Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Homayounieh, Fatemeh; Khera, Ruhani Doda; Bizzo, Bernardo C.; Ebrahimian, Shadi; Primak, Andrew N.; Schmidt, Bernhard; Saini, Sanjay; Kalra, Mannudeep K.

doi:10.1007/s00261-020-02865-0

Cited by 17 publications

(13 citation statements)

References 29 publications

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“…Radiomics analysis allows for the quantitative analysis of a wide range of morphologic and physiologic properties of selected tissues, including shape and histogram‐based and textural features 11,12 . Radiomics has been widely applied in the analysis of various organs and tissues such as the liver, breast, kidney, brain, and lung 13–16 . Therefore, we also evaluated the predictive value of features extracted from ultrasound images to investigate the possibility of integrating radiomics into the prediction model.…”

Section: Introductionmentioning

confidence: 99%

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A nomogram‐based model and ultrasonic radiomic features for gallbladder polyp classification

Zhang

Wang

et al. 2022

J of Gastro and Hepatol

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Background and Aim Gallbladder polyps (GBPs) are relatively common. Many studies have attempted to distinguish between benign and neoplastic GBPs to identify early‐stage gallbladder carcinoma. We have established an accurate neoplastic predictive model and evaluated the effectiveness of radiomics in predicting malignancy in patients with GBPs. Methods A total of 503 patients confirmed through postoperative pathology were included in this retrospective study. Clinical information and ultrasonographic findings were retrospectively analyzed. The model was constructed from independent risk factors using Spearman correlation and logistic regression analysis of a training cohort of 250 GBP patients, and its efficacy was verified using an internal validation group of 253 consecutive patients through the receiver operating characteristic curve (ROC). The area of GBPs was delimited manually, and the texture features of ultrasound images were analyzed using correlation and ROC analysis. Results Independent predictors, including age, gallstones, carcinoembryonic antigen, polyp size, and sessile shape, were incorporated into the nomogram model for the neoplastic potential of GBPs. Compared with other proposed prediction methods, the established nomogram model showed good discrimination ability in the training group (area under the curve [AUC]: 0.865) and validation group (AUC: 0.845). Regarding ultrasonic radiomics, the minimum caliper diameter was identified as the only independent predictor (AUC: 0.841). Conclusions Our preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs using simple clinical data, and our study verified the use of radiomics in GBP identification, which may be valuable for avoiding unnecessary surgery in patients.

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Section: Introductionmentioning

confidence: 99%

“…11,12 Radiomics has been widely applied in the analysis of various organs and tissues such as the liver, breast, kidney, brain, and lung. [13][14][15][16] Therefore, we also evaluated the predictive value of features extracted from ultrasound images to investigate the possibility of integrating radiomics into the prediction model.…”

Section: Introductionmentioning

confidence: 99%

A nomogram‐based model and ultrasonic radiomic features for gallbladder polyp classification

Zhang

Wang

et al. 2022

J of Gastro and Hepatol

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“…Second, manual set for ROI was performed by students and radiologists reading the DWI images. On CT or MRI, automatic segmentation based on deep learning has been successfully established with equivalent accuracy to expert segmentation in kidneys [5,17,18]. Due to the small volume in children's kidneys, it is difficult to tell the kidneys apart from other organs in the body which may have comparable intensity, especially in CAKUT.…”

Section: Discussionmentioning

confidence: 99%

A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

Kang

Zhang

et al. 2022

Front. Phys.

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Background: Assessment of renal lesions and deficiency accurately remains critical in the diagnosis of congenital anomalies of the kidneys and urinary tracts (CAKUT) in children. Advanced imaging such as Magnetic resonance Imaging (MRI) and Diffusion weighted Imaging (DWI) allows structural and functional insufficiency to be detected. Currently, radiomics machine learning models are being explored as full-automated diagnostic tools. We aimed to develop a machine learning integrated radiomics model to predict renal anomalies and deficiency in children.Methods: A retrospective study of 280 children with MRI/DWI were enrolled between 2018 and 202 at a children’s hospital. A total of 1,037 radiomics features were extracted from the DWI images of each participant, which were divided into training set and test set (8:2 split). Using 5-fold cross-validated method, multiple machine learning algorithms were employed to predict renal lesions and deficiency when compared with the radiologist’s diagnosis based on DWI, 99mTc-labeled dimercaptosuccinic acid (DMSA) SPECT cortical renal scintigraphy or 99mTc-labeled diethylenetriamine pentaacetate (DTPA) renal scan.Results: For detecting the kidney lesions, the LASSO + Random Forest algorithm outperformed other classifiers with an accuracy of 0.750 (95% confidence interval, 0.734–0.766) and area under the curve (AUC) of 0.765 (95% confidence interval, 0.700–0.831). The performance of classifiers did not show a significant difference (p > 0.05) in detecting bilateral or unilateral kidney lesions by DWI scanning. The classifiers performed significantly better in bilateral kidney deficit than in unilateral kidney deficit (p < 0.05). We next built prediction models for renal deficiency using the radiomics signature and clinical features compared to renal scintigraphy. The ensemble model had a high-test accuracy of 80.9% ± 4.2% and a sensitivity of 91.7% ± 7.1% with a moderate calibration.Conclusion: An ensemble model integrated with DWI-radiomic and clinical features can be utilized to predict renal lesions and deficiency in children with CAKUT.

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“…19 Homayounieh et al have demonstrated that stone radiomics features (most notably stone volume) are highly predictive of future hydronephrosis, future stone burden, and invasive treatment. 22 Relatively few works have been published that tackle the challenge of computer-aided detection of kidney stones in CT. Lee et al used texture-and intensity-based features to train an artificial neural network to distinguish kidney stones from vascular calcifications. 23 Liu et al segmented the kidneys and then used total-variation flow denoising followed by the maximal stable extremal regions method to segment stones.…”

Section: Introductionmentioning

confidence: 99%

“…Homayounieh et al. have demonstrated that stone radiomics features (most notably stone volume) are highly predictive of future hydronephrosis, future stone burden, and invasive treatment 22 …”

Section: Introductionmentioning

confidence: 99%

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

et al. 2022

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Purpose Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning‐based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low‐dose CT or tested on a large‐scale external dataset. Methods We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient‐level labels were used as an external validation set. A 3D U‐Net model was employed to segment the kidneys, followed by gradient‐based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions. Results The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low‐dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (r2=0.95$r^2 = 0.95$). For patient‐level classification, the system achieved an area under the receiver‐operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney stones. Conclusions Our deep‐learning‐based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.

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Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Cited by 17 publications

References 29 publications

A nomogram‐based model and ultrasonic radiomic features for gallbladder polyp classification

A nomogram‐based model and ultrasonic radiomic features for gallbladder polyp classification

A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

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