Clinical Staging of Primary Lung Cancer

Tisi, Gennaro M.; Friedman, Paul J.; Peters, Richard M.; Pearson, Grif; Carr, David; Lee, Robert E.; Selawry, Oleg S.

doi:10.1164/arrd.1983.127.5.659

Cited by 257 publications

(41 citation statements)

References 3 publications

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“…Performance status was estimated according to the Eastern Cooperative Oncology Group (ECOG) (Zubrod et al, 1960) and the percentage of weight loss during the previous 4 months was recorded. Staging was carried out by exhaustive procedures according to the 4th edition of the Union Internationale Contre le Cancer (UICC) tumour node metastases (TNM) classification (Sobin et al, 1987), the American Thoracic Society map of regional pulmonary nodes (Tisi et al, 1982) and the new Mountain stage grouping (Mountain, 1997). The following investigations were carried out: a clinical examination, a standard chest roentgenography, a computed tomographic (CT) scan of chest and upper abdomen, fibreoptic bronchoscopy, liver sonography and bone scanning.…”

Section: Methodsmentioning

confidence: 99%

A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index

et al. 2005

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Treatment of non-small-cell lung cancer (NSCLC) might take into account comorbidities as an important variable. The aim of this study was to generate a new simplified comorbidity score (SCS) and to determine whether or not it improves the possibility of predicting prognosis of NSCLC patients. A two-step methodology was used.Step 1: An SCS was developed and its prognostic value was compared with classical prognostic determinants in the outcome of 735 previously untreated NSCLC patients.Step 2: the SCS reliability as a prognostic determinant was tested in a different population of 136 prospectively accrued NSCLC patients with a formal comparison between SCS and the classical Charlson comorbidity index (CCI). Prognosis was analysed using both univariate and multivariate (Cox model) statistics. The SCS summarised the following variables: tobacco consumption, diabetes mellitus and renal insufficiency (respective weightings 7, 5 and 4), respiratory, neoplastic and cardiovascular comorbidities and alcoholism (weighting ¼ 1 for each item). In step 1, aside from classical variables such as age, stage of the disease and performance status, SCS was a statistically significant prognostic variable in univariate analyses. In the Cox model weight loss, stage grouping, performance status and SCS were independent determinants of a poor outcome. There was a trend towards statistical significance for age (P ¼ 0.08) and leucocytes count (P ¼ 0.06). In Step 2, both SCS and well-known prognostic variables were found as significant determinants in univariate analyses. There was a trend towards a negative prognostic effect for CCI. In multivariate analysis, stage grouping, performance status, histology, leucocytes, lymphocytes, lactate dehydrogenase, CYFRA 21-1 and SCS were independent determinants of a poor prognosis. CCI was removed from the Cox model. In conclusion, the SCS, constructed as an independent prognostic factor in a large NSCLC patient population, is validated in another prospective population and appears more informative than the CCI in predicting NSCLC patient outcome.

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Section: Methodsmentioning

confidence: 99%

A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index

et al. 2005

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“…In this study, we defined a true-positive scan as a scan with typical, multiple areas of increased uptake or a single area of increased uptake considered in the final analysis to be of tumoral origin by the clinician in charge; a falsepositive scan was defined as one in which the increased uptake was not proved to be tumour. Tumour staging was performed according to the guidelines of the American Thoracic Society [14]. The clinical follow-up period for the study was at least 9 months.…”

Section: Methodsmentioning

confidence: 99%

Fluorine-18 deoxyglucose positron emission tomography for the detection of bone metastases in patients with non-small cell lung cancer

Bury

Barreto

Daenen

et al. 1998

European Journal of Nuclear Medicine and Molecular Imaging

283

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Abstract. Despite advances in morphological imaging, some patients with lung cancer are found to have non resectable disease at surgery or die of recurrence within a year of surgery. At present, metastatic bone involvement is usually assessed using bone scintigraphy, which has a high sensitivity but a poor specificity. We have attempted to evaluate the utility of the fluorine-18 deoxyglucose positron emission tomography (FDG PET) for the detection of bone metastasis. One hundred and ten consecutive patients with histological diagnosis of non-small cell lung cancer (NSCLC) who underwent both FDG PET and bone scintigraphy were selected for this review. In this group, there were 43 patients with metastatic disease (stage IV). Among these, 21 (19% of total group) had one or several bone metastases confirmed by biopsy (n = 8) or radiographic techniques (n = 13). Radionuclide bone scanning correctly identified 54 out of 89 cases without osseous involvement and 19 out of 21 osseous involvements. On the other hand, FDG PET correctly identified the absence of osseous involvement in 87 out of 89 patients and the presence of bone metastasis in 19 out of 21 patients. Thus using PET there were two false-negative and two false-positive cases. PET and bone scanning had, respectively, an accuracy of 96% and 66% in the evaluation of osseous involvement in patients with NSCLC. In conclusion, our data suggest that whole-body FDG PET may be useful in detecting bone metastases in patients with known NSCLC.

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“…Histological sub-classification was done according to the WHO classification (World Health Organization, 1982). Staging was carried out by exhaustive procedures according to the 4th edition of the Union Internationale Contre le Cancer (UICC) tumour node metastases (TNM) classification (Sobin et al, 1987) and the American Thoracic Society map of regional pulmonary nodes (Tisi et al, 1982). By definition, all patients belonged to stage IV of the new Mountain's stage grouping (Mountain, 1997) …”

Section: Patient Selectionmentioning

confidence: 99%

Brain metastases at the time of presentation of non-small cell lung cancer: a multi-centric AERIO* analysis of prognostic factors

et al. 2001

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Patients with lung cancer frequently suffer from brain metastases at the time of presentation. This condition affects approximately 10% of non-small cell lung cancer (NSCLC) patients (Newman and Hansen, 1974;Sorensen et al, 1988). Surgery is feasible only for a small proportion of these patients. Whole brain radiotherapy has been, hitherto, the generally recommended treatment in inoperable patients. The survival of NSCLC patients with brain metastases is poor, reported to be between 3 to 6 months in patients treated with medical therapies, either radiotherapy or chemotherapy (compared to 6-10 months in other advanced NSCLC (Paesmans et al, 1995;Shepherd, 1999)). Furthermore, brain metastases at the time of presentation of lung cancer seems to be a worse prognosis (Sorensen et al, 1988) than metachronous brain metastases.New therapeutic strategies are needed to improve the outcome of these patients. The knowledge of prognostic determinants might be important in both clinical trials and routine practice (Komaki et al, 1993;Charloux et al, 1997;Paesmans et al, 1997;Merrill et al, 1999). In the former setting, prognostic co-variables must be taken into account in survival analyses; by way of illustration, in a given randomized trial, the statement that a difference in survival is related to the effects of the treatment must be supported by a proportional hazards model demonstrating that this effect does not depend on well-known prognostic determinants (Depierre et al, 1999;Furuse et al, 1999). In the second setting, a therapeutic decision might be influenced by the state of prognostic variables (Komaki et al, 1993).Here we especially take a look at the prognostic significance of 2 specific serum markers, CYFRA 21-1 and neuron-specific enolase (NSE). The prognostic value of CYFRA 21-1 (a fragment of cytokeratin subunit 19) in this disease has been suggested (Pujol et al, 1993;Wieskopf et al, 1995;Brechot et al, 1997). NSE, the γ-subunit of enolase, has been widely investigated as a marker of small cell lung cancer (SCLC;Jorgensen et al, 1989). Although only a small proportion of NSCLC presented with a high NSE level, this marker might indirectly reflect; i) a neuroendocrine component of the disease in favour of tumour heterogeneity; ii) a Summary A multi-centre retrospective study involving 4 French university institutions has been conducted in order to identify routine pretherapeutic prognostic factors of survival in patients with previously untreated non-small cell lung cancer and brain metastases at the time of presentation. A total of 231 patients were recorded regarding their clinical, radiological and biological characteristics at presentation. The accrual period was January 1991 to December 1998. Prognosis was analysed using both univariate and multivariate (Cox model) statistics. The median survival of the whole population was 28 weeks. Univariate analysis (log-rank), showed that patients affected by one of the following characteristics proved to have a shorter survival in comparison with the opposite status...

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Clinical Staging of Primary Lung Cancer

Cited by 257 publications

References 3 publications

A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index

A new simplified comorbidity score as a prognostic factor in non-small-cell lung cancer patients: description and comparison with the Charlson's index

Fluorine-18 deoxyglucose positron emission tomography for the detection of bone metastases in patients with non-small cell lung cancer

Brain metastases at the time of presentation of non-small cell lung cancer: a multi-centric AERIO* analysis of prognostic factors

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