BACKGROUND: In current clinical trials of treating @entity43 @entity1 , how to accurately predict @entity1 ' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of @entity43 @entity1 to chemotherapy in clinical trials. MATERIAL AND METHODS: A dataset of 30 @entity1 was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment @entity5 depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the @entity5 volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict @entity1 ' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in @entity5 (RECIST) guideline. RESULTS: The areas under receiver operating characteristic curve (AUC) were 0.773        0.086, 0.680        0.109, and 0.668        0.101, when using each of three features, respectively. AUC value increased to 0.831        0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). CONCLUSION: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of @entity43 @entity1 to the chemotherapy in clinical trials.

Early prediction of clinical benefit of treating XXXX using quantitative CT image feature analysis.

@entity43 :: ('MESH:D010051', 'Disease') :: ['ovarian cancer'] @entity1 :: ('9606', 'Species') :: ['patients'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor', 'Solid Tumors', 'tumors']
@entity43 :: (MESH:D010051,Disease) :: ['ovarian cancer']
