Direct Dose Prediction With Deep Learning for Postoperative Cervical Cancer Underwent Volumetric Modulated Arc Therapy.

PURPOSE: To predict the voxel-based dose distribution for postoperative cervical cancer patients underwent volumetric modulated arc therapy using deep learning models. METHOD: A total of 254 patients with cervical cancer received volumetric modulated arc therapy in authors' hospital from January 2018 to September 2021 were enrolled in this retrospective study. Two deep learning networks (3D deep residual neural network and 3DUnet) were adapted to train (203 cases) and test (51 cases) the feasibility and effectiveness of the prediction method. The performance of deep learning models was evaluated by comparing the results with those of treatment planning system according to metrics of dose-volume histogram of target volumes and organs at risk. RESULTS: The dose distributions predicted by deep learning models were clinically acceptable. The automatic dose prediction time was around 5 to 10 min, which was about one-eighth to one-tenth of the manual optimization time. The maximum dose difference was observed in D98 of rectum with a | δD| of 5.00 ± 3.40% and 4.88 ± 3.99% for Unet3D and ResUnet3D, respectively. The minimum difference was observed in the D2 of clinical target volume with a |δD| of 0.53 ± 0.45% and 0.83 ± 0.45% for ResUnet3D and Unet3D, respectively. CONCLUSION: The 2 deep learning models adapted in the study showed the feasibility and reasonable accuracy in the voxel-based dose prediction for postoperative cervical cancer underwent volumetric modulated arc therapy. Automatic dose distribution prediction of volumetric modulated arc therapy with deep learning models is of clinical significance for the postoperative management of patients with cervical cancer.

RefineNet-based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy.

PURPOSE: An accurate and reliable target volume delineation is critical for the safe and successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic segmentation models based on RefineNet for clinical target volume (CTV) and organs at risk (OARs) for postoperative cervical cancer based on computed tomography (CT) images. METHODS: A 2D RefineNet and 3D RefineNetPlus3D were adapted and built to automatically segment CTVs and OARs on a total of 44 222 CT slices of 313 patients with stage I-III cervical cancer. Fully convolutional networks (FCNs), U-Net, context encoder network (CE-Net), UNet3D, and ResUNet3D were also trained and tested with randomly divided training and validation sets, respectively. The performances of these automatic segmentation models were evaluated by Dice similarity coefficient (DSC), Jaccard similarity coefficient, and average symmetric surface distance when comparing them with manual segmentations with the test data. RESULTS: The DSC for RefineNet, FCN, U-Net, CE-Net, UNet3D, ResUNet3D, and RefineNet3D were 0.82, 0.80, 0.82, 0.81, 0.80, 0.81, and 0.82 with a mean contouring time of 3.2, 3.4, 8.2, 3.9, 9.8, 11.4, and 6.4 s, respectively. The generated RefineNetPlus3D demonstrated a good performance in the automatic segmentation of bladder, small intestine, rectum, right and left femoral heads with a DSC of 0.97, 0.95, 091, 0.98, and 0.98, respectively, with a mean computation time of 6.6 s. CONCLUSIONS: The newly adapted RefineNet and developed RefineNetPlus3D were promising automatic segmentation models with accurate and clinically acceptable CTV and OARs for cervical cancer patients in postoperative radiotherapy.

Efficient capture of radioactive iodine by ZIF-8 derived porous carbon.

Due to the rapid diffusion of radioactive iodine, the demand for safe and efficient capture and storage of radioactive iodine is increasing worldwide. The use of porous carbon materials to capture iodine has aroused great interest. This work prepared porous carbon materials derived from polymetallic oxides of the zeolitic imidazolate framework (ZIF) by pyrolysis at 1000 °C. The carbon materials (CZIF-1000) have a high specific surface area of about 1110 m2/g and a total pore volume of 0.92 cm3/g. Adsorption studies have shown that the CZIF-1000 had significant adsorption performance for iodine, and the adsorption capacity can reach 790.8 mg/g at 8h. The potential mechanism of adsorption is that the carbonization causes the charge-transfer interaction and pore size distribution. Compared with the conventional adsorbents, the adsorbents showed faster kinetics and high extraction capacity for iodine. This experiment provides an effective method for designing a highly efficient adsorbent for iodine and broadens the ideas for developing new iodine extraction adsorbents.

Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images.

OBJECTIVES: It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM). The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with BM based on radiomics from brain contrast-enhanced computer tomography (CECT) images. METHODS: A total of 144 BM patients (94 male, 50 female) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Radiomics features from manually contoured tumors were extracted using python. Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex. RESULTS: Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under the curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age. CONCLUSIONS: Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC. KEY POINTS: • It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM) to define the prognosis and treatment. • Few studies had investigated the feasibility and accuracy of differentiating the pathological subtypes of primary non-small-cell lung cancer between adenocarcinoma (AD) and squamous cell carcinoma (SCC) for patients with BM based on radiomics from brain contrast-enhanced CT (CECT) images, although CECT images are often the initial imaging modality to screen for metastases and are recommended on equal footing with MRI for the detection of cerebral metastases. • Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC with a highest area under the curve (AUC) of 0.828 and an accuracy of 0.758, respectively.

Computer Tomography Radiomics-Based Nomogram in the Survival Prediction for Brain Metastases From Non-Small Cell Lung Cancer Underwent Whole Brain Radiotherapy.

Prognostic parameters and models were believed to be helpful in improving the treatment outcome for patients with brain metastasis (BM). The purpose of this study was to investigate the feasibility of computer tomography (CT) radiomics based nomogram to predict the survival of patients with BM from non-small cell lung cancer (NSCLC) treated with whole brain radiotherapy (WBRT). A total of 195 patients with BM from NSCLC who underwent WBRT from January 2012 to December 2016 were retrospectively reviewed. Radiomics features were extracted and selected from pretherapeutic CT images with least absolute shrinkage and selection operator (LASSO) regression. A nomogram was developed and evaluated by integrating radiomics features and clinical factors to predict the survival of individual patient. Five radiomics features were screened out from 105 radiomics features according to the LASSO Cox regression. According to the optimal cutoff value of radiomics score (Rad-score), patients were stratified into low-risk (Rad-score <= -0.14) and high-risk (Rad-score > -0.14) groups. Multivariable analysis indicated that sex, karnofsky performance score (KPS) and Rad-score were independent predictors for overall survival (OS). The concordance index (C-index) of the nomogram in the training cohort and validation cohort was 0.726 and 0.660, respectively. An area under curve (AUC) of 0.786 and 0.788 was achieved for the short-term and long-term survival prediction, respectively. In conclusion, the nomogram based on radiomics features from CT images and clinical factors was feasible to predict the OS of BM patients from NSCLC who underwent WBRT.

Superhydrophobic and superaerophilic hierarchical Pt@MIL-101/PVDF composite for hydrogen water isotope exchange reactions.

A hierarchical porous composite of Pt@MIL-101/ployvinylidene fluoride (Pt@MIL-101/PVDF) was successfully prepared through a solution-processed method. This composite possesses advanced superhydrophobic and superaerophilic performance which makes it a promising catalyst facilitating liquid phase catalytic exchange techniques (LPCE) in hydrogen-water isotope exchange process. Its superhydrophobic property results in the repellence of water drops from flooding the catalytic surface with a relatively large contact angle in the exchange reaction, and its superaerophilic surface broke hydrogen bubbles into thin film so as to reach higher catalytic reactive efficiency. High reactivity and long-term stability in the reaction process can also be achieved by the configuration of mesoporous cages of MIL-101 confining Pt nanoparticles and preventing them from sintering.

First-principles study of the effect of dopants (Pd, Ni) on the formation and desorption of T2O from a Li2TiO3 (001) surface.

We investigated the effect of Pd and Ni dopants on the formation and desorption of tritiated water (T2O) molecules from the Li2TiO3 (001) surface using first-principles calculations coupled with the climbing-image nudged elastic band method. We calculated the energy barriers for T2O production and desorption on the pure Li2TiO3 surface to be 0.94 and 0.64 eV, respectively. The Pd and Ni dopants enhanced T2O formation by reducing the formation energy of O vacancies, and T2O generated spontaneously on the dopant surface. Moreover, we found that dopant atoms affect the charge transfer of neighboring atoms, which leads to orbital hybridization and the generation of a chemical bond between the O and T on the doped Li2TiO3 surface. In addition, desorption of T2O from the doped Li2TiO3 surface requires a relatively low energy (<0.50 eV). This theoretical study suggests that doping the Li2TiO3 surface with metal atoms is an effective strategy for producing T2O molecules and is beneficial to T release.

[Hydrogen isotopes analysis by gas chromatography using metal-organic framework CPL-1 packed column].

CPL-1 is a metal organic framework (MOF) with potential application as stationary phase material in gas chromatographic (GC) analysis, due to its large specific surface area, uniform pore size and good quantum sieving effect on hydrogen isotopes at low temperatures. Herein, a microporous column packed with CPL-1 was used at cryogenic temperature (77 K), and the column was 1.0 mm in inner diameter, 0.5 m in length. Single crystals of Al2O3 was used to build flow path for chromatographic carrier gas. The results showed that the adsorption of H2 and D2 with CPL-1 was 4 mmol/g at 77 K, which was better than MnCl2/γ -Al2O3 and γ -Al2O3. With the injection volume increasing from 0.25 mL to 2 mL, the results showed good linear relationship, and the relative error was less than 4%. The results indicated that the column packed with CPL-1 has wide linear range, good repeatability and high accuracy, and it has potential use in hydrogen isotope analysis with gas chromatography.

[Analysis of hydrogen isotopes by gas chromatography using a MnCl2 coated γ-Al2O3 capillary packed column].

The conventional packed column gas chromatographic analysis of hydrogen isotopes has low column efficiency, broad peak and long retention time. In this work, a γ-Al2O3 with MnCl2 coated capillary packed column was tested at cryogenic temperature. The systematic column efficiency analysis and the hydrogen isotopes analytical technique research had been carried out. The results showed that, the γ-Al2O3 with MnCl2 coating could greatly improve the surface degree of order, pore structure and adsorption properties. Also the o-H2 peak and p-H2 peak were eluted in a single area. The γ-Al2O3 with MnCl2 coating was packed into a 0.53 mm inner diameter and 1.0 m long fused silica capillary column. It had a good linear relationship used this column with thermal conductivity detector (TCD) to detect the volume concentrations of hydrogen isotopes from 1 to 10 mL/L, and the relative error was less than 5% for low concentration sample testing. For H2, HD and D2, the retention times can be shortened to 39, 46 and 60 s, respectively. The limits of detection were reduced to 0.046, 0.067 and 0.072 mL/L, respectively. Compared with conventional packed column, capillary packed column had sharper peak form, higher separation degree of adjacent components, shorter retention time and lower detection limits. The above results indicate that the capillary packed column with TCD detector can be used for fast detection of low concentration of hydrogen isotopes and their online analysis.

Constructing three-dimensional (3D) nanocrystalline models of Li4SiO4 for numerical modeling and simulation.

The three-dimensional (3D) nanocrystalline models of lithium silicates with the log-normal grain size distribution are constructed by constrained Voronoi tessellation. During evolution process, the algorithm is improved. We proposed a new algorithm idea by combining Genetic Algorithm (GA) with Least Square (LS) method to make up for the disadvantages of traditional genetic algorithm which may be easily trapped in local optimal solution. In the process of modeling, it is the first time, to the best of our knowledge, that we keep the whole sample showing the charge neutrality by deleting the excess atoms on the polyhedron boundary during the modeling. By using the molecular-dynamics method, the relaxation procedure of nanostructured Li4SiO4 is carried out. The results show that the average mass density of the sample is slightly lower than the experimental data of the perfect crystal after relaxation process. In addition, boundary component proportion (BCP) and density reduction proportion (DRP) of the sample is obtained, respectively. The present results display a significantly reduced BCP but an increased DRP when increasing the mean grain size of the sample.