No description available.

The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.

No description available.

Thesis (M.S.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 71-72). / xii, 89 leaves, bound ill. 29 cm

Synovial sarcoma is a soft tissue tumor defined by the presence of t(X;18)(p11.2;q11.2), fusing the SYT (SS18) gene on chromosome 18 and one of three SSX genes on chromosome X. T(X;18) results in production of a fusion protein (SYT-SSX) that is thought to underlie synovial sarcoma pathogenesis through aberrant targeting of both activating (trithorax, SWI/SNF) and repressing (Polycomb) transcription factors when expressed in a stem or progenitor-like cellular background. Clinically, synovial sarcomas present considerable diagnostic and therapeutic challenges. Whereas the classical biphasic histology is distinctive, the more common monophasic histology can be difficult to differentiate from other spindle cell tumors. In these situations, detection of t(X;18) is the gold standard for diagnosis, but it is a specialized and time-consuming process. Immunohistochemistry can be helpful, but no marker that is both highly sensitive and specific is available. Here I describe a fluorescence in situ hybridization based method employing an SYT break-apart probe set that can expedite detection of t(X;18). I also report that TLE1, which was identified in gene expression studies as a good discriminator of synovial sarcoma from other mesenchymal tumors, is a highly sensitive and specific immunohistochemical marker for synovial sarcoma. Both of these novel diagnostic techniques are applicable to small tissue samples such as core needle biopsies and are now being used clinically. The diagnosis of synovial sarcoma carries a poor prognosis and the 10-year overall survival rate is approximately 50%, most of whom are young adults. The addition of chemotherapy to surgical resection (the mainstay of treatment) does not appear to improve overall survival. Thus, there is a strong need for development of a clinically effective systemic therapy to improve patient outcome. I describe preclinical studies that demonstrate the Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) inhibits proliferation of synovial sarcoma by inducing apoptosis and that this is associated with degradation of multiple receptor tyrosine kinases and disruption of the SYT-SSX-β-catenin interaction. I also identify a subset of synovial sarcoma cells, typified by expression of CD133, which exhibit stem-like properties and are relatively resistant to doxorubicin but susceptible to 17-AAG at clinically relevant concentrations.

This thesis proposes four novel geometric neurodynamical classifier models, namely GBAM, Lie-derivative, Lie-Poisson, and FAM, applied to breast cancer detection. All these models have been published in a paper and/or in a book form. All theoretical material of this thesis (Chapter 2) has been published in my monographs (see my publication list), as follows: 2.1 Tensorial Neurodynamics has been published in Natural Biodynamics (Chapters 3, 5 and 7), Geometrical Dynamics of Complex Systems; (Chapter 1 and Appendix), 2006) as well as Applied Differential Geometry:A Modern Introduction(Chapter 3) 2.2 GBAM Neurodynamical Classifier has been published in Natural Biodynamics (Chapter 7) and Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 3), as well as in the KES–Conference paper with the same title; 2.3 Lie-Derivative Neurodynamical Classifier has been published in Geometrical Dynamics of Complex Systems; (Chapter 1) and Applied Differential Geometry: A Modern Introduction (Chapter 3); 2.4 Lie-Poisson Neurodynamical Classifier has been published in Geometrical Dynamics of Complex Systems; (Chapter 1) and Applied Differential Geometry: A Modern Introduction (Chapter 3); 2.5 Fuzzy Associative Dynamical Classifier has been published in Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 4), as well as in the KES-Conference paper with the same title. Besides, Section 1.2 Artificial Neural Networks has been published in Natural Biodynamics (Chapter 7) and Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapter 3). Also, Sections 4.1. and 4.5. have partially been published in Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling (Chapters 3 and 4, respectively) and in the corresponding KES–Conference papers. A. The GBAM (generalized bidirectional associative memory) classifier is a neurodynamical, tensor-invariant classifier based on Riemannian geometry. The GBAM is a tensor-field system resembling a two-phase biological neural oscillator in which an excitatory neural field excites an inhibitory neural field, which reciprocally inhibits the excitatory one. This is a new generalization of Kosko’s BAM neural network, with a new biological (oscillatory, i.e., excitatory/inhibitory)interpretation. The model includes two nonlinearly-coupled (yet non-chaotic and Lyapunov stable) subsystems, activation dynamics and self-organized learning dynamics, including a symmetric synaptic 2-dimensional tensor-field, updated by differential Hebbian associative learning innovations. Biologically, the GBAM describes interacting excitatory and inhibitory populations of neurons found in the cerebellum, olfactory cortex, and neocortex, all representing the basic mechanisms for the generation of oscillating (EEG-monitored) activity in the brain. B. Lie-derivative neurodynamical classifier is an associative-memory, tensor-invariant neuro-classifier, based on the Lie-derivative operator from geometry of smooth manifolds. C. Lie-Poisson neurodynamical classifier is an associative-memory, tensor-invariant neuro-classifier based on the Lie-Poisson bracket from the generalized symplectic geometry. D. The FAM-matrix (fuzzy associative memory) dynamical classifier is a fuzzy-logic classifier based on a FAM-matrix (fuzzy phase-plane). All models are formulated and simulated in Mathematica computer algebra system. All models are applied to breast cancer detection, using the database from the University of Wisconsin and Mammography database. Classification results outperformed those obtained with standard MLP trained with backpropagation algorithm. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2008

Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Nie, Shuming; Committee Member: Bao, Gang; Committee Member: Murthy, Niren; Committee Member: Varma, Vijay; Committee Member: Wang, Zhong Lin. Part of the SMARTech Electronic Thesis and Dissertation Collection.

On the purpose of designing a novel generation of luminescent bioprobes for imaging and inhibition of tumour cells, a series of lanthanide-ruthenium complexes has been synthesized and characterized by 1H NMR, 13C NMR, absorption/emission spectroscopy, high-performance liquid chromatography, and mass spectroscopy. Those complexes are qualified to be considered as photo-activatable anticancer prodrugs which consist of a ruthenium (II) complex linked to a lanthanide-based cyclen chelate via a π-conjugated bridge. Comprehensive studies have been performed to evaluate their efficacy as pro-drugs which requires in cellulo activity, inhibiting ability, instant monitoring possibility, and safety to normal cells. The resulting complexes are proved to be promising agents for controllable anticancer therapy because the prodrug remains inactive in dark and the release of the active drug is induced by visible light. Drug delivery process can be quantitatively monitored by either the long-lived red europium emission under one- or two-photon excitation or potentially by magnetic resonance imaging signals. Besides of these, the correlation among the drug releasing amount, signaling emission intensity, and mass spectroscopy response, has been proposed for quick and simple quantitative analysis.

Synovial sarcoma is a soft tissue tumor defined by the presence of t(X;18)(p11.2;q11.2), fusing the SYT (SS18) gene on chromosome 18 and one of three SSX genes on chromosome X. T(X;18) results in production of a fusion protein (SYT-SSX) that is thought to underlie synovial sarcoma pathogenesis through aberrant targeting of both activating (trithorax, SWI/SNF) and repressing (Polycomb) transcription factors when expressed in a stem or progenitor-like cellular background. Clinically, synovial sarcomas present considerable diagnostic and therapeutic challenges. Whereas the classical biphasic histology is distinctive, the more common monophasic histology can be difficult to differentiate from other spindle cell tumors. In these situations, detection of t(X;18) is the gold standard for diagnosis, but it is a specialized and time-consuming process. Immunohistochemistry can be helpful, but no marker that is both highly sensitive and specific is available. Here I describe a fluorescence in situ hybridization based method employing an SYT break-apart probe set that can expedite detection of t(X;18). I also report that TLE1, which was identified in gene expression studies as a good discriminator of synovial sarcoma from other mesenchymal tumors, is a highly sensitive and specific immunohistochemical marker for synovial sarcoma. Both of these novel diagnostic techniques are applicable to small tissue samples such as core needle biopsies and are now being used clinically. The diagnosis of synovial sarcoma carries a poor prognosis and the 10-year overall survival rate is approximately 50%, most of whom are young adults. The addition of chemotherapy to surgical resection (the mainstay of treatment) does not appear to improve overall survival. Thus, there is a strong need for development of a clinically effective systemic therapy to improve patient outcome. I describe preclinical studies that demonstrate the Hsp90 inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) inhibits proliferation of synovial sarcoma by inducing apoptosis and that this is associated with degradation of multiple receptor tyrosine kinases and disruption of the SYT-SSX-β-catenin interaction. I also identify a subset of synovial sarcoma cells, typified by expression of CD133, which exhibit stem-like properties and are relatively resistant to doxorubicin but susceptible to 17-AAG at clinically relevant concentrations. / Medicine, Faculty of / Pathology and Laboratory Medicine, Department of / Graduate

Ovarian cancer is the fifth leading cause of cancer death among women in the U.S. and kills approximately 14,000 women each year (Nezhat et al., 2015). Survival increases with early diagnosis; the five-year survival rate in stage I is 90%. Symptoms are vague and common to many health diseases, which may well explain why upwards of 70% of women with ovarian cancer are diagnosed at stage III or IV (Slatnik & Duff, 2015). Preventative guidelines in the U.S. do not recommend screening for ovarian cancer in women of average risk (AAFP, 2016b; ACOG, 2011; Doubeni et al., 2016; Moyer, 2012; NCCN, 2015; Qaseem et al., 2014; Wilt et al., 2015). A lack of screening recommendations and a subtle presentation point to the need for greater healthcare professional recognition of symptoms and risk factors of ovarian cancer, which can then lead to a prompt diagnosis. While healthcare professionals have the opportunity to improve women’s health, gaps in knowledge exist related to ovarian cancer risk factors and symptom recognition (Gajjar et al., 2012). Continuing education improves healthcare professionals’ performance and patient health outcomes (Cervero & Gaines, 2015). Increasing healthcare professionals’ knowledge of ovarian cancer may help to detect ovarian cancer in earlier stages and enhance health outcomes of women. Based on the need for an increase in awareness and knowledge among healthcare professionals, a local ovarian cancer conference was developed and offered to healthcare professionals. The conference focused on presenting ovarian cancer risk factors and symptoms. Attendees were provided with an ovarian cancer resource for patient education. The conference was evaluated through pretests and posttests and a conference evaluation survey. Data was collected the evening of the conference with 29 attendees responding. After the conference, correct responses increased in the areas of risk factor and symptom recognition. The number of correct responses increased from 106 on the pretest to 122 on the posttest. In regards to ability to educate women about ovarian cancer, 62% of respondents indicated that they were “very confident” in their ability. / Pam Solseng Ovarian Cancer Endowment Fund