The tumor, node, metastasis (TNM) system of cancer classification was originally described by Denoix and Schwartz  in 1948 and, in 1953, was incorporated by the International Union Against Cancer (Union International Contre le Cancer, UICC) and the International Congress of Radiology into a formal classification system for cancer . The TNM system was adopted in the US in 1959, when the American College of Surgeons, American College of Radiology, College of American Pathologists, American College of Physicians, American Cancer Society, and National Cancer Institute cosponsored the formation of the American Joint Committee for Cancer Staging and End Results Reporting . Its stated purpose was "to develop systems for the clinical classification of cancer which would be of value to practicing American physicians" .
During the past 30 years, the AJCC has worked to develop TNM definitions and stage classifications for all disease sites and subsites, revise definitions and classifications based on the results of clinical studies, and educate physicians and other health-care professionals about the TNM classification system. In 1988, the AJCC reached agreement with the UICC for a common TNM and stage classification system, thereby resolving many minor differences that had previously prevented the creation of a single tumor classification system.
The TNM system classifies a cancer's spread from primary to distant sites. In the general TNM approach, the six T categories-T0, Tis, T1, T2, T3, and T4-refer to the extent of the primary tumor; the four N categories-N0, N1, N2, and N3-denote the degree of involvement of regional nodes; and the two M categories-M0 and M1-designate the absence or presence of distant metastasis.
For each patient, the individual T, N, and M category ratings are combined in tandem to form expressions such as T2, N1, M0 or T3, N2, M1. Because six categories of T, four categories of N, and two categories of M create 48 possible ratings for the TNM expressions, stage groupings (I, II, III, and IV) were created by combining categories to ease statistical analyses. The TNM system is thus an exclusively morphologic classification, offering a reasonably precise description of the anatomic extent of tumor at a specific time.
The form of the tumor can be expressed in gross anatomic terms (TNM index), microscopic terms (eg, cell type, degree of differentiation), and biomolecular terms (eg, tumor markers, ploidy). The function of the tumor can be described by clinical effects that create severity of illness within the patient. The tumor's functional effects are manifested by the type, duration, and severity of cancer symptoms (eg, weight loss, fatigue) [5-10] and by the performance or functional status of the host [11,12].
Another important aspect of clinical biology is the comorbidity, or the "setting" in which the cancer occurs. Although unrelated to the cancer itself, the patient's concomitant disease(s) can affect the clinical course of cancer, choice of treatment, and prognosis [13-17].
Shortcomings of TNM System
Despite its excellence in describing a tumor's size and extent of anatomic spread, the TNM system does not account for the clinical biology of the cancer [18-20]. A substantial amount of clinical research has demonstrated the prognostic shortcomings of the TNM system (unpublished data, JF Piccirillo, MD, and AR Feinstein, MD; and references 5, 12, and 21-24), while several editorials have requested improvements in it (unpublished data, JF Piccirillo, MD, and AR Feinstein, MD; and references 20, 25, and 26). Nevertheless, no significant modifications have been made in the TNM system since its creation. In particular, important patient-based prognostic factors, such as symptom type and severity, severity of comorbidity, and functional capacity, are excluded. Despite unequivocal evidence of the prognostic importance of symptoms, performance status, and comorbidity, the AJCC system remains relentlessly confined to morphologic data.
Many people with cancer also have other nonneoplastic diseases. These comorbidities may be so severe as to prohibit the use of preferred antineoplastic therapie and impact on 3- and 5-year survival statistics. In several studies of cancer prognosis, the presence of comorbidity was found to dramatically affect survival and the evaluation of treatment effectiveness [7,16,21,27-30].
Prognostic comorbidity  refers to comorbidity severe enough to impact on survival rates. Examples of prognostic comorbidity are significant cardiac disease, severe hypertension, far-advanced tuberculosis, severe liver disease, and recent severe stroke. Examples of nonprognostic comorbidity include a history of "mild" hypertension that is well controlled with medication, congestive heart failure or myocardial infarction of more than 6 months duration, recurrent asthma attacks without underlying lung disease, and slight gastrointestinal bleeding not requiring transfusion. As shown in Table 1, 5-year survival rates in a variety of cancers are dramatically worse in patients with prognostic comorbidity than in those without prognostic comorbidity.
Comorbidity vs Tumor Size and Stage
In many cancers, comorbidity is prognostically more important than tumor size or stage. The cancers for which comorbidity is particularly important are those which are not rapidly fatal and which affect people who are middle-aged or older (ie, over age 50 years). These include cancers of the breast, prostate,7 oral cavity, pharynx and larynx [16,29], bladder, ovary, and uterus , as well as non-Hodgkin's lymphoma. Based on recent incidence rates, these cancers represent approximately 61% of all cancers for men and 65% for women.
The importance of comorbidity is clear from these statistics, and yet the present cancer staging system does not contain this important information. As several valid comorbidity instruments now exist, the continued exclusion of standardized comorbidity information from cancer statistics appears to be a major omission.
Comorbidity instruments can be classified into two groups, depending on the origin of the data: (1) instruments that rely on primary data and (2) instruments that are based on secondary data. Primary data are collected from physicians or nurses or through chart reviews. Secondary data are derived from administrative and financial databases main- tained by hospitals, insurance companies, and state and federal governments.
Instruments Derived from Primary Data
Comorbidity measures that rely on primary data include the Kaplan-Feinstein Index , the Charlson Co-Morbidity Index , and the Index of Co-Existent Disease .
The Kaplan-Feinstein Comorbidity Index was developed from a study of the impact of comorbidity on 5-year survival outcomes for patients with diabetes mellitus. In this index, specific diseases and conditions are classified as mild, moderate, or severe based on the severity of organ decompensation. An overall comorbidity score is assigned according to the highest level of decompensation.
The Charlson Comorbidity Index was created from studies of 1-year mortality among patients admitted to a medical unit of a teaching hospital. It is a weighted index that takes into account the number and seriousness of comorbid diseases. The scoring system for this instrument assigns weights of 1, 2, 3, and 6 for each of the comorbid diseases present at initial assessment, and derives from these a total score that determines the patient's overall prognostic status.
The Index of Co-Existent Disease (ICED) predicts length of stay and resource utilization after hospitalization for surgical procedures. To calculate the overall burden of comorbidity, ICED assesses the patient's status in two separate components: physiologic and functional burden.