Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.

In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and motion within an image deformation, was proposed. Data included 57 CT scans (7202 2D slices) of patients with LACC randomly divided into the train (n = 42) and test (n = 15) datasets. In addition to CT images and the corresponding RT structure (bladder, cervix, and rectum), the bone was segmented, and the coaches were eliminated. The correlated stochastic field was simulated using the same size as the target image (used for deformation) to produce the general random deformation. The deformation field was optimized to have a maximum amplitude in the rectum region, a moderate amplitude in the bladder region, and an amplitude as minimum as possible within bony structures. The DIRNet is a convolutional neural network that consists of convolutional regressors, spatial transformation, as well as resampling blocks. It was implemented by different parameters. Mean Dice indices of 0.89 ± 0.02, 0.96 ± 0.01, and 0.93 ± 0.02 were obtained for the cervix, bladder, and rectum (defined as at-risk organs), respectively. Furthermore, a mean average symmetric surface distance of 1.61 ± 0.46 mm for the cervix, 1.17 ± 0.15 mm for the bladder, and 1.06 ± 0.42 mm for the rectum were achieved. In addition, a mean Jaccard of 0.86 ± 0.04 for the cervix, 0.93 ± 0.01 for the bladder, and 0.88 ± 0.04 for the rectum were observed on the test dataset (15 subjects). Deep learning-based non-rigid image registration is, therefore, proposed for the high-dose-rate brachytherapy in inter-fraction cervical cancer since it outperformed conventional algorithms.

Sildenafil improves radiation-induced oral mucositis by attenuating oxidative stress, NF-κB, ERK and JNK signalling pathways.

Radiation-induced oral mucositis is a common and dose-limiting complication of head and neck radiotherapy with no effective treatment. Previous studies revealed that sildenafil, a phosphodiesterase 5 inhibitor, has anti-inflammatory and anti-cancer effects. In this study, we investigated the effect of sildenafil on radiation-induced mucositis in rats. Two doses of radiation (8 and 26 Gy X-ray) were used to induce low-grade and high-grade oral mucositis, separately. A control group and three groups of sildenafil citrate-treated rats (5, 10, and 40 mg/kg/day) were used for each dose of radiation. Radiation increased MDA and activated NF-κB, ERK and JNK signalling pathways. Sildenafil significantly decreased MDA level, nitric oxide (NO) level, IL1ß, IL6 and TNF-α. The most effective dose of sildenafil was 40 mg/kg/day in this study. Sildenafil also significantly inhibited NF-κB, ERK and JNK signalling pathways and increased bcl2/bax ratio. In addition, high-dose radiation severely destructed the mucosal layer in histopathology and led to mucosal cell apoptosis in the TUNEL assay. Sildenafil significantly improved mucosal structure and decreased inflammatory cell infiltration after exposure to high-dose radiation and reduced apoptosis in the TUNEL assay. These findings show that sildenafil can improve radiation-induced oral mucositis and decrease the apoptosis of mucosal cells via attenuation of inflammation and oxidative stress.

The Relationship between Lung and Heart Two-Dimensional Parameters and Three-Dimensional Dose-Volume Data in Adjuvant Radiotherapy for Breast Cancer.

Background: Two-dimensional (2D) radiographic parameters have been used to estimate the amount of heart and lung irradiated for minimizing heart and lung complications in breast cancer patients. The aim of this study was to investigate the correlation between traditionally used 2D radiographic and dose-volume parameters during adjuvant radiotherapy of breast cancer. Methods: In this cross-sectional study, we analyzed 121 female patients treated with breast-conserving surgery (BCS) or modified radical mastectomy (MRM) and 3D conformal radiotherapy (3DCRT) using two-field radiotherapy (2FRT) or three-field radiotherapy (3FRT) technique. All patients underwent computed tomography (CT)-planning. Two-D parameters, including central lung distance (CLD), maximum lung depth (MLD), maximum heart length (MHL), maximum heart distance (MHD), and chest wall separation (CWS), were measured using digitally reconstructed radiographs (DRR) and CT images. DVHs for lung, heart, and target were created. The Pearson correlation test was used to evaluate the correlation between 2D radiographic and dose-volume parameters. Results: There was a correlation between CLD and ipsilateral lung V5-20Gy and Dmean and between MLD and ipsilateral lung V5-20Gy. In 2FRT, only moderate correlation between CLD and ipsilateral lung V20Gy (r = 0.453, P = 0.003) and between MLD and ipsilateral lung V20Gy (r = 0.593, P <0.001) were observed. Poor correlation of MHL and heart V25Gy (r = 0.409, P = 0.007) was seen only in 3FRT. There was a correlation between MHD and heart dose-volume data, with a strong correlation between MHD and heart V5-25Gy and Dmean (r = 0.875-0.934, P<0.001) in the 2FRT group. No correlation between CWS and breast Dmax was found. Conclusion: There was a correlation between 2D parameters (i.e., CLD, MLD, and MHD) and the heart and lung dose-volume parameters during adjuvant breast radiotherapy. Although CLD was correlated to ipsilateral lung V5-20Gy and Dmean, the correlation between CLD and ipsilateral lung V20Gy was greater than other dose-volume parameters. MHD provided a close estimation of heart dose-volume parameters.

Comparison of dosimetric characteristics of physical wedge and enhanced dynamic wedge in inhomogeneous medium using Monte Carlo simulations.

BACKGROUND: Widely used physical wedges in clinical radiotherapy lead to beam intensity attenuation as well as the beam hardening effect, which must be considered. Dynamic wedges devised to overcome the physical wedges (PWs) problems result in dosimetry complications due to jaw movement while the beam is on. This study was aimed to investigate the usability of physical wedge data instead of enhanced dynamic wedge due to the enhanced dynamic wedge (EDW) dosimetry measurement hardships of Varian 2100CD in inhomogeneous phantom by Monte Carlo code as a reliable method in radiation dosimetry. MATERIALS AND METHODS: A PW and EDW-equipped-linac head was simulated using BEAMnrc code. DOSXYZnrc was used for three-dimensional dosimetry calculation in the CIRS phantom. RESULTS: Based on the isodose curves, EDW generated a less scattered as well as lower penumbra width compared to the PW. The depth dose variations of PWs and EDWs were more in soft tissue than the lung tissue. Beam profiles of PW and EDW indicated good coincidence in all points, except for the heel area. CONCLUSION: Results demonstrated that it is possible to apply PW data instead of EDW due to the dosimetry and commissioning hardships caused by EDW in inhomogeneous media.

CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.

PURPOSE: Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. METHODS: In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. RESULTS: Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively. CONCLUSIONS: We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.

Combinatorial effects of radiofrequency hyperthermia and radiotherapy in the presence of magneto-plasmonic nanoparticles on MCF-7 breast cancer cells.

Here, the effects of combinatorial cancer therapy including radiotherapy (RT) and radiofrequency (RF) hyperthermia in the presence of gold-coated iron oxide nanoparticles (Au@IONPs), as a thermo-radio-sensitizer, are reported. The level of cell death and the ratio of Bax/Bcl2 genes, involved in the pathway of apoptosis, were measured to evaluate the synergistic effect of Au@IONPs-mediated RF hyperthermia and RT. MCF-7 human breast adenocarcinoma cells were treated with different concentrations of Au@IONPs. After incubation with NPs, the cells were exposed to RF waves (13.56 MHz; 100 W; 15 min). At the same time, thermometry was performed with an infrared (IR) camera. Then, the cells were exposed to 6 MV X-ray at various doses of 2 and 4 Gy. MTT (3-[4,5-dimethylthiazol-2-y1]-2,5-diphenyltetrazolium bromide) assay was performed to evaluate cell viability and quantitative real-time polymerase chain reaction (qRT-PCR) was used to determine the expression ratio of Bax/Bcl2. Cellular uptake of nanoparticles was confirmed qualitatively and quantitatively. The results obtained from MTT assay and qRT-PCR studies showed that NPs and RF hyperthermia had no significant effect when applied separately, while their combination had synergistic effects on cell viability percentage and the level of apoptosis induction. A synergistic effect was also observed when the cancer cells were treated with a combination of NPs, RF hyperthermia, and RT. On the basis of the obtained results, it may be concluded that the use of magneto-plasmonic NPs in the process of hyperthermia and RT of cancer holds a great promise to develop a new combinatorial cancer therapy strategy.

Rectal retractor application during image-guided dose-escalated prostate radiotherapy.

PURPOSE: To investigate efficacy of a rectal retractor (RR) on rectal dose during image-guided dose-escalated prostate three-dimensional conformal radiotherapy (3DCRT). PATIENTS AND METHODS: In all, 21 patients with localized prostate cancer were treated with a RR for 3DCRT in 40â€¯× 2 Gy. Patient underwent two scans for radiotherapy planning, without and with RR. RR was used for the first half of the treatment sessions. Two plans were created for each patient to compare the effect of RR on rectal doses. PTW-31014 Pinpoint chamber embedded within RR was used for in vivo dosimetry in 6 of 21 patients. The patient tolerance and acute rectal toxicity were surveyed during radiotherapy using Common Terminology Criteria for Adverse Events (CTCAE) v.4.0. RESULTS: Patients tolerated the RR well during 20 fractions with mild degree of anal irritation. Using a RR significantly reduced the rectal wall (RW), anterior RW and posterior RW dose-volume parameters. The average RW Dmean was 29.4 and 43.0 Gy for plans with and without RR, respectively. The mean discrepancy between the measured dose and planned dose was -3.8% (±4.9%). Grade 1 diarrhea, rectal urgency and proctitis occurred in 4, 2 and 3 cases, respectively. There were no grade ≥2 acute rectal toxicities during the treatment. CONCLUSION: Rectal retraction resulted in a significant reduction of rectal doses with a safe toxicity profile, which may reduce rectal toxicity. Dosimeter inserted into the RR providing a practical method for in vivo dosimetric verification. Further prospective clinical studies will be necessary to demonstrate the clinical advantage of RR.

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages. METHODS: Thirty-three Pca patients were included. All patients underwent pre- and post-IMRT T2-weighted (T2 W) and apparent diffusing coefficient (ADC) MRI. IMRT response was calculated in terms of changes in the ADC value, and patients were divided as responders and non-responders. A wide range of radiomic features from different feature sets were extracted from all T2 W and ADC images. Univariate radiomic analysis was performed to find highly correlated radiomic features with IMRT response, and a paired t test was used to find significant features between responders and non-responders. To find high predictive radiomic models, tenfold cross-validation as the criterion for feature selection and classification was applied on the pre-, post- and delta IMRT radiomic features, and area under the curve (AUC) of receiver operating characteristics was calculated as model performance value. RESULTS: Of 33 patients, 15 patients (45%) were found as responders. Univariate analysis showed 20 highly correlated radiomic features with IMRT response (20 ADC and 20 T2). Two and fifteen T2 and ADC radiomic features were found as significant (P-value ≤ 0.05) features between responders and non-responders, respectively. Several cross-combined predictive radiomic models were obtained, and post-T2 radiomic models were found as high predictive models (AUC 0.632) followed by pre-ADC (AUC 0.626) and pre-T2 (AUC 0.61). For GS prediction, T2 W radiomic models were found as more predictive (mean AUC 0.739) rather than ADC models (mean AUC 0.70), while for stage prediction, ADC models had higher prediction performance (mean AUC 0.675). CONCLUSIONS: Radiomic models developed by MR image features and machine learning approaches are noninvasive and easy methods for personalized prostate cancer diagnosis and therapy.

Fiducial markers in prostate cancer image-guided radiotherapy.

Background: Image-guided radiotherapy (IGRT) is recommended to reduce the risk of geometrical miss when modern radiotherapy technologies with high grades of conformity are used. The purpose of this study was to evaluate the efficacy of fiducial markers (FMs) for electronic portal imaging in prostate cancer radiotherapy in term of evaluating the complications associated with FMs implantation, quantifying inter-fraction prostate motion, and determination of optimal planning target volume (PTV) margins. Methods: In this single institution, prospective, consecutive study, 27 patients underwent implantation of three-gold seed FMs into the prostate gland before prostate radiotherapy. Prior to computed tomography planning, all patients were asked to report any complication associated with FMs implantation that have experienced to date. Daily pre-treatment electronic portal images were captured, and prostate position errors were corrected if they were greater than 2 mm along three translational directions. Optimal PTV expansions were computed using van Herk formula [PTV-margin= 2.5Σ + 0.7σ]. Results: FMs implantation was successful with an acceptable toxicity profile in all patients. Without IGRT, margins of 5.4 mm, 5.8 mm and 5.5 mm, in vertical, longitudinal and lateral directions, respectively, are needed for a 95% confidence level of complete clinical target volume (CTV) coverage in each treatment session. The PTV margins of 3.0 mm, 3.3 mm and 4.0 mm in corresponding directions were calculated when FMs based electronic portal imaging was applied. Conclusion: FMs based electronic portal imaging is an effective tool for prostate cancer IGRT.

Evaluation of the effect of hyperthermia and electron radiation on prostate cancer stem cells.

The aim of this study was to investigate the effect of hyperthermia, 6 MeV electron radiation and combination of these treatments on cancer cell line DU145 in both monolayer culture and spheroids enriched for prostate cancer stem cells (CSCs). Flowcytometric analysis of the expression of molecular markers CD133+/CD44+ was carried out to determine the prostate CSCs in cell line DU145 grown as spheroids in serum-free medium. Following monolayer and spheroid culture, DU145 cells were treated with different doses of hyperthermia, electron beam and combination of them. The survival and self-renewing of the cells were evaluated by colony formation assay (CFA) and spheroid formation assay (SFA). Flowcytometry results indicated that the percentage of CD133+/CD44+ cells in spheroid culture was 13.9-fold higher than in the monolayer culture. The SFA showed significant difference between monolayer and spheroid culture for radiation treatment (6 Gy) and hyperthermia (60 and 90 min). The CFA showed significantly enhanced radiosensitivity in DU145 cells grown as monolayer as compared to spheroids, but no effect of hyperthermia. In contrast, for the combination of radiation and hyperthermia the results of CFA and SFA showed a reduced survival fraction in both cultures, with larger effects in monolayer than in spheroid culture. Thus, hyperthermia may be a promising approach in prostate cancer treatment that enhances the cytotoxic effect of electron radiation. Furthermore, determination and characterization of radioresistance and thermoresistance of CSCs in the prostate tumor is the key to develop more efficient therapeutic strategies.

Dose equivalent consideration from neutron contamination in modified radiotherapy vault: a Monte Carlo study.

Laminated barriers incorporating metal sheets provide effective protection for space-restricted radiotherapy centers. This study aimed to assess photoneutron contamination in smaller vaults protected by different compositions of multilayer barriers during simulated pelvic radiotherapy with 18 MV photon beams. Monte Carlo Simulations of 18 MV LINAC (Varian 2100 C/D) and Medical Internal Radiation Dose (MIRD) phantom were used to assess photoneutron contamination within reconstructed vaults incorporating different combinations of metal sheet and borated polyethylene (BPE) during pelvic radiotherapy. The findings highlight a 3.27 and 2.91 times increase in ambient neutron doseHn*(10) along the maze of reconstructed vaults that use lead and steel sheets, respectively, compared to concrete. TheHn*(10) outside the treatment room increased after incorporating a metal sheet, but it remained within the permissible limit of 20µSv/week for uncontrolled areas adjacent to the LINAC bunker, even with a workload of 1000Gy/week. Neutron equivalent doses in the patient's organs ranged from 0.22 to 0.96 mSv Gy-1. There is no notable distinction in the organ's neutron equivalent dose, fatal cancer risk, secondary radiation-induced cancer risk, and cancer mortality for various laminated barrier compositions. Furthermore, the use of metal sheets for vault wall reconstruction keeps the variation in cancer risk induced by photoneutrons below 6%, while risks of fatal cancer and cancer mortality vary less than 11%. While the metal portion of the laminated barrier raises the neutron dose, the addition of a BPE plate reduces concerns of increased effective dose and secondary malignancy risk.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac.

BACKGROUND: Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals. METHODS: Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations. RESULTS: Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models. CONCLUSIONS: This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

An analytical model to determine interseed attenuation effect in low-dose-rate brachytherapy.

Brachytherapy treatment planning systems (BTPS) are employing the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43)-recommended dosimetric parameters of sources, which are measured in water. The majority of brachytherapy implant volumes are not homogeneous media. Particularly, an implant with multiple seeds significantly changes homogeneity of the implant volume. Heterogeneities, such as attenuation by adjacent seeds or interseed attenuation (ISA), are neglected to this day in all BTPS. The goal of this project is to determine a novel analytical method to evaluate the impact of the dose perturbations (P-value) and/or interseed attenuation effect (ISA-value). This method will be validated for low- and high-energy brachytherapy seeds such as 125I and 192Ir using Monte Carlo (MC) simulation techniques. In this analytical model, determination of dose perturbation and interseed attenuation in a multisource brachytherapy implant is based on MC-simulated 3D kernels of P-values and ISA data for single active and single dummy configurations, arranged at different distances and orientations relative to each other. The accuracy of the final model in multisource implant configurations has been examined by a comparison of the calculated P-values and ISA-values with full Monte Carlo water simulations (FMCWS). This model enabled us to determine the total perturbation and ISA values for any multisource implant, and the results are in excellent agreement with the FMCWS data. The advantage of this model to FMCWS for daily clinical application is the speed of the calculations and ease of the implementation. The new perturbation and ISA formulism have shown a better accuracy for 192Ir than 125I due to Compton scattering and its independence of the atomic number of the chemical composition of the phantom materials. The maximum difference between the ISA model and FMCWS for all cases was less than 5%. This new model can provide inputs for brachytherapy planning software to consider the ISA effect in dose calculations based on TG-43U1 algorithm. This approach is applicable for energy range of 125I to192Ir sources.

Response investigation of a new polymer gel dosimeter based on ammonium salt through MRI technique.

Increasing the use of polymer gel dosimetry (PGD) in radiotherapy requires reducing its toxicity. The toxicity of the PGD components causes risks for the users as well as the environment. The aim of this study is to produce a new PGD called PAGBIT (Polymer, Amps ammonium salt, Gelatin, BIs, Thpc) based on the nontoxic monomer of 2-acrylamido-2-methylpropanesulfonic acid ammonium salt. Furthermore, this monomer is ecofriendly. The PAGBIT PGD was prepared in the laboratory in ambient conditions. PGDs were irradiated using a clinical accelerator with a dose range of 0-10 Gy. The incident photon energy and dose rate were 6-MV and 300 cGy/min, respectively. The irradiated PGDs were imaged using a 1.5T MRI scanner 9 times in a time range of 12-720 h post-irradiation. The maximum obtained sensitivity was 0.115 ± 0.005 Gy-1s-1 at 36 h post-irradiation time. The average sensitivity change as a function of post-irradiation time was 0.0017 Gy-1s-1h-1. However, the average sensitivity change as a function of scanning temperature was 0.0006 Gy-1s-1°C-1. Results showed that the differences of effective atomic number and electron density between PAGBIT and soft tissue were 2.3% and 0.3%, respectively. It was concluded that the PAGBIT is a low toxic, water equivalent PGD with noticeable temporal and temperature stabilities.

Effect of 13.56 MHz radiofrequency hyperthermia on mitotic cell cycle arrest in MCF7 breast cancer cell line and suggest a time interval for radiotherapy.

Introduction: After surgery, radiotherapy is the most common technique to treat breast cancer. Over the past decades, the thermal effects of radiofrequency-wave hyperthermia combined with radiotherapy have been used to increase radiosensitivity in cancer treatment. The cells have various radiation and thermal sensitivities at different stages of the mitotic cycle. Furthermore, ionizing radiation and the thermal effect of hyperthermia affect the cells' mitotic cycle and can partly induce cell cycle arrest. However, the time interval between hyperthermia and radiotherapy, as an essential factor influencing hyperthermia effect on cancer cells' cycle arrest, has not been studied before. In this study, we investigated the effect of hyperthermia on the MCF7 cancer cell cycle arrest in mitotic cycles at various selected time intervals after hyperthermia to find and propose appropriate time intervals between hyperthermia and radiotherapy. Method and Materials: In this experimental study, we used the MCF7 breast cancer cell line to investigate the effect of 13.56 MHz hyperthermia (at a temperature of 43°C for a period of 20 min) on their cell cycle arrest. We performed the flowcytometry assay to assess the changes in the mitotic phases of the cell population at different time intervals (1, 6, 24, and 48 h) after hyperthermia. Results: Our flowcytometry results indicated the 24-h time interval has the most significant effect on the cell population at S and G2/M phases. Therefore, the 24-h time interval can be proposed as the most appropriate time after hyperthermia for carrying out combinational radiotherapy procedure. Conclusion: Among various investigated time intervals examined in our research, the 24-h time interval can be proposed as the most appropriate time between hyperthermia and radiotherapy for combinational therapy of breast cancer cells.

Radiosensitization effect of radiofrequency hyperthermia in the presence of PEGylated-gold nanoparticles on the MCF-7 breast cancer cells under 6 MeV electron irradiation.

Purpose: The purpose of the study was to investigate the radiosensitization effect of radiofrequency (RF) hyperthermia in combination with PEGylated gold nanoparticles (PEG-GNPs) on MCF-7 breast cancer cells under electron beam radiotherapy (EBRT) based on the clonogenic assay. Materials and Methods: The cell death of MCF-7 breast cancer cells treated with 13.56 MHz capacitive RF hyperthermia (power: 150W) for 2, 5, 10, and 15 min combined with 6 MeV EBRT, with a dose of 2 Gy, was evaluated in the presence of 20 nm PEG-GNPs with a low nontoxic concentration (20 mg/l). All the treatment groups were incubated for 14 days. Thereafter, survival fractions and viability of the cells were calculated and analyzed against the control group. Results: The presence of PEG-GNPs inside the MCF-7 cancer cells during electron irradiation decreased cell survival significantly (16.7%) compared to irradiated cells without GNPs. Applying hyperthermia before electron irradiation with a capacitive RF system decreased cell survival by about 53.7%, while hyperthermia without irradiation did not show any significant effect on cell survival. Combining the hyperthermia with the presence of PEG-GNPs in the cells decreased the cell survival by about 67% at the electron irradiation, showing their additive radiosensitization effect. Conclusion: Low nontoxic concentration of 20 nm PEG-GNPs increases the radiosensitization effect of combining 6 MeV EBRT and RF hyperthermia on MCF-7 cancer cells. Combining hyperthermia with PEG-GNPs in electron radiotherapy could be an appropriate method for enhancing radiotherapy effectiveness on cancerous cells which can be studied on different cells and electron energies in future research.

The feasibility of the pretreatment verification of 2D dose distributions in radiation therapy with small fields using the electronic portal imaging device.

BACKGROUND: The present study aims to evaluate the performance of an Electronic portal imaging device (EPID) for measuring dosimetric parameters and for verification of dose in small photon fields. MATERIAL AND METHODS: In this study, the beam profiles were obtained using the amorphous silicon (a-Si) EPID for field sizes ranging from 1 × 1 to 10 × 10 cm 2 at energies 6 and 18 mega-voltage (MV). For comparison, the dosimetric parameters, including penumbra widths and field sizes, were measured with the pinpoint, diode, and Semiflex dosimeters. Finally, Rando Phantom was used to compare the two-dimensional (2D) Dose distribution between EPID and Treatment Planning System (TPS). RESULTS: In both 5 cm and 10 cm depths, there were large differences between the measured doses obtained from TPS, Pinpoint detector, and Farmer detector in 1 × 1 field size. The differences become negligible as the field sizes increase and from 3 × 3 field size to 10 × 10 field size, the maximum observed differences are 2 cGy and 2.4 cGy for 5 cm and 10 cm depths, respectively. The results indicate that the penumbra widths are smaller in the Gantry-Target (GT) direction compared to the Right-Left (RL) direction. The maximum difference (47.6%) was observed for EPID in the 10 × 10 field size, and the minimum difference (16.6%) was observed for TPS in the 1 × 1 field size. Finally, 2D dose distributions obtained by EPID and TPS exhibit excellent agreement. CONCLUSION: EPID is an excellent tool for the measurement of dosimetry parameters such as dose profiles, penumbra widths, field sizes, and pretreatment verification of 2D dose distributions, especially in small fields.

Binary Logistic Regression Modeling of Voice Impairment and Voice Assessment in Iranian Patients with Nonlaryngeal Head-and-Neck Cancers after Chemoradiation Therapy: Objective and Subjective Voice Evaluation.

Background: Laryngeal damages after chemoradiation therapy (RT) in nonlaryngeal head-and-neck cancers (HNCs) can cause voice disorders and finally reduce the patient's quality of life (QOL). The aim of this study was to evaluate voice and predict laryngeal damages using statistical binary logistic regression (BLR) models in patients with nonlaryngeal HNCs. Methods: This cross-section experimental study was performed on seventy patients (46 males, 24 females) with an average age of 50.43 ± 16.54 years, with nonlaryngeal HNCs and eighty individuals with assumed normal voices. Subjective and objective voice assessment was carried out in three stages including before, at the end, and 6 months after treatment. Eventually, the Enter method of the BLR was used to measure the odds ratio of independent variables. Results: In objective evaluation, the acoustic parameters except for F0 increased significantly (P < 0.001) at the end treatment stage and decreased 6 months after treatment. The same trend can be seen in the subjective evaluations, whereas none of the values returned to pretreatment levels. Statistical models of BLR showed that chemotherapy (P < 0.05), mean laryngeal dose (P < 0.05), V50 Gy (P = 0.002), and gender (P = 0.008) had the greatest effect on incidence laryngeal damages. The model based on acoustic analysis had the highest percentage accuracy of 84.3%, sensitivity of 87.2%, and the area under the curve of 0.927. Conclusions: Voice evaluation and the use of BLR models to determine important factors were the optimum methods to reduce laryngeal damages and maintain the patient's QOL.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.

Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

Bipolar Androgen Therapy: When Excess Fuel Extinguishes the Fire.

Androgen deprivation therapy (ADT) remains the cornerstone of advanced prostate cancer treatment. However, the progression towards castration-resistant prostate cancer is inevitable, as the cancer cells reactivate androgen receptor signaling and adapt to the castrate state through autoregulation of the androgen receptor. Additionally, the upfront use of novel hormonal agents such as enzalutamide and abiraterone acetate may result in long-term toxicities and may trigger the selection of AR-independent cells through "Darwinian" treatment-induced pressure. Therefore, it is crucial to develop new strategies to overcome these challenges. Bipolar androgen therapy (BAT) is one such approach that has been devised based on studies demonstrating the paradoxical inhibitory effects of supraphysiologic testosterone on prostate cancer growth, achieved through a variety of mechanisms acting in concert. BAT involves rapidly alternating testosterone levels between supraphysiological and near-castrate levels over a period of a month, achieved through monthly intramuscular injections of testosterone plus concurrent ADT. BAT is effective and well-tolerated, improving quality of life and potentially re-sensitizing patients to previous hormonal therapies after progression. By exploring the mechanisms and clinical evidence for BAT, this review seeks to shed light on its potential as a promising new approach to prostate cancer treatment.