Objective To explore the feasibility of constructing a three-classification bone status screening radiomics model on chest CT images.Methods A total of 371 patients who underwent both chest and abdominal plain CT examinations were retrospec-tively selected and randomly divided into training set(296 cases)and test set(75 cases)in a ratio of 8︰2.Additionally,110 patients were included as external validation set using the same criteria.The 120 kVp abdominal images were transmitted to a quantitative compu-ted tomography(QCT)post-processing workstation to measure the bone mineral density(BMD)of the L1-L2 vertebral bodies.Patients were classified into osteoporosis(OP)group(BMD<80 mg/cm3),osteopenia group(80 mg/cm3≤BMD≤120 mg/cm3)and normal bone mass group(BMD>120 mg/cm3)based on QCT BMD results.The automatic segmentation model was used to segment T10-T12 vertebral trabecular bone on chest CT images and the radiomics models based on random forest(RF)and logistic regres-sion(LR)was established to evaluate BMD,enabling it to simultaneously distinguish OP,osteopenia,and normal bone mass.The diag-nostic performance of the two models were evaluated using metrics such as the area under the curve(AUC),sensitivity and specificity.The DeLong test was used to compare the differences between the two models.Results In the test set,the AUC for differentiating normal bone mass were 0.948 and 0.877 for the RF and LR models,respectively;the AUC for differentiating OP were 0.942 and 0.836,respectively;and the AUC for differentiating osteopenia were 0.871 and 0.688,respectively.The performance comparison results of the models showed that there was no statistically significant difference in AUC(0.966 vs 0.907,P>0.05)between RF model and LR model in the external validation set for distinguishing OP,while there was a statistically significant difference in AUC for distinguishing osteopenia(0.895 vs 0.749,P=0.009)and normal bone mass(0.975 vs 0.906,P=0.023).The RF model performance was superior to the LR model.Conclusion The radiomics model developed based on chest plain CT can be used for opportunistic OP screening with good diagnostic efficacy,and the the model based on the RF classifier outperforms the LR model.

Objective To explore the application effect of a mobile learning platform for tumor diagnosis and treatment combined with the case-based learning method(CBL)in the standardized training of oncology residents.Methods Fifty-two 2022-level trainees who underwent the resident physician standardized training in the Oncology Department of the First Affiliated Hospital of Kunming Medical University from August to December 2024 were selected as the research subjects.Using the random number table method,they were divided into the observation group and the control group,with 26 trainees in each group.The control group adopted the CBL teaching method,while the observation group adopted the tumor diagnosis and treatment mobile learning platform combined with the CBL teaching method.During the graduation assessment,the mastery degree of basic knowl-edge in the oncology specialty,the analysis ability of clinical cases,the performance level of clinical operation skills,and the sat-isfaction with the standardized training in oncology for the two groups of trainees were compared.Results The scores of the obser-vation group students in terms of their mastery of basic knowledge in oncology,their ability to analyze clinical cases,and their satis-faction with the oncology residency training teaching were[(84.42±3.43)points,(85.08±2.94)points,(7.50±1.03)points],which were significantly higher than those of the control group[(82.15±4.32)points,(82.12±3.82)points,(5.89±1.28)points](P＜0.05).There was no significant difference in the clinical operation skills scores between the two groups of students(P＞0.05).Conclusion The application of our mobile learning platform for tumor diagnosis and therapy combined with CBL can effectively enhance the educational outcomes and satisfaction in medical oncology standardized residency training.

Objective To explore the feasibility of constructing a three-classification bone status screening radiomics model on chest CT images.Methods A total of 371 patients who underwent both chest and abdominal plain CT examinations were retrospec-tively selected and randomly divided into training set(296 cases)and test set(75 cases)in a ratio of 8︰2.Additionally,110 patients were included as external validation set using the same criteria.The 120 kVp abdominal images were transmitted to a quantitative compu-ted tomography(QCT)post-processing workstation to measure the bone mineral density(BMD)of the L1-L2 vertebral bodies.Patients were classified into osteoporosis(OP)group(BMD<80 mg/cm3),osteopenia group(80 mg/cm3≤BMD≤120 mg/cm3)and normal bone mass group(BMD>120 mg/cm3)based on QCT BMD results.The automatic segmentation model was used to segment T10-T12 vertebral trabecular bone on chest CT images and the radiomics models based on random forest(RF)and logistic regres-sion(LR)was established to evaluate BMD,enabling it to simultaneously distinguish OP,osteopenia,and normal bone mass.The diag-nostic performance of the two models were evaluated using metrics such as the area under the curve(AUC),sensitivity and specificity.The DeLong test was used to compare the differences between the two models.Results In the test set,the AUC for differentiating normal bone mass were 0.948 and 0.877 for the RF and LR models,respectively;the AUC for differentiating OP were 0.942 and 0.836,respectively;and the AUC for differentiating osteopenia were 0.871 and 0.688,respectively.The performance comparison results of the models showed that there was no statistically significant difference in AUC(0.966 vs 0.907,P>0.05)between RF model and LR model in the external validation set for distinguishing OP,while there was a statistically significant difference in AUC for distinguishing osteopenia(0.895 vs 0.749,P=0.009)and normal bone mass(0.975 vs 0.906,P=0.023).The RF model performance was superior to the LR model.Conclusion The radiomics model developed based on chest plain CT can be used for opportunistic OP screening with good diagnostic efficacy,and the the model based on the RF classifier outperforms the LR model.

Objective To explore the application effect of a mobile learning platform for tumor diagnosis and treatment combined with the case-based learning method(CBL)in the standardized training of oncology residents.Methods Fifty-two 2022-level trainees who underwent the resident physician standardized training in the Oncology Department of the First Affiliated Hospital of Kunming Medical University from August to December 2024 were selected as the research subjects.Using the random number table method,they were divided into the observation group and the control group,with 26 trainees in each group.The control group adopted the CBL teaching method,while the observation group adopted the tumor diagnosis and treatment mobile learning platform combined with the CBL teaching method.During the graduation assessment,the mastery degree of basic knowl-edge in the oncology specialty,the analysis ability of clinical cases,the performance level of clinical operation skills,and the sat-isfaction with the standardized training in oncology for the two groups of trainees were compared.Results The scores of the obser-vation group students in terms of their mastery of basic knowledge in oncology,their ability to analyze clinical cases,and their satis-faction with the oncology residency training teaching were[(84.42±3.43)points,(85.08±2.94)points,(7.50±1.03)points],which were significantly higher than those of the control group[(82.15±4.32)points,(82.12±3.82)points,(5.89±1.28)points](P＜0.05).There was no significant difference in the clinical operation skills scores between the two groups of students(P＞0.05).Conclusion The application of our mobile learning platform for tumor diagnosis and therapy combined with CBL can effectively enhance the educational outcomes and satisfaction in medical oncology standardized residency training.

Objective:To explore the effects of the auto-prescription technique combined with noise index (NI) and adaptive statistical iterative reconstruction algorithm-veo (ASIR-V) on the radiation dose and image quality in the computed tomographic angiography (CTA) of the deep inferior epigastric artery (DIEA).Methods:The data of 150 cases who underwent DIEP CTA in the First Affiliated Hospital of Dalian Medical University were prospectively collected, and were randomized into groups A, B, and C, with 50 cases in each group. For group A, the conventional tube voltage was 120 kVp, and images were reconstructed using 40% ASIR-V. For group B, the tube voltage based on the auto-prescription technique was adopted, and images were reconstructed using 40%, 60%, and 80% ASIR-V (corresponding to the three subgroups B1-B3, respectively). For group C, the tube voltage based on the auto-prescription technique was employed, NI = 13, and images were reconstructed by 40%, 60%, and 80% ASIR-V (corresponding to subgroups C1, C2, and C3, respectively). Subsequently, the CT and SD values of the originating femoral artery of DIEA and the rectus abdominis at the same level as DIEA were measured on axial images of each group, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and the radiation doses of the three groups were recorded. Finally, the image quality and noise of DIEA in each group were independently and subjectively scored by two radiologists in a blinded manner.Results:Compared to group A, groups B and C exhibited higher CT values of femoral artery ( t= -5.18, -5.17, -5.06, -6.08, -6.08, -6.07, P < 0.05) and higher image SNR and CNR ( P < 0.05), which increased gradually with the upgrading of ASIR-V ( F = 55.45, 49.70, 53.47, 68.89, P < 0.05). The two radiologists offered consistent subjective evaluations (Kappa = 0.76-0.92, P < 0.05). The images of groups B and C displayed more distinct perforating branches and emerging points of DIEA and received higher subjective scores of intramuscular course compared to those of group A. The subjective scores of image noise in subgroups B2, B3, and C2 were not significantly different from those in group A. The radiation doses in groups B and C decreased by 15.10% and 52.85%, respectively compared to those in group A ( H = 75.21, P < 0.05). Conclusion:The combination of the auto-prescription technique with NI = 13 and 60% ASIR-V can reduce the radiation dose by 52.85% while ensuring a clear display of DIEA.

Objective To explore the application value of deep learning image reconstruction(DLIR)algorithm in low-dose deep inferior epigastric artery(DIEA)computed tomography angiography(CTA).Methods A total of 59 patients undergoing DIEA CTA were prospectively selected and randomly divided into two groups:group A(29 cases)and group B(30 cases).Group A was the conventional radiation dose group(tube voltage 120 kVp),the tube current was dose modulation 3,and the iterative reconstruction algo-rithm was Karl 5.Group B was the low radiation dose group(tube voltage 120 kVp),the tube current was dose modulation 2,with DLIR reconstruction algorithm,and four levels of DLIR(1-4)were reconstructed and labeled as groups B1 to B4.The mean tube current,vol-ume CT dose index(CTDIvol),and dose length product(DLP)of group A and group B were recorded,and the effective dose(ED)was calculated.The CT and standard deviation(SD)values of the external iliac artery and psoas major muscle were measured on axial images of each group,and signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated.Two observers using double-blind method independently evaluated the DIEA on volume rendering(VR)and maximum intensity projection(MIP)images of each group using a 3-point scale,and a 5-point scale was used to evaluate the overall image quality on axial images.Results Compared with group A,the mean tube current and ED in group B were decreased by 39.33％and 44.09％,respectively(P＜0.05).With the increasing of DLIR level from groups B1 to B4,the SD value of the image gradually decreased,while SNR and CNR gradually increased(P＜0.05).The CNR in groups B3 and B4 was higher than that in group A(P＜0.05).The subjective scores of the two observers were consistent(Kappa value 0.779-0.889,P＜0.05),and there was no statistical significance between group A and group B in the perforating vessels from the DIEA,intramuscular course,and the point of emergence(P＞0.05).With the increase in DLIR level,the subjective score of the overall image quality from group B1 to group B4 showed a trend of first increasing and then decreasing,and the score of group B3 was the highest(4.50±0.51)points,which had no statistical significance compared with group A(4.45±0.51)points(P＞0.05).Conclusion DLIR can effectively reduce the radiation dose of the DIEA CT A scan and ensure the image quality,among which DLIR 3 is the recommended best reconstruction grade.

Objective To explore the application value of 1 024×1 024 reconstruction matrix combined with iterative reconstruc-tion algorithm(Karl)in deep inferior epigastric artery(DIEA)computed tomography angiography(CTA).Methods A total of 40 patients who underwent DIEA CTA were prospectively selected and the original data were reconstructed by grouping.Group A was reconstructed using a conventional 512×512 matrix combined with Karl 5 grade.Group B was reconstructed using 1 024×1 024 recon-struction matrix combined with Karl 5,7,and 9 grades,respectively,and 3 subgroups B1-B3 were obtained.The CT and standard devia-tion(SD)values of the external iliac artery and psoas major muscle were measured on axial images,and signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated.A 3-point scale was used to evaluate the perforating vessels from the DIEA,intramuscular course,point of emergence,superficial inferior epigastric artery(SIEA)and superficial inferior epigastric vein(SIEV)on volume ren-dering(VR)and maximum intensity projection(MIP)images by two observers,and a 5-point scale was used to evaluate the overall image quality on axial images.Results With the increase of Karl grade in groups B1 to B3,the SD value of the external iliac artery decreased gradually(P＜0.05),while SNR and CNR increased gradually(P＜0.05).The SD values of the external iliac artery in group B2 and group B3 were lower than those in group A(P＜0.05),and SNR and CNR were higher than those in group A(P＜0.05).There was a good consistency in the subjective evaluation between the two observers(Kappa values=0.773-0.872,P＜0.05).The perforating vessels from the DIEA,intramuscular course,point of emergence,SIEA and SIEV display and overall image quality subjective scores of group B2 and group B3 were better than those of group A(P＜0.05),and the scores of group B2 showed the greatest improvement.Conclusion The 1 024 × 1 024 reconstruction matrix combined with the Karl 7 reconstruction algorithm can optimize the image quality and improve the display of the DIEA and perforator microvessels.

Objective To investigate the clinical application value of posterior adaptive statistical iterative reconstruction-Veo(ASIR-V)algorithm combined with full-field organ dose modulation(ODM)technique in low-dose computed tomography urography(CTU).Methods Ninety patients who underwent urological contrast-enhanced and CTU examination were prospectively selected and divided into conventional dose group(group A,NI=11,posterior ASIR-V of 60％),low-dose group(group B,NI=15,posterior ASIR-V of 60％-100％with 10％interval,denoted as B1-B5 groups);full-field ODM low-dose group(group C,NI=15,posterior ASIR-V of 60％-100％with 10％interval,denoted as C1-C5 groups)according to the noise index(NI).The volume rendering(VR)and maximum intensity projection(MIP)images were reconstructed.The CT values and standard deviation(SD)values of the renal cortex and para-spinal muscles were measured,and the signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were cal-culated.The CT values of the renal pelvis,ureter,and bladder were measured,and the SNR and CNR were calculated with the para-spinal muscles as background.The subjective score was performed on the two-dimensional and three-dimensional images to evaluate the ability of CTU to display lesions.The volume CT dose index(CTDIvol),dose length product(DLP),and effective dose(ED)of the three groups were compared.Results The bladder SNR and CNR of groups B1,C1,and C2 were lower than those of group A(P＜0.05).There was no statistically significant difference in the SNR and CNR of each tissue between groups B2,C3 and group A(P＞0.05).The SNR and CNR of each tissue in groups B4,B5,and C5 were higher than those in group A(P＜0.05).The display rate of lesions in urinary system in groups B2 and C3 could reach 100％.There was no statistically significant difference in the axial image scores between groups B2,C3 and group A(P＞0.05),while the VR and MIP image scores in each group were comparable to those in group A(P＞0.05).The CTDIvol of groups A,B and C were(6.08±2.11)mGy,(3.15±1.15)mGy,and(2.9±0.92)mGy,respectively.Conclusion CTU using the full-field ODM technique combined with posterior 80％ASIR-V can reduce the CTDIvol by 52.30％,and further reduce by 7.93％compared with group B,which is effective to reduce radiation to the gonads.

Immediate-early genes (IEGs) have long been used to visualize neural activations induced by sensory and behavioral stimuli. Recent advances in imaging techniques have made it possible to use endogenous IEG signals to visualize and discriminate neural ensembles activated by multiple stimuli, and to map whole-brain-scale neural activation at single-neuron resolution. In addition, a collection of IEG-dependent molecular tools has been developed that can be used to complement the labeling of endogenous IEG genes and, especially, to manipulate activated neural ensembles in order to reveal the circuits and mechanisms underlying different behaviors. Here, we review these techniques and tools in terms of their utility in studying functional neural circuits. In addition, we provide an experimental strategy to measure the signal-to-noise ratio of IEG-dependent molecular tools, for evaluating their suitability for investigating relevant circuits and behaviors.
Animals ; Brain ; metabolism ; Gene Expression Profiling ; methods ; Genes, Immediate-Early ; Humans ; Molecular Imaging ; methods ; Neural Pathways ; metabolism ; Neurons ; metabolism ; Signal-To-Noise Ratio