Deep Learning Based on <scp>MRI</scp> for Differentiation of Low‐ and High‐Grade in Low‐Stage Renal Cell Carcinoma

Zhao, Yijun; Chang, Marcello; Wang, Robin; Xi, I.; Chang, Ken; Huang, Raymond Y.; Vallières, Martin; Habibollahi, Peiman; Dagli, Mandeep; Palmer, Matthew; Zhang, Paul J.; Silva, Alvin C.; Yang, Li; Soulen, Michael C.; Zhang, Zishu; Bai, Harrison X.; Stavropoulos, S. William

doi:10.1002/jmri.27153

Cited by 35 publications

(22 citation statements)

References 36 publications

(69 reference statements)

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“…Our study was generally in line with prior reports of renal cancer assessed with machine learning radiomics (29,(38)(39)(40)(41). Our model performance was comparable to the prior studies which had AUC values reaching 0.86~0.98 for predicting pathological grade of renal cancers.…”

Section: Discussionsupporting

confidence: 89%

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Xiao

Zeng

et al. 2021

Front. Oncol.

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BackgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.MethodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.ResultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.ConclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

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Section: Discussionsupporting

confidence: 89%

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Xiao

Zeng

et al. 2021

Front. Oncol.

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“…Deep learning models can recognize predictive features directly from images by utilizing a back-propagation algorithm which recalibrates the model's internal parameters after each round of training [10] . Recent studies have shown the potential of deep learning in the assessment of solid liver lesions on ultrasonography [11] , renal lesions [ 12 , 13 ] and glioma on MR Imaging [ 10 , [14] , [15] , [16] , [17] ] and abnormal chest radiographs [18] .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

Pan

Bao

et al. 2020

eBioMedicine

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Background: To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists. Methods: A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and preoperative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests. Findings: For benign vs. not benign, model achieved area under curve (AUC) of 0894 and 0877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0907 and 0916 on cross-validation and external testing, respectively. For three-way classification, model achieved 721% accuracy vs. 746% and 721% for the two subspecialists on cross-validation (p = 003 and p = 052, respectively). On external testing, model achieved 734% accuracy vs. 693%, 734%, 731%, 679%, and 634% for the two subspecialists and three junior radiologists (p = 014, p = 089, p = 093, p = 002, p < 001 for radiologists 1À5, respectively). Interpretation: Deep learning can classify primary bone tumors using conventional radiographs in a multiinstitutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists.

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“…Although numerous efforts have been made to derive the clinical or histological characteristics from radiological images, the majority of these studies solely pertain to a single task, either target segmentation or image classification [10,[29][30][31][32]. A framework that systematically performs cancer segmentation and evaluation is still rare.…”

Section: Discussionmentioning

confidence: 99%

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images

Yang

et al. 2021

Eur Radiol

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Objectives We tried to realize accurate pathological classification, assessment of prognosis, and genomic molecular typing of renal cell carcinoma by CT texture feature analysis. To determine whether CT texture features can perform accurate pathological classification and evaluation of prognosis and genomic characteristics in renal cell carcinoma. Methods Patients with renal cell carcinoma from five open-source cohorts were analyzed retrospectively in this study. These data were randomly split to train and test machine learning algorithms to segment the lesion, predict the histological subtype, tumor stage, and pathological grade. Dice coefficient and performance metrics such as accuracy and AUC were calculated to evaluate the segmentation and classification model. Quantitative decomposition of the predictive model was conducted to explore the contribution of each feature. Besides, survival analysis and the statistical correlation between CT texture features, pathological, and genomic signatures were investigated. Results A total of 569 enhanced CT images of 443 patients (mean age 59.4, 278 males) were included in the analysis. In the segmentation task, the mean dice coefficient was 0.96 for the kidney and 0.88 for the cancer region. For classification of histologic subtype, tumor stage, and pathological grade, the model was on a par with radiologists and the AUC was 0.83 ± 0.1, 0.80 ± 0.1, and 0.77 ± 0.1 at 95% confidence intervals, respectively. Moreover, specific quantitative CT features related to clinical prognosis were identified. A strong statistical correlation (R 2 = 0.83) between the feature crosses and genomic characteristics was shown. The structural equation modeling confirmed significant associations between CT features, pathological (β = − 0.75), and molecular subtype (β = − 0.30). Conclusions The framework illustrates high performance in the pathological classification of renal cell carcinoma. Prognosis and genomic characteristics can be inferred by quantitative image analysis. Key Points• The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.

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Deep Learning Based on MRI for Differentiation of Low‐ and High‐Grade in Low‐Stage Renal Cell Carcinoma

Cited by 35 publications

References 36 publications

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study

A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images

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