PDF(Pubmed)
Abstract：
OBJECTIVE: To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach.
METHODS: Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance.
RESULTS: For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively.
CONCLUSIONS: Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts.
UNASSIGNED: Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making.
CONCLUSIONS: Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
METHODS: Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance.
RESULTS: For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively.
CONCLUSIONS: Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts.
UNASSIGNED: Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making.
CONCLUSIONS: Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
摘要：
目的：使用深度学习方法，使用单模态T2加权成像非侵入性检测前列腺癌并预测Gleason分级。
方法：前列腺癌患者，经组织病理学证实，2015年9月至2022年6月期间在我们医院接受磁共振成像检查的患者被回顾性纳入内部数据集.来自另一个医疗中心的外部数据集和公共挑战数据集用于外部验证。设计了一种深度学习方法用于前列腺癌检测和Gleason等级预测。计算曲线下面积(AUC)以比较模型性能。
结果：对于前列腺癌检测，内部数据集包括来自195名健康个体(年龄:57.27±14.45岁)和302名诊断为前列腺癌的患者(年龄:72.20±8.34岁)的数据.在验证集中我们的前列腺癌检测模型的AUC(n=96,19.7%)为0.918。对于格里森品位预测，数据集包括来自302名前列腺癌患者中的283名的数据，227名（年龄：72.06±7.98岁）和56名（年龄：72.78±9.49岁）患者正在接受培训和测试，分别。外部和公共挑战数据集包括来自48名患者（年龄：72.19±7.81岁）和91名患者（年龄信息不可用）的数据。分别。我们在训练集中的格里森等级预测模型的AUC（n=227）为0.902，而那些验证（n=56），外部验证(n=48)，和公共挑战验证集(n=91)分别为0.854,0.776和0.838.
结论：通过多中心数据集验证，我们提出的深度学习方法可以检测前列腺癌，并比人类专家更好地预测Gleason等级。
■精确的前列腺癌检测和Gleason分级预测对临床治疗和决策具有重要意义。
结论：对于放射科医生来说，前列腺分割比前列腺癌病灶更容易注释。我们的深度学习方法检测到前列腺癌并预测Gleason分级，表现优于人类专家。非侵入性Gleason等级预测可以减少不必要的活检次数。
方法：前列腺癌患者，经组织病理学证实，2015年9月至2022年6月期间在我们医院接受磁共振成像检查的患者被回顾性纳入内部数据集.来自另一个医疗中心的外部数据集和公共挑战数据集用于外部验证。设计了一种深度学习方法用于前列腺癌检测和Gleason等级预测。计算曲线下面积(AUC)以比较模型性能。
结果：对于前列腺癌检测，内部数据集包括来自195名健康个体(年龄:57.27±14.45岁)和302名诊断为前列腺癌的患者(年龄:72.20±8.34岁)的数据.在验证集中我们的前列腺癌检测模型的AUC(n=96,19.7%)为0.918。对于格里森品位预测，数据集包括来自302名前列腺癌患者中的283名的数据，227名（年龄：72.06±7.98岁）和56名（年龄：72.78±9.49岁）患者正在接受培训和测试，分别。外部和公共挑战数据集包括来自48名患者（年龄：72.19±7.81岁）和91名患者（年龄信息不可用）的数据。分别。我们在训练集中的格里森等级预测模型的AUC（n=227）为0.902，而那些验证（n=56），外部验证(n=48)，和公共挑战验证集(n=91)分别为0.854,0.776和0.838.
结论：通过多中心数据集验证，我们提出的深度学习方法可以检测前列腺癌，并比人类专家更好地预测Gleason等级。
■精确的前列腺癌检测和Gleason分级预测对临床治疗和决策具有重要意义。
结论：对于放射科医生来说，前列腺分割比前列腺癌病灶更容易注释。我们的深度学习方法检测到前列腺癌并预测Gleason分级，表现优于人类专家。非侵入性Gleason等级预测可以减少不必要的活检次数。
