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Data Source
Toronto Sunnybrook Health Science Center (TSRCC)
Organizer
Dr. Al Covens, Department of Obstetrics and Gynecology, University of Toronto, Ontario, Canada has provided this Case Study in cooperation with Edmee Franssen, BSc, MSc., Biostatistician, and Milena Kurtinecz, BSc., Statistical Database Coordinator

Introduction

Radical hysterectomy (an operation in which the uterus is removed) has been demonstrated to be the customary method of treatment of early stage of the cervix cancer. Although both surgery and radiation therapy produce equivalent cure rates, surgery is often selected for younger, healthier patients, based on a shorter treatment course, as an opportunity for ovarian preservation, and better post-treatment vaginal function. The cure rate associated with radical surgery (approximately 80%) has not appreciably changed over the last three decades. It has been of interest to identify clinical and pathological factors that predict an increased risk of recurrence following surgery. These include tumor size, cell type, grade, depth of invasion, and lymph node status.
 

Data Description

The study, conducted in Toronto, is of a prospective data collection design where the interest of the doctor is to determine the different attributes predicting survival (i.e. no relapse of disease). It is expected that if there will be a relapse, it will occur during the first two years following surgery. The overall relapse rate is approximately 20%, and in the case of stage I cancer, the relapse rate is less than 5%.
 

The data (excel file, space delimited data file) documents the cases of 905 cervical cancer patients of which only 871 patients, having a record of their last follow-up, are considered. A patient enters this study on her surgery date, also considered to be her diagnosis date and is observed for an unspecified period of time, or until her first relapse. The range of recorded observations is roughly from 1984 to present, however the exact time range can be gotten from the data itself.
 

In addition to determining the different attributes predicting survival, there is a need for patient classification regarding likelihood of relapse. This classification can be into 3 or 4 groups as follows:
 

Classification 1: "Low relapse", "Moderate relapse", "High relapse"
or
Classification 2: "No relapse", "Low relapse","Moderate relapse", "High relapse"


Frequently Asked Questions

Please check this section regularly for updates.

 

Research Question

Nous espérons que ces données nous aideront à réaliser les deux objectifs suivants :

  1. déterminer quels attributs de la table précédente permettent de prévoir la non-rechute;
  2. classer les patientes en fonction de leur risque de rechute individuel.

 

Variables

Les attributs (variables) de cette étude sont les suivants :

Variable

Description
MRNO Numéro de patiente.
SURGDAT Date d’opération (date de diagnostic).
ADJ_RAD Variable catégorique :
0  si la patiente n’a PAS reçu de radiothérapie;
1  si la patiente a reçu une radiothérapie.
(radiothérapie donnée seulement lorsque le médecin juge les paramètres suffisamment sévères)
AGE_1 Âge de la patiente au moment du diagnostic.
CLS_1 Variable catégorique : (Pronostiques)
(Capillary Lymphatic Spaces, espaces lymphatiques capillaires)
0  négatif;
1, 2  positif.
DIS_STA Variable catégorique :
0  aucun signe de la maladie;
1  vivant avec la maladie;
2  morte de la maladie;
3  morte de complications (maladie présente);
4  morte de complications (maladie absente);
5  morte de causes indépendantes.
GRAD_1 Variable catégorique : différenciation cellulaire
1  optimale;
2  moyenne;
3  pire;
0  indique une valeur manquante.
HISTOLOG Variable catégorique : varie de 0 à 6.
MARGINS Variable catégorique : maladie résiduelle après l’opération
0  aucune;
1  zone para-vaginale;
2  zone vaginale;
3  les deux.
MAXDEPT Variable continue : profondeur de la tumeur (mm); une valeur de 0 indique que l’appareil n’a pas pu mesurer la profondeur en raison d’une mesure trop petite.
PELLYMPH 0  négatif;
1  positif.
RECURRN1 Date de récurrence de la maladie (en l’absence de rechute, aucun enregistrement).
SIZE_1 Taille de la tumeur (mm) lors du diagnostic.
FU_DATE Date du dernier contrôle.
 

 

References
Student Presentations
  1. McMaster University: Christine Calzonetti, Simo Goshev, Rongfang Gu, Shahidul Mohammad Islam, Amanda Lafontaine, Marcus Loreti, Maria Porco, William Volterman, Qihao Xie.
  2. University of Toronto: Eshetu Atenafu, Sandra Gardner, So-hee Kang, Anjela Tzontcheva.
  3. University of Calgary: Alberto Nettel Aguirre, Luz Palacious.
  4. University of Guelph: Baktiar Hasan, Mark Kane, Melanie Laframboise, Michael Maschio, Andy Quigley.
  5. York University: Sophia Lee, Noa Rozenblit, Sumanth Sharatchandran, Shirin Yazdanian.