RESUMO
PURPOSE: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019). MATERIALS AND METHODS: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T2-weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis. RESULTS: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T2WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%. CONCLUSION: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference.
RESUMO
BACKGROUND: Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension. PURPOSE: To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs. STUDY TYPE: Retrospective. POPULATION: 797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses. FIELD STRENGTH/SEQUENCES: 3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging. ASSESSMENT: Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions. STATISTICAL TESTS: Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05. RESULTS: The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993). CONCLUSION: The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.
RESUMO
PURPOSE: Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC. METHODS: We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)]. RESULTS: In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780). CONCLUSION: The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.