Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma(ccRcC),non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment.A total of 126345 ...Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma(ccRcC),non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment.A total of 126345 computerized tomography(cT)images from four independent patient cohorts were included for analysis in this study.We propose a V Bottieneck multi-resolution and focus-organ network(VB-MrFo-Net)using a cascade framework for deep learning analysis.The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation,with a Dice score of 0.87.The nuclear-grade prediction model performed best in the logistic regression classifier,with area under curve values from 0.782 to 0.746.Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk,with a hazard ratio(HR)of 2.49[95%confidence interval(CI):1.13-5.45,P=0.023]in the General cohort.Excellent performance had also been verified in the Cancer Genome Atlas cohort,the Clinical Proteomic Tumor Analysis Consortium cohort,and the Kidney Tumor Segmentation Challenge cohort,with HRs of 2.77(95%CI:1.58-4.84,P=0.0019),3.83(95%CI:1.22-11.96,P=0.029),and 2.80(95%CI:1.05-7.47,P=0.025),respectively.In conclusion,we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRcc.The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments,which couid provide practical advicefordecidingtreatmentoptions.展开更多
Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice.We applied a deep learning algorithm to construct a prognostic model and...Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice.We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score(DLFscore)for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer.Based on this prognostic model,there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas(TCGA)cohort(P<0.0001).In the validation cohort GSE116918,we also observed a consistent conclusion with the training set(P=0.02).Additionally,functional enrichment analysis showed that DNA repair,RNA splicing signaling,organelle assembly,and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis.Meanwhile,the prognostic model we constructed also had application value in predicting drug sensitivity.We predicted some potential drugs for the treatment of prostate cancer through AutoDock,which could potentially be used for prostate cancer treatment.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.81972393 and 82002665).
文摘Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma(ccRcC),non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment.A total of 126345 computerized tomography(cT)images from four independent patient cohorts were included for analysis in this study.We propose a V Bottieneck multi-resolution and focus-organ network(VB-MrFo-Net)using a cascade framework for deep learning analysis.The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation,with a Dice score of 0.87.The nuclear-grade prediction model performed best in the logistic regression classifier,with area under curve values from 0.782 to 0.746.Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk,with a hazard ratio(HR)of 2.49[95%confidence interval(CI):1.13-5.45,P=0.023]in the General cohort.Excellent performance had also been verified in the Cancer Genome Atlas cohort,the Clinical Proteomic Tumor Analysis Consortium cohort,and the Kidney Tumor Segmentation Challenge cohort,with HRs of 2.77(95%CI:1.58-4.84,P=0.0019),3.83(95%CI:1.22-11.96,P=0.029),and 2.80(95%CI:1.05-7.47,P=0.025),respectively.In conclusion,we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRcc.The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments,which couid provide practical advicefordecidingtreatmentoptions.
基金supported by the National Natural Science Foundation of China(Grant No.82172920).
文摘Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice.We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score(DLFscore)for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer.Based on this prognostic model,there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas(TCGA)cohort(P<0.0001).In the validation cohort GSE116918,we also observed a consistent conclusion with the training set(P=0.02).Additionally,functional enrichment analysis showed that DNA repair,RNA splicing signaling,organelle assembly,and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis.Meanwhile,the prognostic model we constructed also had application value in predicting drug sensitivity.We predicted some potential drugs for the treatment of prostate cancer through AutoDock,which could potentially be used for prostate cancer treatment.
