Leiomyosarcoma of the gonadal vein is an exceedingly rare entity, representing a small subset of smooth muscle tumors that more commonly arise in the retroperitoneum, uterus, and blood vessels. To date, fewer than 10 ...Leiomyosarcoma of the gonadal vein is an exceedingly rare entity, representing a small subset of smooth muscle tumors that more commonly arise in the retroperitoneum, uterus, and blood vessels. To date, fewer than 10 cases of gonadal vein leiomyosarcoma have been reported in the literature, highlighting its rarity and the limited understanding of its clinical behavior. These tumors are often diagnosed incidentally or present with nonspecific symptoms, such as abdominal pain or a palpable mass, which can complicate early detection. The proximity of gonadal vein leiomyosarcomas to critical structures, such as the ureter, renal vessels, and surrounding organs, introduces unique diagnostic and surgical challenges. Previous reports have underscored the importance of advanced imaging techniques, including CT and MRI, in delineating the tumor’s anatomical relationships and guiding surgical planning. This case, involving a leiomyosarcoma closely associated with the patient’s left ureter, provides an opportunity to build on existing knowledge by addressing the clinical presentation, diagnostic approach, treatment pathway, and long-term follow-up strategies required for optimal management. By presenting this detailed review, we aim to contribute valuable insights into the diagnosis and management of this rare malignancy.展开更多
BACKGROUND Inferior vena cava(IVC)leiomyosarcomas are rare and aggressive tumors.Complete cure depends on achieving R0 resection,which often requires circumferential resection and reconstruction.Synthetic grafts have ...BACKGROUND Inferior vena cava(IVC)leiomyosarcomas are rare and aggressive tumors.Complete cure depends on achieving R0 resection,which often requires circumferential resection and reconstruction.Synthetic grafts have traditionally been used when venous continuity must be restored.However,the use of cadaveric IVC grafts for reconstruction has not been widely reported.CASE SUMMARY Herein,we present the case of a 64-year-old woman diagnosed with an intrahepatic IVC leiomyosarcoma with local invasion.The patient responded favorably to chemotherapy and subsequently underwent an en bloc right hepatectomy,retrohepatic IVC resection,and reconstruction with an interpositional cadaveric IVC graft.Serial imaging follow-ups until 2 years after the operation showed persistent patency of the graft and no graft-related complications.CONCLUSION Cadaveric IVC grafts are an alternative to synthetic grafts for reconstruction,with acceptable outcomes.Larger,long-term studies are necessary to validate these findings.展开更多
Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the result...Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.展开更多
Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest rad...Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.展开更多
BACKGROUND Primary hepatic leiomyosarcoma(PHL)is a rare malignant tumor and has non-specific clinical manifestations and imaging characteristics,making preoperative diagnosis challenging.Here,we report a case of PHL p...BACKGROUND Primary hepatic leiomyosarcoma(PHL)is a rare malignant tumor and has non-specific clinical manifestations and imaging characteristics,making preoperative diagnosis challenging.Here,we report a case of PHL presenting primarily with fever,with computed tomography imaging showing a thick-walled hepatic lesion with low-density areas,resembling liver abscess.CASE SUMMARY The patient was a 34-year-old woman who presented with right upper abdominal pain and fever over 4 days before admission.Based on the patient’s medical history,laboratory examinations,and imaging examinations,liver abscess was suspected.Mesenchymal tumor was diagnosed by percutaneous liverbiopsy and partial hepatectomy was performed.Postoperative pathology revealed PHL.The patient is currently undergoing intravenous chemotherapy with the AD regimen and shows no signs of recurrence.CONCLUSION When there is a thick wall and rich blood supply in the hepatic lesion with a large proportion of uneven low-density areas,PHL should be considered.展开更多
BACKGROUND Pleomorphic leiomyosarcomas make up around 8.6%of all leiomyosarcomas.They behave aggressively and often have poor prognoses.They can affect the gastrointestinal tract and retroperitoneum.To date,pleomorphi...BACKGROUND Pleomorphic leiomyosarcomas make up around 8.6%of all leiomyosarcomas.They behave aggressively and often have poor prognoses.They can affect the gastrointestinal tract and retroperitoneum.To date,pleomorphic leiomyosarcoma involving the mesocolon have been reported in nine patients.CASE SUMMARY The patient was a 44-year-old man with a history of pleomorphic leiomyosarcoma of the left maxilla with metastasis to the lung and liver.His most recent positron emission tomography-computed tomography(PET-CT)scan showed uptake in the ascending and transverse colons.A colonoscopy revealed a 5.0 cm×3.5 cm×3.0 cm pedunculated polyp in the ascending colon.The polyp was removed using hot snare polypectomy technique and retrieved with Rothnet.Histopathologic examination of the polyp showed a metastatic pleomorphic leiomyosarcoma.CONCLUSION Uptake(s)on PET-CT in a patient with pleomorphic leiomyosarcoma should raise suspicion for metastasis.展开更多
Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can b...Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.展开更多
Directional solidification of Al-15% （mass fraction） Cu alloy was investigated by in situ and real time radiography which was performed by Shanghai synchrotron radiation facility （SSRF）. The imaging results reveal...Directional solidification of Al-15% （mass fraction） Cu alloy was investigated by in situ and real time radiography which was performed by Shanghai synchrotron radiation facility （SSRF）. The imaging results reveal that columnar to equiaxed transition （CET） is provoked by external thermal disturbance. The detaching and floating of fragments of dendrite arms are the prelude of the transition when the solute boundary layer in front of the solid-liquid interface is thin. And the dendrite triangular tip is the fracture sensitive zone. When the conditions are suitable, new dendrites can sprout and grow up. This kind of dendrite has no obvious stem and is named anaxial columnar dendrites.展开更多
目的探讨吞咽造影检查在脑卒中早期吞咽障碍患者康复的临床应用价值。方法选取200例脑卒中早期出现吞咽障碍的患者,通过随机数字表的方式分为观察组(100例)和对照组(100例)。对照组常规行洼田饮水试验(kubota water swallow test,KWST)...目的探讨吞咽造影检查在脑卒中早期吞咽障碍患者康复的临床应用价值。方法选取200例脑卒中早期出现吞咽障碍的患者,通过随机数字表的方式分为观察组(100例)和对照组(100例)。对照组常规行洼田饮水试验(kubota water swallow test,KWST)吞咽功能评估与康复训练;观察组在对照组的基础上完善荧光镜录像吞咽检查(video⁃fluoroscopic swal-lowing study,VFSS)并指导康复训练,两组疗程均为2周。对两组在治疗前后的吞咽功能改善、吸入性肺炎的发生率以及临床有效率进行对比分析。结果治疗后两组吞咽功能均得到了显著改善。观察组的吞咽功能改善较对照组更为显著,差异有统计学意义(P<0.05)。治疗后两组吸入性肺炎的发生率均低于治疗前,且观察组显著低于对照组,以及治疗后观察组的临床有效率显著高于对照组,均差异有统计学意义(P<0.05)。结论吞咽造影检查能够精准评估脑卒中早期患者的吞咽功能,并能显著减少吸入性肺炎发生率,检测出隐性误吸(silent aspiration,SA)情况,指导临床实施有效的吞咽康复治疗。展开更多
目的探讨基于CT增强扫描联合直方图分析技术构建判断甲状腺良恶性结节的预测模型,并评价各个模型的诊断效能。方法收集154例符合纳入标准的甲状腺良恶性结节患者的临床及影像资料,其中良性结节80例,恶性结节74例;使用MaZda软件分别从CT...目的探讨基于CT增强扫描联合直方图分析技术构建判断甲状腺良恶性结节的预测模型,并评价各个模型的诊断效能。方法收集154例符合纳入标准的甲状腺良恶性结节患者的临床及影像资料,其中良性结节80例,恶性结节74例;使用MaZda软件分别从CT平扫、动脉期、静脉期3个期相中勾画病灶最大层面作为感兴趣区(region of interest,ROI)并提取直方图参数。采用R语言将所有数据以7:3的比例随机抽样划分为训练集(n=108)和测试集(n=46),将组间差异显著的变量依次纳入二元Logistic回归分析建立预测模型,采用受试者操作特征(receiver operating characteristic,ROC)曲线评价各训练集模型价值并计算曲线下面积(area under curve,AUC),之后预测测试集每个模型的的预测概率,与实际值对比后计算测试集各模型的ROC及AUC。结果在训练集中临床模型、平扫模型、动脉期模型、静脉期模型和联合模型的AUC分别为0.814、0.682、0.630、0.701、0.865;测试集中各个模型的AUC依次为0.722、0.676、0.619、0.655、0.745。结论基于CT增强扫描联合直方图分析技术构建的预测模型对判断甲状腺结节性质的诊断效能较高,可提高术前诊断符合率。展开更多
文摘Leiomyosarcoma of the gonadal vein is an exceedingly rare entity, representing a small subset of smooth muscle tumors that more commonly arise in the retroperitoneum, uterus, and blood vessels. To date, fewer than 10 cases of gonadal vein leiomyosarcoma have been reported in the literature, highlighting its rarity and the limited understanding of its clinical behavior. These tumors are often diagnosed incidentally or present with nonspecific symptoms, such as abdominal pain or a palpable mass, which can complicate early detection. The proximity of gonadal vein leiomyosarcomas to critical structures, such as the ureter, renal vessels, and surrounding organs, introduces unique diagnostic and surgical challenges. Previous reports have underscored the importance of advanced imaging techniques, including CT and MRI, in delineating the tumor’s anatomical relationships and guiding surgical planning. This case, involving a leiomyosarcoma closely associated with the patient’s left ureter, provides an opportunity to build on existing knowledge by addressing the clinical presentation, diagnostic approach, treatment pathway, and long-term follow-up strategies required for optimal management. By presenting this detailed review, we aim to contribute valuable insights into the diagnosis and management of this rare malignancy.
文摘BACKGROUND Inferior vena cava(IVC)leiomyosarcomas are rare and aggressive tumors.Complete cure depends on achieving R0 resection,which often requires circumferential resection and reconstruction.Synthetic grafts have traditionally been used when venous continuity must be restored.However,the use of cadaveric IVC grafts for reconstruction has not been widely reported.CASE SUMMARY Herein,we present the case of a 64-year-old woman diagnosed with an intrahepatic IVC leiomyosarcoma with local invasion.The patient responded favorably to chemotherapy and subsequently underwent an en bloc right hepatectomy,retrohepatic IVC resection,and reconstruction with an interpositional cadaveric IVC graft.Serial imaging follow-ups until 2 years after the operation showed persistent patency of the graft and no graft-related complications.CONCLUSION Cadaveric IVC grafts are an alternative to synthetic grafts for reconstruction,with acceptable outcomes.Larger,long-term studies are necessary to validate these findings.
基金supported by National Natural Science Foundation of China(Nos.11905028,12105040)Scientific Research Project of Education Department of Jilin Province(No.JJKH20231294KJ)。
文摘Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.
文摘Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
基金Supported by the Lishui City Key Research and Development Project,No.2022ZDYF08.
文摘BACKGROUND Primary hepatic leiomyosarcoma(PHL)is a rare malignant tumor and has non-specific clinical manifestations and imaging characteristics,making preoperative diagnosis challenging.Here,we report a case of PHL presenting primarily with fever,with computed tomography imaging showing a thick-walled hepatic lesion with low-density areas,resembling liver abscess.CASE SUMMARY The patient was a 34-year-old woman who presented with right upper abdominal pain and fever over 4 days before admission.Based on the patient’s medical history,laboratory examinations,and imaging examinations,liver abscess was suspected.Mesenchymal tumor was diagnosed by percutaneous liverbiopsy and partial hepatectomy was performed.Postoperative pathology revealed PHL.The patient is currently undergoing intravenous chemotherapy with the AD regimen and shows no signs of recurrence.CONCLUSION When there is a thick wall and rich blood supply in the hepatic lesion with a large proportion of uneven low-density areas,PHL should be considered.
文摘BACKGROUND Pleomorphic leiomyosarcomas make up around 8.6%of all leiomyosarcomas.They behave aggressively and often have poor prognoses.They can affect the gastrointestinal tract and retroperitoneum.To date,pleomorphic leiomyosarcoma involving the mesocolon have been reported in nine patients.CASE SUMMARY The patient was a 44-year-old man with a history of pleomorphic leiomyosarcoma of the left maxilla with metastasis to the lung and liver.His most recent positron emission tomography-computed tomography(PET-CT)scan showed uptake in the ascending and transverse colons.A colonoscopy revealed a 5.0 cm×3.5 cm×3.0 cm pedunculated polyp in the ascending colon.The polyp was removed using hot snare polypectomy technique and retrieved with Rothnet.Histopathologic examination of the polyp showed a metastatic pleomorphic leiomyosarcoma.CONCLUSION Uptake(s)on PET-CT in a patient with pleomorphic leiomyosarcoma should raise suspicion for metastasis.
文摘Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.
基金Project(51001074)supported by the National Natural Science Foundation of ChinaProject(12ZR1414500)supported by Shanghai Municipal Natural Science Fund of ChinaProject(2012CB619505)supported by the National Basic Research Program of China
文摘Directional solidification of Al-15% （mass fraction） Cu alloy was investigated by in situ and real time radiography which was performed by Shanghai synchrotron radiation facility （SSRF）. The imaging results reveal that columnar to equiaxed transition （CET） is provoked by external thermal disturbance. The detaching and floating of fragments of dendrite arms are the prelude of the transition when the solute boundary layer in front of the solid-liquid interface is thin. And the dendrite triangular tip is the fracture sensitive zone. When the conditions are suitable, new dendrites can sprout and grow up. This kind of dendrite has no obvious stem and is named anaxial columnar dendrites.
文摘目的探讨基于CT增强扫描联合直方图分析技术构建判断甲状腺良恶性结节的预测模型,并评价各个模型的诊断效能。方法收集154例符合纳入标准的甲状腺良恶性结节患者的临床及影像资料,其中良性结节80例,恶性结节74例;使用MaZda软件分别从CT平扫、动脉期、静脉期3个期相中勾画病灶最大层面作为感兴趣区(region of interest,ROI)并提取直方图参数。采用R语言将所有数据以7:3的比例随机抽样划分为训练集(n=108)和测试集(n=46),将组间差异显著的变量依次纳入二元Logistic回归分析建立预测模型,采用受试者操作特征(receiver operating characteristic,ROC)曲线评价各训练集模型价值并计算曲线下面积(area under curve,AUC),之后预测测试集每个模型的的预测概率,与实际值对比后计算测试集各模型的ROC及AUC。结果在训练集中临床模型、平扫模型、动脉期模型、静脉期模型和联合模型的AUC分别为0.814、0.682、0.630、0.701、0.865;测试集中各个模型的AUC依次为0.722、0.676、0.619、0.655、0.745。结论基于CT增强扫描联合直方图分析技术构建的预测模型对判断甲状腺结节性质的诊断效能较高,可提高术前诊断符合率。
