Exploring the Relation between Rate Pressure Product and Ankle Brachial Pressure Index in Sickle Cell Patients: A Cross-Sectional Analysis

Figures

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  Fig. 1. Flowchart of the inclusion criteria and exclusion criteria of the multicenter study. center 1, SUH1st, the first affiliated hospital of Soochow University; center 2, SUH2nd, the second affiliated hospital of Soochow University; center 3, ZJGH, People’s Hospital of Zhangjiagang; center 4, CSH, Changshu no. 1 People’s Hospital; center 5, TZH, People’s Hospital of Taizhou; center 6, SKH, Suzhou Kowloon Hospital; PCa, prostate cancer

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  Fig. 2. Flowchart of the development of the AttenNet models. a The architecture of the AttenNet Model with channel attention and soft attention module. First, the VOIs were fed into a module of ResNet3D with channel attention; then, the three parallel and independent branches were used as inputs; finally, the three branches were integrated into a fusion network with a soft attention module. b Details of ResNet3D with channel attention. c Details of the block in the ResNet3D with channel attention. PCa, prostate cancer; csPCa, clinically significant prostate cancer; PZ, peripheral zone; TZ, transitional zone; Self-learning, the automatic and iterative change in the values of the elements of embedding vectors during the pretraining and training processes

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  Fig. 3. Diagnosis performances of different deep learning models for predicting PCa and csPCa. a Diagnosis performances of the ResNet, ResNet-T, ResNet-TC, and AttenNet models for predicting PCa in three external testing cohorts. The AttenNet model yields the highest AUC compared with the other three models in predicting PCa. b ROC curves of the AttenNet model and the other three models for predicting PCa in three external testing cohorts. c Diagnosis performances of the ResNet, ResNet-T, ResNet-TC, and AttenNet models for predicting csPCa in two external testing cohorts. Similarly, the AttenNet model yields the highest AUC among all the models in predicting csPCa. d ROC curves of the AttenNet model and the other three models for predicting csPCa in two external testing cohorts. ROC, receiver operating characteristics; AUC, area under ROC curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; center 4, TZH, People’s Hospital of Taizhou; center 5, CSH, Changshu No.1 People’s Hospital; center 6, SKH, Suzhou Kowloon Hospital; PCa, prostate cancer; csPCa, clinically significant prostate cancer; ResNet-T, ResNet with transfer module; ResNet-TC, ResNet with transfer and channel attention…

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  Fig. 4. Results of DCA of AttenNet for predicting PCa and csPCa. a Results of DCA of AttenNet for predicting PCa in three external testing cohorts. b Results of DCA of AttenNet for predicting csPCa in two external testing cohorts. The red lines indicate the net benefit of patients when using a biopsy based on the AttenNet model for detecting PCa (a) and csPCa (b). The blue lines indicate the net benefit of patients when they were all predicted to be positive (i.e., all patients with PI-RADS 3 underwent the biopsy) for the detection of PCa (a) and csPCa (b). The black lines indicate the net benefit of patients when they were all predicted to be negative for the detection of PCa (a) and csPCa (b)

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  Fig. 5. Clinical practice of AttenNet models for predicting PCa and csPCa in PI-RADS category 3 patients. a The downgrading and upgrading results of PI-RADS category 3 patients using the AttenNet model for predicting PCa in three external testing cohorts of center 4, center 5, and center 6. b The downgrading and upgrading results of PI-RADS category 3 patients using the AttenNet model for predicting csPCa in two external testing cohorts of center 4 and center 5. PI-RADS, prostate imaging-reporting and data system version; PI-RADS 3U, PI-RADS category 3 upgrade; PI-RADS 3D, PI-RADS category 3 downgrade; center4, CSH, Changshu No.1 People’s Hospital; center5, TZH, People’s Hospital of Taizhou; center6, SKH, Suzhou Kowloon Hospital; PCa, prostate cancer; ciPCa, clinically insignificant prostate cancer; csPCa, clinically significant prostate cancer

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  Fig. 6. The results of subgroup analysis with AttenNet models for different levels of tumor size and PSA. a The performance of the AttenNet model for predicting PCa in each subgroup. b The performance of the AttenNet model for predicting csPCa in each subgroup. Center 4, CSH, Changshu No.1 People’s Hospital; center 5, TZH, People’s Hospital of Taizhou; center 6, SKH, Suzhou Kowloon Hospital; PCa, prostate cancer; csPCa, clinically significant prostate cancer; PSA, prostate-specific antigen; ROC, receiver operating characteristics; AUC, area under ROC curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; D-max, diameter in greatest dimension