Management Options For Cancer Research Articles

PROGRESSION DETECTION OF STAGE I NONSEMINOMATOUS TESTIS CANCER ON SURVEILLANCE: IMPLICATIONS FOR THE FOLLOWUP PROTOCOL

To optimize followup in patients with stage I nonseminomatous testis cancer on surveillance we evaluated the contribution of each followup modality to the detection of progression as well as morbidity and mortality outcomes. After orchiectomy 170 patients with clinical stage I nonseminoma were prospectively placed on a surveillance protocol. History, physical examination, serum tumor markers, abdominal and pelvic computerized tomography (CT), and chest x-ray were used for followup. The number of failures, methods and timing of progression detection, treatments required, mortality rate and subsequent contralateral primary tumors were recorded. The 170 surveillance patients were followed a median of 6.3 years. Within 2 years (median 6.9 months) postoperatively 48 patients (28.2%) had disease progression. History, physical examination, markers, CT and chest radiography provided the initial evidence of progression in 18 (37.5%), 34 (70.8%), 34 (70.8%), and 4 (8.3%) patients, respectively. Each modality was the only indicator of failure in 2 (4.2%), 4 (8.3%), 10 (20.8%) and 0 cases, respectively. Of the 170 patients 122 (71.8%) required no additional treatment beyond orchiectomy, 26 (15.3%) received 1 and 22 (12.9%) underwent more than 1 therapeutic modality. Only 1 patient (0.6%) died of disease. Contralateral tumors developed in 5 cases (2.9%) therapeutic a mean of 8.1 years after orchiectomy. In stage I nonseminoma patients, surveillance history, physical examination, tumor markers and abdominopelvic CT are necessary components of the followup protocol. Removal of routine chest x-ray from the protocol would not have changed progression detection. The initial surveillance visit must occur by 2 months postoperatively. Patients should be followed beyond 5 years and likely for life in addition to regular patient self-examination.

Author reply

Although we certainly would not argue with the sentiments expressed by Janjan et al., regarding priorities and strategies for cancer control in developing as well as developed countries, we take mild exception to their criticism of the program of a conference that to our knowledge none of them attended. The conference had a very large interactive component and those who attended were not shortchanged with regard to opportunities to discuss the issues raised by Dr. Janjan and colleagues. Their assertion that "the only discussion regarding the management of cancer-related pain in this conference was within a symposium entitled 'Strategic Issues and Resource Allocation for Cancer Organisations in Developed Countries' " is contradicted by a statement made in our report on the Conference (which they evidently overlooked), which reads as follows: "Mittra gave an overview of cancer management options, including a call for an emphasis on palliative care in developing countries. . ."1 As a plenary lecturer, Dr Mittra's message reached all conference participants. David Hill Ph.D.*, Donald C. Iverson Ph.D.

A computational model of reasoning form the clinical literature

This article explores the premise that a formalized representation of empirical studies can play a central role in computer- based decision support. The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. (2) A computational model based on a declarative representation for published reports of clinical studies can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. (3) The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model can help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system that draws on structured representations of the clinical literature to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer-management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help bring a critical analysis of the relevant literature, structured around a particular patient and treatment decision, to the physician.

A computational model of reasoning from the clinical literature

This paper explores the premise that a formalized representation of empirical studies can play a central role in computer-based decision support. The specific motivations underlying this research include the following propositions: (1) Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. (2) A computational model, based upon a declarative representation for published reports of clinical studies, can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. (3) The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model may help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system which draws upon structured representations of the clinical literature in order to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help to bring a critical analysis of the relevant literature to the physician, structured around a particular patient and treatment decision.