The Gleason grading system for prostate adenocarcinoma has evolved from its original scheme established in the 1960s-1970s, to a significantly modified system after two major consensus meetings conducted by the Intern...The Gleason grading system for prostate adenocarcinoma has evolved from its original scheme established in the 1960s-1970s, to a significantly modified system after two major consensus meetings conducted by the International Society of Urologic Pathology （ISUP） in 2005 and 2014, respectively. The Gleason grading system has been incorporated into the WHO classification of prostate cancer, the AJCC/ UICC staging system, and the NCCN guidelines as one of the key factors in treatment decision. Both pathologists and clinicians need to fully understand the principles and practice of this grading system. We here briefly review the historical aspects of the original scheme and the recent developments of Gleason grading system, focusing on major changes over the years that resulted in the modern Gleason grading system, which has led to a new ＂Grade Group＂ system proposed by the 2014 ISUP consensus, and adopted by the 2016 WHO classification of tumours of the prostate.展开更多
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
基金supported by grants from the Natural Science Foundation of China (NSFC 81272848, 81272820, 81302225, 81572540)
文摘The Gleason grading system for prostate adenocarcinoma has evolved from its original scheme established in the 1960s-1970s, to a significantly modified system after two major consensus meetings conducted by the International Society of Urologic Pathology （ISUP） in 2005 and 2014, respectively. The Gleason grading system has been incorporated into the WHO classification of prostate cancer, the AJCC/ UICC staging system, and the NCCN guidelines as one of the key factors in treatment decision. Both pathologists and clinicians need to fully understand the principles and practice of this grading system. We here briefly review the historical aspects of the original scheme and the recent developments of Gleason grading system, focusing on major changes over the years that resulted in the modern Gleason grading system, which has led to a new ＂Grade Group＂ system proposed by the 2014 ISUP consensus, and adopted by the 2016 WHO classification of tumours of the prostate.
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.
