Benign prostatic hyperplasia (BPH) and prostate cancer (PCa) belong to the most frequent diseases in ageing men. It has been proposed that prostate chronic inflammation is a risk factor for the development of both BPH and PCa. However, potential stimuli that cause or maintain inflammation in the prostate gland are still poorly characterized. Bacterial infections seems to be one of the potential sources of prostatitis. Recent studies show that Propionibacterium acnes (P. acnes) is the most prevalent microorganism in the prostate gland and may be a predisposing factor for inflammation of prostatic tissue. It indicates that P. acnes may contribute to cancer development by enhancing proinflammatory responses, as well as by modifying the prostate extracellular environment. In this review, we discuss the potential role of P. acnes in the development of BPH and PCa and highlight the importance of regulatory T CD4(+)FoxP3(+) (Treg) and Th17 cells in response to P. acnes infection in the context of both prostate diseases.

All men, almost, suffer from prostatic disorders in average life expectancy. In the year of 1950s, the first autopsy of prostate gland discovered the link between Benign prostatic hyperplasia (BPH) and Prostate Cancer (PCa). After that, many histology, biochemistry, epidemiology studies explained the association and associated risk factor for the same. From the various scientific evidence, it is proved that both diseases share some common transcription factors and signalling pathways. Still, BPH cannot be considered as the first step of PCa progression. To define, the relationship between both of the diseases, a well-defined large epidemiological study is needed. Along with androgen signalling, imbalanced apoptosis, oxidative stress, and microbial infection also crucial factors that significantly affect the pathogenesis of BPH. Various signalling pathways are involved in the progression of BPH. Androgen signalling is the driving force for the progress of PCa. In PCa androgen signalling is upregulated as compared to a healthy prostate. Some dominant Androgen-regulated genes and their functions have been discussed in this work.

The aims of the study were to explore the feasibility of generating a monoexponential model (MEM), stretched-exponential model (SEM) based diffusion-weighted imaging (DWI), and diffusion kurtosis imaging (DKI) by applying the same set of reduced b values and to compare their effectiveness in distinguishing prostate cancer from stromal hyperplasia (SH) in the transition zone (TZ) area.

To investigate the diagnostic value of quantitative parameters of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in prostate cancer.From January 2019 to June 2020, 96 patients with prostatic tumor admitted in the department of urological surgery of our hospital were selected as subjects. Magnetic resonance imaging data of 48 cases of benign prostatic hyperplasia and 48 cases of prostate cancer were retrospectively analyzed. The patients included in this study received conventional MRI and IVIM-DWI examinations. Quantitative parameters of IVIM-DWI including D value, D* value, apparent diffusion coefficient (ADC) value and f value in lesions of prostatic tumor were calculated through the double exponential model fitting algorithm. D value, D* value, ADC value and f value were compared between benign prostatic hyperplasia (BPH) group and prostate cancer group. Quantitative parameters of IVIM-DWI were also compared among patients from different Gleason scores groups. The correlation of quantitative parameters of IVIM-DWI with Gleason scores and PSA concentration was analyzed. Diagnostic efficiency of quantitative parameters of IVIM-DWI for prostate cancer was evaluated by ROC curve.Compared with those in BPH group, D value, ADC value and f value in prostate cancer group were significantly lower, but D* value was obviously higher. With the Gleason score increased, D value, ADC value and f value gradually decreased, while D* value gradually increased. The diagnostic efficiency of parameters ADC and D was higher among other parameters. D value, ADC value and f value of prostate cancer were negatively correlated with Gleason score and PSA concentration, respectively (all P<0.05), while D* value was positively correlated with Gleason score and PSA concentration.Quantitative parameters of IVIM-DWI could be used for the diagnosis and evaluation of prostate cancer, and quantitative parameters of IVIM-DWI were associated with Gleason score and PSA concentration.AJTR Copyright © 2021.

<b>Objective</b> To explore the diagnosis of lung adenocarcinoma with brain metastases based on MRI texture features and predictive value of EGFR gene mutation in lung adenocarcinoma with brain metastases. <b>Methods</b> A total of 154 patients with lung adenocarcinoma admitted were selected,and patients were divided into BMS group (n=71) and non-BMS group (n=83) according to whether they had BMS.MRI texture characteristics in the two groups were compared to evaluate the diagnostic value of energy,entropy,moment of inertia,correlation and deficit moment for lung adenocarcinoma brain metastases.EGFR gene detection was performed for patients in BMS group,and patients were divided into EGFR mutation group (n=33) and EGFR wild group (n=38).Univariate and multivariate Logistic regression analysis were performed to determine the influencing factors of MRI texture features on EGFR gene mutations in patients with lung adenocarcinoma with brain metastasis. <b>Results</b> The entropy and correlation in BMS group were significantly higher than those in the non-BMS group,and the energy,moment of inertia and deficit moment in BMS group were significantly lower than those in the non-BMS group.According to the ROC curve,the critical value of energy,sensitivity,specificity and AUC were 0.00121,69.01%,71.08% and 0.801(95%CI:0.750-0.852).The entropy critical value was 10.42,the sensitivity was 73.24%,the specificity was 69.88%,and the AUC was 0.760(95%CI:0.707-0.812).The critical value of moment of inertia was 7139.86,the sensitivity was 64.79%,the specificity was 60.24%,and the AUC was 0.708(95%CI:0.645-0.772).The critical correlation value was 0.000026,the sensitivity was 59.15%,the specificity was 60.24%,and the AUC was 0.631(95%CI:0.564-0.698).The critical value of the negative moment was 0.0301,the sensitivity was 74.65%,the specificity was 78.31%,and the AUC was 0.816(95%CI:0.768-0.864).At the optimal critical value of regression analysis,the corresponding sensitivity,specificity and AUC were 80.28%,80.72% and 0.869(95%CI:0.831-0.907).The sensitivity and specificity of regression analysis were significantly higher than that of MRI texture feature alone.Logistic analysis showed that energy,entropy,moment of inertia,correlation and deficit moment was the influencing factor of EGFR gene mutation in lung adenocarcinoma brain metastases (P<0.05). <b>Conclusion</b> Energy,entropy,moment of inertia,correlation and deficit moment were abnormally expressed in lung adenocarcinoma with brain metastases,single and regression analysis could be applied to improve the diagnostic efficiency of brain metastases.MRI based texture feature analysis can be used to predict EGFR gene mutations in lung adenocarcinoma with brain metastases.

目的 探讨基于MRI纹理特征的肺腺癌脑转移瘤诊断及对肺腺癌脑转移EGFR基因突变的预测价值。方法 选取154例肺腺癌患者,根据是否脑转移将患者分成脑转移组(n=71)和非脑转移组(n=83)。比较两组患者MRI纹理特征指标,评估能量、熵、惯性矩、相关和逆差矩对肺腺癌脑转移的诊断价值。针对脑转移组患者行EGFR基因检测,将患者分成EGFR突变组(n=33)和EGFR野生组(n=38),单因素和多因素Logistic回归分析确定MRI纹理特征对肺腺癌脑转移患者EGFR基因突变的影响因素。结果 脑转移组患者熵和相关等明显高于非脑转移组,脑转移组患者能量、惯性矩和逆差矩明显低于非脑转移组。根据ROC曲线可得,能量的临界值为0.00121,敏感度为69.01%,特异度为71.08%,AUC为0.801(95%CI:0.750~0.852);熵的临界值为10.42,敏感度为73.24%,特异度为69.88%,AUC为0.760(95%CI:0.707~0.812);惯性矩的临界值为7139.86,敏感度为64.79%,特异度为60.24%,曲线下面积(AUC)为0.708(95%CI:0.645~0.772);相关的临界值为0.000026,敏感度为59.15%,特异度为60.24%,AUC为0.631(95%CI:0.564~0.698);逆差矩的临界值为0.0301,敏感度为74.65%,特异度为78.31%,AUC为0.816(95%CI:0.768~0.864)。回归分析在最佳临界值是对应的敏感度为80.28%,特异性为80.72%,AUC为0.869(95%CI:0.831~0.907),回归分析的敏感度和特异性明显高于MRI纹理特征的单独诊断。Logistic分析显示,能量、熵、惯性矩、相关和逆差矩是肺腺癌脑转移EGFR基因突变的影响因素(P<0.05)。结论 能量、熵、惯性矩、相关及逆差矩等MRI纹理特征在肺腺癌脑转移瘤中异常表达,单独及回归分析联合诊断可应用于提高肺癌脑转移的诊断效能;且基于MRI纹理特征分析可以用于预测肺腺癌脑转移EGFR基因突变。

针对深度学习训练成本高，以及基于磁共振图像的前列腺癌临床诊断需要大量医学常识且极为耗时的问题，本文提出了一种基于级联卷积神经网络（Convolutional Neural Network，CNN）和磁共振图像的前列腺癌（Prostate Cancer，PCa）自动分类诊断方法，该网络以Faster-RCNN作为前网络，对前列腺区域进行提取分割，用于排除前列腺附近组织器官的干扰；以基于ResNet改进的网络结构CNN40bottleneck作为后网络，用于对前列腺区域病变进行分类.后网络由瓶颈结构串联组成，其中使用批量标准化（Batch Normalization，BN）、全局平均池化（Global Average Pooling，GAP）进行优化.实验结果证明，本文方法对前列腺癌诊断结果较好，而且缩减了训练时间和参数量，有效降低了训练成本.

Background Deep learning reconstruction (DLR) may improve image quality. However, its impact on diffusion-weighted imaging (DWI) of the prostate has yet to be assessed. Purpose To determine whether DLR can improve image quality of diffusion-weighted MRI at values ranging from 1000 sec/mm to 5000 sec/mm in patients with prostate cancer. Materials and Methods In this retrospective study, images of the prostate obtained at DWI with a value of 0 sec/mm, DWI with a value of 1000 sec/mm (DWI), DWI with a value of 3000 sec/mm (DWI), and DWI with a value of 5000 sec/mm (DWI) from consecutive patients with biopsy-proven cancer from January to June 2020 were reconstructed with and without DLR. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) from region-of-interest analysis and qualitatively assessed using a five-point visual scoring system (1 [very poor] to 5 [excellent]) for each high--value DWI sequence with and without DLR. The SNR, CNR, and visual score for DWI with and without DLR were compared with the paired test and the Wilcoxon signed rank test with Bonferroni correction, respectively. Apparent diffusion coefficients (ADCs) from DWI with and without DLR were also compared with the paired test with Bonferroni correction. Results A total of 60 patients (mean age, 67 years; age range, 49-79 years) were analyzed. DWI with DLR showed significantly higher SNRs and CNRs than DWI without DLR (<.001); for example, with DWI the mean SNR was 38.7 ± 0.6 versus 17.8 ± 0.6, respectively (<.001), and the mean CNR was 18.4 ± 5.6 versus 7.4 ± 5.6, respectively (<.001). DWI with DLR also demonstrated higher qualitative image quality than DWI without DLR (mean score: 4.8 ± 0.4 vs 4.0 ± 0.7, respectively, with DWI [ =.001], 3.8 ± 0.7 vs 3.0 ± 0.8 with DWI [ =.002], and 3.1 ± 0.8 vs 2.0 ± 0.9 with DWI [<.001]). ADCs derived with and without DLR did not differ substantially (>.99). Conclusion Deep learning reconstruction improves the image quality of diffusion-weighted MRI scans of prostate cancer with no impact on apparent diffusion coefficient quantitation with a 3.0-T MRI system. © RSNA, 2022. See also the editorial by Turkbey in this issue.