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.

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.

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.

Catalase-gold nanoaggregates manipulate the tumor microenvironment and enhance the effect of low-dose radiation therapy by reducing hypoxia.

Radiotherapy as a standard method for cancer treatment faces tumor recurrence and antitumoral unresponsiveness. Suppressive tumor microenvironment (TME) and hypoxia are significant challenges affecting efficacy of radiotherapy. Herein, a versatile method is introduced for the preparation of pH-sensitive catalase-gold cross-linked nanoaggregate (Au@CAT) having acceptable stability and selective activity in tumor microenvironment. Combining Au@CAT with low-dose radiotherapy enhanced radiotherapy effects via polarizing protumoral immune cells to the antitumoral landscape. This therapeutic approach also attenuated hypoxia, confirmed by downregulating hypoxia hallmarks, such as hypoxia-inducible factor α-subunits (HIF-α), vascular endothelial growth factor (VEGF), and EGF. Catalase stability against protease digestion was improved significantly in Au@CAT compared to the free catalase. Moreover, minimal toxicity of Au@CAT on normal cells and increased reactive oxygen species (ROS) were confirmed in vitro compared with radiotherapy. Using the nanoaggregates combined with radiotherapy led to a significant reduction of immunosuppressive infiltrating cells such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells (T-regs) compared to the other groups. While, this combined therapy could significantly increase the frequency of CD8+ cells as well as M1 to M2 macrophages (MQs) ratio. The combination therapy also reduced the tumor size and increased survival rate in mice models of colorectal cancer (CRC). Our results indicate that this innovative nanocomposite could be an excellent system for catalase delivery, manipulating the TME and providing a potential therapeutic strategy for treating CRC.

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.

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.

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.

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.

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.

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.

Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.

PURPOSE: The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients. METHODS: The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment. RESULTS: The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively. CONCLUSION: This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.

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.

Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters.

PURPOSE: To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. METHODS: Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. RESULTS: One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). CONCLUSION: Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.

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.

Sensitization of glioblastoma cancer cells to radiotherapy and magnetic hyperthermia by targeted temozolomide-loaded magnetite tri-block copolymer nanoparticles as a nanotheranostic agent.

AIMS: Recently, the development of new strategies in the treatment and diagnosis of cancer cells such as thermo-radiation-sensitizer and theranostic agents have received a great deal of attention. In this work, folic acid-conjugated temozolomide-loaded SPION@PEG-PBA-PEG nanoparticles (TMZ-MNP-FA NPs) were proposed for use as magnetic resonance imaging (MRI) contrast agents and to enhance the cytotoxic effects of hyperthermia and radiotherapy. MAIN METHODS: Nanoparticles were synthesized by the Nano-precipitation method and their characteristics were determined by dynamic light scattering (DLS), scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). To evaluate the thermo-radio-sensitization effects of NPs, C6 cells were treated with nanoparticles for 24 h and then exposed to 6-MV X-ray radiation. After radiotherapy, the cells were subjected to an alternating magnetic field (AMF) hyperthermia. The therapeutic potential was assessed using clonogenic assay, ROS generation measurement, flow cytometry assay, and qRT-PCR analysis. Also, the diagnostic properties of the nanoparticles were assessed by MRI. KEY FINDINGS: MRI scanning indicated that nanoparticles accumulated in C6 cells could be tracked by T2-weighted MR imaging. Colony formation assay proved that TMZ-MNP-FA NPs enhanced the anti-proliferation effects of AMF by 1.94-fold compared to AMF alone (P < 0.0001). Moreover, these NPs improved the radiation effects with a dose enhancement factor of 1.65. All results showed that the combination of carrier-based chemotherapy with hyperthermia and radiotherapy caused a higher anticancer efficacy than single- or two-modality treatments. SIGNIFICANCE: The nanoparticles advanced in this study can be proposed as the promising theranostic and thermo-radio-sensitizer platform for the diagnosis and tri-modal synergistic cancer therapy.

Effect of Post IORT Wound Fluid Secretion (PIWFS) on the Behavior of Breast Cancer Cells: Stimulator or Inhibitor; Report of an Experimental Study on Breast Cancer.

BACKGROUND: Although investigating the probable side effects of post intraoperative radiotherapy wound fluid secretion (PIWFS) is crucial, especially in clinical cases, no report has been published on the effect of PIWFS on the remaining tumor cells (in the vital state) in cavity side margins or surrounding regions. These tumor cells might be directly/indirectly exposed to intraoperative radiation therapy (IORT). Here, for the first time, we investigated the effect of PIWFS on tumor cells of the same patient extracted from the excised tumor in the spheroid form. METHODS: We generated 8 human-derived breast tumor spheroids from 4 patient specimens who received to IORT, dissociated and cultured them in microfluidic devices. The spheroids from each sample were treated with the patients' PIWFS and DMEM medium separately. Two different parameters, called area and number of detached cells (NDCs), were determined and investigated to evaluate the spheroids' vital and proliferative states. RESULTS: The results showed severe transformation in tumor spheroids' function into more invasive and proliferative functions after treatment with PIWFS. CONCLUSION: Although the radiation-induced bystander effect may have a role in this observation, further experiments must be done to better clarify the probable desired or non-desired effects of post-IORT secretion for both the remaining tumor cells and the surrounding immune cells.

Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy.

PURPOSE: Absorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. MATERIAL AND METHODS: Overall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. RESULTS: The CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. CONCLUSIONS: A promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.

Neural Network Performance Evaluation of Simulated and Genuine Head-and-Neck Computed Tomography Images to Reduce Metal Artifacts.

Background: This study evaluated the performances of neural networks in terms of denoizing metal artifacts in computed tomography (CT) images to improve diagnosis based on the CT images of patients. Methods: First, head-and-neck phantoms were simulated (with and without dental implants), and CT images of the phantoms were captured. Six types of neural networks were evaluated for their abilities to reduce the number of metal artifacts. In addition, 40 CT patients' images with head-and-neck cancer (with and without teeth artifacts) were captured, and mouth slides were segmented. Finally, simulated noisy and noise-free patient images were generated to provide more input numbers (for training and validating the generative adversarial neural network [GAN]). Results: Results showed that the proposed GAN network was successful in denoizing artifacts caused by dental implants, whereas more than 84% improvement was achieved for images with two dental implants after metal artifact reduction (MAR) in patient images. Conclusion: The quality of images was affected by the positions and numbers of dental implants. The image quality metrics of all GANs were improved following MAR comparison with other networks.

Target motion management in breast cancer radiation therapy.

BACKGROUND: Over the last two decades, breast cancer remains the main cause of cancer deaths in women. To treat this type of cancer, radiation therapy (RT) has proved to be efficient. RT for breast cancer is, however, challenged by intrafractional motion caused by respiration. The problem is more severe for the left-sided breast cancer due to the proximity to the heart as an organ-at-risk. While particle therapy results in superior dose characteristics than conventional RT, due to the physics of particle interactions in the body, particle therapy is more sensitive to target motion. CONCLUSIONS: This review highlights current and emerging strategies for the management of intrafractional target motion in breast cancer treatment with an emphasis on particle therapy, as a modern RT technique. There are major challenges associated with transferring real-time motion monitoring technologies from photon to particles beams. Surface imaging would be the dominant imaging modality for real-time intrafractional motion monitoring for breast cancer. The magnetic resonance imaging (MRI) guidance and ultra high dose rate (FLASH)-RT seem to be state-of-the-art approaches to deal with 4D RT for breast cancer.

Investigating dose homogeneity in radiotherapy of oral cancers in the presence of a dental implant system: an in vitro phantom study.

BACKGROUND: Materials with high atomic numbers are part of the composition of dental implant systems. In radiotherapy of oral cavity cancers, an implant can cause dose perturbations that affect target definition, dose calculation, and dose distribution. In consequence, this may result in poor tumor control and higher complications. In this study, we evaluated dose homogeneity when a dental implant replaced a normal tooth. We also aimed to evaluate the concordance of dose calculations with dose measurements. MATERIALS AND METHODS: In this study, 2 sets of planning CT scans of a phantom with a normal tooth and the same phantom with the tooth replaced by a Z1 TBR dental implant system were used. The implant system was composed of a porcelain-fused-to-metal crown and titanium with a zirconium collar. Three radiotherapy plans were designed when the density of the implant material was corrected to match their elements, or when all were set to the density of water, or when using the default density conversion. Gafchromic EBT-3 films at the level of isocenter and crowns were used for measurements. RESULTS: At the level of crowns, upstream and downstream dose calculations were reduced when metal kernels were applied (M-plan). Moreover, relatively measured dose distribution patterns were most similar to M-plan. At this level, relative to the non-implanted phantom, mean doses values were higher with the implant (215.93 vs. 192.25), also, new high-dose areas appeared around a low-dose streak forward to the implant (119% vs. 95%). CONCLUSIONS: Implants can cause a high dose to the oral cavity in radiotherapy because of extra scattered radiation. Knowledge of the implant dimensions and defining their material enhances the accuracy of calculations.