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Objective: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance. Methods: We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets. Results: For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM). Conclusion: The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) accounts for approximately 15–20% of all breast cancer types, indicating a poor survival prognosis with a more aggressive biology of metastasis to the lung and a short response duration to available therapies. Ibulocydine (IB) is a novel (cyclin-dependent kinase) CDK7/9 inhibitor prodrug displaying potent anti-cancer effects against various cancer cell types. We performed in vitro and in vivo experiments to determine whether IB inhibits metastasis and eventually overcomes the poor drug response in TNBC. The result showed that IB inhibited the growth of TNBC cells by inducing caspase-mediated apoptosis and blocking metastasis by reducing MMP-9 expression in vitro. Concurrently, in vivo experiments using the metastasis model showed that IB inhibited metastasis of MDA-MB-231-Luc cells to the lung. Collectively, these results demonstrate that IB inhibited the growth of TNBC cells and blocked metastasis by regulating MMP-9 expression, suggesting a novel therapeutic agent for metastatic TNBC. [ABSTRACT FROM AUTHOR]

Background: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects. Purpose: In this study, a deep learning method utilized to QA was used to predict the beam range and spread‐out Bragg peak (SOBP) for two‐dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom. Methods: The 2D residual U‐net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction. Result: The estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field‐marginal regions. Conclusion: The deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy. [ABSTRACT FROM AUTHOR]

Radiotherapy (RT) plays an important role in localized lung cancer treatments. Although RT locally targets and controls malignant lesions, RT resistance prevents RT from being an effective treatment for lung cancer. In this study, we identified phosphomevalonate kinase (PMVK) as a novel radiosensitizing target and explored its underlying mechanism. We found that cell viability and survival fraction after RT were significantly decreased by PMVK knockdown in lung cancer cell lines. RT increased apoptosis, DNA damage, and G2/M phase arrest after PMVK knockdown. Also, after PMVK knockdown, radiosensitivity was increased by inhibiting the DNA repair pathway, homologous recombination, via downregulation of replication protein A1 (RPA1). RPA1 downregulation was induced through the ubiquitin–proteasome system. Moreover, a stable shRNA PMVK mouse xenograft model verified the radiosensitizing effects of PMVK in vivo. Furthermore, PMVK expression was increased in lung cancer tissues and significantly correlated with patient survival and recurrence. Our results demonstrate that PMVK knockdown enhances radiosensitivity through an impaired HR repair pathway by RPA1 ubiquitination in lung cancer, suggesting that PMVK knockdown may offer an effective therapeutic strategy to improve the therapeutic efficacy of RT. [ABSTRACT FROM AUTHOR]