bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring the Accuracy of Prognostic Judgments
Authors: Mackillop William J. , Bartfay Emma , Groome Patti A.
Published: November 2003

The practical test of a true science is the power it confers of prevision, or of knowing now what will follow hereafter. When we can prognosticate with certainty, medicine will have become a science.

—H. Hartshorne
A system of medicine

Prognosis can be defined as, “a forecasting of the probable course and termination of an illness.” In the clinical practice of oncology the prognosis is important because it may determine the optimal choice of treatment, and because patients often want to know what is going to happen to them in order to prepare themselves for the future. Given that prognostic judgments are used as a basis for important decisions, we need to know how accurate they are. We may not yet be able to achieve the “certainty” in prognosis that Hartshorne envisaged as a characteristic of the scientific medicine, but we should at least be able to measure the predictive value of our prognostic judgments, and take this into account in our decision making. It is generally accepted today that we should not place reliance on the results of diagnostic tests without knowing their accuracy, and the same principle should apply to prognostic judgments. However, while modern textbooks of clinical epidemiology deal thoroughly with methods for measuring the accuracy of diagnostic tests, much less has been written about how to measure the accuracy of prognosis.

Although there is a very extensive literature about prognostic factors in cancer, there have been very few reports relating to the accuracy of prognostic judgments in practice in individual cases. In this chapter, we are concerned mainly with the quality of prognostic judgments at the level of the individual patient. This is much more of a challenge than predicting the outcome of groups of cases. Once the average outcome of a specific medical problem has been established in a large group of cases, the average outcome in another large group of similar cases may be predicted with great precision. However, a precise knowledge of the average outcome of an illness may have little or no predictive value in the individual case. For example, if a large group of patients with a specific stage of a specific cancer has been observed to have a 5‐year survival of 50% then we can be confident that if a large group of similar cases is managed in the same way in future, it will also have a 5‐year survival of about 50%. When we try to use this information to predict the outcome in the next case of this illness that we encounter, however, it translates into a probability of 5‐year survival of 0.5; In other words, it leaves us in a state of complete uncertainty.

The main objectives of this chapter are to describe methods that can be used to measure the accuracy of prognosis at the level of the individual case, and to review what little is known about the accuracy of prognostic judgments in oncology. We end by considering factors that may limit the degree of “certainty” that we can ever expect to achieve in prognosis in oncology.
Thyroid Cancer
Authors: Brierley James D. , Asa Sylvia L.
Published: November 2003

The thyroid gland is composed of follicular cells, parafollicular cells, and connective tissue with occasional lymphocytes. Each of these cell types can give rise to histologically distinct tumors that differ significantly in behavior, prognosis, and response to treatment. The commonest tumors by far are those derived from follicular epithelial cells; these tumors are classified as differentiated (papillary and follicular), poorly differentiated (insular), and undifferentiated (anaplastic). Medullary thyroid cancers are derived from parafollicular cells. Tumors derived from lymphocytes and connective tissue cells are similar to such lesions elsewhere in the body, but a detailed discussion of those entities is beyond the scope of this chapter.

In one of the earliest studies on prognostic factors in thyroid cancer, the multivariate analysis of the European Organization for Research and Treatment of Cancer (EORTC) Thyroid Cancer Co‐operative Group, all histologies were combined. The 5‐year survival ranged from 80% in differentiated carcinoma (papillary and follicular), to 55% in medullary thyroid carcinoma (MTC), to 10% in anaplastic carcinoma. Most subsequent studies of prognostic factors in thyroid cancer were developed for individual histologic types [differentiated papillary–follicular, poorly differentiated (insular), medullary, and anaplastic thyroid carcinomas] and they will be considered separately in this review. Some studies combine papillary and follicular, while others do not. The focus of this chapter is a review of the tumor‐, host‐ and environment‐related prognostic factors in differentiated thyroid cancer, with an additional discussion of prognostic factors in other thyroid histologies.
Extrahepatic Biliary Tract and the Ampulla ofVater Cancers
Authors: Henson Donald Earl , Ries Lynn G. , Albores‐Saavedra Jorge
Published: November 2003

This chapter reviews the most useful prognostic factors that are available for carcinomas arising in the gallbladder, extrahepatic bile ducts, or the ampulla of Vater. In considering these prognostic indicators, the complex anatomy of the extrahepatic biliary system and its relation to adjacent structures becomes critical. For this reason, the prognostic factors are described separately for the gallbladder, extrahepatic bile ducts, and ampulla. For all three sites, however, the single most important factor having the greatest effect on survival is the extent of tumor at the time of diagnosis. In preparing this chapter, data were reviewed from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute for the years 1973–1995. In the SEER Program, the extent of disease can be classified as “localized,” regional, and “distant” or by the standard TNM classification. A “localized” tumor is confined to the organ of origin without nodal involvement; “regional,” denotes regional lymph‐node involvement or direct extension to adjacent organs; and “distant” indicates that distant metastases have been found. The prognostic factors are presented as tumor‐related, host‐related, and environment‐related.
Cancer of the Pancreas
Authors: Roder Jürgen D. , Ott Katja
Published: November 2003

Pancreatic cancer remains an important cause of death in many nations. In most patients pancreatic adenocarcinoma is diagnosed at a stage of disease that is not curable by surgery and can only be little affected by multimodality treatment options (chemotherapy, radiotherapy, immunotherapy). Surgery is the only curative approach, but success with resection is low and the prognosis remains extremely poor, for example, patients with completely resected tumors have a 5‐year survival rate of 15–28%. The overall 5‐year survival remains extremely low despite a slight improvement during the last few years: figures of 1–3% for all, and 5–25% for resected patients are reported.

More than 90–95% of carcinomas of the pancreas are exocrine tumors. Over 90% of these tumors, as referred to in the World Health Organization (WHO) classification, are ductal adenocarcinomas, including rare types, such as mucinous noncystic, adenosquamous, anaplastic, and osteoblast‐like giant‐cell carcinoma. The mucinous cystadenocarcinoma (about 1% of all cases or 5% of resected cases) had a relatively good prognosis and a 5‐year survival rate of 54.3% in a Japanese study. Also, the uncommon intraductal papillary mucinous adenocarcinoma has a better prognosis than the common ductal adenocarcinoma. These two tumor types, as well as the carcinoma in situ, are excluded in the following review of prognostic factors.

The most important prognostic factor is the possibility of curative resection. The outcome of patients who undergo resection is definitely superior to those not undergoing resection. In the literature the resection rates for exocrine pancreatic carcinoma range from 2.6% to 30%. In contrast to patients without resection, who practically never show a long‐term survival rate, in specialized centers resected patients show a 5‐year survival rate of 20–25%. The median survival time for resected patients is 10–20 months. Therefore, the prognostic factors for those patients who have not been resected will be discussed separately in the section on treatment‐related prognostic factors.

Due to the small number of resected patients and the poor prognosis, the importance of several prognostic factors still remains unclear, because most studies are based on univariate analysis. Only a few studies with more than 100 resected patients analyzed by multivariate analysis have been published.

In the following analysis of prognostic factors, the relevant international literature will be considered. Problems in reviewing the actual literature are that often carcinomas of the head of the pancreas are not separated from those of the ampulla of Vater and the distal bile duct, which have a better prognosis. Furthermore, in 1997 the new International Union Against Cancer Tumors, Nodules, Metastases (UICC TNM) classification (5th ed.) was published this manual relates better to prognosis than does the old classification (4th ed.). Also, in the interpretation of prognostic factors, the reduction of perioperative mortality to less than 5% also has to be considered.
Lung Cancer
Authors: Brundage Michael D. , Mackillop William J.
Published: November 2003

Lung cancer is best conceptualized as a group of heterogeneous clinical entities with common molecular and cellular origins, but with different clinical behaviors, and hence, different prognoses. Lung cancer has a major impact on population health; taken together, lung cancers are the most common cause of cancer death in both males and females in North America. Overall, only 13% of patients survive more than 5 years after being diagnosed.

Determining the prognosis for an individual patient with lung cancer is difficult, in part, because the clinical course of the disease in the individual case can evolve along a seemingly infinite combination of branching pathways. Over 90% of lung cancer patients present with signs or symptoms of disease, and since no symptoms or signs are pathognomonic for lung cancer, the disease has a capacity clinically to mimic other cancers and other nonmalignant diseases with a constellation of possible presenting symptoms. These varying presentations, and potential clinical evolutions, are in turn due to the multiple potential manifestations of the primary tumor, metastatic sites of involvement, and paraneoplastic syndromes.

Despite the heterogeneity of the clinical manifestations of lung cancer, the prognosis for a population of patients with lung cancer is remarkably predictable. The overall mortality rates for lung cancer in North America over the last 15 years have remained unchanged. A population‐based study of over 12,000 patients with unresected non‐small‐cell lung cancer registered in seven Ontario Regional Cancer Centers (1982–1991) demonstrated no significant differences in patient survival either between Centers or over time. The predictability of population survival outcomes is of limited usefulness, however, due to the marked heterogeneity of patients making up the overall population. Prognostic factors are thus used to divide the overall population of lung cancer patients into subgroups in order to realize the benefits of prognostic stratification. A number of studies have addressed the identification and application of such factors.
Soft Tissue Sarcoma
Authors: Pisters Peter W.T. , O'Sullivan Brian , Pollock Raphael E.
Published: November 2003

Significant advances have been made over the past decade in the understanding of clinicopathologic prognostic factors for soft tissue sarcoma. Foremost among these advances is an improved ability to recognize the subset of patients at high risk for recurrent disease and tumor‐related death based on clinicopathologic data available at the time of initial presentation. Recent advances have also helped to elucidate specific molecular factors that have independent prognostic significance. This review summarizes the available data on traditional clinicopathologic, medical, and molecular prognostic factors for adult soft tissue sarcoma. Expected outcomes will be provided, drawing in particular from the results of a large series of patients managed at one center. Although there are many potential outcomes that could be assessed, the discussion will focus almost entirely on the traditional oncology outcomes of local control, metastatic risk, and survival. Other important outcomes, such as limb preservation; function, and quality of life will not be emphasized because of space limitations.

The factors will be discussed in a framework that focuses on thetumor‐related,host‐related(or patient associated), andenvironment‐relatedbackground for these factors. The review will conclude with a summary tabulation identifying strata of the importance of factors to everyday clinical practice using the principles outlined in Chapter 2. These includeessentialfactors (needed for treatment decision‐making),additionalfactors (of benefit for describing a cohort of patients), andnew and promisingfactors that may of benefit in the future to exploit new treatment or diagnostic strategies.
Breast Cancer
Authors: Fitzgibbons Patrick L.
Published: November 2003

Breast cancer is a heterogeneous disease that exhibits a wide variety of clinical presentations, histologic types, and growth rates. Because of these variations, determining prognosis for an individual patient at the time of initial diagnosis requires careful assessment of multiple clinical and pathological parameters. This evaluation includes consideration of an expanding list of tumor‐ and patient‐related prognostic factors, which are especially important for decisions related to systemic treatment and predictions for overall survival.
Uterine Cervix Cancer
Authors: Pitson Graham , Fyles Anthony
Published: November 2003

Carcinoma of the cervix accounts for approximately 20% of all gynecologic cancers and 2% of all malignancies in women. Numerous prognostic factors have been studied in patients with cervical carcinoma, using both univariate and multivariate analysis. Differences in endpoints of analysis, whether survival (disease‐free or overall), relapse‐free rate, or local control rate, make comparison of such studies difficult. The failure to perform a multivariate analysis, or the use of different covariates in multivariate analyses, can further complicate comparisons between studies. Not all factors are relevant to all patients; for example, depth of tumor invasion and presence of vascular‐space invasion can only be reliably determined in patients treated with surgery, whereas hemoglobin level is important in patients treated with radiation. This review concentrates largely on those factors identified using multivariate techniques, such as log rank or Cox regression analysis, in order to account for interactions between various factors. Where available, estimates such as hazard ratios will be included in order to indicate the strengths of the individual variables.
Cancer of the Ovary and
F
allopian Tube
Authors: Pecorelli Sergio , Zigliani Lucia , Odicino Franco E.
Published: November 2003

Ovarian Cancer

In both treatment and management of carcinoma of the ovary, a series of endpoints has been identified. Such endpoints are widely mentioned in the literature and will be analyzed in this chapter by following the guidelines suggested in the first part of this book, “Principles of Prognostic Factors.” Overall, the probability of cure of ovarian cancer is low, 41.6% 5‐year survival (Volume 23 of the
FIGO Annual Report on the Results of Treatment in Gynecological Cancer
), even though in the last 20 years it has increased at least by one third. The improvement of overall survival thus represents the main objective of prospective clinical research, with study designs derived from the results obtained in other fields of clinical and basic research.

The different prognostic factors are discussed according to their subdivision into
essential, additional
, and
new and promising
. Each of these factors will be cross‐referenced to
tumor‐, host‐
and
environment‐related
characteristics, although some of them cannot be strictly classified under these specific categories. Prognostic factors will be dealt with in four different
scenarios
, chosen according to the importance they have in the management of this cancer.
Gestational Trophoblastic Disease
Authors: Ngan Hextan Y.S. , Wong Ling‐Chui
Published: November 2003

Gestational trophoblastic disease (GTD) is a disease of the proliferative trophoblastic allografts and includes partial mole (PM), complete hydatidiform mole (CM), invasive mole, metastatic mole, choriocarcinoma (CC), and placental site trophoblastic tumor (PSTT). While CC and PSTT are definitely neoplastic, the various types of molar pregnancies are basically benign, but with a potential to behave like a malignant disease that requires chemotherapy. This potential malignant behavior is identified by the failure of regression of the human chorionic gonadotrophin (hCG) in the absence of a normal pregnancy and is termed gestational trophoblastic tumor (GTT) or gestational persistent trophoblastic disease (GPTD). The criteria for the diagnosis of postmolar GTT varies among different centers in the world. Basically, if the hCG level fails to fall satisfactory over 3–4 weeks, or when there is a rise in the hCG level over 2–3 weeks, GTT is diagnosed and further investigations should be done to assess the extent of the disease. The risk or predictive factors of PTD following mole will be discussed in the first major section below.

In GTT, unlike other solid malignancies, metastasis does not always mean poor prognosis. If the metastatic lesion is a mole rather than CC, the prognosis is much better. However, histological confirmation is uncommon because most cases of GTT are treated with chemotherapy. Thus, other prognostic factors in addition to metastasis have to be taken into consideration in assessing the prognosis of a GTT. The prognostic factors of GTT are be discussed in the second major section below. PSTT arises from the intermediate trophoblasts and behaves quite differently from that of GTT and will be discussed separately in the final major section below.
Germ‐cell Testis Tumors
Authors: Schmoll Hans‐Joachim , Rie Christoph
Published: November 2003

Prognostic factors are of major importance for the treatment and cure of patients with testicular cancer. The histologic type, seminoma versus nonseminomatous germ‐cell tumors (NSGCT) and the (TNM) stage constitute the fundamental prognostic factors that govern clinical practice. For early stages, prognostic factors are the critical determinants for the decisions about treatment. They don't necessarily predict the survival, which is greater than 98% independent of treatment strategy, but for the risk of relapse with different treatment options. In advanced stages, prognostic factors indicate the chance of survival following chemotherapy, either first‐line or salvage. In this chapter, the different prognostic factors, relevant for decision making in early and late stage germ‐cell testis tumors, are discussed.
Leukemia
Authors: Minden Mark
Published: November 2003

Leukemia, the malignant transformation of bone marrow blood‐forming cells, is not a single entity. Prior to any form of therapy, which would include antibiotics and transfusion, the leukemias were classified as acute or chronic. The acute leukemias were rapidly fatal, with individuals dying in days to weeks from the time of diagnosis. In contrast, individuals with chronic leukemias could live for many years without any therapy. With the recognition of the stem‐cell nature of blood formation and the identification of different lineages, it was possible to further subdivide the leukemias into those of the myeloid and lymphoid lineages; with the development of therapeutics, this distinction has proven to be important.
Gliomas
Authors: Ohgaki Hiroko , Reifenberger Guido , Nomura Kazuhiro , Kleihues Paul
Published: November 2003

Tumors of the nervous system amount to less than 2% of the total human cancer burden, the overall incidence being in the range of 7–9 new cases a year per 100,000 population. They affect all age groups and their etiology is largely unknown. More than 40 clinicopathological disease entities have been classified by the World Health Organization (WHO) and this makes the identification of factors with unequivocal prognostic value difficult. Large, randomized therapy studies with long‐term follow‐up of a significant number of patients have concentrated on malignant gliomas of astrocytic and oligodendroglial origin.

During the past decade, numerous studies have been published that, in addition to conventional clinical and histopathological parameters, suggested certain genetic alterations as potential prognostic factors, but for tumors of the nervous system, few of these have been confirmed in subsequent reports. The establishment of reliable prognostic factors requires the critical, unbiased evaluation of large databases and often remains an elusive goal.
Explanatory Notes—General
Authors: Wittekind Ch. , Henson D. E. , Hutter R. V. P. , Sobin L. H.
Published: November 2003

TNM Supplement: A Commentary on Uniform Use, Second Edition, extends and clarifies the most recent version of the International Union Against Cancer (UICC) TNM Classification of Malignant Tumours, providing a monograph that amplifies and unifies the principles and practice of cancer staging. The TNM (Tumour‐Node‐Metastasis) classification remains the most widely used system for staging of cancer and the anatomic extent of disease represented by the TNM system is the most important predictor of outcome for most tumours.

This chapter provides additional explanatory information for the general application of TNM providing more precise definitions for anatomical sites/subsites, regional lymph nodes and T, N, and M categories that are generic or ambiguous.
Explanatory Notes–Specific Anatomical Sites
Authors: Wittekind Ch. , Henson D. E. , Hutter R. V. P. , Sobin L. H.
Published: November 2003

This new edition of TNM Supplement: A Commentary on Uniform Use, Second Edition, promotes the uniform application of the TNM classification in cancer practice. The text of the second edition of TNM Supplement provides explanations and examples to answer many questions that might arise during the daily use of TNM, particularly in unusual cases.

This chapter provides additional explanatory information for application of TNM to specific anatomical sites by providing more precise definitions for anatomical sites/subsites, regional lymph nodes and T, N, and M categories that are generic or ambiguous.
Optional Proposals for Testing New Telescopic Ramifications of
TNM
Authors: Wittekind Ch. , Henson D. E. , Hutter R. V. P. , Sobin L. H.
Published: November 2003

In this chapter, various proposals for optional subdivision of the existing T, N and M categories, i.e., “telescopic” ramification, are presented. Telescoping accommodates the collection of additional data without altering the definitions of the existing TNM categories.

The concept of telescoping permits an orderly expansion of TNM elements to allow for (1) testing of subcategories for prognosis and (2) treatment planning considerations. Telescoping accommodates “splitters” and “lumpers”, permits data from expansions to collapse into the standard categories and promotes testing of new hypotheses uniformly in different centres.

The editors recognize that staging must be simple enough for universal use in both highly developed and developing countries and sufficiently uncomplicated so that medical professionals are not discouraged from using the system. On the other hand, for specialized institutions and for investigational purposes a relatively simple staging system is not sufficient and runs the risk of not being used. For these specialized institutions the TNM system may be made more attractive by further subdivision of the existing categories (telescopic ramification) and by including additional descriptors.

The proposals for subdivisions and additional designations in this section are presented for investigational use and are
entirely optional
. Some proposals relate to subclassifications of M1 and are of interest to oncologists. Justification for each proposal is given based on published data or clinical experience.
Frequently Asked Questions
Authors: Wittekind Ch. , Henson D. E. , Hutter R. V. P. , Sobin L. H.
Published: November 2003

The TNM Supplement: A Commentary on Uniform Use, provides explanations and examples to answer many questions that might arise during the daily use of TNM, particularly in unusual cases. The Frequently Asked Questions (FAQ) presented here were derived from the activities of the TNM web site's “Help Desk” based on questions received by the TNM Prognostic Factors Project Committee concerning the use of TNM and how to interpret rules in specific situations.
Head and Neck Tumours
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The following sites are included:

Lip, Oral cavity

Pharynx: Oropharynx, Nasopharynx, Hypopharynx

Larynx

Maxillary sinus

Nasal Cavity and Ethmoid sinus

Salivary glands

Thyroid gland

Carcinomas arising in minor salivary glands of the upper aerodigestive tract are classified according to the rules for tumours of their anatomic site of origin, e.g., oral cavity.

Each site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Lip and Oral Cavity
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Nasal Cavity and Paranasal Sinuses
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Salivary Glands
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Thyroid Gland
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Digestive System Tumours
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The following sites are included:

Oesophagus

Stomach

Small Intestine

Colon and Rectum

Anal Canal

Liver

Gallbladder

Extrahepatic Bile Ducts

Ampulla of Vater

Pancreas

Each site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological Classification

G Histopathological grading

Stage grouping

Summary
Oesophagus
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Stomach
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Pancreas
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Pleural Mesothelioma
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Tumours of Bone and Soft Tissues
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The following sites are included:

Bone

Soft tissues

Each site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Bone
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Malignant Melanoma of Skin
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Vagina
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Cervix Uteri
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Corpus Uteri
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Ovary
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Gestational Trophoblastic Tumours
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Testis
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Kidney
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Urinary Bladder
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Urethra
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
Retinoblastoma
Authors: Sobin L.H. , Wittekind Ch.
Published: November 2003

The TNM system is used to describe and classify cancer stages and progression based on the assessment of three components:

T

The extent of the primary tumour

N

The absence or presence and extent of regional lymph node metastasis

M

The absence or presence of distant metastasis.

TNM classification for the above site is described under the following headings:

Rules for classification with the procedures for assessing T, N, and M categories; additional methods may be used when they enhance the accuracy of appraisal before treatment

Anatomical sites and subsites where appropriate

Definition of the regional lymph nodes

TNM Clinical classification

pTNM Pathological classification

G Histopathological grading

Stage grouping

Summary
In search of meaningful online learning experiences
Authors: Yoon Seung‐won
Published: November 2003

Effective online education is a blend of pedagogy, technology, and organizational support. For meaningful
online learning experiences, prospective online learners should evaluate the strengths of these three elements
and
play an active role in exploring the increased interaction opportunity that online learning provides.
Soft Solution Route to Directionally Grown ZnO Nanorod Arrays on Si Wafer; Room‐Temperature Ultraviolet Laser
Authors: Choy J.‐H. , Jang E.‐S. , Won J.‐H. , Chung J.‐H. , Jang D.‐J. , Kim Y.‐W.
Published: November 2003

A high‐quality ZnO nanorod array (NRA) has been successfully grown on a Si wafer by a wet‐chemical process, where the Si wafer was dip‐coated with 4 nm sized ZnO nanoparticles as a buffer and seed layer prior to the crystal growth. It is found that the as‐prepared ZnO NRA has a threshold power density of ∼ 70 kW cm–2, which is comparable to the lowest one determined for ZnO NRAs on Al2O3 substrates (40 kW cm–2). The ultraviolet lasing efficiency of the ZnO NRAs is thus similar for both substrates.
ZnO Nanoribbon Microcavity Lasers
Authors: Yan H. , Johnson J. , Law M. , He R. , Knutsen K. , McKinney J.R. , Pham J. , Saykally R. , Yang P.
Published: November 2003

ZnO nanoribbons with pseudo‐rectangular cross‐sections (see Figure) are demonstarted to be excellent microcavities with a high quality factor (∼ 3000). The lasing threshold is shown to be inversely proportional to the length of the ribbon for pumping intensities lower than the saturation region. Analysis of the emission spectra points to the possibility of the existence of both pure axial modes and “bow‐tie” cavity modes.
New aspects of aging in epoxy networks. II. Hydrothermal aging
Authors: Bockenheimer Clemens , Fata Davis , Possart Wulff
Published: November 2003

An amine cured epoxy is prepared in two different network states for hydrothermal aging. The “low‐crosslinked” network has a considerable amount of residual reactive groups and a relatively high‐molecular mobility. The low‐crosslinked matrix contains high‐crosslinked regions. In contrast, the “highly crosslinked” epoxy system has little reactive groups and a lower molecular mobility. Here, low‐crosslinked regions are found in a high‐crosslinked matrix. Hydrothermal loading for both networks is performed in demineralized water at temperatures below their glass transition. The water plasticizes both kinds of networks which remain in the glassy state, however. As a consequence, in the low‐crosslinked epoxy, the increased molecular mobility promotes an ongoing curing reaction leading to the consumption of epoxy groups until an almost complete network has formed. As a new aging process, phase separation occurs in the highly crosslinked epoxy. The new phase is more mobile than the matrix because it has its own glass transition at a lower temperature. In addition, thermooxidative degradation is observed for both network states. Certainly, these chemical and structural changes in the epoxy networks should influence the performance of an adhesive joint, a coating, or a fiber‐reinforced composite. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 369–377, 2004
New polymeric Schiff base and its metal complexes
Authors: Kaliyappan T. , Rajagopan S. , Kannan P.
Published: November 2003

A novel polymeric Schiff base was synthesized by the reaction of a Schiff base from 2,4‐dihydroxy benzaldehyde and aniline with acryloyl chloride and was polymerized in methyl ethyl ketone at 70°C with benzoyl peroxide as a free‐radical initiator. Polychelates were obtained in an alkaline solution of poly(2‐hydroxy‐4‐acryloyloxy‐N‐phenylbenzylidine) with aqueous solutions of metal ions such as Cu(II), Ni(II), Co(II), Ca(II), Cd(II), Mn(II), and Zn(II). The polymeric Schiff base and polychelates were characterized with elemental analysis and spectral studies. The elemental analysis of the polychelates suggested that the metal‐to‐ligand ratio was 1:2. The IR spectral data of the polychelates indicated that the metals were coordinated through the nitrogen and oxygen of the phenolic OH group. Diffuse reflectance spectra, electron paramagnetic resonance, and magnetic moment studies revealed that the polychelates of the Cu(II) complex were square‐planar, those of the Ni(II), Mn(II), and Co(II) complexes were octahedral, and those of the Ca(II), Cd(II), and Zn(II) complexes were tetrahedral. X‐ray diffraction studies revealed that the polychelates were highly crystalline. The thermal properties of the Schiff base and polychelates were also examined. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 494–500, 2004