TRAN, William T. (2017). Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy. Doctoral, Sheffild Hallam University. [Thesis]
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Tran_2018_PhD_MeasuringChemotherapyResponse.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Tran_2018_PhD_MeasuringChemotherapyResponse.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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Abstract
Purpose: This study comprises two subprojects. In subproject one, the study
purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using
quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS)
in locally advanced breast cancer (LABC) during chemotherapy. In subproject
two, DOS-based functional maps were analysed with texture-based image
features to predict breast cancer response before the start of NAC.
Patients and Measurements: The institution’s ethics review board approved
this study. For subproject one, subjects (n=22) gave written consent before
participating in the study. Participants underwent non-invasive, DOS and QUS
imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before
surgical removal of the tumour (mastectomy and/or lumpectomy);
corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were
determined from tumour ultrasound data using spectral analysis. In the same
patients, DOS was used to measure parameters relating to tumour haemoglobin
and tissue composition such as %Water and %Lipids. Discriminant analysis
and receiver-operating characteristic (ROC) analyses were used to correlate the
measured imaging parameters to Miller-Payne pathological response during
treatment. Additionally, multivariate analysis was carried out for pairwise DOS
and QUS parameter combinations to determine if an increase in the
classification accuracy could be obtained using combination DOS and QUS
parametric models.
For subproject two, 15 additional patients we recruited after first giving
their written informed consent. A pooled analysis was completed for all DOS
baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients
planned for NAC had functional DOS maps and associated textural features
generated. A grey-level co-occurrence matrix (texture) analysis was completed
for parameters associated with haemoglobin, tissue composition, and optical
properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total
haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering
amplitude [SA]) prior to treatment. Textural features included contrast (con),
vi
correlation (cor), energy (ene), and homogeneity (hom). Patients were
classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological
response criteria after treatment completion. In order to test if baseline
univariate texture features could predict treatment response, a receiver
operating characteristic (ROC) analysis was performed, and the optimal
sensitivity, specificity and area under the curve (AUC) was calculated using
Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to
test 40 DOS-texture features and all possible bivariate combinations using a
naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were
included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and
validation and ROC analysis were performed to find the maximum sensitivity
and specificity of bivariate DOS-texture features.
Results: For subproject one, individual DOS and QUS parameters, including
the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin
(HbT) were significant markers for response outcome after one week of
treatment (p<0.01). Multivariate (pairwise) combinations increased the
sensitivity, specificity and AUC at this time; the SI+HbO2 showed a
sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment.
For subproject two, the results indicated that textural characteristics of
pre-treatment DOS parametric maps can differentiate treatment response
outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst
univariate parameters in predicting response to chemotherapy: sensitivity (%Sn)
and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of
87.8%. The highest predictors using multivariate (binary) combination features
were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0,
a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model.
Conclusion: DOS and QUS demonstrated potential as coincident markers for
treatment response and may potentially facilitate response-guided therapies.
Also, the results of this study demonstrated that DOS-texture analysis can be
used to predict breast cancer response groups prior to starting NAC using
baseline DOS measurements.
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