Literature DB >> 20421814

Assessment of therapy responses and prediction of survival in malignant pleural mesothelioma through computer-aided volumetric measurement on computed tomography scans.

Fan Liu1, Binsheng Zhao, Lee M Krug, Nicole M Ishill, Remy C Lim, Pingzhen Guo, Matthew Gorski, Raja Flores, Chaya S Moskowitz, Valerie W Rusch, Lawrence H Schwartz.   

Abstract

PURPOSE: The purposes of this study were (1) to calculate the tumor volume in patients with malignant pleural mesothelioma using computed tomography (CT) scan images and a computer-aided measurement technique and (2) to investigate whether the baseline volume, or volume change after chemotherapy, predicts patient survival.
METHODS: We compiled the clinical characteristics and outcome from 30 patients enrolled in two clinical trials at our cancer center in which the patients were treated with induction chemotherapy followed by surgery and radiation. CT scans of 30 patients were obtained at baseline and after two cycles of chemotherapy. Tumor volumes were calculated using a semiautomated computer algorithm. Overall survival was measured using a landmark time at 3 months post-treatment start date such that all patients had already received two cycles of chemotherapy and a follow-up scan. Association of volume changes with overall survival were determined by a Cox Proportional Hazards Model or log-rank test. The relationship between both pre and postoperative clinical stage and baseline tumor volume was analyzed using the rank sum test.
RESULTS: The median baseline tumor volume was 473 cm(3) (range, 61 cm(3)-2108 cm(3)). Patients with high preoperative stages (III and IV) had larger baseline tumor volume than those with low preoperative stages (I and II) (p = 0.05). Patients with baseline volumes smaller than 619 cm(3) tended to survive longer than those with baseline volumes larger than or equal to 619 cm(3) (p = 0.07). Percentage change of tumor volume from baseline to first follow-up CT after two cycles of chemotherapy was significantly associated with overall survival (hazard ratio: 1.94 [95% confidence interval, 1.05-3.60], p = 0.04). Whereas the relative change in modified RECIST measurements was not significantly associated with overall survival (hazard ratio: 1.06 [95% confidence interval, 0.96-1.16], p = 0.25). By classifying changes of tumor volumes between two scans into two groups, i.e., "increase" and "decrease," a significant difference in survival was found between those who increased and decreased after two cycles of chemotherapy (p = 0.03).
CONCLUSIONS: Changes in tumor volume after two cycles of chemotherapy predicted overall survival in patients with malignant pleural mesothelioma. Tumor volume at baseline was shown to be associated with preoperative clinical stage and survival. Computer-aided volumetric measurements may enable more reliable therapeutic response assessment and could provide additional prognostic information.

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Year:  2010        PMID: 20421814     DOI: 10.1097/JTO.0b013e3181dd0ef1

Source DB:  PubMed          Journal:  J Thorac Oncol        ISSN: 1556-0864            Impact factor:   15.609


  29 in total

1.  A practical guide of the Southwest Oncology Group to measure malignant pleural mesothelioma tumors by RECIST and modified RECIST criteria.

Authors:  Anne S Tsao; Linda Garland; Mary Redman; Kemp Kernstine; David Gandara; Edith M Marom
Journal:  J Thorac Oncol       Date:  2011-03       Impact factor: 15.609

2.  Variability of tumor area measurements for response assessment in malignant pleural mesothelioma.

Authors:  Zacariah E Labby; Christopher Straus; Philip Caligiuri; Heber MacMahon; Ping Li; Alexandra Funaki; Hedy L Kindler; Samuel G Armato
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Review 3.  Volumetric assessment in malignant pleural mesothelioma.

Authors:  David J Murphy; Ritu R Gill
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Review 4.  The eighth TNM classification for malignant pleural mesothelioma.

Authors:  Lawek Berzenji; Paul E Van Schil; Laurens Carp
Journal:  Transl Lung Cancer Res       Date:  2018-10

5.  Assessment of objective responses using volumetric evaluation in advanced thymic malignancies and metastatic non-small cell lung cancer.

Authors:  Jeremy Force; Arun Rajan; Eva Dombi; Seth M Steinberg; Giuseppe Giaccone
Journal:  J Thorac Oncol       Date:  2011-07       Impact factor: 15.609

6.  Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion.

Authors:  Eyjolfur Gudmundsson; Christopher M Straus; Feng Li; Samuel G Armato
Journal:  J Med Imaging (Bellingham)       Date:  2020-01-29

7.  Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans.

Authors:  Eyjolfur Gudmundsson; Christopher M Straus; Samuel G Armato
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-24

8.  Clinical Implementation of a Free-Breathing, Motion-Robust Dynamic Contrast-Enhanced MRI Protocol to Evaluate Pleural Tumors.

Authors:  Thomas S C Ng; Ravi T Seethamraju; Raphael Bueno; Ritu R Gill
Journal:  AJR Am J Roentgenol       Date:  2020-04-29       Impact factor: 3.959

9.  FDG PET-CT aids in the preoperative assessment of patients with newly diagnosed thymic epithelial malignancies.

Authors:  Marcelo F K Benveniste; Cesar A Moran; Osama Mawlawi; Patricia S Fox; Stephen G Swisher; Reginald F Munden; Edith M Marom
Journal:  J Thorac Oncol       Date:  2013-04       Impact factor: 15.609

10.  Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma.

Authors:  Zacariah E Labby; Samuel G Armato; James J Dignam; Christopher Straus; Hedy L Kindler; Anna K Nowak
Journal:  J Thorac Oncol       Date:  2013-04       Impact factor: 15.609

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