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- Dahl, Olav, et al.
(författare)
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Evaluation of the stage classification of anal cancer by the TNM 8th version versus the TNM 7th version
- 2020
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Ingår i: Acta Oncologica. - 0284-186X .- 1651-226X. ; 59:9, s. 1016-1023
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Tidskriftsartikel (refereegranskat)abstract
- Background: The UICC TNM 7th edition introduced stage groups for anal cancer which in 2019 has not yet come into general use. The new TNM 8th edition from 2016 defines 7 sub-stages. Background data for these changes are lacking. We aimed to investigate whether the new classification for anal cancer reliably predict the prognosis in the different stages.Patients and methods: The Nordic Anal Cancer Group (NOAC) conducted a large retrospective study of all anal cancers in Norway, Sweden and most of Denmark in 2000–2007. From the Nordic cohort 1151 anal cancer patients with follow-up data were classified by the TNM 4th edition which has identical T, N and M definitions as the TNM 7th edition, and therefore also can be classified by the TNM 7th stage groups. We used the Nordic cohort to translate the T, N and M stages into the TNM 8th stages and sub-stages. Overall survival for each stage was assessed.Results: Although the summary stage groups for TNM 8th edition discriminates patients with different prognosis reasonably well, the analyses of the seven sub-stages show overlapping overall survival: HR for stage IIA 1.30 (95%CI 0.80–2.12) is not significantly different from stage I (p = .30) and HR for stage IIB 2.35 (95%CI 1.40–3.95) and IIIA 2.48 (95%CI 1.43–4.31) are also similar as were HRs for stage IIIB 3.41 (95%CI 1.99–5.85) and IIIC 3.22 (95%CI 1.99–5.20). Similar overlapping was shown for local recurrence and distant spread.Conclusion: The results for the sub-stages calls for a revision of the staging system. We propose a modification of the TNM 8th edition for staging of anal cancer into four stages based on the T, N and M definitions of the TNM 8th classification.
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| 2. |
- Osterman, Erik, et al.
(författare)
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Accurate population-based model for individual prediction of colon cancer recurrence
- 2021
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Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 60:10, s. 1241-1249
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Tidskriftsartikel (refereegranskat)abstract
- Background: Prediction models are useful tools in the clinical management of colon cancer patients, particularly when estimating the recurrence rate and, thus, the need for adjuvant treatment. However, the most used models (MSKCC, ACCENT) are based on several decades-old patient series from clinical trials, likely overestimating the current risk of recurrence, especially in low-risk groups, as outcomes have improved over time. The aim was to develop and validate an updated model for the prediction of recurrence within 5 years after surgery using routinely collected clinicopathologic variables.Material and methods: A population-based cohort from the Swedish Colorectal Cancer Registry of 16,134 stage I–III colon cancer cases was used. A multivariable model was constructed using Cox proportional hazards regression. Three-quarters of the cases were used for model development and one quarter for internal validation. External validation was performed using 12,769 stage II–III patients from the Norwegian Colorectal Cancer Registry. The model was compared to previous nomograms.Results: The nomogram consisted of eight variables: sex, sidedness, pT-substages, number of positive and found lymph nodes, emergency surgery, lymphovascular and perineural invasion. The area under the curve (AUC) was 0.78 in the model, 0.76 in internal validation, and 0.70 in external validation. The model calibrated well, especially in low-risk patients, and performed better than existing nomograms in the Swedish registry data. The new nomogram’s AUC was equal to that of the MSKCC but the calibration was better.Conclusion: The nomogram based on recently operated patients from a population registry predicts recurrence risk more accurately than previous nomograms. It performs best in the low-risk groups where the risk-benefit ratio of adjuvant treatment is debatable and the need for an accurate prediction model is the largest.
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