Dataset Information

Changes of lung tumour volume on CT - prediction of the reliability of assessments.

ABSTRACT:

Background

For oncological evaluations, quantitative radiology gives clinicians significant insight into patients' response to therapy. In regard to the Response Evaluation Criteria in Solid Tumours (RECIST), the classification of disease evolution partly consists in applying thresholds to the measurement of the relative change of tumour. In the case of tumour volumetry, response thresholds have not yet been established. This study proposes and validates a model for calculating thresholds for the detection of minimal tumour change when using the volume of pulmonary lesions on CT as imaging biomarker.

Methods

Our work is based on the reliability analysis of tumour volume measurements documented by the Quantitative Imaging Biomarker Alliance. Statistics of measurements were entered into a multi-parametric mathematical model of the relative changes derived from the Geary-Hinkley transformation. The consistency of the model was tested by comparing modelled thresholds against Monte Carlo simulations of tumour volume measurements with additive random error. The model has been validated by repeating measurements on real patient follow ups.

Results

For unchanged tumour volume, relying on a normal distribution of error, the agreement between model and simulations featured a type I error of 5.25%. Thus, we established that a threshold of 35% of volume reduction corresponds to a partial response (PR) and a 55% volume increase corresponds to progressive disease (PD). Changes between -35 and +55% are categorized as stable disease (SD). Tested on real clinical data, 97.1% [95.7; 98.0] of assessments fall into the range of variability predicted by our model of confidence interval.

Conclusions

Our study indicates that the Geary Hinkley model, using published statistics, is appropriate to predict response thresholds for the volume of pulmonary lesions on CT.

SUBMITTER: Beaumont H

PROVIDER: S-EPMC4628325 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Publications

Changes of lung tumour volume on CT - prediction of the reliability of assessments.

Beaumont Hubert H Souchet Simon S Labatte Jean Marc JM Iannessi Antoine A Tolcher Anthony William AW

Cancer imaging : the official publication of the International Cancer Imaging Society 20151031

<h4>Background</h4>For oncological evaluations, quantitative radiology gives clinicians significant insight into patients' response to therapy. In regard to the Response Evaluation Criteria in Solid Tumours (RECIST), the classification of disease evolution partly consists in applying thresholds to the measurement of the relative change of tumour. In the case of tumour volumetry, response thresholds have not yet been established. This study proposes and validates a model for calculating threshold ...[more]

PMID: 26521238

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Project description:BackgroundIdentification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis.ResultsIn this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used.ConclusionsPrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.

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Dataset Information

Changes of lung tumour volume on CT - prediction of the reliability of assessments.

Background

Methods

Results

Conclusions

Publications

Changes of lung tumour volume on CT - prediction of the reliability of assessments.

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