The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is prop...The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.展开更多
One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of p...One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients.Traditionally,urological pathologists perform the grading by scoring the morphological pattern,known as the Gleason pattern,in histopathology images.However,thismanual grading is highly subjective,suffers intra-and inter-pathologist variability and lacks reproducibility.An automated grading system could be more efficient,with no subjectivity and higher accuracy and reproducibility.Automated methods presented previously failed to achieve sufficient accuracy,lacked reproducibility and depended on high-resolution images such as 40×.This paper proposes an automated Gleason grading method,ProGENET,to accurately predict the grade using low-resolution images such as 10×.This method first divides the patient’s histopathology whole slide image(WSI)into patches.Then,it detects artifacts and tissue-less regions and predicts the patch-wise grade using an ensemble network of CNN and transformer models.The proposed method adapted the International Society of Urological Pathology(ISUP)grading system and achieved 90.8%accuracy in classifying the patches into healthy and Gleason grades 1 through 5 using 10×WSI,outperforming the state-of-the-art accuracy by 27%.Finally,the patient’s grade was determined by combining the patch-wise results.The method was also demonstrated for 4−class grading and binary classification of prostate cancer,achieving 93.0%and 99.6%accuracy,respectively.The reproducibility was over 90%.Since the proposedmethod determined the grades with higher accuracy and reproducibility using low-resolution images,it is more reliable and effective than existing methods and can potentially improve subsequent therapy decisions.展开更多
集成磁共振成像(synthetic magnetic resonance imaging,SyMRI)是一种新型快速定量MRI技术,能通过短时间扫描获得多种定量图谱和对比加权图像,无创性地获得组织客观定量参数,从微观角度提供更多组织成分信息。该技术获得的纵向弛豫时间T...集成磁共振成像(synthetic magnetic resonance imaging,SyMRI)是一种新型快速定量MRI技术,能通过短时间扫描获得多种定量图谱和对比加权图像,无创性地获得组织客观定量参数,从微观角度提供更多组织成分信息。该技术获得的纵向弛豫时间T1、横向弛豫时间T2和质子密度(proton density,PD)在前列腺癌的鉴别诊断、侵袭性预测和预后评价等方面发挥了重要作用。本文通过阐述SyMRI技术基本原理,就现有文献对SyMRI在前列腺癌中的相关应用进行综述,旨在提高对前列腺癌的早期诊断,并为前列腺癌治疗提供额外信息。此外,本文就该技术在前列腺癌的应用现状,探讨其未来发展方向,以期为后续的研究提供参考。展开更多
目的:探讨临床、影像与双参数磁共振(Biparametric magnetic resonance imaging,bpMRI)影像组学特征联合构建的列线图模型在预测高级别前列腺癌中的应用价值。方法:2022年1月至2023年12月我院收治的前列腺癌患者137例,根据病理结果分成...目的:探讨临床、影像与双参数磁共振(Biparametric magnetic resonance imaging,bpMRI)影像组学特征联合构建的列线图模型在预测高级别前列腺癌中的应用价值。方法:2022年1月至2023年12月我院收治的前列腺癌患者137例,根据病理结果分成低级别组(Gleason≤3+4)和高级别组(Gleason≥4+3)。患者按照3∶1比例随机分为训练组(n=102)和测试组(n=35),提取、筛选T2加权成像(T2-weighted imaging,T2WI)与表观弥散系数(Apparent diffusion coefficient,ADC)图像中的影像组学特征,构建影像组学模型,计算影像组学评分值(Radiomics score,Rad-Score)。利用训练组样本分别将临床、影像及影像组学参数进行单、多因素分析并筛选预测高级别前列腺癌的独立危险因素,分别建立临床影像、影像组学以及联合参数的Logistic模型并绘制列线图。运用Delong检验以及受试者工作特征曲线(Receiver operating characteristic curve,ROC)比较各模型间的差异、效能,采用决策曲线评估各模型的临床获益。结果:最终筛选6个与前列腺癌Gleason分级相关的影像组学特征,建立Rad-Score预测模型并计算Rad-Score。经过单、多因素分析,将MRI成像下前列腺特异性抗原密度(MRI-defined prostate specific antigen density,mPSAD)、前列腺影像报告和数据系统(Prostate imaging reporting and data system,PIRADS)评分、Rad-Score纳入列线图模型。该模型在训练组、测试组的曲线下面积(Area under the curve,AUC)分别是0.881、0.853,高于临床影像模型(AUC:0.793、0.755)及Rad-Score模型(AUC:0.808、0.788),差异均有统计学意义(P<0.05)。决策曲线结果显示,列线图模型的临床获益优于其他模型。结论:基于临床、影像及T2WI+ADC影像组学的列线图模型对预测高级别前列腺癌具有较高的预测效能与临床应用潜力。展开更多
基金Foundation item:the Suzhou Municipal Health and Family Planning Commission's Key Diseases Diagnosis and Treatment Program(No.LCzX202001)the Science and Technology Development Project ofSuzhou(Nos.SS2019012andSKY2021031)+1 种基金the Youth Innovation Promotion Association CAS(No.2021324)the Medical Research Project of Jiangsu Provincial Health and Family Planning Commission(No.M2020068)。
文摘The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘One in every eight men in the US is diagnosed with prostate cancer,making it the most common cancer in men.Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients.Traditionally,urological pathologists perform the grading by scoring the morphological pattern,known as the Gleason pattern,in histopathology images.However,thismanual grading is highly subjective,suffers intra-and inter-pathologist variability and lacks reproducibility.An automated grading system could be more efficient,with no subjectivity and higher accuracy and reproducibility.Automated methods presented previously failed to achieve sufficient accuracy,lacked reproducibility and depended on high-resolution images such as 40×.This paper proposes an automated Gleason grading method,ProGENET,to accurately predict the grade using low-resolution images such as 10×.This method first divides the patient’s histopathology whole slide image(WSI)into patches.Then,it detects artifacts and tissue-less regions and predicts the patch-wise grade using an ensemble network of CNN and transformer models.The proposed method adapted the International Society of Urological Pathology(ISUP)grading system and achieved 90.8%accuracy in classifying the patches into healthy and Gleason grades 1 through 5 using 10×WSI,outperforming the state-of-the-art accuracy by 27%.Finally,the patient’s grade was determined by combining the patch-wise results.The method was also demonstrated for 4−class grading and binary classification of prostate cancer,achieving 93.0%and 99.6%accuracy,respectively.The reproducibility was over 90%.Since the proposedmethod determined the grades with higher accuracy and reproducibility using low-resolution images,it is more reliable and effective than existing methods and can potentially improve subsequent therapy decisions.
文摘集成磁共振成像(synthetic magnetic resonance imaging,SyMRI)是一种新型快速定量MRI技术,能通过短时间扫描获得多种定量图谱和对比加权图像,无创性地获得组织客观定量参数,从微观角度提供更多组织成分信息。该技术获得的纵向弛豫时间T1、横向弛豫时间T2和质子密度(proton density,PD)在前列腺癌的鉴别诊断、侵袭性预测和预后评价等方面发挥了重要作用。本文通过阐述SyMRI技术基本原理,就现有文献对SyMRI在前列腺癌中的相关应用进行综述,旨在提高对前列腺癌的早期诊断,并为前列腺癌治疗提供额外信息。此外,本文就该技术在前列腺癌的应用现状,探讨其未来发展方向,以期为后续的研究提供参考。
文摘目的:探讨临床、影像与双参数磁共振(Biparametric magnetic resonance imaging,bpMRI)影像组学特征联合构建的列线图模型在预测高级别前列腺癌中的应用价值。方法:2022年1月至2023年12月我院收治的前列腺癌患者137例,根据病理结果分成低级别组(Gleason≤3+4)和高级别组(Gleason≥4+3)。患者按照3∶1比例随机分为训练组(n=102)和测试组(n=35),提取、筛选T2加权成像(T2-weighted imaging,T2WI)与表观弥散系数(Apparent diffusion coefficient,ADC)图像中的影像组学特征,构建影像组学模型,计算影像组学评分值(Radiomics score,Rad-Score)。利用训练组样本分别将临床、影像及影像组学参数进行单、多因素分析并筛选预测高级别前列腺癌的独立危险因素,分别建立临床影像、影像组学以及联合参数的Logistic模型并绘制列线图。运用Delong检验以及受试者工作特征曲线(Receiver operating characteristic curve,ROC)比较各模型间的差异、效能,采用决策曲线评估各模型的临床获益。结果:最终筛选6个与前列腺癌Gleason分级相关的影像组学特征,建立Rad-Score预测模型并计算Rad-Score。经过单、多因素分析,将MRI成像下前列腺特异性抗原密度(MRI-defined prostate specific antigen density,mPSAD)、前列腺影像报告和数据系统(Prostate imaging reporting and data system,PIRADS)评分、Rad-Score纳入列线图模型。该模型在训练组、测试组的曲线下面积(Area under the curve,AUC)分别是0.881、0.853,高于临床影像模型(AUC:0.793、0.755)及Rad-Score模型(AUC:0.808、0.788),差异均有统计学意义(P<0.05)。决策曲线结果显示,列线图模型的临床获益优于其他模型。结论:基于临床、影像及T2WI+ADC影像组学的列线图模型对预测高级别前列腺癌具有较高的预测效能与临床应用潜力。
