joint in several coupled degrees-of-freedom simultaneously. Informally, it is a rate-of-change of joint acceleration in all 6-degrees-of-freedom simultaneously, times the corresponding portion of the body mass. In the case of a human knee, this happens when most of the body mass is on one leg with a semi-flexed knee -- and then, caused by some external shock, the knee suddenly `jerks; this can happen in running, skiing, sports games (e.g., soccer, rugby) and various crashes/impacts. To show this formally, based on the previously defined covariant force law and its application to traumatic brain injury (Ivancevic, 2008), we formulate the coupled Newton--Euler dynamics of human joint motions and derive from it the corresponding coupled SE(3)-jolt dynamics of the joint in case. The SE(3)-jolt is the main cause of two forms of discontinuous joint injury: (i) mild rotational disclinations and (ii) severe translational dislocations. Both the joint disclinations and dislocations, as caused by the SE(3)-jolt, are described using the Cosserat multipolar viscoelastic continuum joint model. Keywords: musculo-skeletal injury, coupled-loading--rate hypothesis, coupled Newton-Euler dynamics, Euclidean jolt dynamics, joint dislocations and disclinations
Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. However, training effective deep learning models usually require a large amount of high-quality labeled data, which are often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep learning methods, the new approach can utilize both annotated and un-annotated data for training. It generates un-annotated synthetic data by a data augmentation scheme using generative adversarial networks (GANs). We applied the new approach to segmenting multiple organs in male pelvic CT images, where CT images without annotations and GAN-synthesized un-annotated images were used in semi-supervised learning. Experimental results, evaluated by three metrics (Dice similarity coefficient, average Hausdorff distance, and average surface Hausdorff distance), showed that the new method achieved either comparable performance with substantially fewer annotated images or better performance with the same amount of annotated data, outperforming the existing state-of-the-art methods.
Muscle uses Ca2+ as a messenger to control contraction and relies on ATP to maintain the intracellular Ca2+ homeostasis. Mitochondria are the major sub-cellular organelle of ATP production. With a negative inner membrane potential, mitochondria take up Ca2+ from their surroundings, a process called mitochondrial Ca2+ uptake. Under physiological conditions, Ca2+ uptake into mitochondria promotes ATP production. Excessive uptake causes mitochondrial Ca2+ overload, which activates downstream adverse responses leading to cell dysfunction. Moreover, mitochondrial Ca2+ uptake could shape spatio-temporal patterns of intracellular Ca2+ signaling. Malfunction of mitochondrial Ca2+ uptake is implicated in muscle degeneration. Unlike non-excitable cells, mitochondria in muscle cells experience dramatic changes of intracellular Ca2+ levels. Besides the sudden elevation of Ca2+ level induced by action potentials, Ca2+ transients in muscle cells can be as short as a few milliseconds during a single twitch or as long as minutes during tetanic contraction, which raises the question whether mitochondrial Ca2+ uptake is fast and big enough to shape intracellular Ca2+ signaling during excitation-contraction coupling and creates technical challenges for quantification of the dynamic changes of Ca2+ inside mitochondria. This review focuses on characterization of mitochondrial Ca2+ uptake in skeletal muscle and its role in muscle physiology and diseases.
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