Study on a Pig Vocalization Classification Method Based on Multi-Feature Fusion.

To improve the classification of pig vocalization using vocal signals and improve recognition accuracy, a pig vocalization classification method based on multi-feature fusion is proposed in this study. With the typical vocalization of pigs in large-scale breeding houses as the research object, short-time energy, frequency centroid, formant frequency and first-order difference, and Mel frequency cepstral coefficient and first-order difference were extracted as the fusion features. These fusion features were improved using principal component analysis. A pig vocalization classification model with a BP neural network optimized based on the genetic algorithm was constructed. The results showed that using the improved features to recognize pig grunting, squealing, and coughing, the average recognition accuracy was 93.2%; the recognition precisions were 87.9%, 98.1%, and 92.7%, respectively, with an average of 92.9%; and the recognition recalls were 92.0%, 99.1%, and 87.4%, respectively, with an average of 92.8%, which indicated that the proposed pig vocalization classification method had good recognition precision and recall, and could provide a reference for pig vocalization information feedback and automatic recognition.

Preparation and application of biocontrol formulation of nematode-trapping fungus-Duddingtonia flagrans.

The use of nematophagous fungi as a biological control strategy for parasitic gastrointestinal nematodes (GINs) in livestock holds promise as an innovative alternative approach. This study aimed to evaluate the clinical efficacy of a lyophilized Duddingtonia flagrans preparation, utilized in association with the anthelmintics ivermectin or albendazole, to control GINs in Tibetan sheep on a farm based in Qinghai Province. The experimental design included five groups: D. flagrans lyophilized preparation group; D. flagrans+ ivermectin combination tablets treatment group (0.6 tablets for each 10 kg b.w. containing 106 chlamydospores of D. flagrans); D. flagrans+ albendazole combination capsules treatment group (5 capsules for each 10 kg b.w. containing 106 chlamydospores of D. flagrans); ivermectin group (0.2 mg/kg); albendazole group (15 mg/kg), and a control group; The effect of these strategies was evaluated through the analysis of feces collected directly from the animals in each group at 24 h, 48 h, 72 h,96 h and 120 h after administration, by estimating the counts of fecal egg count reduction percentage (FECR) and larval development reduction percentage (LDR). The combination of D. flagrans lyophilized preparation with either ivermectin or albendazole yielded fecal egg and larval reduction rates of up to 100% within 72 h after oral administration, outperforming the groups treated with a single anthelmintic. Moreover, the application of the lyophilized preparation of D. flagrans chlamydospores in isolation demonstrated an 89.8% larval reduction rate. The formulation containing D. flagrans showed high predatory capacity after passage through the gastrointestinal tract of sheep and was effective for controlling gastrointestinal nematodes, which greatly reduced the pollution of the grassland, and avoid reinfection.

Comparative analysis of dose calculation algorithms for CyberKnife-based stereotactic radiotherapy in lung cancer.

Purpose: The accuracy of dose calculation is the prerequisite for CyberKnife (CK) to implement precise stereotactic body radiotherapy (SBRT). In this study, CK-MLC treatment planning for early-stage non-small cell lung cancer (NSCLC) were compared using finite-size pencil beam (FSPB) algorithm, FSPB with lateral scaling option (FSPB_LS) and Monte Carlo (MC) algorithms, respectively. We concentrated on the enhancement of accuracy with the FSPB_LS algorithm over the conventional FSPB algorithm and the dose consistency with the MC algorithm. Methods: In this study, 54 cases of NSCLC were subdivided into central lung cancer (CLC, n=26) and ultra-central lung cancer (UCLC, n=28). For each patient, we used the FSPB algorithm to generate a treatment plan. Then the dose was recalculated using FSPB_LS and MC dose algorithms based on the plans computed using the FSPB algorithm. The resultant plans were assessed by calculating the mean value of pertinent comparative parameters, including PTV prescription isodose, conformity index (CI), homogeneity index (HI), and dose-volume statistics of organs at risk (OARs). Results: In this study, most dose parameters of PTV and OARs demonstrated a trend of MC > FSPB_LS > FSPB. The FSPB_LS algorithm aligns better with the dose parameters of the target compared to the MC algorithm, which is particularly evident in UCLC. However, the FSPB algorithm significantly underestimated the does of the target. Regarding the OARs in CLC, differences in dose parameters were observed between FSPB and FSPB_LS for V10 of the contralateral lung, as well as between FSPB and MC for mean dose (Dmean) of the contralateral lung and maximum dose (Dmax) of the aorta, exhibiting statistical differences. There were no statistically significant differences observed between FSPB_LS and MC for the OARs. However, the average dose deviation between FSPB_LS and MC algorithms for OARs ranged from 2.79% to 11.93%. No significant dose differences were observed among the three algorithms in UCLC. Conclusion: For CLC, the FSPB_LS algorithm exhibited good consistency with the MC algorithm in PTV and demonstrated a significant improvement in accuracy when compared to the traditional FSPB algorithm. However, the FSPB_LS algorithm and the MC algorithm showed a significant dose deviation in OARs of CLC. In the case of UCLC, FSPB_LS showed better consistency with the MC algorithm than observed in CLC. Notwithstanding, UCLC's OARs were highly sensitive to radiation dose and could result in potentially serious adverse reactions. Consequently, it is advisable to use the MC algorithm for dose calculation in both CLC and UCLC, while the application of FSPB_LS algorithm should be carefully considered.

Deciphering the functional landscape of phosphosites with deep neural network.

Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.

(-)-Epigallocatechin Gallate is a Noncompetitive Inhibitor of NAD Kinase.

Nicotinamide adenine dinucleotide kinase (NADK) controls the intracellular NADPH content and provides reducing power for the synthesis of macromolecules and anti-ROS. Moreover, NADK is considered to be a synthetic lethal gene for KRAS mutations. To discover NADK-targeted probes, a high-throughput screening assay was established and optimized with a Z factor of 0.71. The natural product (-)-epigallocatechin gallate (EGCG) was found to be a noncompetitive inhibitor of NADK with K i = 3.28 ± 0.32 µΜ. The direct binding of EGCG to NADK was determined by several biophysical methods, including NMR spectroscopy, surface plasmon resonance (SPR) assay, and hydrogen-deuterium exchange mass spectrometry (HDX-MS). The SPR assay showed a K d of 1.78 ± 1.15 µΜ. The HDX-MS experiment showed that EGCG was bound at the non-substrate-binding sites of NADK. Besides, binding mode prediction and derivative activity analysis revealed a potential structure-activity relationship between EGCG and NADK. Furthermore, EGCG can specifically inhibit the proliferation of KRAS-mutated lung cancer cell lines without affecting KRAS wild-type lung cancer cell lines.

A potent PGK1 antagonist reveals PGK1 regulates the production of IL-1ß and IL-6.

Glycolytic metabolism enzymes have been implicated in the immunometabolism field through changes in metabolic status. PGK1 is a catalytic enzyme in the glycolytic pathway. Here, we set up a high-throughput screen platform to identify PGK1 inhibitors. DC-PGKI is an ATP-competitive inhibitor of PGK1 with an affinity of K d = 99.08 nmol/L. DC-PGKI stabilizes PGK1 in vitro and in vivo, and suppresses both glycolytic activity and the kinase function of PGK1. In addition, DC-PGKI unveils that PGK1 regulates production of IL-1ß and IL-6 in LPS-stimulated macrophages. Mechanistically, inhibition of PGK1 with DC-PGKI results in NRF2 (nuclear factor-erythroid factor 2-related factor 2, NFE2L2) accumulation, then NRF2 translocates to the nucleus and binds to the proximity region of Il-1ß and Il-6 genes, and inhibits LPS-induced expression of these genes. DC-PGKI ameliorates colitis in the dextran sulfate sodium (DSS)-induced colitis mouse model. These data support PGK1 as a regulator of macrophages and suggest potential utility of PGK1 inhibitors in the treatment of inflammatory bowel disease.

Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face.

Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.

A fast Tikhonov regularization method based on homotopic mapping for electrical resistance tomography.

Electrical resistance tomography (ERT) is considered a novel sensing technique for monitoring conductivity distribution. Image reconstruction of ERT is an ill-posed inverse problem. In this paper, an improved regularization reconstruction method is presented to solve this issue. We adopted homotopic mapping to choose the regularization parameter of the iterative Tikhonov algorithm. The standard normal distribution function was used to continuously adjust the regularization parameter. Subsequently, the resultant image vector was deployed as the initial value of the iterative Tikhonov algorithm to improve the image quality. Finally, the improved method was combined with a projection algorithm based on the Krylov subspace, which was also effective in reducing the computational time. Both simulation and experimental results indicated that the new algorithm could improve the real-time performance and imaging quality.

A preliminary investigation of re-evaluating the irradiation dose in hepatocellular carcinoma radiotherapy applying 4D CT and deformable registration.

PURPOSE: To investigate the effect of breathing motion on dose distribution for hepatocellular carcinoma (HCC) patients using four-dimensional (4D) CT and deformable registration. METHODS: Fifty HCC patients who were going to receive radiotherapy were enrolled in this study. All patients had been treated with transarterial chemoembolization beforehand. Three-dimensional (3D) and 4D CT scans in free breathing were acquired sequentially. Volumetric modulated arc therapy (VMAT) was planned on the 3D CT images and maximum intensity projection (MIP) images. Thus, the 3D dose (Dose-3D ) and MIP dose (Dose-MIP ) were obtained, respectively. Then, the Dose-3D and Dose-MIP were recalculated on 10 phases of 4D CT images, respectively, in which the end-inhale and end-exhale phase doses were defined as Dose-3D-EI , Dose-3D-EE , Dose-MIP-EI , and Dose-MIP-EE . The 4D dose (Dose-4D-3D and Dose-4D-MIP ) were obtained by deforming 10 phase doses to the end-exhale CT to accumulate. The dosimetric difference in Dose-3D , Dose-EI3D , Dose-EE3D , Dose-4D-3D , Dose-MIP , Dose-EIMIP , Dose-EEMIP , and Dose-4D-MIP were compared to evaluate the motion effect on dose delivery to the planning target volume (PTV) and normal liver. RESULTS: Compared with Dose-3D , PTV D99 in Dose-EI3D , Dose-EE3D and Dose-4D-3D decreased by an average of 6.02%, 1.32%, 2.43%, respectively (P < 0.05); while PTV D95 decreased by an average of 3.34%, 1.51%, 1.93%, respectively (P < 0.05). However, CI and HI of the PTV in Dose-3D was superior to the other three distributions (P < 0.05). There was no significant differences for the PTV between Dose-EI and Dose-EE , and between the two extreme phase doses and Dose-4D (P> 0.05). Negligible difference was observed for normal liver in all dose distributions (P> 0.05). CONCLUSIONS: Four-dimensional dose calculations potentially ensure target volume coverage when breathing motion may affect the dose distribution. Dose escalation can be considered to improve the local control of HCC on the basis of accurately predicting the probability of radiation-induced liver disease.

Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.

BACKGROUND: The reproducibility and non-redundancy of radiomic features are challenges in accelerating the clinical translation of radiomics. In this study, we focused on the robustness and non-redundancy of radiomic features extracted from computed tomography (CT) scans in hepatocellular carcinoma (HCC) patients with respect to different tumor segmentation methods. METHODS: Arterial enhanced CT images were retrospectively randomly obtained from 106 patients. As a training data set, 26 HCC patients were used to calculate the features' reproducibility and redundancy. Another data set (55 HCC patients and 25 healthy volunteers) was used for classification. The GrowCut and GraphCut semiautomatic segmentation methods were implemented in 3D Slicer software by two independent observers, and manual delineation was performed by five abdominal radiation oncologists to acquire the gross tumor volume (GTV). Seventy-one radiomic features were extracted from GTVs using Imaging Biomarker Explorer (IBEX) software, including 17 tumor intensity statistical features, 16 shape features and 38 textural features. For each radiomic feature, intraclass correlation coefficient (ICC) and hierarchical clustering were used to quantify its reproducibility and redundancy. Features with ICC values greater than 0.75 were considered reproducible. To generate the number of non-redundancy feature subgroups, the R2 statistic method was used. Then, a classification model was built using a support vector machine (SVM) algorithm with 10-fold cross validation, and area under ROC curve (AUC) was used to evaluate the utility of non-redundant feature extraction by hierarchical clustering. RESULTS: The percentages of excellent reproducible features in the manual delineation group, GraphCut and GrowCut segmentation group were 69% [49], 73% [52] and 79% [56], respectively. Sixty-five percent [46] of the features showed strong robustness for all segmentation methods. The optimal number of cluster subgroup were 9, 13 and 11 for manual delineation, GraphCut and GrowCut segmentation, respectively. The optimal cluster subgroup number was 6 for all groups when the collectively high reproducibility features were selected for clustering. The receiver operating characteristic (ROC) analysis of radiomics classification model with and without feature reduction for healthy liver and HCC had an AUC value of 0.857 and 0.721 respectively. CONCLUSIONS: Our study demonstrates that variations exist in the reproducibility of quantitative imaging features extracted from tumor regions segmented using different methods. The reproducibility and non-redundancy of the radiomic features rely greatly on the tumor segmentation in HCC CT images. We recommend that the most reliable and uniform radiomic features should be selected in the clinical use of radiomics. Classification experiments with feature reduction showed that radiomic features were effective in identifying healthy liver and HCC.

Quantification of variation in dose-volume parameters for the heart, pericardium and left ventricular myocardium during thoracic tumor radiotherapy.

Cardiac activity can induce dose-volume evaluation errors for cardiac structures. The purpose of this study was to quantify the variation in dose-volume parameters for the heart, pericardium and left ventricular myocardium (LVM) throughout the cardiac circle. The heart, pericardium and LVM of 22 patients were contoured on 20 phases of electrocardiography-gated 4D computed tomography (4DCT) images acquired during breath-hold. Radiotherapy plans were designed on 0% phase of the 4DCT images, and the dose distributions of the plans were imported into MIM Maestro and deformed to each phase to generate distributions for all phases. Variations in dose-volume parameters for the heart, pericardium and LVM were compared among different phases. The rates of variation in Dmean for the heart and pericardium were 3.33 ± 1.04% and 2.66 ± 1.15%, respectively. The mean values of the maximum difference in V5, V10, V20, V30 and V40 were all <2% for the heart and pericardium and were not statistically significant (P > 0.05). The rate of variation in Dmean for the LVM reached 87.05 ± 38.34%, and the maximum differences in V5, V10, V20, V30 and V40 were 13.76 ± 4.46%, 13.64 ± 4.33%, 12.84 ± 4.55%, 11.62 ± 4.85% and 3.63 ± 2.56%, respectively; all differences were statistically significant (P < 0.05). Variations in dose-volume parameters were more significant in the LVM than in the heart and pericardium (P < 0.05). The dose-volume parameters for the LVM were significantly influenced by cardiac activity, whereas those for the heart and pericardium were not; therefore, individual dosimetric evaluation and limitation must be performed for the LVM.

Quantification of heart, pericardium, and left ventricular myocardium movements during the cardiac cycle for thoracic tumor radiotherapy.

PURPOSE: The purpose of this study was to quantify variations in the heart, pericardium, and left ventricular myocardium (LVM) caused by cardiac movement using the breath-hold technique. PATIENTS AND METHODS: In this study, the electrocardiography-gated four-dimensional computed tomography (CT) images of 22 patients were analyzed, which were sorted into 20 phases (0-95%) according to the cardiac cycle. The heart, pericardium, and LVM were contoured on each phase of the CT images. The positions, volume, dice similarity coefficient (DSC) in reference to 0% phase, and morphological parameters (max 3D diameter, roundness, spherical disproportion, sphericity, and surface area) in different phases of the heart, pericardium, and LVM were analyzed, which were presented as mean ± standard deviation. RESULTS: The mean values of displacements along the X, Y, and Z axes respectively were as follows: 1.2 mm, 0.6 mm, and 0.6 mm for the heart; 0.5 mm, 0.4 mm, and 0.8 mm for the pericardium; and 1.0 mm, 4.1 mm, and 1.9 mm for the LVM. The maximum variations in volume and DSC respectively were 16.49%±3.85% and 10.08%±2.14% for the heart, 12.62%±3.94% and 5.20%±1.54% for the pericardium, and 24.23%±11.35% and 184.33%±128.61% for the LVM. The differences in the morphological parameters between the maximum and minimum DSC phases for the heart and pericardium were not significantly different (p>0.05) but were significantly different for the LVM (p<0.05). CONCLUSION: The volumetric and morphological variations of the heart were similar to those of pericardium, and all were significantly smaller than those of the LVM. This inconsistency in the volumetric and morphological variations between the LVM and the heart and pericardium indicates that special protection of the LVM should be considered.

The heterogeneous CTV-PTV margins should be given for different parts of tumors during tomotherapy.

OBJECTIVE: The purpose of this study was to evaluate CTV-PTV margins of tumors for tomotherapy. METHODS: Setup errors were analyzed for 151 patients receiving helical tomotherapy treatment. 53 patients had head and neck tumors, 45 had thoracic tumors, 20 had abdominal tumors, and 33 had pelvic tumors. The setup errors were calculated in six directions, i.e. +X (left), -X (right), +Y (head), -Y (foot), +Z (ventral), and -Z (dorsal), after Megavoltage CT (MVCT) images were registered to simulation CT images. And then the CTV-PTV margins were calculated. RESULTS: The setup errors along the +Z direction were significantly higher than that along the -Z direction (p<0.05). The CTV-PTV margins on +X, -X, +Y, -Y, +Z, and -Z directions were asymmetric for all tumors, and the heterogeneity were more remarkable on the +Z and -Z directions. The CTV-PTV margins on +Z and -Z were 4.1 mm, 4.6 mm, 5.2 mm, and 8.4 mm; and 3.9 mm, 7.7 mm, 3.3 mm, and 7.7 mm for head and neck tumors, thoracic tumors, abdominal tumors, and pelvic tumors, respectively. CONCLUSIONS: The CTV-PTV margins for patients with different types of tumors were heterogeneous during tomotherapy. The individual margins of six directions should be given for those patients who accept tomotherapy.

Capability of leaf interdigitation with different inverse planning strategies in Monaco: an investigation of representative tumour sites.

PURPOSE: The aim of this study was to experimentally assess the dosimetric impact of leaf interdigitation using different inverse treatment strategies for representative tumour sites and to identify the situations in which leaf interdigitation can benefit these tumour sites. MATERIAL AND METHODS: Sixty previously treated patients (15 nasopharyngeal carcinoma (NPC), 15 multiple brain metastasis (MBM), 15 cervical cancer and 15 prostate cancer) were re-planned for volumetric modulated arc therapy (VMAT), sliding window IMRT (dMLC) and step-and-shoot IMRT (ssIMRT) with and without leaf interdigitation. Various dosimetric variables, such as PTV coverage, OARs sparing, delivery efficiency and planning time, were evaluated for each plan. In addition, a protocol developed by our group was applied to identify the situations in which leaf interdigitation can achieve benefits in clinical practice. RESULTS: Leaf interdigitation produced few benefits in PTV homogeneity for the MBM VMAT plans and NPC ssIMRT plans. For OARs, sparing was equivalent with and without leaf interdigitation. Leaf interdigitation showed an increase in MUs for dMLC plans and a decrease in MUs for ssIMRT plans. Leaf interdigitation resulted in an increase in segments for dMLC plans and a decrease in segments for NPC and MBM ssIMRT plans. For beam on time, leaf interdigitation showed an increase in MBM dMLC, NPC ssIMRT and prostate ssIMRT plans. In addition, leaf interdigitation saved planning time for VMAT and dMLC plans but increased planning time for ssIMRT plans. CONCLUSION: Leaf interdigitation does not improve plan quality when performing inverse treatment strategies, regardless of whether the target is simple or complex. However, it influences the delivery efficiency and planning time. Based on these observations, our study suggests that leaf interdigitation should be utilized when performing MBM VMAT plans and NPC ssIMRT plans.

A comparison of dosimetric variance for external-beam partial breast irradiation using three-dimensional and four-dimensional computed tomography.

PURPOSE: To investigate the potential dosimetric benefits from four-dimensional computed tomography (4DCT) compared with three-dimensional computed tomography (3DCT) in radiotherapy treatment planning for external-beam partial breast irradiation (EB-PBI). PATIENTS AND METHODS: 3DCT and 4DCT scan sets were acquired for 20 patients who underwent EB-PBI. The volume of the tumor bed (TB) was determined based on seroma or surgical clips on 3DCT images (defined as TB3D) and the end inhalation (EI) and end exhalation (EE) phases of 4DCT images (defined as TBEI and TBEE, respectively). The clinical target volume (CTV) consisted of the TB plus a 1.0 cm margin. The planning target volume (PTV) was the CTV plus 0.5 cm (defined as PTV3D, PTVEI, and PTVEE). For each patient, a conventional 3D conformal plan (3D-CRT) was generated (defined as EB-PBI3D, EB-PBIEI, and EB-PBIEE). RESULTS: The PTV3D, PTVEI, and PTVEE were similar (P=0.549), but the PTV coverage of EB-PBI3D was significantly less than that of EB-PBIEI or EB-PBIEE (P=0.001 and P=0.025, respectively). There were no significant differences in the homogeneity or conformity indexes between the three treatment plans (P=0.125 and P=0.536, respectively). The EB-PBI3D plan resulted in the largest organs at risk dose. CONCLUSION: There was a significant benefit for patients when using 3D-CRT based on 4DCT for EB-PBI with regard to reducing nontarget organ exposure. Respiratory motion did not affect the dosimetric distribution during free breathing, but might result in poor dose coverage when the PTV is determined using 3DCT.

Different methods for target volume delineation of glandular breast tissue following breast-conserving surgery in breast cancer: A comparative study.

The present study aimed to investigate an optimal and feasible method for delineating the target volume of glandular breast tissue following breast-conserving surgery. A total of 15 patients who underwent radiotherapy following breast-conserving surgery were recruited into the study. Clinical target volume was delineated by the following three methods based on computed tomography (CT): Anatomical landmarks (CTVan), breast palpation (CTVpa) and CT scan images (CTVgl). The target volume, degree of inclusion (DI) and conformal index (CI) defined by these methods were compared. The difference was significant between CTVan and CTVgl, and CTVpa and CTVgl (P<0.0001). The CI between CTVan and CTVpa was 0.644±0.122, significantly higher than that between CTVan and CTVgl (0.264±0.108; P<0.0001) or between CTVpa and CTVgl (0.328±0.115; P<0.0001). The DI of CTVpa in CTVan was 0.890±0.08 and the opposite was 0.709±0.144, while that of DI of CTVgl in CTVan or CTVpa was 0.994±0.005 and 0.989±0.008, respectively. The boundary difference between CTVan and CTVpa was 3.35±7.23, 5.57±13.37, 1.75±11.62 and 11.25±4.07 mm for the medial, lateral, cephalic and caudal boundaries, respectively. A significant difference was observed in the target volume of the breast defined by the three methods. The target volume defined by CTVgl was significantly smaller than that identified by the other two methods. Overall, the combination of palpation marks and anatomical landmarks to define the contouring scope of the breast was indicated to be a relatively rational method for delineating the target volume of the breast.

Gradient-based delineation of the primary GTV on FLT PET in squamous cell cancer of the thoracic esophagus and impact on radiotherapy planning.

BACKGROUND: To validate a gradient-based segmentation method for gross tumor volume(GTV) delineation on (8)F-fluorothymidine (FLT)positron emission tomography (PET)/ computer tomography (CT) in esophageal squamous cell cancer through pathologic specimen, in comparison with standardized uptake values (SUV) threshold-based methods and CT. The corresponding impact of this GTV delineation method on treatment planning was evaluated. METHODS AND MATERIALS: Ten patients with esophageal squamous cell cancer were enrolled. Before radical surgery, all patients underwent FLT-PET/CT. GTVs were delineated by using four methods. GTVGRAD, GTV1.4 and GTV30%max were segmented on FLT PET using a gradient-based method, a fixed threshold of 1.4 SUV and 30% of SUVmax, respectively. GTVCT was based on CT data alone. The maximum longitudinal tumor length of each segmented GTV was compared with the measured tumor length of the pathologic gross tumor length (LPath). GTVGRAD, GTV1.4 and GTV30%max were compared with GTVCT by overlap index. Two radiotherapy plannings (planGRAD) and (planCT) were designed for each patient based on GTVGRAD and GTVCT. The dose-volume parameters for target volume and normal tissues, CI and HI of planGRAD and planCT were compared. RESULTS: The mean ± standard deviation of LPath was 6.47 ± 2.70 cm. The mean ± standard deviation of LGRAD,L1.4, L30%max and LCT were 6.22 ± 2.61, 6.23 ± 2.80, 5.95 ± 2.50,7.17 ± 2.28 cm, respectively. The Pearson correlation coefficients between LPath and each segmentation method were 0.989, 0.920, 0.920 and 0.862, respectively. The overlap indices of GTVGRAD, GTV1.4, GTV30%max when compared with GTVCT were 0.75 ± 0.12, 0.71 ± 0.12, 0.57 ± 0.10, respectively. The V5, V10, V20, V30 and mean dose of total-lung,V30 and mean dose of heart of planGRAD were significantly lower than planCT. CONCLUSIONS: The gradient-based method provided the closest estimation of target length. The radiotherapy plannings based on the gradient-based segmentation method reduced the irradiated volume of lung, heart in comparison to CT.

Dosimetric impact of tumor bed delineation variability based on 4DCT scan for external-beam partial breast irradiation.

This study sought to evaluate the dosimetric impact of tumor bed delineation variability (based on clips, seroma or both clips and seroma) during external-beam partial breast irradiation (EB-PBI) planned utilizing four-dimensional computed tomography (4DCT) scans. 4DCT scans of 20 patients with a seroma clarity score (SCS) 3~5 and ≥5 surgical clips were included in this study. The combined volume of the tumor bed formed using clips, seroma, or both clips and seroma on the 10 phases of 4DCT was defined as the internal gross target volume (termed IGTVC, IGTVS and IGTVC+S, respectively). A 1.5-cm margin was added by defining the planning target volume (termed PTVC, PTVS and PTVC+S, respectively). Three treatment plans were established using the 4DCT images (termed EB-PBIC, EB-PBIS, EB-PBIC+S, respectively). The results showed that the volume of IGTVC+S was significantly larger than that of IGTVCand IGTVS. Similarly, the volume of PTVC+S was markedly larger than that of PTVC and PTVS. However, the PTV coverage for EB-PBIC+S was similar to that of EB-PBIC and EB-PBIS, and there were no significant differences in the homogeneity index or conformity index between the three treatment plans (P=0.878, 0.086). The EB-PBIS plan resulted in the lowest ipsilateral normal breast and ipsilateral lung doses compared with the EB-PBIC and EB-PBIC+S plans. To conclude, the volume variability delineated based on clips, seroma or both clips and seroma resulted in dosimetric variability for organs at risk, but did not show a marked influence on the dosimetric distribution.

18FDG PET-CT standardized uptake value for the prediction of radiation pneumonitis in patients with lung cancer receiving radiotherapy.

The present study aimed to determine if the standardized uptake value (SUV) determined with 18F-FDG PET-CT can be used to predict radiation pneumonitis (RP) in lung cancer patients who receive radiotherapy. A total of 40 patients with non-small cell lung cancer received 18F-FDG PET-CT examinations prior to and following radiotherapy. The average SUV of lung tissue prior to and following radiation were measured at differing radiation doses. SUV differences between patients with and without RP, and the SUV ratio of the irradiated lung tissues compared with that of non-irradiated lung tissues (L/B) were compared. There were no differences in the mean SUV between the RP and no RP groups prior to radiotherapy. There were also no significant differences in the mean SUV of lung tissue within groups or between the no RP and RP groups with radiation doses of <5 Gy, 5 to ≤14.9 Gy and 15 to ≤34.9 Gy (all P>0.05) following radiotherapy. There were, however, statistically significant differences in the mean SUV of lung tissue within groups or between the no RP and RP groups with doses of ≥60 Gy prior to therapy and 35 to ≤59.9 Gy and ≥60 Gy following therapy (all P<0.05). When the L/B ratio was ≥3, the incidence of RP was 50%, and when the L/B ratio was ≥2.5 the incidence was 40.7%. When the L/B ratio was <2, there were no cases of RP. In conclusion, the present study indicates that 18F-FDG PET-CT can be used to predict RP by L/B ratio.

Feasibility and potential benefits of defining the internal gross tumor volume of hepatocellular carcinoma using contrast-enhanced 4D CT images obtained by deformable registration.

OBJECTIVE: To study the feasibility and the potential benefits of defining the internal gross tumor volume (IGTV) of hepatocellular carcinoma (HCC) using contrast-enhanced 4D CT images obtained by combining arterial-phase (AP) contrast-enhanced (CE) 3D CT and non-contrast-enhanced (NCE) 4D CT images using deformable registration (DR). METHODS: Ten HCC patients who had received radiotherapy beforehand were selected for this study. The following CT simulation images were acquired sequentially: NCE 4D CT in free breathing, NCE 3D CT and APCE 3D CT in end-expiration breath holding. All 4D CT images were sorted into ten phases according to breath cycle (CT00 ~ CT90). Gross tumor volumes (GTVs) were contoured on all CT images and the IGTV-1 was obtained by merging the GTVs in each phase of 4D CT images. The GTV on the APCE 3D CT image was deformably registered to each 4D CT phase image according to liver shape using RayStation(TM) 3.99.0.7 version treatment planning system. The IGTV-DR was obtained by merging the GTVs after DR on the 4D CT images. Volume differences among the GTVs and between the IGTV-1 and the IGTV-DR were compared. RESULTS: The edge of most lesions could be definitively identified using APCE 3D CT images compared to NCE 4D and 3D CT images. The GTV volume on APCE 3D CT images increased by an average of 34.79% (P<0.05). There was no significant difference among the GTV volumes obtained using NCE 4D and 3D CT images (P>0.05). The GTV volumes after DR on 4D CT different phase images increased by an average of 36.29% (P<0.05), as was observed using the APCE 3D CT image (P>0.05). Lastly, the volume of IGTV-DR increased by an average of 19.91% compared to that of IGTV-1 (P<0.05). CONCLUSION: NCE 4D CT imaging alone has the potential risk of missing a partial volume of the HCC. The combination of APCE 3D CT and NCE 4D CT images using the DR technique improved the accuracy of the definition of the IGTV in HCC.