Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
BMC Medical Imaging
Published in: BMC Medical Imaging 1/2016

Open Access 01-12-2016 | Research article

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome

Authors: Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen Moore, Hong Liu, Bin Zheng

Published in: BMC Medical Imaging | Issue 1/2016

Login to get access

Abstract

Background

To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome.

Methods

A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients.

Results

The correlation coefficients between automated and radiologist’s manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients’ PFS (p < 0.01).

Conclusion

Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.
Literature
1.
2.
Chiarle R, Voena C, Ambrogio C, Piva R, Inghirami G. The anaplastic lymphoma kinase in the pathogenesis of cancer. Nat Rev Cancer. 2008;8(1):11–23.CrossRefPubMed
3.
Goetsch CM. Genetic tumor profiling and genetically targeted cancer therapy. In: Seminars in oncology nursing: 2011. Philadelphia, USA: WB Saunders; 2011. p. 34–44.
4.
Hagen AI, Kvistad KA, Maehle L, Holmen MM, Aase H, Styr B, Vabø A, Apold J, Skaane P, Møller P. Sensitivity of MRI versus conventional screening in the diagnosis of BRCA-associated breast cancer in a national prospective series. Breast. 2007;16(4):367–74.CrossRefPubMed
5.
Kobayashi S, Boggon TJ, Dayaram T, Jänne PA, Kocher O, Meyerson M, Johnson BE, Eck MJ, Tenen DG, Halmos B. EGFR mutation and resistance of non–small-cell lung cancer to gefitinib. N Engl J Med. 2005;352(8):786–92.CrossRefPubMed
6.
7.
Diamandis EP. Mass spectrometry as a diagnostic and a cancer biomarker discovery tool opportunities and potential limitations. Mol Cell Proteomics. 2004;3(4):367–78.CrossRefPubMed
8.
Bhooshan N, Giger M, Edwards D, Yuan Y, Jansen S, Li H, Lan L, Sattar H, Newstead G. Computerized three-class classification of MRI-based prognostic markers for breast cancer. Phys Med Biol. 2011;56(18):5995.CrossRefPubMedPubMedCentral
9.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.CrossRefPubMedPubMedCentral
10.
Tan M, Pu J, Cheng S, Liu H, Zheng B. Assessment of a four-view mammographic image feature based fusion model to predict near-term breast cancer risk. Ann Biomed Eng. 2015;43(10):2416–28.CrossRefPubMedPubMedCentral
11.
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentral
12.
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234–48.CrossRefPubMedPubMedCentral
13.
Aghaei F, Tan M, Hollingsworth AB, Zheng B. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy. J. Magn. Reson. Imaging. 2016. DOI: 10.1002/jmri.25276
14.
Emaminejad N, Qian W, Guan Y, Tan M, Qiu Y, Liu H, Zheng B. Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng. 2016;63(5):1034–43.CrossRefPubMed
15.
Qiu Y, Tan M, McMeekin S, Thai T, Ding K, Moore K, Liu H, Zheng B. Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis. Acta Radiologica. 2015. doi:10.1177/0284185115620947
16.
Tan M, Li Z, Qiu Y, McMeekin SD, Thai TC, Ding K, Moore KN, Liu H, Zheng B. A new approach to evaluate drug treatment response of ovarian cancer patients based on deformable image registration. IEEE Trans Med Imaging. 2016;35(1):316–25.CrossRefPubMed
17.
Ramírez J, Górriz J, Salas-Gonzalez D, Romero A, López M, Álvarez I, Gómez-Río M. Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf Sci. 2013;237:59–72.CrossRef
18.
Olsen GF, Brilliant SS, Primeaux D, Najarian K: An image-processing enabled dental caries detection system. In: Complex Medical Engineering, 2009 CME ICME International Conference on: 2009: NJ, USA: IEEE; 2009. p. 1–8.
19.
Abulafia O, Triest WE, Sherer DM. Angiogenesis in primary and metastatic epithelial ovarian carcinoma. Am J Obstet Gynecol. 1997;177(3):541–7.CrossRefPubMed
20.
Burger RA, Brady MF, Bookman MA, Monk BJ, Walker JL, Homesley HD, Fowler J, Greer BE, Boente M, Fleming GF. Risk factors for GI adverse events in a phase III randomized trial of Bevacizumab in first-line therapy of advanced ovarian cancer: a gynecologic oncology group study. J Clin Oncol. 2014;32(12):1210–7.CrossRefPubMedPubMedCentral
21.
Burger RA, Brady MF, Rhee J, Sovak MA, Kong G, Nguyen HP, Bookman MA. Independent radiologic review of the gynecologic oncology group study 0218, a phase III trial of Bevacizumab in the primary treatment of advanced epithelial ovarian, primary peritoneal, or fallopian tube cancer. Gynecol Oncol. 2013;131(1):21–6.CrossRefPubMedPubMedCentral
22.
Perren TJ, Swart AM, Pfisterer J, Ledermann JA, Pujade-Lauraine E, Kristensen G, Carey MS, Beale P, Cervantes A, Kurzeder C. A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med. 2011;365(26):2484–96.CrossRefPubMed
23.
Teoh D, Secord AA. Antiangiogenic therapies in epithelial ovarian cancer. Cancer Control. 2011;18(1):31–43.PubMed
24.
Guiu B, Petit JM, Bonnetain F, Ladoire S, Guiu S, Cercueil J-P, Krausé D, Hillon P, Borg C, Chauffert B. Visceral fat area is an independent predictive biomarker of outcome after first-line bevacizumab-based treatment in metastatic colorectal cancer. Gut. 2010;59(3):341–7.CrossRefPubMed
25.
Slaughter KN, Thai T, Penaroza S, Benbrook DM, Thavathiru E, Ding K, Nelson T, McMeekin DS, Moore KN. Measurements of adiposity as clinical biomarkers for first-line bevacizumab-based chemotherapy in epithelial ovarian cancer. Gynecol Oncol. 2014;133(1):11–5.CrossRefPubMed
26.
Leader JK, Zheng B, Rogers RM, Sciurba FC, Perez A, Chapman BE, Patel S, Fuhrman CR, Gur D. Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme 1. Acad Radiol. 2003;10(11):1224–36.CrossRefPubMed
27.
Yoshizumi T, Nakamura T, Yamane M, Waliul Islam AHM, Menju M, Yamasaki K, Arai T, Kotani K, Funahashi T, Yamashita S. Abdominal fat: Standardized technique for measurement at ct 1. Radiology. 1999;211(1):283–6.CrossRefPubMed
28.
Liou T-H, Chan WP, Pan L-C, Lin P, Chou P, Chen C-H. Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging. Int J Obes. 2006;30(5):844–52.CrossRef
29.
Stroom J, Blaauwgeers H, van Baardwijk A, Boersma L, Lebesque J, Theuws J, van Suylen R-J, Klomp H, Liesker K, van Pel R. Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. Int J Radiat Oncol Biol Phys. 2007;69(1):267–75.CrossRefPubMed
30.
Hosmer Jr DW, Lemeshow S. Applied logistic regression. Hoboken: John Wiley & Sons; 2004
31.
Pudil P, Ferri F, Novovicova J, Kittler J: Floating search methods for feature selection with nonmonotonic criterion functions. In: In Proceedings of the Twelveth International Conference on Pattern Recognition, IAPR: 1994. NJ, USA: IEEE; 1994.
32.
Li Q, Doi K. Reduction of bias and variance for evaluation of computer-aided diagnostic schemes. Med Phys. 2006;33(4):868–75.CrossRefPubMed
33.
Wang Y, Thai T, Moore K, Ding K, McMeekin S, Liu H, Zheng B. Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients. Oncol Letter. 2016;12:680–6.
Metadata
Title
Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome
Authors
Yunzhi Wang
Yuchen Qiu
Theresa Thai
Kathleen Moore
Hong Liu
Bin Zheng
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2016
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-016-0157-5

Other articles of this Issue 1/2016

BMC Medical Imaging 1/2016 Go to the issue

Research Article

A deformable template method for describing and averaging the anatomical variation of the human nasal cavity

Research article

Radiation dosimetry of 18F-FDG PET/CT: incorporating exam-specific parameters in dose estimates

Reviewer acknowledgement

BMC Medical Imaging reviewer acknowledgement 2015

Research article

Qualitative analysis of small (≤2 cm) regenerative nodules, dysplastic nodules and well-differentiated HCCs with gadoxetic acid MRI

Research article

Comparison of three 18F-labeled carboxylic acids with 18F-FDG of the differentiation tumor from inflammation in model mice

Research article

How to assess intra- and inter-observer agreement with quantitative PET using variance component analysis: a proposal for standardisation