METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.

RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.

MATERIALS AND METHODS: SEA country-specific cancer incidence by tumor site for 2015, 2025 and 2035 was extracted from the GLOBOCAN database. We utilized the optimal radiotherapy utilization rate model by Wong et al. (2016) to calculate the optimal number of fractions for all tumor sites in each SEA country. The available machines (LINAC & Co-60) were extracted from the IAEA's Directory of Radiotherapy Centres (DIRAC) from which the number of available fractions was calculated.

MATERIALS AND METHODS: A retrospective study on the symptoms and results of TFT according to the dosage of intensity-modulated radiotherapy (IMRT) given to patients with NPC. Data were traced and analysed.

RESULTS: A total of 78 patients were identified. All patients received IMRT with 33-35 fractions of radiotherapy (RT) with total dosage of 66-70 Gray given. Not all patients had their thyroid function status measured routinely. Twelve patients did have symptoms of hypothyroidism. TFT were obtained in this group but the results were normal. No correlation was found between RT and hypothyroidism.

MATERIALS AND METHODS: Ninety-seven ROs were randomly assigned to either manual or AI-assisted contouring of eight OARs for two head-and-neck cancer cases with an in-between teaching session on contouring guidelines. Thereby, the effect of teaching (yes/no) and AI-assisted contouring (yes/no) was quantified. Second, ROs completed short-term and long-term follow-up cases all using AI assistance. Contour quality was quantified with Dice Similarity Coefficient (DSC) between ROs' contours and expert consensus contours. Groups were compared using absolute differences in medians with 95% CIs.

RESULTS: AI-assisted contouring without previous teaching increased absolute DSC for optic nerve (by 0.05 [0.01; 0.10]), oral cavity (0.10 [0.06; 0.13]), parotid (0.07 [0.05; 0.12]), spinal cord (0.04 [0.01; 0.06]), and mandible (0.02 [0.01; 0.03]). Contouring time decreased for brain stem (-1.41 [-2.44; -0.25]), mandible (-6.60 [-8.09; -3.35]), optic nerve (-0.19 [-0.47; -0.02]), parotid (-1.80 [-2.66; -0.32]), and thyroid (-1.03 [-2.18; -0.05]). Without AI-assisted contouring, teaching increased DSC for oral cavity (0.05 [0.01; 0.09]) and thyroid (0.04 [0.02; 0.07]), and contouring time increased for mandible (2.36 [-0.51; 5.14]), oral cavity (1.42 [-0.08; 4.14]), and thyroid (1.60 [-0.04; 2.22]).

METHODS: Data from 585 eligible patients who received palliative radiotherapy between January 2012 and December 2014 were analysed. Median overall survival was calculated from the commencement of first fraction of the last course of radiotherapy to date of death or when censored. 30-DM was calculated as the proportion of patients who died within 30 days from treatment start date. Kaplan-Meier survival analysis was used to estimate survival. Chi-square test and logistic regression was used to assess the impact of potential prognostic factors on median survival and 30-DM.

RESULTS: The most common diagnoses were lung and breast cancers and most common irradiated sites were bone and brain. Median survival and 30-DM were 97 days and 22.7% respectively. Primary cancer, age, treatment course, performance status, systemic treatment post radiotherapy and intended radiotherapy treatment completed had an impact on median survival whereas mainly the latter three factors had an impact on 30-DM.

METHODS: Dose measurement of a standard pear-shaped plan carried out in phantom to verify the MOSkin dose measurement accuracy. With MOSkin attached to the third diode, RP3 of the PTW 9112, both detectors were inserted into patients' rectum. The RP3 and MOSkin measured doses in 18 sessions as well as the maximum measured doses from PTW 9112, RPmax in 48 sessions were compared to the planned doses.

RESULTS: Percentage dose differences ΔD (%) in phantom study for two MOSkin found to be 2.22 ± 0.07% and 2.5 ± 0.07%. IVD of 18 sessions resulted in ΔD(%) of -16.3% to 14.9% with MOSkin and ΔD(%) of -35.7% to -2.1% with RP3. In 48 sessions, RPmax recorded ΔD(%) of -37.1% to 11.0%. MOSkin_measured doses were higher in 44.4% (8/18) sessions, while RP3_measured were lower than planned doses in all sessions. RPmax_measured were lower in 87.5% of applications (42/47).

METHODS: To explore differences between these two modalities, we assessed the immune cell infiltrate into EMT6.5 mammary tumors after CRT and MRT.

RESULTS: CRT induced marked increases in tumor-associated macrophages and neutrophils while there were no increases in these populations following MRT. In contrast, there were higher numbers of T cells in the MRT treated tumors. There were also increased levels of CCL2 by immunohistochemistry in tumors subjected to CRT, but not to MRT. Conversely, we found that MRT induced higher levels of pro-inflammatory genes in tumors than CRT.