Proposal for revision of the TNM classification system for renal cell carcinoma

Ficarra, Vincenzo; Guilhot, François; Schips, Luigi; Taille, A. De La; Galetti, T. Prayer; Tostain, Jacques; Cindolo, Luca; Novara, Giacomo; Zigeuner, Richard; Bratti, Emiliano; Li, Guorong; Altieri, Vincenzo; Abbou, Claude C.; Zanolla, Luisa; Artibani, Walter; Patard, Jean‐Jacques

doi:10.1002/cncr.21465

Cited by 73 publications

(33 citation statements)

References 33 publications

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“…Such division was based on a study that showed the prognostic difference between tumors less than 4 cm and those greater than 4 cm [29]. This finding was also confirmed by a recent European multi-institutional study [30]. Another rationale for such division is that tumors less than 4 cm are amenable to nephron-sparing surgery.…”

Section: Tnm Stagingmentioning

confidence: 80%

The changing face of renal cell carcinoma pathology

Aydın¹,

Zhou

2008

Curr Oncol Rep

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Section: Tnm Stagingmentioning

confidence: 80%

The changing face of renal cell carcinoma pathology

Aydın¹,

Zhou

2008

Curr Oncol Rep

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“…7,8 On revision, renal tumors were grossly stratified into <5.5 cm and >5.5 cm according to the new optimal tumor size breakpoint put forward by Ficarra et al, 9 and were classified according to the histologic and immunohistochemical criteria put forward by Eble et al in the WHO bluebook partially devoted to tumors of the urinary system and published in 2004. 10 We used the criteria put forward by Renshaw et al, 2 Grignon and Staerbel, 11 and Renshaw 12 for review of FNA smears.…”

Section: Methodsmentioning

confidence: 99%

Solid renal masses in adults: Image‐guided fine‐needle aspiration cytology and imaging techniques—“Two Heads Better Than One?”

García‐Solano

Acosta-Ortega

Pérez-Guillermo

et al. 2007

Diagnostic Cytopathology

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We have compared the diagnostic accuracy of image-guided 25G-FNA (fine-needle aspiration) and imaging modalities in a group of 31 patients with solid space-occupying renal lesions. All patients had undergone total nephrectomy and histologic sections were available for review.By FNA there were 24 malignant diagnoses, I benign diagnosis, and 6 cases with yield inadequate for diagnosis. The FNA accuracy for malignancy was 100% with no false positive cases; cancer typing by FNA matched the final histologic diagnoses in 91.6% of cases. Sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 14%, 80%, and 14%, respectively.Radiologically there were 26 diagnoses of malignancy, I of benignity, and 4 indeterminate lesions (IL). Accuracy for malignancy was 100%, with one false positive case; cancer typing matched the final histologic diagnoses in 84%. Sensitivity of imaging modalities was 86%, specificity 17%, positive predictive value 83%, and negative predictive value 20%. Four IL corresponded to renal cell carcinoma in the final histologic report: two IL had a previous diagnosis of malignancy by FNA, and the yield of two was inadequate for cytologic diagnosis.Both techniques have 100% accuracy for the diagnosis of malignancy. The sensitivity, specificity, positive predictive value, and negative predictive value of imaging techniques are slightly higher than those obtained by FNA. Imaging techniques and FNA of solid renal masses complement each other in IL and in nondiagnostic FNAs. Diagn. Cytopathol. 2008;36:8-12.' 2007 Wiley-Liss, Inc.Key Words: solid renal masses; fine-needle aspiration; needle biopsy; cytology; imaging techniques Fine-needle aspiration (FNA) has a well-defined and traditional role in the diagnosis and treatment of numerous conditions in almost every organ of the body. Its utility is undeniable in space-occupying lesions of various locations; however, the use of FNA in space-occupying lesions of the kidney has recently tended to be confined to specific clinico-pathological conditions, mainly because of the refinement of imaging techniques. [1][2][3][4][5][6] The purpose of this article is 3-fold: to assess the diagnostic accuracy of image-guided 25G-FNA and imaging modalities in a group of 31 patients with solid space-occupying renal lesions who later underwent total nephrectomy (TN) for histologic confirmation; to compare the diagnostic accuracy of the two techniques; and to investigate whether FNA provides valuable information for the clinical management of patients with solid renal masses. Material and MethodsBetween January 2000 and July 2006 a total number of 129 patients of our hospital had been referred to either image-guided 25G-FNA as preoperative diagnostic procedure of renal space-occupying lesions, or to some type of imaging modality for planning clinical management, or had been referred to both. Of these 129 patients, the 31 in whom image-guided FNA, imaging studies, and TN were available were chosen for the present study.The cytologic specimens of im...

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“…Almost all available mathematical models use the 1997 version of the TNM staging system that is very similar to the more recent 2002 version [32] . Nevertheless, it is possible to hypothesize that the forthcoming TNM version will satisfy some of the proposals for reclassification of organ-confined (T1-2), locally advanced (T3-4), and lymph node involving (N1-2) tumors published in the last years [33][34][35][36] .…”

Section: Discussionmentioning

confidence: 99%

Mathematical Models for Prognostic Prediction in Patients with Renal Cell Carcinoma

et al. 2008

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Objectives: The objectives of this study are to catalogue all models developed to predict survival of RCC patients and to identify the ones to be used in different situations. Methods: A systematic review was performed searching with a free text and MeSH strategy 3 electronic databases. For each model, the following parameters were identified: number, features of the patients; evaluation endpoints; clinical and/or pathological variables included; concordance indexes (cI). Results: The research retrieved 156 records. Eleven articles proposed new models, 5 articles external validations. We retrieved 2 mathematical models including clinical variables only (Yaycioglu, cI 0.651; Cindolo, cI 0.672); 2 algorithms including also pathological variables (SSIGN, cI 0.819; UISS, cI 0.79–0.84), 5 nomograms (Kattan, cI 0.76–0.86; Sorbellini, cI 0.82; Kim 2004, cI 0.79, Kim 2005, cI 0.68; Karakiewicz, cI 0.86); 2 algorithms for patients with metastatic disease (Motzer, Leibovich). Conclusions: The SSIGN was the most accurate algorithm for conventional RCC, while the UISS allowed the evaluation of patients regardless of tumor histotype. The Sorbellini nomogram is applicable only for patients with conventional RCC, while the Kattan and Karakiewicz nomograms also provide information for other histotypes. Metastatic patients can be evaluated with Leibovich and Motzer algorithms. Two models combine molecular markers and clinical features (Kim 2004–2005).

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Proposal for revision of the TNM classification system for renal cell carcinoma

Abstract: The authors thank Christine M. Lohse from the Division of Biostatistics, Mayo Clinic (Rochester, MN) for her excellent and kind collaboration with martingale residuals analysis. Receiving this kind of collaboration was a great honor for all the authors.

Cited by 73 publications

References 33 publications

The changing face of renal cell carcinoma pathology

The changing face of renal cell carcinoma pathology

Solid renal masses in adults: Image‐guided fine‐needle aspiration cytology and imaging techniques—“Two Heads Better Than One?”

Mathematical Models for Prognostic Prediction in Patients with Renal Cell Carcinoma

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