Purpose: To evaluate accuracy of MRI in detecting renal tumor pseudocapsule (PC) invasion and to propose a classification based on imaging of PC status in patients with renal cell carcinoma. Methods: From January 2017 to June 2018, 58 consecutive patients with localized renal cell carcinoma were prospectively enrolled. MRI was performed preoperatively and PC was classified, according to its features, as follows: MRI-Cap 0 (absence of PC), MRI-Cap 1 (presence of a clearly identifiable PC), MRI-Cap 2 (focally interrupted PC), and MRI-Cap 3 (clearly interrupted and infiltrated PC). A 3D image reconstruction showing MRI-Cap score was provided to both surgeon and pathologist to obtain complete preoperative evaluation and to compare imaging and pathology reports. All patients underwent laparoscopic partial nephrectomy. In surgical specimens, PC was classified according to the renal tumor capsule invasion scoring system (i-Cap). Results: A concordance between MRI-Cap and i-Cap was found in 50/58 (86%) cases. ρ coefficient for each MRI-cap and iCap categories was: MRI-Cap 0: 0.89 (p < 0.0001), MRI-Cap1: 0.75 (p < 0.0001), MRI-Cap 2: 0.76 (p < 0.0001), and MRI-Cap3: 0.87 (p < 0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and AUC were: MRI-Cap 0: Se 97.87% Spec 83.3%, PPV 95.8%, NPV 90.9%, and AUC 90.9; MRI-Cap 1: Se 77% Spec 95.5%, PPV 83.3%, NPV 93.5%, and AUC 0.86; MRI-Cap 2- iCap 2: Se 88% Spec 90%, PPV 79%, NPV 95%, and AUC 0.89; MRI-Cap 3: Se 94% Spec 95%, PPV 88%, NPV 97%, and AUC 0.94. Conclusions: MRI-Cap classification is accurate in evaluating renal tumor PC features. PC features can provide an imaging-guided landmark to figure out where a minimal margin could be preferable during nephron-sparing surgery.
Accuracy of magnetic resonance imaging to identify pseudocapsule invasion in renal tumors / Papalia, Rocco; Panebianco, Valeria; Mastroianni, Riccardo; Del Monte, Maurizio; Altobelli, Emanuela; Faiella, Eliodoro; Grasso, Francesco Rosario; Bellangino, Mariangela; Simone, Giuseppe; Ciccozzi, Massimo; Angeletti, Silvia; D’Ovidio, Giulia; Catalano, Carlo; Gallucci, Michele; Scarpa, Roberto Mario; Muto, Giovanni. - In: WORLD JOURNAL OF UROLOGY. - ISSN 0724-4983. - 38:2(2020), pp. 407-415. [10.1007/s00345-019-02755-1]
Accuracy of magnetic resonance imaging to identify pseudocapsule invasion in renal tumors
Panebianco, ValeriaSecondo
;Mastroianni, Riccardo;Del Monte, Maurizio;Bellangino, Mariangela;D’ovidio, Giulia;Catalano, Carlo;Gallucci, Michele;
2020
Abstract
Purpose: To evaluate accuracy of MRI in detecting renal tumor pseudocapsule (PC) invasion and to propose a classification based on imaging of PC status in patients with renal cell carcinoma. Methods: From January 2017 to June 2018, 58 consecutive patients with localized renal cell carcinoma were prospectively enrolled. MRI was performed preoperatively and PC was classified, according to its features, as follows: MRI-Cap 0 (absence of PC), MRI-Cap 1 (presence of a clearly identifiable PC), MRI-Cap 2 (focally interrupted PC), and MRI-Cap 3 (clearly interrupted and infiltrated PC). A 3D image reconstruction showing MRI-Cap score was provided to both surgeon and pathologist to obtain complete preoperative evaluation and to compare imaging and pathology reports. All patients underwent laparoscopic partial nephrectomy. In surgical specimens, PC was classified according to the renal tumor capsule invasion scoring system (i-Cap). Results: A concordance between MRI-Cap and i-Cap was found in 50/58 (86%) cases. ρ coefficient for each MRI-cap and iCap categories was: MRI-Cap 0: 0.89 (p < 0.0001), MRI-Cap1: 0.75 (p < 0.0001), MRI-Cap 2: 0.76 (p < 0.0001), and MRI-Cap3: 0.87 (p < 0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and AUC were: MRI-Cap 0: Se 97.87% Spec 83.3%, PPV 95.8%, NPV 90.9%, and AUC 90.9; MRI-Cap 1: Se 77% Spec 95.5%, PPV 83.3%, NPV 93.5%, and AUC 0.86; MRI-Cap 2- iCap 2: Se 88% Spec 90%, PPV 79%, NPV 95%, and AUC 0.89; MRI-Cap 3: Se 94% Spec 95%, PPV 88%, NPV 97%, and AUC 0.94. Conclusions: MRI-Cap classification is accurate in evaluating renal tumor PC features. PC features can provide an imaging-guided landmark to figure out where a minimal margin could be preferable during nephron-sparing surgery.File | Dimensione | Formato | |
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