Potentially lethal damage (PLD) and its repair (PLDR) were studied in confluent human fibroblasts by analyzing the kinetics of chromosome break rejoining after X-ray or heavy-ion exposures. Cells were either held in the non-cycling G0 phase of the cell cycle for 12 h, or forced to proliferate immediately after irradiation. Fusion premature chromosome condensation (PCC) was combined with fluorescence in situ hybridization (FISH) to study chromosomal aberrations in interphase. The culture condition had no impact on the rejoining kinetics of PCC breaks during the 12 h after X-ray or heavy-ion irradiation. However, 12 h after X-ray and silicon irradiation, cycling cells had more chromosome exchanges than non-cycling cells. After 6 Gy X-rays, the yield of exchanges in cycling cells was 2.8 times higher than that in non-cycling cells, and after 2 Gy of 55 keV/μm silicon ions the yield of exchanges in cycling cells was twice that of non-cycling cells. In contrast, after exposure to 2 Gy 200-keV/μm or 440-keV/μm iron ions the yield of exchanges was similar in non-cycling and cycling cells. Since the majority of repair in G0/G1 occurs via the non-homologous end joining process (NHEJ), increased PLDR in X-ray and silicon-ion irradiated cells may result from improved cell cycle-specific rejoining fidelity through the NHEJ pathway, which is not the case in high-LET iron-ion irradiated cells.

Under hypoxic conditions, nitroimidazoles can replace oxygen as electron acceptors, thereby enhancing the effects of radiation on malignant cells. These compounds also accumulate in hypoxic cells, where they can act as cytotoxins or imaging agents. However, whether these effects apply to cancer stem cells has not been sufficiently explored. Here we show that the 2-nitroimidazole doranidazole potentiates radiation-induced DNA damage in hypoxic glioma stem cells (GSCs) and confers a significant survival benefit in mice harboring GSC-derived tumors in radiotherapy settings. Furthermore, doranidazole and misonidazole, but not metronidazole, manifested radiation-independent cytotoxicity for hypoxic GSCs that was mediated by ferroptosis induced partially through blockade of mitochondrial complexes I and II and resultant metabolic alterations in oxidative stress responses. Doranidazole also limited the growth of GSC-derived subcutaneous tumors and that of tumors in orthotopic brain slices. Our results thus reveal the theranostic potential of 2-nitroimidazoles as ferroptosis inducers that enable targeting GSCs in their hypoxic niche.

We report the feasibility and treatment outcomes of image-guided three-dimensional conformal arc radiotherapy (3D-CART) using a C-arm linear accelerator with a computed tomography (CT) on-rail system for localized prostate cancer.

We investigated dose perturbations caused by 125I seeds in patients undergoing supplemental external beam radiotherapy (EBRT) for prostate cancer. We examined two types of nonradioactive seed models: model 6711 and model STM1251. All experiments were performed using a water-equivalent phantom. Radiochromic film was used to measure the dose distributions adjacent to the seeds upstream and downstream of the external beam source. Single and clusters of multiple seeds were placed in slots in a solid water (SW) slab to measure dose perturbations with separate versus dense seed placement at beam energies of 6 or 10 MV. Monte Carlo simulations (MCSs) were also performed to include the theoretical basis against film dosimetry. Distinct patterns of dose enhancement (buildup [BU]) were upstream, and dose reduction (builddown [BD]) were downstream of the radiation source. Model 6711 with lower photon beam energies produced larger dose perturbations of BU and BD than the model STM1251. The results showed the same tendency with different seed placements and beam energies. However, these differences were not observed in the rotational irradiation measurement, which replicated a clinical plan. Dose perturbations around seeds result in dose enhancement and dose reduction with varying impact depending on the photon beam energy and seed type. This has the potential to cancel out these perturbations using multiple beam direction fields.

This study analyzed the impact of the coronavirus disease 2019 (COVID-19) pandemic on radiotherapy delivery in Japan using a high-quality Japanese national database based on universal health coverage.

Prostate-specific antigen nadir (nPSA) after radiotherapy for localized prostate cancer has been investigated as a predictor. However, nPSA usually requires several years, limiting its clinical utility. We investigated the significance of nPSA within 12 months (nPSA12) after low-dose-rate prostate brachytherapy (LDR-PB) or external beam radiotherapy (EBRT) on treatment outcomes. Between 2006 and 2014, 663 patients with prostate cancer were treated with LDR-PB or EBRT at two institutions. Four hundred and seventy-four men received LDR-PB and 189 men received EBRT, without androgen deprivation therapy. The Kaplan-Meier method was used for biochemical failure (BF)-free survival (BFFS) and distant metastasis (DM)-free survival (DMFS) analyses, and multivariable Cox regression analysis was performed. The median follow-up was 61.3 months. The median nPSA12 in the LDR-PB and EBRT cohorts was 0.7 and 1.0 ng/mL, respectively. The 7-year BFFS and DMFS rates in LDR-PB patients with nPSA12 ≤ 0.7 ng/mL were 99.1% and 99.5%, respectively; when nPSA12 was >0.7 ng/mL, they were 90.2% and 94.8%, respectively. In EBRT patients with nPSA12 ≤ 1.0 ng/mL, BFFS and DMFS rates were 85.4% and 98.5%, respectively; when nPSA12 was >1.0 ng/mL, they were 67.1% and 87.2%, respectively. nPSA12 was an independent predictor of BF and DM in both cohorts (LDR-PB, P = 0.004 and 0.020, respectively; EBRT, P = 0.005 and 0.041, respectively). The nPSA12 after LDR-PB or EBRT is significantly associated with treatment outcomes of prostate cancer. Higher nPSA12 may identify patients at high risk of relapse who might benefit from salvage treatment.

We investigated the normal tissue complication probability (NTCP) of the incidence of pericardial effusion (PCE) based on the mean heart dose (MHD) in patients with oesophageal cancer treated with definitive chemoradiotherapy. The incidences of PCE in any grade (A-PCE) and symptomatic PCE (S-PCE) were evaluated separately. To identify predictors for PCE, several clinical and dose-volume parameters were analysed using a receiver operating characteristic (ROC) curve and multivariate regression analysis. To validate its clinical applicability, the generated NTCP model was compared to the Lyman-Kutcher-Burman (LKB) model. Among 229 eligible patients, A-PCE and S-PCE were observed in 100 (43.7%) and 18 (7.9%) patients, respectively. MHD showed a preferable area under the curve (AUC) value for S-PCE (AUC = 0.821) and A-PCE (AUC = 0.734). MHD was the only significant predictor for A-PCE; MHD and hypertension were selected as significant factors for S-PCE. The estimated NTCP, using the MHD-based model, showed excellent correspondence to the LKB model in A-PCE and S-PCE. The NTCP curve of A-PCE was gentler than that of S-PCE and had no threshold. The MHD-based NTCP model was simple but comparable to the LKB model for both A-PCE and S-PCE. Therefore, the estimated NTCP may provide clinically useful parameters for predicting PCE.

This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with regards to delineating normal lung excluding the trachea and main bronchi. A total of 232 non-small-cell lung cancer cases were examined. The computed tomography (CT) images of these cases were converted from Digital Imaging and Communications in Medicine (DICOM) Radiation Therapy (RT) formats to arrays of 32 × 128 × 128 voxels and input into both 2D and 3D U-Net, which are deep learning networks for semantic segmentation. The number of training, validation and test sets were 160, 40 and 32, respectively. Dice similarity coefficients (DSCs) of the test set were evaluated employing Smart SegmentationⓇ Knowledge Based Contouring (Smart segmentation is an atlas-based segmentation tool), as well as the 2D and 3D U-Net. The mean DSCs of the test set were 0.964 [95% confidence interval (CI), 0.960-0.968], 0.990 (95% CI, 0.989-0.992) and 0.990 (95% CI, 0.989-0.991) with Smart segmentation, 2D and 3D U-Net, respectively. Compared with Smart segmentation, both U-Nets presented significantly higher DSCs by the Wilcoxon signed-rank test (P < 0.01). There was no difference in mean DSC between the 2D and 3D U-Net systems. The newly-devised 2D and 3D U-Net approaches were found to be more effective than a commercial auto-segmentation tool. Even the relatively shallow 2D U-Net which does not require high-performance computational resources was effective enough for the lung segmentation. Semantic segmentation using deep learning was useful in radiation treatment planning for lung cancers.