Limits...
Modelling tumour volume variations in head and neckcancer: contribution of magnetic resonance imaging for patients undergoing induction chemotherapy

View Article: PubMed Central - PubMed

ABSTRACT

Primary tumour volume evaluation has predictive value for estimating survival outcomes. Using volumetric data acquired by MRI in patients undergoing induction chemotherapy (IC) these outcomes were estimated before the radiotherapy course in head and neck cancer (HNC) patients. MRI performed before and after IC in 36 locally advanced HNC patients were analysed to measure primary tumour volume. The two volumes were correlated using the linear-log ratio (LLR) between the volume in the first MRI and the volume in the second. Cox's proportional hazards models (CPHM) were defined for loco-regional control (LRC), disease-free survival (DFS) and overall survival (OS). Strict evaluation of the influence of volume delineation uncertainties on prediction of final outcomes has been defined. LLR showed good predictive value for all survival outcomes in CPHM. Predictive models for LRC and DFS at 24 months showed optimal discrimination and prediction capability. Evaluation of primary tumour volume variations in HNC after IC provides an example of modelling that can be easily used even for other adaptive treatment approaches. A complete assessment of uncertainties in covariates required for running models is a prerequisite to create reliable clinically models.

No MeSH data available.


Related in: MedlinePlus

Nomogram for calculating disease-free survival (DFS) at 24 months. Two vertical lines show the values of mean tumour volume before (mV1) and after (mV2) induction chemotherapy. Using a ruler to draw a straight line connecting the values of the two volumes on the oblique outcome line the predicted survival probability can be directly read.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5384316&req=5

Figure 3: Nomogram for calculating disease-free survival (DFS) at 24 months. Two vertical lines show the values of mean tumour volume before (mV1) and after (mV2) induction chemotherapy. Using a ruler to draw a straight line connecting the values of the two volumes on the oblique outcome line the predicted survival probability can be directly read.

Mentions: In Figure 3 the nomogram showing predicted DFS at 24months is shown. The use of this nomogram does not require calculating sums, but only placing a ruler connecting the value of the mV1 on the right with the value of the mV2 on the left. The predicted value of DFS at 24 months can be read on the oblique line showing the outcome prediction where the connection intersects this line. An aspect usually not analysed using current predictive models evaluation and nomogram drawing procedures 30 is the need to assess the level of uncertainty (that is error) in measures of covariates put into the model. Covariates as sex, age and similar, used within predictive models, usually do not need to be evaluated for errors in detecting the value (especially when considering simple dummy variables). Indeed, when managing variables such as the value of delineated volume, as in our study, or similar measures subject to some kind of detection error, a verification of the variability in outcome prediction due to uncertainties in covariates should always be considered: as cited previously, there is much evidence for great variability in volume delineation procedures, that can vary to according anatomical site, imaging modality etc. 15-2431.


Modelling tumour volume variations in head and neckcancer: contribution of magnetic resonance imaging for patients undergoing induction chemotherapy
Nomogram for calculating disease-free survival (DFS) at 24 months. Two vertical lines show the values of mean tumour volume before (mV1) and after (mV2) induction chemotherapy. Using a ruler to draw a straight line connecting the values of the two volumes on the oblique outcome line the predicted survival probability can be directly read.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5384316&req=5

Figure 3: Nomogram for calculating disease-free survival (DFS) at 24 months. Two vertical lines show the values of mean tumour volume before (mV1) and after (mV2) induction chemotherapy. Using a ruler to draw a straight line connecting the values of the two volumes on the oblique outcome line the predicted survival probability can be directly read.
Mentions: In Figure 3 the nomogram showing predicted DFS at 24months is shown. The use of this nomogram does not require calculating sums, but only placing a ruler connecting the value of the mV1 on the right with the value of the mV2 on the left. The predicted value of DFS at 24 months can be read on the oblique line showing the outcome prediction where the connection intersects this line. An aspect usually not analysed using current predictive models evaluation and nomogram drawing procedures 30 is the need to assess the level of uncertainty (that is error) in measures of covariates put into the model. Covariates as sex, age and similar, used within predictive models, usually do not need to be evaluated for errors in detecting the value (especially when considering simple dummy variables). Indeed, when managing variables such as the value of delineated volume, as in our study, or similar measures subject to some kind of detection error, a verification of the variability in outcome prediction due to uncertainties in covariates should always be considered: as cited previously, there is much evidence for great variability in volume delineation procedures, that can vary to according anatomical site, imaging modality etc. 15-2431.

View Article: PubMed Central - PubMed

ABSTRACT

Primary tumour volume evaluation has predictive value for estimating survival outcomes. Using volumetric data acquired by MRI in patients undergoing induction chemotherapy (IC) these outcomes were estimated before the radiotherapy course in head and neck cancer (HNC) patients. MRI performed before and after IC in 36 locally advanced HNC patients were analysed to measure primary tumour volume. The two volumes were correlated using the linear-log ratio (LLR) between the volume in the first MRI and the volume in the second. Cox's proportional hazards models (CPHM) were defined for loco-regional control (LRC), disease-free survival (DFS) and overall survival (OS). Strict evaluation of the influence of volume delineation uncertainties on prediction of final outcomes has been defined. LLR showed good predictive value for all survival outcomes in CPHM. Predictive models for LRC and DFS at 24 months showed optimal discrimination and prediction capability. Evaluation of primary tumour volume variations in HNC after IC provides an example of modelling that can be easily used even for other adaptive treatment approaches. A complete assessment of uncertainties in covariates required for running models is a prerequisite to create reliable clinically models.

No MeSH data available.


Related in: MedlinePlus