Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning.

BACKGROUND: Multicenter non-small cell lung cancer (NSCLC) patient data is information-rich. However, its direct integration becomes exceptionally challenging due to constraints involving different healthcare organizations and regulations. Traditional centralized machine learning methods require centralizing these sensitive medical data for training, posing risks of patient privacy leakage and data security issues. In this context, federated learning (FL) has attracted much attention as a distributed machine learning framework. It effectively addresses this contradiction by preserving data locally, conducting local model training, and aggregating model parameters. This approach enables the utilization of multicenter data with maximum benefit while ensuring privacy safeguards. Based on pre-radiotherapy planning target volume images of NSCLC patients, a multicenter treatment response prediction model is designed by FL for predicting the probability of remission of NSCLC patients. This approach ensures medical data privacy, high prediction accuracy and computing efficiency, offering valuable insights for clinical decision-making. METHODS: We retrospectively collected CT images from 245 NSCLC patients undergoing chemotherapy and radiotherapy (CRT) in four Chinese hospitals. In a simulation environment, we compared the performance of the centralized deep learning (DL) model with that of the FL model using data from two sites. Additionally, due to the unavailability of data from one hospital, we established a real-world FL model using data from three sites. Assessments were conducted using measures such as accuracy, receiver operating characteristic curve, and confusion matrices. RESULTS: The model's prediction performance obtained using FL methods outperforms that of traditional centralized learning methods. In the comparative experiment, the DL model achieves an AUC of 0.718/0.695, while the FL model demonstrates an AUC of 0.725/0.689, with real-world FL model achieving an AUC of 0.698/0.672. CONCLUSIONS: We demonstrate that the performance of a FL predictive model, developed by combining convolutional neural networks (CNNs) with data from multiple medical centers, is comparable to that of a traditional DL model obtained through centralized training. It can efficiently predict CRT treatment response in NSCLC patients while preserving privacy.

Iterative Reconstruction of Micro Computed Tomography Scans Using Multiple Heterogeneous GPUs.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

A simple and precise alignment calibration method for cone-beam computed tomography with the verifications.

Cone-beam Computed Tomography (CBCT) is widely used in dental imaging, small animal imaging, radiotherapy, and non-destructive industrial inspection. The quality of CBCT images depends on the precise knowledge of the CBCT system's alignment. We introduce a distinct procedure, "precision alignment loop (PAL)", to calibrate any CBCT system with a circular trajectory. We describe the calibration procedure by using a line-beads phantom, and how PAL determines the misalignments from a CBCT system. PAL also yields the uncertainties in the simulated calibration to give an estimate of the errors in the misalignments. From the analytical simulations, PAL can precisely obtain the source-to-rotation axis distance (SRD), and the geometric center G, "the point in z-axis meets the detector", where the z-axis is coincident with the line from the X-ray source that intersects the axis of the rotation (AOR) orthogonally. The uncertainties of three misalignment angles of the detector are within ±0.05°, which is close to ±0.04° for the results of Yang et al. [18], but our method is easy and simple to implement. Our distinct procedure, on the other hand, yields the calibration of a micro-CT system and an example of reconstructed images, showing our calibration method for the CBCT system to be simple, precise, and accurate.

Cone-beam x-ray luminescence computed tomography (CB-XLCT) prototype development and performance evaluation.

This study developed a prototype for a rotational cone-beam x-ray luminescence computed tomography (CB-XLCT) system, considering its potential application in pre-clinical theranostic imaging. A geometric calibration method applicable to both imaging chains (XL and CT) was also developed to enhance image quality. The results of systematic performance evaluations were presented to assess the feasibility of commercializing XLCT technology. Monte Carlo GATE simulation was performed to determine the optimal imaging conditions for nanophosphor particles (NPs) irradiated by 70 kV x-rays. We acquired a low-dose transmission x-ray tube and designed a prone positioning platform and a rotating gantry, using mice as targets from commercial small animalµ-CT systems. We then employed the image cross-correlation (ICC) automatic geometric calibration method to calibrate XL and CT images. The performance of the system was evaluated through a series of phantom experiments with a linearity of 0.99, and the contrast-to-noise ratio (CNR) between hydroxyl-apatite (HA) and based epoxy resin is 19.5. The XL images of the CB-XLCT prototype achieved a Dice similarity coefficient (DICE) of 0.149 for a distance of 1 mm between the two light sources. Finally, the final XLCT imaging results were demonstrated using the Letter phantoms with NPs. In summary, the CB-XLCT prototype developed in this study showed the potential to achieve high-quality imaging with acceptable radiation doses for small animals. The performance of CT images was comparable to current commercial machines, while the XL images exhibited promising results in phantom imaging, but further efforts are needed for biomedical applications.

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.

BACKGROUND: Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low signal-to-noise ratio. Deep learning (DL) techniques such as generative adversarial networks (GANs) and convolutional neural network (CNN) have the capability to enhance the quality of images derived from noisy or low-quality PET data, which encodes critical information about radioactivity distribution in the body. PURPOSE: Our objective was to optimize the image quality and reduce noise in preclinical PET images by utilizing the sinogram domain as input for DL models, resulting in improved image quality as compared to LD-PET images. METHODS: A GAN and CNN model were utilized to predict high-dose (HD) preclinical PET sinograms from the corresponding LD preclinical PET sinograms. In order to generate the datasets, experiments were conducted on micro-phantoms, animal subjects (rats), and virtual simulations. The quality of DL-generated images was weighted by performing the following quantitative measures: structural similarity index measure (SSIM), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Additionally, DL input and output were both subjected to a spatial resolution calculation of full width half maximum (FWHM) and full width tenth maximum (FWTM). DL outcomes were then compared with the conventional denoising algorithms such as non-local means (NLM), block-matching, and 3D filtering (BM3D). RESULTS: The DL models effectively learned image features and produced high-quality images, as reflected in the quantitative metrics. Notably, the FWHM and FWTM values of DL PET images exhibited significantly improved accuracy compared to LD, NLM, and BM3D PET images, and just as precise as HD PET images. The MSE loss underscored the excellent performance of the models, indicating that the models performed well. To further improve the training, the generator loss (G loss) was increased to a value higher than the discriminator loss (D loss), thereby achieving convergence in the GAN model. CONCLUSIONS: The sinograms generated by the GAN network closely resembled real HD preclinical PET sinograms and were more realistic than LD. There was a noticeable improvement in image quality and noise factor in the predicted HD images. Importantly, DL networks did not fully compromise the spatial resolution of the images.

Whole slide imaging-based deep learning to predict the treatment response of patients with non-small cell lung cancer.

Background: This study developed and validated a deep learning (DL) model based on whole slide imaging (WSI) for predicting the treatment response to chemotherapy and radiotherapy (CRT) among patients with non-small cell lung cancer (NSCLC). Methods: We collected the WSI of 120 nonsurgical patients with NSCLC treated with CRT from three hospitals in China. Based on the processed WSI, two DL models were established: a tissue classification model which was used to select tumor-tiles, and another model which predicted the treatment response of the patients based on the tumor-tiles (predicting the treatment response of each tile). A voting method was employed, by which the label of tiles with the greatest quantity from 1 patient would be used as the label of the patient. Results: The tissue classification model had a great performance (accuracy in the training set/internal validation set =0.966/0.956). Based on 181,875 tumor-tiles selected by the tissue classification model, the model for predicting the treatment response demonstrated strong predictive ability (accuracy of patient-level prediction in the internal validation set/external validation set 1/external validation set 2 =0.786/0.742/0.737). Conclusions: A DL model was constructed based on WSI to predict the treatment response of patients with NSCLC. This model can help doctors to formulate personalized CRT plans and improve treatment outcomes.

Effects of food hardness on temporomandibular joint osteoarthritis: Qualitative and quantitative micro-CT analysis of rats in vivo.

BACKGROUND: Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative joint disease in which quantitative analysis based on magnetic resonance image (MRI) or cone-beam computed tomography (CBCT) remains limited. Moreover, the long-term effects of soft food on the adaptive condylar remodeling process in TMJ-OA remain unclear. This study aimed to assess the effects of food hardness on adaptive condylar remodeling in a healthy TMJ, TMJ-OA, and controlled TMJ-OA. METHODS: Complete Freund's adjuvant (CFA) was used for TMJ-OA induction and Link-N (LN) for TMJ repair. Eighteen mature rats were randomly divided into six groups: (1) control/normal diet (Ctrl-N); (2) control/soft diet (Ctrl-S); (3) TMJ-OA/normal diet (CFA-N); (4) TMJ-OA/soft diet (CFA-S); (5) Link-N-controlled TMJ-OA/normal diet (LN-N); and (6) Link-N-controlled TMJ-OA/soft diet (LN-S). Micro-CT was performed 14, 21, and 28 days after CFA injection to analyze the bone volume, bone volume fraction (BVF), bone mineral density (BMD), and trabecular bone number and thickness (Tb.N, Tb.Th). MRI and histological imaging were performed to support the analysis. RESULTS: Under CFA treatment, the BVF and BMD decreased significantly (p < 0.01) and later recovered to normal. However, more significant improvements occurred in normal-diet groups than soft-diet groups. Additionally, bone volume changes were more predictable in the normal-diet groups than in the soft-diet groups. The normal-diet groups presented a significant decrease and increase in the Tb.N and Tb.Th, respectively (p < 0.05), while the Tb.N and Tb.Th in the soft-diet groups remained largely unchanged. Furthermore, a significantly higher frequency of irregularities on the condylar articular surface was found in the soft-diet groups. CONCLUSIONS: Compared with a soft diet, a normal diet may be beneficial for preserving condyle articular surface and directing bone remodeling in TMJ-OA rats.

Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data.

Reducing the radiation dose will cause severe image noise and artifacts, and degradation of image quality will also affect the accuracy of diagnosis. To find a solution, we comprise a 2D and 3D concatenating convolutional encoder-decoder (CCE-3D) and the structural sensitive loss (SSL), via transfer learning (TL) denoising in the projection domain for low-dose computed tomography (LDCT), radiography, and tomosynthesis. The simulation and real-world practicing results show that many of the figures-of-merit (FOMs) increase in both projections (2-3 times) and CT imaging (1.5-2 times). From the PSNR and structural similarity index of measurement (SSIM), the CCE-3D model is effective in denoising but keeps the shape of the structure. Hence, we have developed a denoising model that can be served as a promising tool to be implemented in the next generation of x-ray radiography, tomosynthesis, and LDCT systems.

Heat shock protein 90 inhibitor 17-AAG down-regulates thymidine phosphorylase expression and potentiates the cytotoxic effect of tamoxifen and erlotinib in human lung squamous carcinoma cells.

Elevated thymidine phosphorylase (TP) levels, a key enzyme in the pyrimidine nucleoside salvage pathway, in cancer cells, are related to a poor prognosis in a variety of cancers. Heat shock protein 90 (Hsp90) is a ubiquitous molecular chaperone that is involved in the stabilization and maturation of many oncogenic proteins. The aim of this study is to elucidate whether Hsp90 inhibitor 17-AAG could enhance tamoxifen- and erlotinib-induced cytotoxicity in nonsmall cell lung cancer (NSCLC) cells via modulating TP expression in two squamous NSCLC cell lines, H520 and H1703. We found that 17-AAG reduced TP expression via inactivating the MKK1/2-ERK1/2-mitogen-activated protein kinase (MAPK) pathway. TP knockdown with siRNA or ERK1/2 MAPK inactivation with the pharmacological inhibitor U0126 could enhance the cytotoxic and growth inhibitory effects of 17-AAG. In contrast, MKK1-CA or MKK2-CA (a constitutively active form of MKK1/2) vector-enforced expression could reduce the cytotoxic and cell growth inhibitory effects of 17-AAG. Furthermore, 17-AAG enhanced the cytotoxic and cell growth inhibitory effects of tamoxifen and erlotinib in NSCLC cells, which were associated with TP expression downregulation and MKK1/2-ERK1/2 signal inactivation. Taken together, Hsp90 inhibition downregulates TP, enhancing the tamoxifen- and erlotinib-induced cytotoxicity in H520 and H1703 cells.

Nitroglycerin-induced downregulation of AKT- and ERK1/2-mediated radiation-sensitive 52 expression to enhance pemetrexed-induced cytotoxicity in human lung cancer cells.

Nitroglycerin (NTG)-a nitric oxide-donating drug-is traditionally administered via the sublingual route to treat acute myocardial angina attacks. NTG also increases tumor blood flow and, consequently, cancer drug delivery to tumor cells. In the homologous recombination pathway, radiation-sensitive 52 (Rad52) plays a crucial role in DNA repair by promoting the annealing of complementary single-stranded DNA and stimulating radiation-sensitive 51 (Rad51) recombinase activity. Pemetrexed-a multitargeted antifolate agent-exhibits satisfactory clinical activity in wild-type nonsquamous non-small-cell lung cancer (NSCLC) cells. However, the synergistic activity of combination therapy with NTG and pemetrexed against NSCLC cells has not yet been clarified. In 2 NSCLC cell lines (i.e. lung squamous cell carcinoma H520 and lung adenocarcinoma H1975 cells), NTG reduced Rad52 expression; in addition, decreased phospho-AKT and phospho-ERK1/2 protein levels were observed. Enhancement of AKT or ERK1/2 activity through transfection with a constitutively active AKT (AKT-CA) vector or constitutively active mitogen-activated protein kinase kinase 1 (MKK1-CA) vector increased the Rad52 protein level and cell survival, which were suppressed by NTG. The knockdown of Rad52 expression by using small interfering RNA or by inhibiting AKT and ERK1/2 activity enhanced the cytotoxicity and cell growth inhibition induced by NTG. Moreover, NTG synergistically enhanced the cytotoxicity and cell growth inhibition induced by pemetrexed in NSCLC cells; these effects were associated with AKT and ERK1/2 inactivation and, consequently, Rad52 downregulation in H520 and H1975 cells. The results provide a rationale for combining NTG and pemetrexed in lung cancer treatment to improve lung cancer control.

Three-dimensional semiquantitative analysis of gastric emptying SPECT.

OBJECTIVE: We used dynamic single-photon emission computed tomography (D-SPECT) to overcome the interference of the planar dynamic imaging due to the overlap of internal organs, thus more accurate physiological function can be obtained. METHODS: 3D printed gastric phantom was used to simulate gastric emptying (GE). First, the planar dynamic liquid GE procedure was used and served as the reference value; second, D-SPECT followed by repeated liquid GE procedures with three gamma cameras were used. The emptying flow rate of the gastric phantom simulated three flow rates of liquid, semisolid and solid. Third, we simulated the intestinal activity that interfered with the residual value obtained by 2D dynamic imaging, which was compared with D-SPECT. Then, we brought the 3D VOI data into the postprocessing program to obtain the residual activity curve and residual percentage. RESULTS: The residual amount obtained in the phantom at 60th minutes in the first stage is 14.57%; the residual amount of liquid emptying are Siemens: 3.33%, GE: 15.06%, PHILIPS: 1.12%; residual amount for semisolid are Siemens: 47.36%, GE: 54.25%, PHILIPS: 51.57%; residual amount for solids are Siemens: 63.98%, GE: 66.88%, PHILIPS: 63.76%. All values are within the normal range. Then, we simulated the intestinal activity that interfered with the residual value obtained by 2D dynamic imaging: 75-90 min: 10.42, 19.48, 19.51 and 11.02%; however, the residual values obtained with 3D SPECT VOI data: 75-90 min: 1.42, 1.41, 1.35 and 1.02%. These results show that the emptying data errors caused by intestinal overlap can be effectively corrected (P = 0.017). CONCLUSION: D-SPECT imaging can overcome the interference in the semiquantitative data of residual GE caused in 2D mode.

Downregulation of p38 MAPK Activation and Radiation-Sensitive 52 Expression Enhances 5-Fluorouracil and Erlotinib-Induced Cytotoxicity in Human Lung Squamous Cells.

INTRODUCTION: 5-Fluorouracil (5-FU) is used to treat various cancers, including non-small-cell lung cancer (NSCLC). It inhibits nucleotide synthesis and induces single- and double-strand DNA breaks. In the homologous recombination pathway, radiation-sensitive 52 (Rad52) plays a crucial role in DNA repair by promoting the annealing of complementary single-stranded DNA and stimulating Rad51 recombinase activity. Erlotinib (Tarceva) is a selective epidermal growth factor receptor tyrosine kinase inhibitor with clinical activity against NSCLC cells. However, whether the combination of 5-FU and erlotinib has synergistic activity against NSCLC cells is unknown. METHODS: After the 5-FU and/or erlotinib treatment, the expressions of Rad52 mRNA were determined by quantitative real-time polymerase chain reaction analysis. Protein levels of Rad52 and phospho-p38 MAPK were determined by Western blot analysis. We used specific Rad52 or p38 MAPK small interfering RNA and p38 MAPK inhibitor (SB2023580) to examine the role of p38 MAPK-Rad52 signal in regulating the chemosensitivity of 5-FU and/or erlotinib. Cell viability was assessed by MTS assay and trypan blue exclusion assay. RESULTS: In 2 squamous cell carcinoma cell lines, namely, H520 and H1703, 5-FU reduced Rad52 expression in a p38 MAPK inactivation-dependent manner. Enhancement of p38 MAPK activity by transfection with MKK6E (a constitutively active form of MKK6) vector increased the Rad52 protein level and cell survival by 5-FU. However, in human lung bronchioloalveolar cell adenocarcinoma A549 cells, 5-FU reduced Rad52 expression and induced cytotoxicity independent of p38 MAPK. Moreover, 5-FU synergistically enhanced the cytotoxicity and cell growth inhibition of erlotinib in NSCLC cells; these effects were associated with Rad52 downregulation and p38 MAPK inactivation in H520 and H1703 cells. CONCLUSION: The results provide a rationale for combining 5-FU and erlotinib in lung cancer treatment.

Brachytherapy Approach Using 177Lu Conjugated Gold Nanostars and Evaluation of Biodistribution, Tumor Retention, Dosimetry and Therapeutic Efficacy in Head and Neck Tumor Model.

Brachytherapy can provide sufficient doses to head and neck squamous cell carcinoma (HNSCC) with minimal damage to nearby normal tissues. In this study, the ß--emitter 177Lu was conjugated to DTPA-polyethylene glycol (PEG) decorated gold nanostars (177Lu-DTPA-pAuNS) used in surface-enhanced Raman scattering and photothermal therapy (PTT). The accumulation and therapeutic efficacy of 177Lu-DTPA-pAuNS were compared with those of 177Lu-DTPA on an orthotopic HNSCC tumor model. The SPECT/CT imaging and biodistribution studies showed that 177Lu-DTPA-pAuNS can be accumulated in the tumor up to 15 days, but 177Lu-DTPA could not be detected at 24 h after injection. The tumor viability and growth were suppressed by injected 177Lu-DTPA-pAuNS but not nonconjugated 177Lu-DTPA, as evaluated by bioluminescent imaging. The radiation-absorbed dose of the normal organ was the highest in the liver (0.33 mSv/MBq) estimated in a 73 kg adult, but that of tumorsphere (0.5 g) was 3.55 mGy/MBq, while intravenous injection of 177Lu-DTPA-pAuNS resulted in 1.97 mSv/MBq and 0.13 mGy/MBq for liver and tumorsphere, respectively. We also observed further enhancement of tumor-suppressive effects by a combination of 177Lu-DTPA-pAuNS and PTT compared to 177Lu-DTPA-pAuNS alone. In conclusion, 177Lu-DTPA-pAuNS may be considered as a potential radiopharmaceutical agent for HNSCC brachytherapy.

Technical Note: Performance evaluation of a small-animal PET/CT system based on NEMA NU 4-2008 standards.

PURPOSE: The MetisTM PET/CT is a self-developed, silicon photomultiplier (SiPM) detector-based, rodent PET/CT system. The objective of this study was to evaluate the performance of the system using the National Electrical Manufacturers Association (NEMA) NU 4-2008 standard protocol. METHODS: Energy resolution, spatial resolution, sensitivity, scatter fraction (SF), noise-equivalent count rate (NECR), and image quality (IQ) characteristics were measured. A micro Derenzo phantom experiment was performed to evaluate the spatial resolution using three-dimensional ordered-subsets expectation maximization (3D-OSEM) and maximum likelihood expectation maximization (MLEM) reconstructed images. In addition, the CT imaging agent Ioverol 350 was mixed with fluorine-18 (18 F)-fluorodeoxyglucose (FDG) and then injected into the micro Derenzo phantom to evaluate the PET/CT imaging. In vivo PET/CT imaging studies were also conducted in a healthy mouse and rat using 18 F-FDG. RESULTS: The mean energy resolution of the system was 15.3%. The tangential resolution was 0.82 mm full-width half-maximum (FWHM) at the center of the field of the view (FOV), and the radial and axial resolution were generally lower than 2.0 mm FWHM. The spatial resolution was significantly improved when using 3D-OSEM, especially the axial FWHM could be improved by up to about 57%. The system absolute sensitivity was 7.7% and 6.8% for an energy window of 200-750 and 350-750 keV respectively. The scatter fraction was 8.2% and 12.1% for the mouse- and rat-like phantom respectively. The peak NECR was 1343.72 kcps at 69 MBq and 640.32 kcps at 53 MBq for the mouse- and rat-like phantom respectively. The 1-mm fillable rod in the IQ phantom can be clearly observed. We can identify the 0.6-mm aperture of the micro Derenzo phantom image clearly using 3D-OSEM (10 subsets, 5 iterations). We also performed the fusion of the PET and CT images of the mouse and the brain imaging of the rat. CONCLUSIONS: The results show that the system has the characteristics of high-resolution, high-sensitivity, and excellent IQ and is suitable for rodent imaging-based research.

Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.

The purpose of this study was to develop Bi-parametric Magnetic Resonance Imaging (BP-MRI) based radiomics models for differentiation between benign and malignant prostate lesions, and to cross-vendor validate the generalization ability of the models. The prebiopsy BP-MRI data (T2-Weighted Image [T2WI] and the Apparent Diffusion Coefficient [ADC]) of 459 patients with clinical suspicion of prostate cancer were acquired using two scanners from different vendors. The prostate biopsies are the reference standard for diagnosing benign and malignant prostate lesions. The training set was 168 patients' data from Siemens (Vendor 1), and the inner test set was 70 patients' data from the same vendor. The external test set was 221 patients' data from GE (Vendor 2). The lesion Region of Interest (ROI) was manually delineated by experienced radiologists. A total of 851 radiomics features including shape, first-order statistical, texture, and wavelet features were extracted from ROI in T2WI and ADC, respectively. Two feature-ranking methods (Minimum Redundancy Maximum Relevance [MRMR] and Wilcoxon Rank-Sum Test [WRST]) and three classifiers (Random Forest [RF], Support Vector Machine [SVM], and the Least Absolute Shrinkage and Selection Operator [LASSO] regression) were investigated for their efficacy in building single-parametric radiomics signatures. A biparametric radiomics model was built by combining the optimal single-parametric radiomics signatures. A comprehensive diagnosis model was built by combining the biparametric radiomics model with age and Prostate Specific Antigen (PSA) value using multivariable logistic regression. All models were built in the training set and independently validated in the inner and external test sets, and the performances of models in the diagnosis of benign and malignant prostate lesions were quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). The mean AUCs of the inner and external test sets were calculated for each model. The non-inferiority test was used to test if the AUC of model in external test was not inferior to the AUC of model in inner test. Combining MRMR and LASSO produced the best-performing single-parametric radiomics signatures with the highest mean AUC of 0.673 for T2WI (inner test AUC = 0.729 vs. external test AUC = 0.616, p = 0.569) and the highest mean AUC of 0.810 for ADC (inner test AUC = 0.822 vs. external test AUC = 0.797, p = 0.102). The biparametric radiomics model produced a mean AUC of 0.833 (inner test AUC = 0.867 vs. external test AUC = 0.798, p = 0.051). The comprehensive diagnosis model had an improved mean AUC of 0.911 (inner test AUC = 0.935 vs. external test AUC = 0.886, p = 0.010). The comprehensive diagnosis model for differentiating benign from malignant prostate lesions was accurate and generalizable.

Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients.

Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, "Cycle-Deblur GAN", combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.

Comparison of Calcium Scoring Between 64-Multidetector Computed Tomography and 320-Multidetector Computed Tomography Using a Cardiac Phantom: Achieving Consistent Image Quality With Dose Optimization.

ABSTRACT: The purpose of this study was to evaluate the relationship between radiation dose and noise level on various coronary calcium scoring protocols between 64-multidetector computed tomography (MDCT) and 320-MDCT. The cardiac QRM phantoms (1 small size and 1 medium size) were used in this study. Lower-dose imaging protocols were proposed for optimization with the parameters of 120 kVp and 10 mAs for small-size phantom (0.336 mSv) in 64-MDCT imaging and small-size phantom (0.2 mSv) in 320-MDCT case, and 120 kVp and 80 mAs for medium-size phantom (2.73 mSv) in 64-MDCT imaging and medium-size phantom (1.58 mSv) in 320-MDCT case. Our results suggest that people can apply lower-dose protocols in the clinical use for early diagnosis of coronary disease without sacrificing diagnostic accuracy.

Downregulation of Xeroderma Pigmentosum Complementation Group C Expression by 17-Allylamino-17-Demethoxygeldanamycin Enhances Bevacizumab-Induced Cytotoxicity in Human Lung Cancer Cells.

INTRODUCTION: Xeroderma pigmentosum complementation group C (XPC) protein is an important DNA damage recognition factor involved in nucleotide excision repair and regulation of non-small-cell lung cancer (NSCLC) cell proliferation and viability. 17-Allylamino-17-demethoxygeldanamycin (17-AAG) blocks ATP binding to heat shock protein 90 (Hsp90), resulting in destabilization of Hsp90-client protein complexes. Vascular endothelial growth factor (VEGF) is a potent angiogenic growth factor expressed by many types of tumors. Bevacizumab (Avastin) is a humanized monoclonal antibody against human VEGF used as an antiangiogenesis agent in the therapy of many cancers, proving successful in increasing objective tumor response rate and prolonging overall survival in NSCLC patients. METHODS: After the bevacizumab and/or 17-AAG treatment, the expressions of XPC mRNA were determined by quantitative real-time PCR analysis. Protein levels of XPC and phospho-AKT were determined by Western blot analysis. We used specific XPC small interfering RNA and PI3K inhibitor (LY294002) to examine the role of the AKT-XPC signal in regulating the chemosensitivity of bevacizumab and 17-AAG. Cell viability was assessed by the MTS assay and trypan blue exclusion assay. RESULTS: In this study, bevacizumab decreased XPC expression in human lung squamous cell carcinoma H520 and H1703 cells via AKT inactivation. Enhancement of AKT activity by transfection with constitutively active AKT vectors increased XPC expression and cell survival after treatment with bevacizumab. In addition, 17-AAG synergistically enhanced bevacizumab-induced cytotoxicity and cell growth inhibition in H520 and H1703 cells, associated with downregulation of XPC expression and inactivation of AKT. DISCUSSION/CONCLUSION: Together, these results may provide a rationale to combine bevacizumab with Hsp90 inhibitors in future to enhance therapeutic effects for lung cancer.

Nitroglycerin Enhances Cisplatin-Induced Cytotoxicity via AKT Inactivation and Thymidylate Synthase Downregulation in Human Lung Cancer Cells.

Nitroglycerin (NTG), a nitric oxide-donating drug, may increase tumor blood flow and consequently increase cancer drug delivery to tumor cells. Thymidylate synthase (TS) is an essential enzyme for the de novo synthesis of deoxythymidine monophosphate; we had found that knocking down the expression of TS sensitizes lung cancer cells to cisplatin-induced cytotoxicity. However, whether NTG and cisplatin could induce synergistic cytotoxicity in nonsmall cell lung cancer (NSCLC) cells through modulating TS expression is unknown. In this study, NTG decreased TS expression in an AKT, also known as Protein kinase B (PKB) inactivation dependent manner in human lung adenocarcinoma A549 and squamous cell carcinoma H1703 cells. Enhancement of AKT activity by transfection with constitutive active AKT vectors increased the TS expression level as well as the cell survival pretreated by NTG. Moreover, NTG synergistically enhanced cytotoxicity and cell growth inhibition by cisplatin treatment in NSCLC cells, which were associated with downregulation of TS expression and inactivation of AKT in A549 and H1703 cells. Together, these results may provide a rationale to combine NTG with cisplatin-based chemotherapy to enhance the therapeutic effect for lung cancer in the future.

Capsaicin enhances erlotinib-induced cytotoxicity via AKT inactivation and excision repair cross-complementary 1 (ERCC1) down-regulation in human lung cancer cells.

Capsaicin, a natural active ingredient of green and red peppers, has been demonstrated to exhibit anti-cancer properties in several malignant cell lines. Excision repair cross-complementary 1 (ERCC1) has a leading role in the nucleotide excision repair (NER) process because of its involvement in the excision of DNA adducts. Erlotinib (TarcevaR) is a selective epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that has demonstrated clinical activity in non-small cell lung cancer (NSCLC) cells. However, whether capsaicin and erlotinib could induce synergistic cytotoxicity in NSCLC cells through modulating ERCC1 expression is unknown. In this study, capsaicin decreased the ERCC1 expression in an AKT inactivation dependent manner in two human lung adenocarcinoma cells, namely, A549 and H1975. Enhancement of AKT activity by transfection with constitutive active AKT vectors increased the ERCC1 protein level as well as the cell survival by capsaicin. Moreover, capsaicin synergistically enhanced the cytotoxicity and cell growth inhibition of erlotinib in NSCLC cells, which were associated with the down-regulation of ERCC1 expression and inactivation of AKT in A549 and H1975 cells. Together, these results may provide a rationale to combine capsaicin with erlotinib for lung cancer treatment.