Principles of artificial intelligence in radiooncology.

PURPOSE: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology. METHODS: In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively. RESULTS: This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size. CONCLUSION: This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data. MATERIALS AND METHODS: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, New York University (NYU), and BraTS Challenge 2023 were used. First, the performance of the DeepMedic network for BM autosegmentation was established for exclusive single-center training and mixed multicenter training, respectively. Subsequently privacy-preserving bilateral collaboration was evaluated, where a pretrained model is shared to another center for further training using transfer learning (TL) either with or without LWF. RESULTS: For single-center training, average F1 scores of BM detection range from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed multicenter training notably improves F1 scores at Stanford and NYU, with negligible improvement at other centers. When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL (0.570) and single-center training (0.688) on combined UKER and USZ test data. Naive TL improves sensitivity and contouring accuracy, but compromises precision. Conversely, LWF demonstrates commendable sensitivity, precision and contouring accuracy. When applied to Stanford, similar performance was observed. CONCLUSION: Data heterogeneity (e.g., variations in metastases density, spatial distribution, and image spatial resolution across centers) results in varying performance in BM autosegmentation, posing challenges to model generalizability. LWF is a promising approach to peer-to-peer privacy-preserving model training.

PRL1 and PRL3 promote macropinocytosis via its lipid phosphatase activity.

PRL1 and PRL3, members of the protein tyrosine phosphatase family, have been associated with cancer metastasis and poor prognosis. Despite extensive research on their protein phosphatase activity, their potential role as lipid phosphatases remains elusive. Methods: We conducted comprehensive investigations to elucidate the lipid phosphatase activity of PRL1 and PRL3 using a combination of cellular assays, biochemical analyses, and protein interactome profiling. Functional studies were performed to delineate the impact of PRL1/3 on macropinocytosis and its implications in cancer biology. Results: Our study has identified PRL1 and PRL3 as lipid phosphatases that interact with phosphoinositide (PIP) lipids, converting PI(3,4)P2 and PI(3,5)P2 into PI(3)P on the cellular membranes. These enzymatic activities of PRLs promote the formation of membrane ruffles, membrane blebbing and subsequent macropinocytosis, facilitating nutrient extraction, cell migration, and invasion, thereby contributing to tumor development. These enzymatic activities of PRLs promote the formation of membrane ruffles, membrane blebbing and subsequent macropinocytosis. Additionally, we found a correlation between PRL1/3 expression and glioma development, suggesting their involvement in glioma progression. Conclusions: Combining with the knowledge that PRLs have been identified to be involved in mTOR, EGFR and autophagy, here we concluded the physiological role of PRL1/3 in orchestrating the nutrient sensing, absorbing and recycling via regulating macropinocytosis through its lipid phosphatase activity. This mechanism could be exploited by tumor cells facing a nutrient-depleted microenvironment, highlighting the potential therapeutic significance of targeting PRL1/3-mediated macropinocytosis in cancer treatment.

System analysis based on Anoikis-related genes identifies MAPK1 as a novel therapy target for osteosarcoma with neoadjuvant chemotherapy.

BACKGROUND: Osteosarcoma (OS) is the most common bone malignant tumor in children, and its prognosis is often poor. Anoikis is a unique mode of cell death.However, the effects of Anoikis in OS remain unexplored. METHOD: Differential analysis of Anoikis-related genes was performed based on the metastatic and non-metastatic groups. Then LASSO logistic regression and SVM-RFE algorithms were applied to screen out the characteristic genes. Later, Univariate and multivariate Cox regression was conducted to identify prognostic genes and further develop the Anoikis-based risk score. In addition, correlation analysis was performed to analyze the relationship between tumor microenvironment, drug sensitivity, and prognostic models. RESULTS: We established novel Anoikis-related subgroups and developed a prognostic model based on three Anoikis-related genes (MAPK1, MYC, and EDIL3). The survival and ROC analysis results showed that the prognostic model was reliable. Besides, the results of single-cell sequencing analysis suggested that the three prognostic genes were closely related to immune cell infiltration. Subsequently, aberrant expression of two prognostic genes was identified in osteosarcoma cells. Nilotinib can promote the apoptosis of osteosarcoma cells and down-regulate the expression of MAPK1. CONCLUSIONS: We developed a novel Anoikis-related risk score model, which can assist clinicians in evaluating the prognosis of osteosarcoma patients in clinical practice. Analysis of the tumor immune microenvironment and chemotherapeutic drug sensitivity can provide necessary insights into subsequent mechanisms. MAPK1 may be a valuable therapeutic target for neoadjuvant chemotherapy in osteosarcoma.

Discovery of novel macrocyclic derivatives as potent and selective cyclin-dependent kinase 2 inhibitors.

Cyclin-dependent kinase 2 (CDK2) is a member of CDK family of kinases (CDKs) that regulate the cell cycle. Its inopportune or over-activation leads to uncontrolled cell cycle progression and drives numerous types of cancers, especially ovarian, uterine, gastric cancer, as well as those associated with amplified CCNE1 gene. However, developing selective lead compound as CDK2 inhibitors remains challenging owing to similarities in the ATP pockets among different CDKs. Herein, we described the optimization of compound 1, a novel macrocyclic inhibitor targeting CDK2/5/7/9, aiming to discover more selective and metabolically stable lead compound as CDK2 inhibitor. Molecular dynamic (MD) simulations were performed for compound 1 and 9 to gain insights into the improved selectivity against CDK5. Further optimization efforts led to compound 22, exhibiting excellent CDK2 inhibitory activity, good selectivity over other CDKs and potent cellular effects. Based on these characterizations, we propose that compound 22 holds great promise as a potential lead candidate for drug development.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed. MATERIAL AND METHODS: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.5 T MRI-scan on nominal day 30 after implantation. For MRI-based seed visualization, DIXON-sequences were acquired and deep learning-based quantitative susceptibility maps (QSM) were generated from 3D-gradient-echo-sequences from 20 patients. Seed segmentations created on CT served as ground truth. For automatic seed segmentation on MRI, a 3D nnU-net model was trained using QSM and DIXON, both solely and combined. RESULTS: Of the implanted seeds 94.8 ± 2.4% were detected with deep learning automatic segmentation entrained on both QSM and DIXON data. Models trained on the individual sequence data-sets performed worse with detection rates of 87.5 ± 2.6% or 88.6 ± 7.5% for QSM and DIXON respectively. The seed centers identified on CT versus QSM and DIXON were on average 1.8 ± 1.3 mm apart. Postimplant dosimetry for evaluation of positioning inaccuracies revealed only small variations of up to 0.4 ± 4.26 Gy in D90 (dose 90% of the prostate receives) between the standard CT-approach and our MRI-only workflow. CONCLUSION: The proposed deep learning-based MRI-only workflow provided a promisingly accurate and robust seed localization and thus has the potential to compete with current state-of-the-art CT-based postimplant dosimetry in the future.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology. Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases. Results: For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Conclusion: Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising.

Revealing the Mechanisms of Byu dMar 25 in the Treatment of Alzheimer's Disease through Network Pharmacology, Molecular Docking, and In Vivo Experiment.

BACKGROUND: Alzheimer's disease (AD) is the most common neurodegenerative disease, severely reducing the cognitive level and life quality of patients. Byu dMar 25 (BM25) has been proved to have a therapeutic effect on AD. However, the pharmacological mechanism is still unclear. Therefore, this study aims to reveal the potential mechanism of BM25 affecting AD from the perspective of network pharmacology and experimental validation. METHODS: The potential active ingredients of BM25 were obtained from the TCMSP database and literature. Possible targets were predicted using SwissTargetPrediction tools. AD-related genes were identified by using GeneCards, OMIM, DisGeNET, and Drugbank databases. The candidate genes were obtained by extraction of the intersection network. Additionally, the "drug-target-disease" network was constructed by Cytoscape 3.7.2 for visualization. The PPI network was constructed by the STRING database, and the core network modules were filtered by Cytoscape 3.7.2. Enrichment analysis of GO and KEGG was carried out in the Metascape platform. Ledock software was used to dock the critical components with the core target. Furthermore, protein levels were evaluated by immunohistochemistry. RESULTS: In this study, 112 active components, 1112 disease candidate genes, 3084 GO functions, and 277 KEGG pathways were obtained. Molecular docking showed that the effective components of BM25 in treating AD were ß-asarone and hydroxysafflor yellow A. The most important targets were APP, PIK3R1, and PIK3CA. Enrichment analysis indicated that the Golgi genetic regulation, peroxidase activity regulation, phosphatidylinositol 3-kinase complex IA, 5-hydroxytryptamine receptor complexes, cancer pathways, and neuroactive ligand-receptor interactions played vital roles against AD. The rat experiment verified that BM25 affected PI3K-Akt pathway activation in AD. CONCLUSIONS: This study reveals the mechanism of BM25 in treating AD with network pharmacology, which provides a foundation for further study on the molecular mechanism of AD treatment.

Research Progress on the Effect of Autophagy and Exosomes on Liver Fibrosis.

Chronic liver disease is a known risk factor for the development of liver cancer, and the development of microRNA (miRNA) liver therapies has been hampered by the difficulty of delivering miRNA to damaged tissues. In recent years, numerous studies have shown that hepatic stellate cell (HSC) autophagy and exosomes play an important role in maintaining liver homeostasis and ameliorating liver fibrosis. In addition, the interaction between HSC autophagy and exosomes also affects the progression of liver fibrosis. In this paper, we review the research progress of mesenchymal stem cell-derived exosomes (MSC-EVs) loaded with specific miRNA and autophagy, and their related signaling pathways in liver fibrosis, which will provide a more reliable basis for the use of MSC-EVs for therapeutic delivery of miRNAs targeting the chronic liver disease.

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Background: Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. Methods: An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Results: Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). Conclusions: We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

To Explore the Molecular Mechanism of Acupuncture Alleviating Inflammation and Treating Obesity Based on Text Mining.

Objective: To explore the related mechanism of acupuncture affecting obesity by regulating inflammation using bioinformatics methods. Methods: The genes related to obesity, inflammation, and acupuncture and inflammation were mined using GenCLiP 3, and the intersecting genes were extracted using Venn diagram. The DAVID database was employed for pathway enrichment analysis and functional annotation of coexpressed genes. Then, the protein-protein interaction (PPI) network was constructed with the STRING database and visualized by the Cytoscape software and screened out important hub genes. Finally, the Boxplot and Survival Analysis of the hub genes in various cancers were performed by GEPIA. Results: 755 genes related to obesity and inflammation and 38 genes related to acupuncture and inflammation were identified, and 24 coexpressed genes related to obesity, inflammation, and acupuncture were extracted from the Venn diagram. Eight hub genes including interleukin-6 (IL-6), interleukin-10 (IL-10), Toll-like receptor 4 (TLR4), signal transduction and transcriptional activation factor 3 (STAT3), C-X-C motif chemokine 10 (CXCL10), interleukin-17A (IL-17A), prostaglandin peroxide synthesis-2 (PTGS2), signal transistors, and transcriptional activation factor 6 (STAT6) were identified by gene ontology (GO), Kyoto Encyclopedia of Genes (KEGG), and PPI network analysis. Among them, IL-6 is suggested to play an essential role in the treatment of obesity and inflammation by acupuncture, and IL-6 was significant in both Boxplot and Survival Analysis of pancreatic cancer (PAAD). Therefore, in this study, the core gene, IL-6 was used as the breakthrough point to explore the possible mechanism of acupuncture in treating obesity and pancreatic cancer by regulating IL-6. Conclusion: (1) Acupuncture can regulate the expression of IL-6 through the TLR4/nuclear factor-κB (NF-κB) pathway, thereby alleviating inflammation, which can be used as a potential strategy for the treatment of obesity. (2) IL-6/STAT3 is closely related to the occurrence, development, and metastasis of pancreatic cancer. Acupuncture affecting pancreatic cancer through TLR4/NF-κB/IL-6/STAT3 pathway may be a potential method for the treatment of pancreatic cancer.

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data.

PURPOSE: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small BM are very difficult to detect manually. With the recent development of deep learning technologies, several res earchers have reported promising results in automated brain metastasis detection. However, the detection sensitivity is still not high enough for tiny BM, and integration into clinical practice in regard to differentiating true metastases from false positives (FPs) is challenging. METHODS: The DeepMedic network with the binary cross-entropy (BCE) loss is used as our baseline method. To improve brain metastasis detection performance, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates metastasis detection sensitivity and specificity at a (sub)volume level. As sensitivity and precision are always a trade-off, either a high sensitivity or a high precision can be achieved for brain metastasis detection by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as FP metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Combining a high-sensitivity VSS loss and a high specificity loss for DeepMedic+, the majority of true positive metastases are confirmed with high specificity, while additional metastases candidates in each patient are marked with high sensitivity for detailed expert evaluation. RESULTS: Our proposed VSS loss improves the sensitivity of brain metastasis detection, increasing the sensitivity from 85.3% for DeepMedic with BCE to 97.5% for DeepMedic with VSS. Alternatively, the precision is improved from 69.1% for DeepMedic with BCE to 98.7% for DeepMedic with VSS. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the FP metastases are reduced in the high-sensitivity model and the precision reaches 99.6% for the high-specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high-sensitivity and high-specificity models, on average only 1.5 FP metastases per patient need further check, while the majority of true positive metastases are confirmed. CONCLUSIONS: Our proposed VSS loss and temporal prior improve brain metastasis detection sensitivity and precision. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice. This facilitates metastasis detection and segmentation for neuroradiologists in diagnostic and radiation oncologists in therapeutic clinical applications.

Effect of benzo[a]pyrene on proliferation and metastasis of oral squamous cell carcinoma cells: A transcriptome analysis based on RNA-seq.

Benzo[a]pyrene (BaP), a representative polycyclic aromatic hydrocarbon compound, is a carcinogen that causes head and neck cancers. Despite intensive research, the molecular mechanism of BaP in the development of oral squamous cell carcinoma (OSCC) remains largely unknown. In the present study, the SCC-9 human OSCC cell line was cultured in vitro, separated into treatment groups, and treated with dimethyl sulfoxide or BaP at various concentrations. The malignant behavior ascribed to the BaP treatment was investigated by cell proliferation, clony formation assay, and Transwell assays. Furthermore, transcriptome sequencing was performed to detect the differentially expressed genes, followed by quantitative real-time PCR to measure the expression levels of nine of these genes. Moreover, the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed the biological processes and signaling pathways in which the target genes were involved. Significant effects on SCC-9 cell proliferation, tumorigenicity, cell migration, and invasion were observed after exposure to 8 µM BaP. Additional results revealed that BaP inhibited apoptosis in a dose-dependent manner. The transcriptome sequencing results showed 137 upregulated genes and 135 downregulated genes induced by BaP, associated with tumor-related biological processes and signaling pathways, mainly including transcriptional dysregulation in cancer, the tumor necrosis factor signaling pathway, metabolism of xenobiotics by cytochrome P450, mitogen-activated protein kinase signaling pathway, and so forth. Our study demonstrates that BaP may regulate the expression of certain genes involved in tumor-associated signaling pathways, thereby promoting the proliferative, tumorigenic, and metastatic behaviors of OSCC cells while suppressing their apoptosis.

Transient Enlargement in Meningiomas Treated with Stereotactic Radiotherapy.

To investigate the occurrence of pseudoprogression/transient enlargement in meningiomas after stereotactic radiotherapy (RT) and to evaluate recently proposed volumetric RANO meningioma criteria for response assessment in the context of RT. Sixty-nine meningiomas (benign: 90%, atypical: 10%) received stereotactic RT from January 2005-May 2018. A total of 468 MRI studies were segmented longitudinally during a median follow-up of 42.3 months. Best response and local control were evaluated according to recently proposed volumetric RANO criteria. Transient enlargement was defined as volumetric increase ≥20% followed by a subsequent regression ≥20%. The mean best volumetric response was -23% change from baseline (range, -86% to +19%). According to RANO, the best volumetric response was SD in 81% (56/69), MR in 13% (9/69) and PR in 6% (4/69). Transient enlargement occurred in only 6% (4/69) post RT but would have represented 60% (3/5) of cases with progressive disease if not accounted for. Transient enlargement was characterized by a mean maximum volumetric increase of +181% (range, +24% to +389 %) with all cases occurring in the first year post-RT (range, 4.1-10.3 months). Transient enlargement was significantly more frequent with SRS or hypofractionation than with conventional fractionation (25% vs. 2%, p = 0.015). Five-year volumetric control was 97.8% if transient enlargement was recognized but 92.9% if not accounted for. Transient enlargement/pseudoprogression in the first year following SRS and hypofractionated RT represents an important differential diagnosis, especially because of the high volumetric control achieved with stereotactic RT. Meningioma enlargement during subsequent post-RT follow-up and after conventional fractionation should raise suspicion for tumor progression.

Fiducial marker recovery and detection from severely truncated data in navigation-assisted spine surgery.

PURPOSE: Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. METHODS: In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained based on simulated data. For the direct method, 6800 images and 10 000 images are generated, respectively, to train the U-Net and ResNet50. For the recovery method, the training set includes 1360 images for FBPConvNet and Pix2pixGAN. The simulated data set with 166 markers and four cadaver cases with real fiducials are used for evaluation. RESULTS: The two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1 mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5 mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2 mm. CONCLUSIONS: Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional data preparation.

Traditional machine learning for limited angle tomography.

PURPOSE: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated. METHODS: Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images. RESULTS: REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp-Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images. CONCLUSION: REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTree.

In vivo long-term investigation of tumor bearing mKate2 by an in-house fluorescence molecular imaging system.

BACKGROUND: Optical imaging is one of the most common, low-cost imaging tools used for investigating the tumor biological behavior in vivo. This study explores the feasibility and sensitivity of a near infrared fluorescent protein mKate2 for a long-term non-invasive tumor imaging in BALB/c nude mice, by using a low-power optical imaging system. METHODS: In this study, breast cancer cell line MDA-MB-435s expressing mKate2 and MDA-MB-231 expressing a dual reporter gene firefly luciferase (fLuc)-GFP were used as cell models. Tumor cells were implanted in different animal body compartments including subcutaneous, abdominal and deep tissue area and closely monitored in real-time. A simple and low-power optical imaging system was set up to image both fluorescence and bioluminescence in live animals. RESULTS: The presence of malignant tissue was further confirmed by histopathological assay. Considering its lower exposure time and no need of substrate injection, mKate2 is considered a superior choice for subcutaneous imaging compared with fLuc. On the contrary, fLuc has shown to be a better option when monitoring the tumor in a diffusive area such as abdominal cavity. Furthermore, both reporter genes have shown good stability and sensitivity for deep tissue imaging, i.e. tumor within the liver. In addition, fLuc has shown to be an excellent method for detecting tumor cells in the lung. CONCLUSIONS: The combination of mKate2 and fLuc offers a superior choice for long-term non-invasive real-time investigation of tumor biological behavior in vivo.