Prediction of renal mass aggressiveness using clinical and radiographic features: a global, multicentre prospective study

Golan, Shay; Eggener, Scott E.; Subotic, S.; Barret, Éric; Cormio, Luigi; Naito, Seiji; Tefekli̇, Ahmet; Pes, M. Pilar Laguna

doi:10.1111/bju.13331

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…There is an increasing likelihood of malignancy and higher grade with increasing lesion size [ 34 , 44 , 45 ]. Of benign SRM that are surgically excised, 10–38% are found to be angiomyolipomas and 34–58% are found to be oncocytomas [ 46 – 48 ]. Ultrasound, contrast-enhanced CT and MRI may aid diagnosis (Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

Imaging for the diagnosis and response assessment of renal tumours

et al. 2018

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Section: Resultsmentioning

confidence: 99%

Imaging for the diagnosis and response assessment of renal tumours

et al. 2018

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“…For patients with small renal masses, age, sex, tumor size/location, symptoms, and smoking history are associated with risk of malignancy and aggressive malignancy. However, the discriminative accuracy of these clinical and imaging characteristics is poor to moderate 17,55–57 . It is possible that the addition of mpMRI to existing clinical prediction models improves accuracy and allows more patients to avoid tumor biopsy and/or treatments.…”

Section: Discussionmentioning

confidence: 99%

“…However, the discriminative accuracy of these clinical and imaging characteristics is poor to moderate. 17,[55][56][57] It is possible that the addition of mpMRI to existing clinical prediction models improves accuracy and allows more patients to avoid tumor biopsy and/or treatments.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta‐analysis

Frank

Dawit

Bossuyt

et al. 2022

Magnetic Resonance Imaging

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Background Biparametric (bp)‐MRI and multiparametric (mp)‐MRI may improve the diagnostic accuracy of renal mass histology. Purpose To evaluate the available evidence on the diagnostic accuracy of bp‐MRI and mp‐MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. Study Type Systematic review. Population MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. Field Strength/Sequence 1.5 or 3 Tesla. Assessment Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion‐weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS‐2. Statistical Tests Meta‐analysis using a bivariate logitnormal random effects model. Results We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%–99%), and specificity was 63% (95% CI: 46%–77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%–90%) and specificity was 77% (95% CI: 73%–81%). Data Conclusion Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. Evidence Level 3 Technical Efficacy Stage 2

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“…In our study, Logistic analyses demonstrated that ERTs with higher R score (OR, 3.32; 95% CI, 1.17-9.41; P=0.024) and N score (OR, 2.87; 95% CI, 1.66-4.96; P=0.021) were independently associated with MH and higher R score (OR, 3.74; 95% CI, 1.71-8.17; P=0.001) was independently associated with UP. Several studies have demonstrated that tumor size was significantly correlated with malignant and adverse pathological features ( 4 , 21 , 42 , 43 , 45 , 46 ). A retrospective study that included 592 patients with renal tumors by Violette, P. et al.…”

Section: Discussionmentioning

confidence: 99%

Pathological diagnostic nomograms for predicting malignant histology and unfavorable pathology in patients with endophytic renal tumor

Deng

Liu

et al. 2022

Front. Oncol.

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PurposeTo develop and validate nomograms for pre-treatment prediction of malignant histology (MH) and unfavorable pathology (UP) in patients with endophytic renal tumors (ERTs).MethodsWe retrospectively reviewed the clinical information of 3245 patients with ERTs accepted surgical treatment in our center. Eventually, 333 eligible patients were included and randomly enrolled into training and testing sets in a ratio of 7:3. We performed univariable and multivariable logistic regression analyses to determine the independent risk factors of MH and UP in the training set and developed the pathological diagnostic models of MH and UP. The optimal model was used to construct a nomogram for MH and UP. The area under the receiver operating characteristics (ROC) curves (AUC), calibration curves and decision curve analyses (DCA) were used to evaluate the predictive performance of models.ResultsOverall, 172 patients with MH and 50 patients with UP were enrolled in the training set; and 74 patients with MH and 21 patients with UP were enrolled in the validation set. Sex, neutrophil-to-lymphocyte ratio (NLR), R score, N score and R.E.N.A.L. score were the independent predictors of MH; and BMI, NLR, tumor size and R score were the independent predictors of UP. Single-variable and multiple-variable models were constructed based on these independent predictors. Among these predictive models, the malignant histology-risk nomogram consisted of sex, NLR, R score and N score and the unfavorable pathology-risk nomogram consisted of BMI, NLR and R score performed an optimal predictive performance, which reflected in the highest AUC (0.842 and 0.808, respectively), the favorable calibration curves and the best clinical net benefit. In addition, if demographic characteristics and laboratory tests were excluded from the nomograms, only the components of the R.E.N.A.L. Nephrometry Score system were included to predict MH and UP, the AUC decreased to 0.781 and 0.660, respectively (P=0.001 and 0.013, respectively).ConclusionIn our study, the pathological diagnostic models for predicting malignant and aggressive histological features for patients with ERTs showed outstanding predictive performance and convenience. The use of the models can greatly assist urologists in individualizing the management of their patients.

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Prediction of renal mass aggressiveness using clinical and radiographic features: a global, multicentre prospective study

Cited by 7 publications

References 33 publications

Imaging for the diagnosis and response assessment of renal tumours

Imaging for the diagnosis and response assessment of renal tumours

Diagnostic Accuracy of MRI for Solid Renal Masses: A Systematic Review and Meta‐analysis

Pathological diagnostic nomograms for predicting malignant histology and unfavorable pathology in patients with endophytic renal tumor

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