Dataset Information

The annual recurrence risk model for tailored surveillance strategy in patients with cervical cancer.

ABSTRACT:

Purpose

Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the same strategy for all survivors. The aim of this study was to develop a robust model allowing for individualised surveillance based on a patient's risk profile.

Methods

Data of 4343 early-stage patients with cervical cancer treated between 2007 and 2016 were obtained from the international SCCAN (Surveillance in Cervical Cancer) consortium. The Cox proportional hazards model predicting disease-free survival (DFS) was developed and internally validated. The risk score, derived from regression coefficients of the model, stratified the cohort into significantly distinctive risk groups. On its basis, the annual recurrence risk model (ARRM) was calculated.

Results

Five variables were included in the prognostic model: maximal pathologic tumour diameter; tumour histotype; grade; number of positive pelvic lymph nodes; and lymphovascular space invasion. Five risk groups significantly differing in prognosis were identified with a five-year DFS of 97.5%, 94.7%, 85.2% and 63.3% in increasing risk groups, whereas a two-year DFS in the highest risk group equalled 15.4%. Based on the ARRM, the annual recurrence risk in the lowest risk group was below 1% since the beginning of follow-up and declined below 1% at years three, four and >5 in the medium-risk groups. In the whole cohort, 26% of recurrences appeared at the first year of the follow-up, 48% by year two and 78% by year five.

Conclusion

The ARRM represents a potent tool for tailoring the surveillance strategy in early-stage patients with cervical cancer based on the patient's risk status and respective annual recurrence risk. It can easily be used in routine clinical settings internationally.

SUBMITTER: Cibula D

PROVIDER: S-EPMC9406128 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

The annual recurrence risk model for tailored surveillance strategy in patients with cervical cancer.

Cibula David D Dostálek Lukáš L Jarkovsky Jiri J Mom Constantijne H CH Lopez Aldo A Falconer Henrik H Fagotti Anna A Ayhan Ali A Kim Sarah H SH Isla Ortiz David D Klat Jaroslav J Obermair Andreas A Landoni Fabio F Rodriguez Juliana J Manchanda Ranjit R Kosťun Jan J Dos Reis Ricardo R Meydanli Mehmet M MM Odetto Diego D Laky Rene R Zapardiel Ignacio I Weinberger Vit V Benešová Klára K Borčinová Martina M Pari Darwin D Salehi Sahar S Bizzarri Nicolò N Akilli Huseyin H Abu-Rustum Nadeem R NR Salcedo-Hernández Rosa A RA Javůrková Veronika V Sláma Jiří J van Lonkhuijzen Luc R C W LRCW

European journal of cancer (Oxford, England : 1990) 20211016

<h4>Purpose</h4>Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the same strategy for all survivors. The aim of this study was to develop a robust model allowing for individualised surveillance based on a patient's risk profile.<h4>Methods</h4>Data of 4343 early-stage patients with cervical cancer treated between 2007 and 2016 were obtained from the international SCCAN (Surveillance in Cervical Cancer) consortium. The Cox proportional hazards model predict ...[more]

PMID: 34666213

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Project description:BACKGROUND:Approximately one-third of those treated curatively for colorectal cancer (CRC) will experience recurrence. No evidence-based consensus exists on how best to follow patients after initial treatment to detect asymptomatic recurrence. Here, a new approach for simulating surveillance and recurrence among CRC survivors is outlined, and development and calibration of a simple model applying this approach is described. The model's ability to predict outcomes for a group of patients under a specified surveillance strategy is validated. METHODS:We developed an individual-based simulation model consisting of two interacting submodels: a continuous-time disease-progression submodel overlain by a discrete-time Markov submodel of surveillance and re-treatment. In the former, some patients develops recurrent disease which probabilistically progresses from detectability to unresectability, and which may produce early symptoms leading to detection independent of surveillance testing. In the latter submodel, patients undergo user-specified surveillance testing regimens. Parameters describing disease progression were preliminarily estimated through calibration to match five-year disease-free survival, overall survival at years 1-5, and proportion of recurring patients undergoing curative salvage surgery from one arm of a published randomized trial. The calibrated model was validated by examining its ability to predict these same outcomes for patients in a different arm of the same trial undergoing less aggressive surveillance. RESULTS:Calibrated parameter values were consistent with generally observed recurrence patterns. Sensitivity analysis suggested probability of curative salvage surgery was most influenced by sensitivity of carcinoembryonic antigen assay and of clinical interview/examination (i.e. scheduled provider visits). In validation, the model accurately predicted overall survival (59% predicted, 58% observed) and five-year disease-free survival (55% predicted, 53% observed), but was less accurate in predicting curative salvage surgery (10% predicted; 6% observed). CONCLUSIONS:Initial validation suggests the feasibility of this approach to modeling alternative surveillance regimens among CRC survivors. Further calibration to individual-level patient data could yield a model useful for predicting outcomes of specific surveillance strategies for risk-based subgroups or for individuals. This approach could be applied toward developing novel, tailored strategies for further clinical study. It has the potential to produce insights which will promote more effective surveillance-leading to higher cure rates for recurrent CRC.

Dataset Information

The annual recurrence risk model for tailored surveillance strategy in patients with cervical cancer.

Purpose

Methods

Results

Conclusion

Publications

The annual recurrence risk model for tailored surveillance strategy in patients with cervical cancer.

Similar Datasets

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