Automatic contouring of normal tissues with deep learning for preclinical radiation studies

Objective. Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (mu CBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies. Approach. A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of the mu CBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (Delta CoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed - 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout uncertainty was performed for delineation of the heart. Main results. The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD95p < 1 mm and Delta CoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%). Significance. A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

Objective. Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (mu CBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies. Approach. A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of the mu CBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (Delta CoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed - 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout uncertainty was performed for delineation of the heart. Main results. The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD95p < 1 mm and Delta CoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%). Significance. A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low

Inhibition of 4E-BP1 sensitizes U87 glioblastoma xenograft tumors to irradiation by decreasing hypoxia tolerance

PURPOSE: Eukaryotic initiation factor 4E (eIF4E) is an essential rate-limiting factor for cap-dependent translation in eukaryotic cells. Elevated eIF4E activity is common in many human tumors and is associated with disease progression. The growth-promoting effects of eIF4E are in turn negatively regulated by 4E-BP1. However, although 4E-BP1 harbors anti-growth activity, its expression is paradoxically elevated in some tumors. The aim of this study was to investigate the functional role of 4E-BP1 in the context of solid tumors. METHODS AND MATERIALS: In vitro and in vivo growth properties, hypoxia tolerance, and response to radiation were assessed for HeLa and U87 cells, after stable expression of shRNA specific for 4E-BP1. RESULTS: We found that loss of 4E-BP1 expression did not significantly alter in vitro growth but did accelerate the growth of U87 tumor xenografts, consistent with the growth-promoting function of deregulated eIF4E. However, cells lacking 4E-BP1 were significantly more sensitive to hypoxia-induced cell death in vitro. Furthermore, 4E-BP1 knockdown cells produced tumors more sensitive to radiation because of a reduction in the viable fraction of radioresistant hypoxic cells. Decreased hypoxia tolerance in the 4E-BP1 knockdown tumors was evident by increased cleaved caspase-3 levels and was associated with a reduction in adenosine triphosphate (ATP). CONCLUSIONS: Our results suggest that although tumors often demonstrate increases in cap-dependent translation, regulation of this activity is required to facilitate energy conservation, hypoxia tolerance, and tumor radioresistance. Furthermore, we suggest that targeting translational control may be an effective way to target hypoxic cells and radioresistance in metabolically hyperactive tumors

Caffeic Acid Phenethyl Ester (CAPE), a natural polyphenol to increase the therapeutic window for lung adenocarcinomas

BACKGROUND AND PURPOSE: Lung cancers are highly resistant to radiotherapy, necessitating the use of high doses, which leads to radiation toxicities such as radiation pneumonitis and fibrosis. Caffeic Acid Phenethyl Ester (CAPE) has been suggested to have anti-proliferative and pro-apoptotic effects in tumour cells, while radioprotective anti-inflammatory and anti-oxidant effects in the normal tissue. We investigated the radiosensitizing and radioprotective effects of CAPE in lung cancer cell lines and normal tissue in vitro and ex vivo, respectively. MATERIALS AND METHODS: The cytotoxic and radiosensitizing effects of CAPE in lung cancer were investigated using viability and clonogenic survival assays. The radioprotective effects of CAPE were assessed in vitro and ex vivo using precision cut lung slices (PCLS). Potential underlying molecular mechanisms of CAPE focusing on cell cycle, cell metabolism, mitochondrial function and pro-inflammatory markers were investigated. RESULTS: Treatment with CAPE decreased cell viability in a dose-dependent manner (IC 57.6±16.6µM). Clonogenic survival assays showed significant radiosensitization by CAPE in lung adenocarcinoma lines (p&lt;0.05), while no differences were found in non-adenocarcinoma lines (p=0.13). Cell cycle analysis showed an increased S-phase (p&lt;0.05) after incubation with CAPE in the majority of cell lines. Metabolic profiling showed that CAPE shifted cellular respiration towards glycolysis (p&lt;0.01), together with mitochondrial membrane depolarization (p&lt;0.01). CAPE induced a decrease in NF-?B activity in adenocarcinomas and decreased pro-inflammatory gene expression in PCLS. CONCLUSION: The combination of CAPE and radiotherapy may be a potentially effective approach to increase the therapeutic window in lung cancer patients

Preclinical evaluation and validation of [18F]HX4, a promising hypoxia marker for PET imaging

Hypoxia has been shown to be an important microenvironmental parameter influencing tumor progression and treatment efficacy. Patient guidance for hypoxia-targeted therapy requires evaluation of tumor oxygenation, preferably in a noninvasive manner. The aim of this study was to evaluate and validate the uptake of [18F]HX4, a novel developed hypoxia marker for PET imaging. A heterogeneous accumulation of [18F]HX4 was found within rat rhabdomyosarcoma tumors that was significantly (P < 0.0001) higher compared with the surrounding tissues, with temporal increasing tumor-to-blood ratios reaching a plateau of 7.638 ± 0.926 and optimal imaging properties 4 h after injection. [18F]HX4 retention in normal tissues was found to be short-lived, homogeneous and characterized by a fast progressive temporal clearance. Heterogeneity in [18F]HX4 tumor uptake was analyzed based on 16 regions within the tumor according to the different orthogonal planes at the largest diameter. Validation of heterogeneous [18F]HX4 tumor uptake was shown by a strong and significant relationship (r = 0.722; P < 0.0001) with the hypoxic fraction as calculated by the percentage pimonidazole-positive pixels. Furthermore, a causal relationship with tumor oxygenation was established, because combination treatment of nicotinamide and carbogen resulted in a 40% reduction (P < 0.001) in [18F]HX4 tumor accumulation whereas treatment with 7% oxygen breathing resulted in a 30% increased uptake (P < 0.05). [18F]HX4 is therefore a promising candidate for noninvasive detection and evaluation of tumor hypoxia at a macroscopic level

Independent and functional validation of a multi-tumour-type proliferation signature.

Item does not contain fulltextBackground:Previously we demonstrated that an mRNA signature reflecting cellular proliferation had strong prognostic value. As clinical applicability of signatures can be controversial, we sought to improve our marker's clinical utility by validating its biological relevance, reproducibility in independent data sets and applicability using an independent technique.Methods:To facilitate signature evaluation with quantitative PCR (qPCR) a novel computational procedure was used to reduce the number of signature genes without significant information loss. These genes were validated in different human cancer cell lines upon serum starvation and in a 168 xenografts panel. Analyses were then extended to breast cancer and non-small-cell lung cancer (NSCLC) patient cohorts.Results:Expression of the qPCR-based signature was dramatically decreased under starvation conditions and inversely correlated with tumour volume doubling time in xenografts. The signature validated in breast cancer (hazard ratio (HR)=1.63, P<0.001, n=1820) and NSCLC adenocarcinoma (HR=1.64, P<0.001, n=639) microarray data sets. Lastly, qPCR in a node-negative, non-adjuvantly treated breast cancer cohort (n=129) showed that patients assigned to the high-proliferation group had worse disease-free survival (HR=2.25, P<0.05).Conclusion:We have developed and validated a qPCR-based proliferation signature. This test might be used in the clinic to select (early-stage) patients for specific treatments that target proliferation