https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45273 Wed 26 Oct 2022 16:03:37 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45142 Wed 26 Oct 2022 15:43:14 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48213 Wed 24 Jan 2024 14:25:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53472 Tue 28 Nov 2023 15:54:21 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:52542 Tue 17 Oct 2023 10:18:54 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51605  20% compared to treatment without IMR. Calculated D98% of IMR monitored treatments with motion was within 1.5% of plans without motion.]]> Tue 12 Sep 2023 13:35:22 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:34592 in vivo EPID dosimetry for patients treated at the specific centre. Sampling patients for generating the control limits were limited to 100 patients. Whilst the quantitative results are specific to the clinical techniques and equipment used, the described method is generally applicable to IMRT and VMAT treatment QA. Whilst more work is required to determine the level of clinical significance, the authors have demonstrated the capability of the method for both treatment specific QA and continuing quality improvement. Practical implications: The proposed method is a valuable tool for assessing the accuracy of treatment delivery whilst also improving treatment quality and patient safety. Originality/value: Assessing in vivo EPID dosimetry with SPC can be used to improve the quality of radiation treatment for cancer patients.]]> Thu 28 Oct 2021 12:37:07 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:29628 Thu 27 Jan 2022 15:55:44 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53393 Thu 23 Nov 2023 13:36:38 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38734 Thu 21 Jul 2022 09:53:03 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38735 Thu 20 Jan 2022 14:53:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38728 Thu 20 Jan 2022 13:36:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40626 Thu 11 Aug 2022 11:20:50 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42724 Thu 01 Sep 2022 13:18:31 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:31482 Sat 24 Mar 2018 08:45:13 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:10094 Sat 24 Mar 2018 08:07:12 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:48497 1H MR spectroscopic imaging (3D ¹H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels. Study Type: Prospective. Subjects: Thirty-six patients with biopsy-proven central gland prostate cancer. Field Strength/Sequence: 3T MRI / 3D ¹H MRSI using GOIA-sLASER. Assessment: Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and ¹H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing. Statistical Tests: Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset. Results: High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%. Data Conclusion: The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer. Level of Evidence: 2. Technical Efficacy: Stage 2.]]> Mon 20 Mar 2023 14:05:29 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51930 Fri 22 Sep 2023 11:07:30 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51714 Fri 15 Sep 2023 17:46:38 AEST ]]>