BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic mal...BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
Objective: To determine whether the categories defined in the Breast Imaging Reporting and Data System (BI-RADS) are useful predictors of malignancy. Methods: A total of 348 cases with benign and malignant breast dise...Objective: To determine whether the categories defined in the Breast Imaging Reporting and Data System (BI-RADS) are useful predictors of malignancy. Methods: A total of 348 cases with benign and malignant breast diseases were collected. Mammographic and pathologic findings were reviewed. Mammograms of 348 cases were characterized according to BI-RADS descriptors and were categorized by the final assessment categories. Results: Of the all 348 patients, 232 (67%) were carcinomas. Significantly more masses with calcification and speculation were found in breast cancers than in benign breast diseases. BI-RADS final assessment categories were category 2 and 3 in 106 cases, in which 75% (79/106) were benign; category 4 and 5 in 242 cases, in which 85% (205/242) were carcinomas. BI-RADS categories 4 and 5 are useful predictors of relative likelihood of malignancy. The features with higher positive predictive values for carcinomas were irregular shape, indistinct or speculated margins, fine or linear calcification morphology, and regional calcification distribution. Conclusion: BI-RADS lexicon is successful in providing a standardized language for physicians to describe lesion morphology. BI-RADS category is useful for predicting the presence of malignancy.展开更多
目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶...目的探讨超微血流成像(SMI)联合高级动态血流成像(ADF)鉴别最大径≤10 mm BI-RADS 4类乳腺结节良恶性的临床价值。方法选取我院经手术病理证实的78例女性乳腺结节患者(共81个病灶),其中良性结节47个,恶性结节34个,均行SMI和ADF获取病灶血流分级和血管形态特征,比较良恶性结节上述检查结果的差异。分析SMI、ADF及两者联合应用鉴别BI-RADS 4类乳腺结节良恶性的诊断效能,采用Kappa检验分析其与病理结果的一致性。结果SMI检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001);ADF检查显示乳腺良恶性结节血流分级和血管形态特征比较差异均有统计学意义(均P<0.001)。SMI准确诊断BI-RADS 4类乳腺良性结节38个,恶性结节28个,诊断灵敏度、特异度、准确率分别为82.35%、80.85%、81.48%;ADF准确诊断BIRADS 4类乳腺良性结节32个,恶性结节25个,诊断灵敏度、特异度、准确率分别为73.53%、68.09%、70.37%;两者联合应用准确诊断BI-RADS 4类乳腺良性结节35个,恶性结节33个,诊断灵敏度、特异度、准确率分别为97.06%、74.47%、83.95%。SMI、ADF及两者联合应用与病理结果的一致性均中等(Kappa=0.632、0.406、0.685,均P<0.05)。结论SMI联合ADF可以提高最大径≤10 mm BI-RADS 4类乳腺结节良恶性的鉴别诊断效能,具有一定的临床价值。展开更多
超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的...超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。展开更多
In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Ela...In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.展开更多
文摘BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金a grant from ICRETT Foundation of UICC (No. 706).
文摘Objective: To determine whether the categories defined in the Breast Imaging Reporting and Data System (BI-RADS) are useful predictors of malignancy. Methods: A total of 348 cases with benign and malignant breast diseases were collected. Mammographic and pathologic findings were reviewed. Mammograms of 348 cases were characterized according to BI-RADS descriptors and were categorized by the final assessment categories. Results: Of the all 348 patients, 232 (67%) were carcinomas. Significantly more masses with calcification and speculation were found in breast cancers than in benign breast diseases. BI-RADS final assessment categories were category 2 and 3 in 106 cases, in which 75% (79/106) were benign; category 4 and 5 in 242 cases, in which 85% (205/242) were carcinomas. BI-RADS categories 4 and 5 are useful predictors of relative likelihood of malignancy. The features with higher positive predictive values for carcinomas were irregular shape, indistinct or speculated margins, fine or linear calcification morphology, and regional calcification distribution. Conclusion: BI-RADS lexicon is successful in providing a standardized language for physicians to describe lesion morphology. BI-RADS category is useful for predicting the presence of malignancy.
文摘超声因其便捷、无辐射等优势成为乳腺癌早期筛查的重要方式^([1]),根据美国放射学会发布的第5版乳腺影像报告与数据系统(breast imaging reporting and data system,BI-RADS),4类乳腺病变的恶性可能性为2%~95%,其跨度相对较大,且病变的超声特征多样,易受诊断医师主观判断影响。在乳腺癌诊治指南中^([2]),BI-RADS 4类结节均建议行细胞学检查或病理活检,最终导致非必要穿刺活检及手术率较高。
文摘In the article“A Lightweight Approach for Skin Lesion Detection through Optimal Features Fusion”by Khadija Manzoor,Fiaz Majeed,Ansar Siddique,Talha Meraj,Hafiz Tayyab Rauf,Mohammed A.El-Meligy,Mohamed Sharaf,Abd Elatty E.Abd Elgawad Computers,Materials&Continua,2022,Vol.70,No.1,pp.1617–1630.DOI:10.32604/cmc.2022.018621,URL:https://www.techscience.com/cmc/v70n1/44361,there was an error regarding the affiliation for the author Hafiz Tayyab Rauf.Instead of“Centre for Smart Systems,AI and Cybersecurity,Staffordshire University,Stoke-on-Trent,UK”,the affiliation should be“Independent Researcher,Bradford,BD80HS,UK”.
