Objective To investigate the effects of numerous re-planning strategies on the anatomic and dosimetric outcomes of target volume and organs at risk (OARs) in patients with head and neck cancer receiving fractionated radiotherapy.Methods From 2015 to 2016,28 patients with head and neck cancer were enrolled in this study with Shandong Cancer Hospital,consisting of 19 patients with nasopharyngeal carcinoma, 4 patients with laryngocarcinoma, and 5 patients with carcinoma of the maxillary sinus.All of them received conventionally fractionated radiotherapy.Each patient had six weekly cone-beam CT (CBCT) scans, which were performed on the first day of every week, to obtain reference images.A virtual CT image was generated by registration of planning CT and each weekly CBCT image.The four re-planning strategies were used for the reconstruction of re-planned dose, while the initial planning was used as a reference.The weekly doses calculated using virtual CT were summed together to obtain the actual dose.The actual and initial planned doses were evaluated.The nonparametric Friedman test was used to evaluate the differences between multiple groups, and the differences between any two groups were analyzed by paired t test.Results The sizes of planning target volume, clinical target volume, and left/right parotid glands (PGs) changed significantly within the six weeks (P=0.041, 0.046, 0.024, and 0.017, respectively).For these four re-planning strategies, there were significant differences between the actual dose and the initial planned dose to the PGs (all P<0.05), with average values decreased by 5.02%, 11.17%, 12.08%, and 13.19%, respectively, compared with that in the reference strategy.Conclusions Re-planning during treatment course could ensure the sparing of OARs and allow for sufficient dose to the target volume.The higher the number of re-planning strategies, the more the actual dose is close to the initial planed dose;the efficiency of two re-planning strategies is the highest.

Objective To study the morphology of normal liver and tumors by breathing motion of hepatocellular carcinoma patients,through comparing the modified demons algorithm and FFD algorithm based on B-spline,and combing four-dimensional computed tomography (4DCT).Methods The 4DCT images of 8 HCC patients were segmented into 10-series which were named CT0,CT10 … CT80,CT90 according to the respiratory phases,CT0 and CT50 are defined to be end-inhale and end-exhale respectively.CT50 was chosen as the reference image.We used the modified demons algorithm and FFD algorithm based on B-spline to deform the images.Linear interpolation was used in both mode 1 and mode 2.The normalized mutual information (NMI),Hausdorff distance (dH) and registration speed were used to verify the registration performance.Results The average NMI for the end-inhale and end-exhale images of 8 HCC patients after demons registration in mode 1 improved 4.75％ with FFD algorithm based on B-spline(P =0.002).And the difference of dH after demons reduced 15.2％ comparing with FFD model algorithm (P =0.02).In addition,demons algorithm has the absolute advantage in registration speed(P =0.036).Conclusions The breathing movement for deformation of normal liver and tumor targets is significant.These two algorithms can achieve the registration of 4DCT images and the modified demons registration can deform 4DCT images effectively.

A 8-channel neural signal′s simultaneous transducer detection micro system was developed to research the neural loop located at the brain hippocampus zone. The components of the system contained the neural probe manufactured with the Micro-electro-mechanical-systems (MEMS) technique based on silicon-on-insulator (SOI) substrate, biological low noise chopper-stabilization amplifier, low noise and intermediate speed SAR-ADC converter, reduced and low power ASK/FSK modulation radio transmitter. The micro system was applicable with the characters of small volume, interferences free, neural electrophysiology and neurotransmitter simultaneous detection, high sensitivity, high linearity, etc. The electrode resistance was optimized to 35.0 kΩ after depositing nanometer platinum black on the 4 electrophysiological sites on the Pt electrode. With the modification enzyme technique, nanomaterial enzyme membrane (Pt-mPD-GluOx) was directly fixed on the glutamate detection locus for selectively detecting special neural neurotransmitter matter. In addition, the electrochemistry measurement results indicated that the linear range of glutamate was 6-35 μmol/L with correlation coefficient of 0.97, the sensitivity was 0.0069 pA/(μmol/L). The current response error was less than 3.0 pA, which showed that the neural needle satisfied differential selection. Also, the logic/analog mixed signal 180-nm Application specific integrated circuit (ASIC ) technique (SmicRF180 nm 1Poly6M) was used to manufacture the transducer back-end disposing IC chip, and the test results provided some key parameters such as chopper-stabilization amplifier (equivalent in putting noise voltage ≤0.7 μV rms@1 kHz, gain of 71-82 dB, CMRR/PSRR>100 dB), SAR-ADC (ENOB is 12 bits, power consumption is 1.2 mW when maxmium conversion speed is 1 Msps, signal-noise-ratio is 60.9 dB, etc), and ASK/FSK modulation radio transmitter (the PA′s outputting power of 4-5 dBm, the radiation range of 10 meters). The micro neural transducer integrated system was convenient and wireless wearable for the research of brain hippocampus region.

Objective To investigate the impact of multi-b-value on texture features of DWI in liver cirrhosis.Methods DWI manifestations of liver cirrhosis in 37 patients were analyzed retrospectively,and DWI of 27 healthy volunteers (control group) were enrolled as controls.The b values were set as 0,20,50,100,200,400,800,1 000,1 200 and 1500 s/mm2,respectively.Three ROIs at different levels of every set image were selected,and 37 texture features within these ROIs were extracted.Unstable texture features affected by different b-values were screened with the percent coefficient of variation (％COV),and the fitting degree between the unstable texture features and b values were analyzed with exponential fitting.Results Among 37 texture features,20 (20/37,54.05 ％) were unstable.With the increase of b values,exponential upward trend was found in 10 texture features,exponential downward trend was found in 4 texture features,and the relative trends could not be defined in other 6 unstable texture features.Conclusion The b values of DWI impact the texture features in liver cirrhosis.Correlations exist among some texture features and b values.

Objective To investigate the changes of the parameters related to planning and re-planning CT imaging features in lung cancer patients presenting with radiation pneumonitis ( RP) by using radiomics technique,and identify the parameters intimately related to the incidence of RP. Methods A total of 31 lung cancer patients who were diagnosed with grade ≥ 2 RP after receiving radiation therapy were selected in this study. For each patient, planning CT images before radiation therapy and re-planning CT images after 40 Gy radiation therapy were obtained. The affected and contralateral lungs were considered as the region of interest (ROI).After the automatic segmentation of normal lung tissues,the parameters related to radiomics features were extracted from ROI by using radiomics software. The differences of these parameters between planning and re-planning CT images were statistically compared. Results ( 1 ) For unilateral lung within each time interval,86 parameters related to radiomics features were extracted; ( 2) Twenty-two parameters significantly differed between the affected and contralateral lungs prior to radiotherapy;(3) Twelve parameters significantly differed between the affected and contralateral lungs on re-planning CT images;(4) Twenty-eight parameters significantly differed in the affected lung before and after radiation therapy;(5) Twenty-eight parameters significantly differed in the contralateral lung before and after radiation therapy. Conclusions The CT imaging radiomics features significantly differ between planning and re-planning CT scan in partial lung cancer patients presenting with RP.Monitoring the dynamic changes of these parameters plays a potential role in predicting the incidence of RP.

Objective: To establish radiomics models based on different CT scaning phases to distinguish mediastinal metastatic lymph nodes in NSCLC and to explore the diagnostic efficacy of these models. Methods: The CT images of 86 preoperative patients with NSCLC who were performed both plain and enhanced CT scans were analyzed retrospectively. The 231 mediastinal lymph nodes were enrolled in this study which were divided into two independent cohorts: 163 lymph nodes enrolled from January 2015 to June 2017 constituted the training cohort, and 68 lymph nodes enrolled from July 2017 to June 2018 constituted the validation cohort. The regions of interest (ROIs) were delineated on plain scan phase, arterial phase and venous phase CT images respectively, and 841 features were extracted from each ROI. LASSO-logistic regression analysis was used to select features and develop models. The area under the ROC curve (AUC value), sensitivity, specificity, accuracy, positive predictive value and negative predictive value of different models for distinguishing metastatic lymph nodes were compared. Results: A total of 6 models were established, and the AUC values were all greater than 0.800. The plain CT model yielded the highest AUC, specificity, accuracy and positive predictive value with 0.926, 0.860, 0.871, 0.906 in the training cohort and 0.925, 0.769, 0.882, 0.870 in the validation cohort. When plain and venous phase CT images were combined with arterial phase CT images, the sensitivity and negative predictive value of the models increased from 0.879, 0.821 and 0.919, 0.789 to 0.949, 0.878 and 0.979, 0.900 respectively. Conclusions The CT radiomics model could be used to assist the clinical diagnosis of lymph nodes. The AUC value of the model based on plain scanning was the highest, while the sensitivity and negative predictive value of the model could be improved by combining the arterial phase CT images.