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
目的研究前列腺癌磁共振扩散张量成像(DTI)参数表观扩散系数(ADC)与各向异性分数(FA)与其病理分级(Gleason评分)的关系,评价ADC值与FA值对于前列腺癌病理分级的诊断价值及评价前列腺癌危险度分级的诊断效能。方法采用3.0 T MR对70例经...目的研究前列腺癌磁共振扩散张量成像(DTI)参数表观扩散系数(ADC)与各向异性分数(FA)与其病理分级(Gleason评分)的关系,评价ADC值与FA值对于前列腺癌病理分级的诊断价值及评价前列腺癌危险度分级的诊断效能。方法采用3.0 T MR对70例经病理证实的前列腺癌患者行DTI检查,b值为0和800 s/mm2。测量前列腺癌的ADC值及FA值,根据病理结果将患者按Gleason评分系统分为高、中、低危三组:Gleason≥8分、Gleason=7分、Gleason≤6分。对三组数据进行单因素方差分析(one-way ANOVA),并进行组间两两比较。采用Pearson相关分析检验前列腺癌ADC值及FA值与Gleason评分的相关性。将前列腺癌Gleason评分≤7与Gleason评分≥8两组之间进行受试者工作特性(ROC)曲线分析,判断低中危组与高危组诊断界值。结果三组前列腺癌区平均ADC值分别为(0.96±0.10)×10-3mm2/s、(0.76±0.15)×10-3mm2/s和(0.62±0.12)×10-3mm2/s,FA值分别为0.39±0.06、0.31±0.09和0.22±0.06;三组ADC值、FA值组间差异均具有统计学意义(P<0.05);ADC值、FA值与Gleason评分之间均呈负相关(ADC值r=-0.768,P<0.05;FA值r=-0.662,P<0.05),两者均随Gleason评分的增高而减小。以ADC=0.68×10-3mm2/s为临界点,区分低中危组与高危组癌灶的诊断敏感性84.2%,特异性76.9%,准确性87.1%;以FA=0.24为临界点,区分低中危组与高危组癌灶的诊断敏感性79.5%,特异性75.8%,准确性78.4%。结论前列腺癌ADC值及FA值与病理分级(Gleason评分)之间呈负相关,具有预测癌灶恶性程度的潜力,有助于预测前列腺癌的恶性程度。展开更多
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.
文摘目的研究前列腺癌磁共振扩散张量成像(DTI)参数表观扩散系数(ADC)与各向异性分数(FA)与其病理分级(Gleason评分)的关系,评价ADC值与FA值对于前列腺癌病理分级的诊断价值及评价前列腺癌危险度分级的诊断效能。方法采用3.0 T MR对70例经病理证实的前列腺癌患者行DTI检查,b值为0和800 s/mm2。测量前列腺癌的ADC值及FA值,根据病理结果将患者按Gleason评分系统分为高、中、低危三组:Gleason≥8分、Gleason=7分、Gleason≤6分。对三组数据进行单因素方差分析(one-way ANOVA),并进行组间两两比较。采用Pearson相关分析检验前列腺癌ADC值及FA值与Gleason评分的相关性。将前列腺癌Gleason评分≤7与Gleason评分≥8两组之间进行受试者工作特性(ROC)曲线分析,判断低中危组与高危组诊断界值。结果三组前列腺癌区平均ADC值分别为(0.96±0.10)×10-3mm2/s、(0.76±0.15)×10-3mm2/s和(0.62±0.12)×10-3mm2/s,FA值分别为0.39±0.06、0.31±0.09和0.22±0.06;三组ADC值、FA值组间差异均具有统计学意义(P<0.05);ADC值、FA值与Gleason评分之间均呈负相关(ADC值r=-0.768,P<0.05;FA值r=-0.662,P<0.05),两者均随Gleason评分的增高而减小。以ADC=0.68×10-3mm2/s为临界点,区分低中危组与高危组癌灶的诊断敏感性84.2%,特异性76.9%,准确性87.1%;以FA=0.24为临界点,区分低中危组与高危组癌灶的诊断敏感性79.5%,特异性75.8%,准确性78.4%。结论前列腺癌ADC值及FA值与病理分级(Gleason评分)之间呈负相关,具有预测癌灶恶性程度的潜力,有助于预测前列腺癌的恶性程度。
