Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

Xi, I.; Zhao, Yijun; Wang, Robin; Chang, Marcello; Purkayastha, Subhanik; Chang, Ken; Huang, Raymond Y.; Silva, Alvin C.; Vallières, Martin; Habibollahi, Peiman; Fan, Yong; Zou, Beiji; Gade, T.; Zhang, Paul J.; Soulen, Michael C.; Zhang, Zishu; Bai, Harrison X.; Stavropoulos, S. William

doi:10.1158/1078-0432.ccr-19-0374

Cited by 106 publications

(69 citation statements)

References 48 publications

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“…Similarly, Xi et al developed a DL model based on MRI data and compared it to four expert radiologists to distinguish benign renal tumors from RCC. The DL model showed overall higher accuracy, sensitivity, and specificity and was comparable to expert diagnostic opinion [19]. Table 1 summarizes articles investigating the differentiation of benign from malignant lesions using radiomics strategies.…”

Section: Renal Mass Differentiationmentioning

confidence: 81%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.

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Section: Renal Mass Differentiationmentioning

confidence: 81%

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Suarez-Ibarrola

Basulto-Martínez

Heinze

et al. 2020

Cancers

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“…Images were padded and resized to 512 by 512 pixels. Single-channel images were converted to 3-channel images by repeating the single channel 3 times [12,19,20]. Pixel values were normalized by scaling values into the range [0, 1], then subtracting (0485, 0456, 0406) and dividing by (0229, 0224, 0225) channel-wise.…”

Section: Implications Of All the Available Evidencementioning

confidence: 99%

“…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 [10,14À17] 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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“…The cohort was partly overlapped with our previous work. 27 Histopathologic diagnosis was obtained for all 430 tumors after surgical excision; Fuhrman grade was available for 353 tumors according to the 4-tiered Fuhrman classification, while the other 77 tumors were graded by the 4-tiered WHO/ISUP grading system. RCCs were grouped into low-grade (grades I and II) and high-grade (grades III and IV).…”

Section: Patient Cohortmentioning

confidence: 99%

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

Zhao

Chang

Wang

et al. 2020

Magnetic Resonance Imaging

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Background Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision‐making. Purpose To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low‐grade (grade I–II) from high‐grade (grade III–IV) in stage I and II renal cell carcinoma. Study Type Retrospective. Population In all, 376 patients with 430 renal cell carcinoma lesions from 2008–2019 in a multicenter cohort were acquired. The 353 Fuhrman‐graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. Field Strength/Sequence 1.5T and 3.0T/T2‐weighted and T1 contrast‐enhanced sequences. Assessment The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision‐recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. Statistical Tests Mann–Whitney U‐test for continuous data and the chi‐square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low‐ and high‐grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. Results The final deep‐learning model achieved a test accuracy of 0.88 (95% CI: 0.73–0.96), sensitivity of 0.89 (95% CI: 0.74–0.96), and specificity of 0.88 (95% CI: 0.73–0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73–0.90), sensitivity of 0.92 (95% CI: 0.84–0.97), and specificity of 0.78 (95% CI: 0.68–0.86) in the WHO/ISUP test set. Data Conclusion Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. Level of Evidence 3 Technical Efficacy Stage 2

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Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

Cited by 106 publications

References 48 publications

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

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

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

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