Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. PURPOSE: The ureter was quantitatively measured on 3D MRU images using a deep learning model. METHODS: A retrospective cohort of 445 3D MRU scans (443 patients, 52 ± 18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55 ± 21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. RESULTS: In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p  < â€¯0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p  < â€¯0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p = 0.08) or in the non-dilated (p = 0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p > 0.05). CONCLUSION: The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

OBJECTIVES: To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view. METHODS: A total of 1431 consecutive Laurin views of the PFJ were retrospectively collected and divided into two parts: (1) the model development dataset (dataset 1, n = 1230) and (2) the hold-out test set (dataset 2, n = 201). Dataset 1 was used to develop the U-shaped fully convolutional network (U-Net) model to segment the landmarks of the PFJ. Based on the predicted landmarks, the PFJ parameters were calculated, including the sulcus angle (SA), congruence angle (CA), patellofemoral ratio (PFR), and lateral patellar tilt (LPT). Dataset 2 was used to assess the model performance. The mean of three radiologists who independently measured the PFJ parameters was defined as the reference standard. Model performance was assessed by the intraclass correlation coefficient (ICC), mean absolute difference (MAD), and root mean square (RMS) compared to the reference standard. Ninety-five percent limits of agreement (95% LoA) were calculated pairwise for each radiologist, reference standard, and model. RESULTS: Compared with the reference standard, U-Net showed good performance for predicting SA, CA, PFR, and LPT, with ICC = 0.85-0.97, MAD = 0.06-5.09, and RMS = 0.09-6.90 in the hold-out test set. Except for the PFR, the remaining parameters measured between the reference standard and the model were within the 95% LoA in the hold-out test dataset. CONCLUSIONS: The U-Net-based deep learning approach had a relatively high model performance in automatically measuring SA, CA, PFR, and LPT. KEY POINTS: • The U-Net model could be used to segment the landmarks of the PFJ and calculate the SA, CA, PFR, and LPT, which could be used to evaluate the patellar instability. • In the hold-out test, the automatic measurement model yielded comparable performance with reference standard. • The automatic measurement model could still accurately predict SA, CA, PFR, and LPT in patients with PI and/or PFOA.

Automatic diagnosis and grading of patellofemoral osteoarthritis from the axial radiographic view: a deep learning-based approach.

BACKGROUND: Patellofemoral osteoarthritis (PFOA) has a high prevalence and is assessed on axial radiography of the patellofemoral joint (PFJ). A deep learning (DL)-based approach could help radiologists automatically diagnose and grade PFOA via interpreting axial radiographs. PURPOSE: To develop and assess the performance of a DL-based approach for diagnosing and grading PFOA on axial radiographs. MATERIAL AND METHODS: A total of 1280 (dataset 1) axial radiographs were retrospectively collected and utilized to develop the high-resolution network (HRNet)-based classification models. The ground truth was the interpretation from two experienced radiologists in consensus according to the K-L grading system. A binary-class model was trained to diagnose the presence (K-L 2∼4) or absence (K-L 0∼1) of PFOA. A multi-class model was used to grade the stage of PFOA, i.e. from K-L 0 to K-L 4. Model performances were evaluated using the receiver operating characteristics (ROC), confusion matrix, and the corresponding evaluation metrics (positive predictive value [PPV], negative predictive value [NPV], F1 score, sensitivity, specificity, accuracy) of the internal test set (n = 129) from dataset 1 and an external validation set (dataset 2, n = 187). RESULTS: For the binary-class model, the area under the curve (AUC) was 0.91 in the internal test set and 0.90 in the external validation set. For grading PFOA, moderate to severe stage of PFOA exhibited a good performance in these two datasets (AUC = 0.91-0.98, PPV = 0.69-0.90, NPV = 0.92-0.99, F1 score = 0.72-0.87, sensitivity = 0.75-0.87, specificity = 0.90-0.99, accuracy = 0.87-0.98). CONCLUSION: The HRNet-based approach performed well in diagnosing and grading radiographic PFOA, especially for the moderate to severe cases.

Prognostic risk factors and survival models for T3 locally advanced rectal cancer: what can we learn from the baseline MRI?

OBJECTIVES: To evaluate the baseline MRI characteristics for predicting survival outcomes and construct survival models for risk stratification to facilitate personalized treatment and follow-up strategies in patients with MRI-defined T3 (mrT3) locally advanced rectal cancer (LARC). METHODS: We retrospectively reviewed 256 mrT3 LARC patients evaluated between 2008 and 2012 in our institution, with an average follow-up period of 6.8 ± 1.2 years. The baseline MRI characteristics, clinical data, and follow-up information were evaluated. The patients were randomized into a training cohort (TC, 186 patients) and validation cohort (VC, 70 patients). The TC dataset was used to develop multivariate nomograms for disease-free survival (DFS) and overall survival (OS), while the VC dataset was used for independent validation of the models. Harrell concordance (C) indices and Hosmer-Lemeshow calibration were used to evaluate the performances of the models. RESULTS: Baseline mrT3 substage, extramural venous invasion (EMVI) grading, mucinous adenocarcinoma, mesorectal fascia involvement, elevated pretreatment carcinoembryonic antigen level, and neoadjuvant chemoradiotherapy (NCRT) were independent predictors of DFS. T3 substage, EMVI grading, and NCRT were also independent predictors of OS. The nomograms constructed permitted the individualized prediction of 3-year and 5-year DFS and 5-year OS with high discrimination (C-index range, 0.833-0.892) and good calibration in the TC and VC. CONCLUSIONS: We have identified baseline MRI characteristics that help independently predict survival outcomes in patients with mrT3 LARC. The survival models based on these characteristics allow for the individualized pretreatment risk stratification in patients with mrT3 LARC. KEY POINTS: • Baseline MRI characteristics can independently stratify risk and predict survival outcomes in patients with mrT3 LARC. • The nomograms built using selected baseline MRI characteristics facilitate the individualized pretreatment risk stratification and help with clinical decision-making in patients with mrT3 LARC. • MR-defined risk factors should, therefore, be carefully reported in the baseline MRI evaluation.