Martin G Carolan

Bio: Martin G Carolan is an academic researcher from Wollongong Hospital. The author has contributed to research in topics: Dosimetry & Dosimeter. The author has an hindex of 20, co-authored 80 publications receiving 1352 citations. Previous affiliations of Martin G Carolan include Illawarra Health & Medical Research Institute & University of Wollongong.

A new radiotherapy surface dose detector: The MOSFET

Abstract: Radiotherapy x-ray and electron beam surface doses are accurately measurable by use of a MOS-FET detector system. The MOSFET (Metal Oxide Semiconductor Field Effect Transistor) is approximately 200-microns in diameter and consists of a 0.5-microns Al electrode on top of a 1-microns SiO2 and 300-microns Si substrate. Results for % surface dose were within +/- 2% compared to the Attix chamber and within +/- 3% of TLD extrapolation results for normally incident beams. Detectors were compared using different energies, field size, and beam modifying devices such as block trays and wedges. Percentage surface dose for 10 x 10-cm and 40 x 40-cm field size for 6-MV x rays at 100-cm SSD using the MOSFET were 16% and 42% of maximum, respectively. Factors such as its small size, immediate retrieval of results, high accuracy attainable from low applied doses, and as the MOSFET records its dose history make it a suitable in vivo dosimeter where surface and skin doses need to be determined. This can be achieved within part of the first fraction of dose (i.e., only 10 cGy is required.)

The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review

Abstract: This paper reviews objective methods for prognostic modelling of cancer tumours located within radiology images, a process known as radiomics. Radiomics is a novel feature transformation method for detecting clinically relevant features from radiological imaging data that are difficult for the human eye to perceive. To facilitate the detection machine learning and deep learning methods are increasingly investigated with the aim of improving patient diagnosis, treatment options and outcomes. A review of the relevant works in the expanding field of radiomics for survival prediction from cancer is provided. Research works outside the field of radiomics which define techniques that may be of future use to improve feature extraction and analysis are also reviewed. Radiomics is a rapidly advancing field of clinical image analysis with a vast potential for supporting decision making involved in the diagnosis and treatment of cancer. The realisation of this goal of more effective decision making requires significant individual and integrated expertise from domain experts in medicine, biology and computer science to allow advances in computer vision and machine learning techniques to be applied effectively. Deep learning combined with machine learning has the potential to advance the field of radiomics significantly in the years to come, provided that mechanisms for data sharing or distributed learning are established to increase the availability of data across all patient and tumour types.

Gastric cancer stem cells: evidence, potential markers, and clinical implications

Abstract: Gastric cancer is a significant global health problem. It is the fifth most common cancer and third leading cause of cancer-related death worldwide (Torre et al. in CA Cancer J Clin 65(2):87-108, 2015). Despite advances in treatment, overall prognosis remains poor, due to tumour relapse and metastasis. There is an urgent need for novel therapeutic approaches to improve clinical outcomes in gastric cancer. The cancer stem cell (CSC) model has been proposed to explain the high rate of relapse and subsequent resistance of cancer to current systemic treatments (Vermeulen et al. in Lancet Oncol 13(2):e83-e89, 2012). CSCs have been identified in many solid malignancies, including gastric cancer, and have significant clinical implications, as targeting the CSC population may be essential in preventing the recurrence and spread of a tumour (Dewi et al. in J Gastroenterol 46(10):1145-1157, 2011). This review seeks to summarise the current evidence for CSC in gastric cancer, with an emphasis on candidate CSC markers, clinical implications, and potential therapeutic approaches.

Characterization of a novel two dimensional diode array the "magic plate" as a radiation detector for radiation therapy treatment.

Abstract: Purpose: Intensity modulated radiation therapy (IMRT) utilizes the technology of multileaf collimators to deliver highly modulated and complex radiation treatment. Dosimetric verification of the IMRT treatment requires the verification of the delivered dose distribution. Two dimensional ion chamber or diode arrays are gaining popularity as a dosimeter of choice due to their real time feedback compared to film dosimetry. This paper describes the characterization of a novel 2D diode array, which has been named the “magic plate” (MP). It was designed to function as a 2D transmission detector as well as a planar detector for dose distribution measurements in a solid water phantom for the dosimetric verification of IMRT treatment delivery. Methods: The prototype MP is an 11 � 11 detector array based on thin (50 lm) epitaxial diode technology mounted on a 0.6 mm thick Kapton substrate using a proprietary “drop-in” technology developed by the Centre for Medical Radiation Physics, University of Wollongong. A full characterization of the detector was performed, including radiation damage study, dose per pulse effect, percent depth dose comparison with CC13 ion chamber and build up characteristics with a parallel plane ion chamber measurements, dose linearity, energy response and angular response. Results: Postirradiated magic plate diodes showed a reproducibility of 2.1%. The MP dose per pulse response decreased at higher dose rates while at lower dose rates the MP appears to be dose rate independent. The depth dose measurement of the MP agrees with ion chamber depth dose measurements to within 0.7% while dose linearity was excellent. MP showed angular response dependency due to the anisotropy of the silicon diode with the maximum variation in angular response of 10.8% at gantry angle 180 � . Angular dependence was within 3.5% for the gantry angles 675 � . The field size dependence of the MP at isocenter agrees with ion chamber measurement to within 1.1%. In the beam perturbation study, the surface dose increased by 12.1% for a 30 � 30 cm 2 field size at the source to detector distance (SDD) of 80 cm whilst the transmission for the MP was 99%. Conclusions: The radiation response of the magic plate was successfully characterized. The array of epitaxial silicon based detectors with “drop-in” packaging showed properties suitable to be used as a simplified multipurpose and nonperturbing 2D radiation detector for radiation therapy dosimetric verification. V C 2012 American Association of Physicists in Medicine.

sources and effects of ionizing radiation

Abstract: NOTE The report of the Committee without its annexes appears as Official Records of the General Assembly, Sixty-third Session, Supplement No. 46. The designations employed and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The country names used in this document are, in most cases, those that were in use at the time the data were collected or the text prepared. In other cases, however, the names have been updated, where this was possible and appropriate, to reflect political changes. Scientific Annexes Annex A. Medical radiation exposures Annex B. Exposures of the public and workers from various sources of radiation INTROdUCTION 1. In the course of the research and development for and the application of atomic energy and nuclear technologies, a number of radiation accidents have occurred. Some of these accidents have resulted in significant health effects and occasionally in fatal outcomes. The application of technologies that make use of radiation is increasingly widespread around the world. Millions of people have occupations related to the use of radiation, and hundreds of millions of individuals benefit from these uses. Facilities using intense radiation sources for energy production and for purposes such as radiotherapy, sterilization of products, preservation of foodstuffs and gamma radiography require special care in the design and operation of equipment to avoid radiation injury to workers or to the public. Experience has shown that such technology is generally used safely, but on occasion controls have been circumvented and serious radiation accidents have ensued. 2. Reviews of radiation exposures from accidents have been presented in previous UNSCEAR reports. The last report containing an exclusive chapter on exposures from accidents was the UNSCEAR 1993 Report [U6]. 3. This annex is aimed at providing a sound basis for conclusions regarding the number of significant radiation accidents that have occurred, the corresponding levels of radiation exposures and numbers of deaths and injuries, and the general trends for various practices. Its conclusions are to be seen in the context of the Committee's overall evaluations of the levels and effects of exposure to ionizing radiation. 4. The Committee's evaluations of public, occupational and medical diagnostic exposures are mostly concerned with chronic exposures of …

Stem Cells,Cancer and Cancer Stem Cells

Abstract: Stem cells are defined as cells able to both extensively self-renew and differentiate into progenitors. Research data show that more resistant stem cells than common cancer cells exist in cancer patients.To identify unrecognized differences between cancer stem cells and cancer cells might be able to develope effective classification,diagnose and treat ment for cancer.

The potential for artificial intelligence in healthcare.

Abstract: The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.