Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer

Abstract

Background: The aim was to evaluate the feasibility of radiomics features based on diffusion-weighted imaging (DWI) at high b-values for grading bladder cancer and to compare the possible advantages of high-b-value DWI over the standard b-value DWI. Methods: Seventy-four participants with bladder cancer were included in this study. DWI sequences using a 3 T MRI with b-values of 1000, 1700, and 3000 s/mm2 were acquired, and the corresponding ADC maps were generated, followed with feature extraction. Patients were randomly divided into training and testing cohorts with a ratio of 8:2. The radiomics features acquired from the ADC1000, ADC1700, and ADC3000 maps were compared between low- and high-grade bladder cancers by using the Wilcox analysis, and only the radiomics features with significant differences were selected. The least absolute shrinkage and selection operator method and a logistic regression were performed for the feature selection and establishing the radiomics model. A receiver operating characteristic (ROC) analysis was conducted to assess the diagnostic performance of the radiomics models. Results: In the training cohorts, the AUCs of the ADC1000, ADC1700, and ADC3000 model for discriminating between low- from high-grade bladder cancer were 0.901, 0.920, and 0.901, respectively. In the testing cohorts, the AUCs of ADC1000, ADC1700, and ADC3000 were 0.582, 0.745, and 0.745, respectively. Conclusions: The radiomics features extracted from the ADC1700 maps could improve the diagnostic accuracy over those extracted from the conventional ADC1000 maps.

MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment

“…A total of 1092 studies were initially identified through the literature search. After removing duplicates and screening for eligibility, 26 studies were finally included in the analysis [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Figure 1 depicts the PRISMA flowchart.…”

“…Of the 26 eligible studies, 6 had the preoperative prediction of BCa pathological grade as their main objective, all of them dividing the data into low-grade and high-grade tumors [ 13 , 18 , 26 , 27 , 30 , 35 ].…”

“…In 23 articles, multiple segmentation was performed by two or three radiologists. However, the intraclass correlation coefficient was employed only in 14 studies [ 15 , 19 , 21 , 23 , 26 , 27 , 28 , 29 , 30 , 32 , 33 , 34 , 35 , 37 ] in order to select the most robust radiomics features, while in the remaining articles, a consensus between radiologists was made regarding the final segmented volume.…”

“…Two papers built a combined model for the prediction of bladder cancer grade, all o them using a separate data set for validation [27,35]. Also in this case, the model devel oped in the paper of Zheng et al [27] [18,27,30,35] conad-Score in a validation group with AUC values higher than 0.9. The best as achieved by Zheng et al [27], who reported an AUC of 0.961 in the train-952 in the validation set.…”

Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

“…Feng et al. ( 23 ) used a nomogram that combines radiomics features to predict the pathological grade of BCa. The AUC value of the ADC1700 model for differentiating low-grade BCa from high-grade BCa was 0.920 in the training cohort and 0.745 in the testing cohort.…”