Literature DB >> 16641170

Measuring tumor response and shape change on CT: esophageal cancer as a paradigm.

L H Schwartz1, J A C Colville, M S Ginsberg, L Wang, M Mazumdar, J Kalaigian, H Hricak, D Ilson, G K Schwartz.   

Abstract

BACKGROUND: Accurate response assessment is essential for evaluating new cancer treatments. We evaluated the impact of Response Evaluation Criteria in Solid Tumors (RECIST), World Health Organization (WHO) criteria and tumor shape on response assessment in patients with metastatic esophageal cancer. PATIENTS AND METHODS: In 19 patients with metastatic esophageal cancer in a phase II trial of bryostatin-1 and paclitaxel, response was retrospectively assessed for 89 lesions with RECIST and WHO criteria on baseline and serial follow-up CT scans. The eccentricity factor (EF) was introduced for measuring the degree to which tumor shape diverges from a perfect sphere [EF = radical1-(LPD/MD)(2), where LPD is the largest perpendicular diameter and MD is the maximal diameter].
RESULTS: The disagreement rate in best overall response categorization between RECIST (unidimensional) and WHO (bidimensional) criteria was 26.3%. Change in eccentricity was significantly greater (P < 0.01) for patients with disagreement (mean 0.31, range 0-0.91). When the short axis was used for unidimensional lymph node measurement, disagreement between WHO and RECIST lessened.
CONCLUSIONS: Response assessment by WHO and RECIST differs substantially. Greater change in eccentricity is associated with greater discordance between WHO and RECIST. The discordance between WHO and RECIST may impact on how effective a therapy is judged to be.

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Year:  2006        PMID: 16641170     DOI: 10.1093/annonc/mdl058

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  6 in total

1.  Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer.

Authors:  Binsheng Zhao; Leonard P James; Chaya S Moskowitz; Pingzhen Guo; Michelle S Ginsberg; Robert A Lefkowitz; Yilin Qin; Gregory J Riely; Mark G Kris; Lawrence H Schwartz
Journal:  Radiology       Date:  2009-07       Impact factor: 11.105

2.  Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy.

Authors:  Adam D Yock; Arvind Rao; Lei Dong; Beth M Beadle; Adam S Garden; Rajat J Kudchadker; Laurence E Court
Journal:  Med Phys       Date:  2014-08       Impact factor: 4.071

3.  Imaging response assessment in oncology.

Authors:  S D Curran; A U Muellner; L H Schwartz
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

4.  A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

Authors:  Sebastian Echegaray; Viswam Nair; Michael Kadoch; Ann Leung; Daniel Rubin; Olivier Gevaert; Sandy Napel
Journal:  Tomography       Date:  2016-12

Review 5.  RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline.

Authors:  Els L van Persijn van Meerten; Hans Gelderblom; Johan L Bloem
Journal:  Eur Radiol       Date:  2009-12-22       Impact factor: 5.315

6.  Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

Authors:  Diego Vera-Yunca; Pascal Girard; Zinnia P Parra-Guillen; Alain Munafo; Iñaki F Trocóniz; Nadia Terranova
Journal:  AAPS J       Date:  2020-03-16       Impact factor: 4.009

  6 in total

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