Flow cytometric S-phase fraction in soft-tissue sarcoma: prognostic importance analysed in 160 patients

Gustafson, Pelle; Fernö, Mårten; Åkerman, Måns; Baldetorp, Bo; Willén, Helena; Killander, D.; Rydholm, Anders

doi:10.1038/bjc.1997.15

Cited by 21 publications

(18 citation statements)

References 16 publications

(17 reference statements)

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“…When comparing the discriminative values of different cut-off points, it became evident that the weaker prognostic effect of SPF seems to be due to the poor discriminative power of high SPF values. With low cut-off points, SPF seemed to be an equally effective prognostic factor as Ki-67, which is in accordance with previous results (Collin et al, 1997;Gustafson et al, 1997). The results of Gustafson and of Collin are not directly comparable to ours, however, because both of these investigators excluded 35-37% of their tumours because of uninterpretable histograms.…”

Section: Discussionsupporting

confidence: 77%

Comparison of the Ki-67 score and S-phase fraction as prognostic variables in soft-tissue sarcoma

et al. 1999

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Immunohistochemically determined Ki-67 scores and flow cytometrically determined S-phase fractions were successfully evaluated from the primary tumours of 123 patients with soft-tissue sarcoma. All patients had either limb or superficial trunk tumours. Ki-67 score correlated strongly with ploidy, S-phase fraction and grade. Ki-67 did not correlate with the size of the primary tumour. When analysed as a continuous variable, Ki-67 was a stronger predictor of both metastasis-free survival and disease-specific overall survival ( P = 0.003 and 0.04 respectively) than was the S-phase fraction ( P = 0.06 and 0.07 respectively). We tested the relevance of different cut-point values by dividing the whole material into two parts at every 10% (e.g. 10% of patients vs. the remaining 90%, 20% vs. 80%, etc.). We counted the relative risk and confidence interval at all these cut-off points. Ki-67 had good prognostic discriminating power irrespective of the cut-point value, but S-phase fraction lost its prognostic power at higher cut-point values. In conclusion, we found that Ki-67 is a useful prognostic tool in the treatment of soft-tissue sarcoma patients irrespective of the cut-point value. S-phase fraction can be used at lower cut-point values. © 1999 Cancer Research Campaign

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Section: Discussionsupporting

confidence: 77%

Comparison of the Ki-67 score and S-phase fraction as prognostic variables in soft-tissue sarcoma

et al. 1999

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“…We have earlier shown that patients in whom calculation of SPF according to Baisch failed have a different distribution of histotypes and malignancy grades, and a worse 5-year MFSR, implying that they may belong to a different subset of STS patients (Gustafson et al, 1997). In the present series, using the MultiCycle software, it was possible to calculate the SPF in the majority of cases where the Baisch method failed.…”

Section: Discussionmentioning

confidence: 66%

“…SPF has been identified as a prognostic factor in several malignancies: breast cancer, non-small cell lung cancer, colorectal cancer, carcinoma of the ovary, endometrial cancer, prostate cancer (Merkel and McGuire, 1990;Sigurdsson et al, 1990;Gudmundsson et al, 1995;Bratt et al, 1996) and recently in STS (Huuhtanen et al, 1996;Collin et al, 1997;Gustafson et al, 1997).…”

Section: Discussionmentioning

confidence: 99%

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Prognostic implications of various models for calculation of S-phase fraction in 259 patients with soft tissue sarcoma

et al. 1999

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The S-phase fraction (SPF) in flow cytometric DNA histograms in soft tissue sarcoma (STS) can be calculated in various ways. The traditional planimetric method of Baisch has been shown to be prognostic, but is hampered by a failure rate of around 40%. We therefore tested other models to see if this rate could be decreased with retained prognostic value. In 259 STS of the locomotor system the SPF was calculated according to Baisch and with commercial parametric MultiCycle software using different corrections for background. Using the Baisch model, 159 histograms could be evaluated for SPF. The 5-year metastasis-free survival rate (MFSR) was 0.94 for the low-risk group (defined with SPF), and 0.53 for the high-risk group. In the low-risk group, four of the seven patients who developed metastasis did so after 5 years. Using the MultiCycle software, SPF could be calculated in 253 tumours. Depending on type of background correction used, the 5-year MFSR varied between 0.67 and 0.82 for the low-risk group, and between 0.47 and 0.53 for the high-risk group. The late metastasis pattern in the low-risk group was never seen using the MultiCycle software. We conclude that in paraffin archival material, calculation of SPF according to Baisch is preferable in clinical use due to better separation between low-risk and high-risk groups, and also the possibility to identify patients who metastasize late. © 1999 Cancer Research Campaign

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“…S100 was not found to retain its prognostic value in a Cox analysis. Gustafson et al (1997) probably also followed the four steps above, although they do not state explicitly that they minimized the P-value. These authors examined multiple cutpoints in the study of SPF in soft-tissue sarcoma.…”

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Suboptimal analysis using 'optimal' cutpoints

Altman¹

1998

Br J Cancer

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(1993) Elevated levels of insulin-like growth factor-binding protein-2 in the serum of prostate cancer patients. J Clin Endocrinol Metab 76: 1031-1035 Ho PJ and Baxter RC (1997) Insulin-like growth factor-binding protein-2 in patients with prostate carcinoma and benign prostatic hyperplasia. Clin Endocrinol 46: 333-342Mantzoros CS, Tzonou A, Signorello LB, Stampfer MJ, Trichopoulos D and Adami H-O (1997) Insulin-like growth factor 1 in relation to prostate cancer and benign prostatic hyperplasia. Br J Cancer 76: 1115-1118 Suboptimal analysis using 'optimal' cutpoints Sir, Oncology journals continue to publish many papers evaluating the prognostic importance of tumour markers in patients with various cancers. There is no agreed statistical methodology for handling such data, but some analyses are widely agreed to be misleading. One in particular, the so-called 'optimal' cutpoint method, is unsatisfactory for this purpose (Altman, 1991;Hilsenbeck et al, 1992;Hill, 1993;Altman et al, 1994 Altman et al, , 1995. Regrettably, this method, which is better referred to as the minimum P-value method, continues to appear in papers published in the British Journal of Cancer. Briefly, the minimum P-value method is as follows:(1) each distinct observed value of the marker is taken in turn as a cutpoint and two groups of patients created with values below or above this level (a variation is to use equi-spaced values across the observed range); (2) for each such grouping a log-rank test is performed and the P-value determined; (3) the cutpoint with the lowest P-value is called 'optimal', Kaplan-Meier curves are constructed for groups created with this cutpoint and the P-value reported; (4) in most cases the resulting binary variable is included with other variables in a Cox multiple regression analysis.The dangers of this approach have been outlined and include:(a) because of multiple testing the false-positive rate is around 40% rather than the nominal 5%; (b) the P-value is far too small (P = 0.002 corresponds to a genuine P = 0.05); (c) the value of the cutpoint has no clinical meaning; (d) the analysis gives no information about the shape of the relation between the level of the tumour marker and prognosis.In addition, when step (4) above is followed, the bias from the univariate analysis is transferred to the multivariate setting . It is not surprising, therefore, that such analyses often show that the tumour marker is apparently more important (i.e. has a smaller P-value) than other variables in univariate analyses, and that they usually retain significance after adjustment for standard risk factors. These problems arise from the search for the 'best' result. The consequence is a cutpoint that may be best in the narrow sense described, but which will not offer a true indication of the importance of the tumour marker.Looking quickly through a few recent issues of the journal I found three such studies. Dettmar et al (1997) studied the prognostic relevance of MIBl (Ki-67) and S-phase fraction (SPF) for disease-free sur...

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Flow cytometric S-phase fraction in soft-tissue sarcoma: prognostic importance analysed in 160 patients

Cited by 21 publications

References 16 publications

Comparison of the Ki-67 score and S-phase fraction as prognostic variables in soft-tissue sarcoma

Comparison of the Ki-67 score and S-phase fraction as prognostic variables in soft-tissue sarcoma

Prognostic implications of various models for calculation of S-phase fraction in 259 patients with soft tissue sarcoma

Suboptimal analysis using 'optimal' cutpoints

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