TNM: evolution and relation to other prognostic factors.

The TNM Classification describes the anatomic extent of cancer. TNM's ability to separately classify the individual tumor (T), node (N), and metastasis (M) elements and then group them into stages differs from other cancer staging classifications (e.g., Dukes), which are only concerned with summarized groups. The objectives of the TNM Classification are to aid the clinician in the planning of treatment, give some indication of prognosis, assist in the evaluation of the results of treatment, and facilitate the exchange of information. During the past 50 years, the TNM system has evolved under the influence of advances in diagnosis and treatment. Radiographic imaging (e.g., endoscopic ultrasound for the depth of invasion of esophageal and rectal tumors) has improved the accuracy of the clinical T, N, and M classifications. Advances in treatment have necessitated more detail in some T4 categories. Developments in multimodality therapy have increased the importance of the "y" symbol and the R (residual tumor) classification. New surgical techniques have resulted in the elaboration of the sentinel node (sn) symbol. The use of immunohistochemistry has resulted in the classification of isolated tumor cells and their distinction from micrometastasis. The most important challenge facing users of the TNM Classification is how it should interface with the large number of non-anatomic prognostic factors that are currently in use or under study. As non-anatomic prognostic factors become widely used, the TNM system provides an inviting foundation upon which to build a prognostic classification; however, this carries a risk that the system will be overwhelmed by a variety of prognostic data. An anatomic extent-of-disease classification is needed to aid practitioners in selecting the initial therapeutic approach, stratifying patients for therapeutic studies, evaluating non-anatomic prognostic factors at specific anatomic stages, comparing the weight of non-anatomic factors with extent of disease, and communicating the extent of disease data in a uniform manner. Methods are needed to express the overall prognosis without losing the vital anatomic content of TNM. These methods should be able to integrate multiple prognostic factors, including TNM, while permitting the TNM system to remain intact and distinct. This article discusses examples of such approaches.

[1]  J. Cuzick,et al.  The grading of rectal cancer: historical perspectives and a multivariate analysis of 447 cases , 1986, Histopathology.

[2]  M W Kattan,et al.  Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  J. Blasko,et al.  Pretreatment nomogram for predicting freedom from recurrence after permanent prostate brachytherapy in prostate cancer. , 2001, Urology.

[4]  M. Kattan,et al.  Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  Pierre I Karakiewicz,et al.  International validation of a preoperative nomogram for prostate cancer recurrence after radical prostatectomy. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  M. Kattan,et al.  Postoperative nomogram for 12-year sarcoma-specific death. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  A. Partin,et al.  Evaluation of artificial neural networks for the prediction of pathologic stage in prostate carcinoma , 2001, Cancer.

[8]  S. Tucker,et al.  International prognostic index‐based outcomes for diffuse large B‐cell lymphomas , 2002, Cancer.

[9]  K. Leung,et al.  Construction of the Chinese University Prognostic Index for hepatocellular carcinoma and comparison with the TNM staging system, the Okuda staging system, and the Cancer of the Liver Italian Program staging system , 2002, Cancer.