Multimodal approach in assessment of the response of breast cancer to neoadjuvant chemotherapy

Павлов, М. В.; Kalganova, Tatiana; Lyubimtseva, Yekaterina Sergeevna; Плеханов, В. И.; Golubyatnikov, German Yurievich; Ilyinskaya, Olga Yevgenyevna; Orlova, Anna; Subochev, Pavel; Сафонов, Д. В.; Shakhova, Natalia M.; Maslennikova, Anna

doi:10.1117/1.jbo.23.9.091410

Cited by 14 publications

(14 citation statements)

References 55 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shear wave is a transverse wave, that is, the direction of particle displacement in the elastic medium is perpendicular to the direction of wave propagation. A faster shear wave indicates a higher elasticity value of the medium, and a slower shear wave indicates a lower elasticity value of the medium. , Observing the changes in the blood flow and elastography in tumors before and after treatment is also beneficial to evaluate the effect of tumor treatment. − In this study, the images of the blood flow (CDFI, color Doppler flow imaging) and elastography before and after treatment were recorded. The CDFI images were semiquantitatively analyzed using the Adler grade, and the elastography was quantitatively analyzed using the shear wave velocity.…”

Section: Results and Discussionmentioning

confidence: 99%

Novel Multifunctional Nanoagent for Visual Chemo/Photothermal Therapy of Metastatic Lymph Nodes via Lymphatic Delivery

Cheng

Liu

He³

et al. 2020

ACS Omega

View full text Add to dashboard Cite

Breast cancer is one of the major diseases that threaten women’s health. Lymph node (LN) metastasis is the most common metastatic path of breast cancer. Finding a simple, effective, and safe strategy to eliminate metastatic tumors in LNs is highly desired for clinical use. Carbon nanoparticles (CNs), as an LN tracer, have been widely used in the clinical setting. In addition, previous experiments have confirmed that CNs have good photoacoustic imaging and photothermal effects. In this study, we used CNs as a photothermal conversion material and drug carrier, poly(lactic-co-glycolic acid) (PLGA) as a film-forming material, and docetaxel as a chemotherapy drug to prepare multifunctional nanoparticles (DOC-CNPs). The prepared DOC-CNPs present as a black solution, which shows smooth spherical particles under light microscopy and transmission electron microscopy (TEM), and they have a good ability for liquid–gas phase transition, good dispersibility, high drug-loading capacity, and low cytotoxicity. In vitro, they can release drugs and inhibit tumor cells after laser irradiation. The photoacoustic (PA) signal intensity and the photothermal conversion efficiency increased with an increase in the concentration of DOC-CNPs. In vivo, after administration, the DOC-CNPs reached the LNs. After laser irradiation, the DOC-CNPs absorbed laser energy, and the temperature of the LNs increased high enough to achieve photothermal therapy under PA and ultrasound monitoring. Fracture of the DOC-CNPs was caused by the liquid–gas phase transition with the increased temperature, and the ruptured DOC-CNPs released docetaxel to achieve targeted chemotherapy. These findings suggested that DOC-CNPs can achieve precise treatment for metastatic LNs of breast cancer with PA and ultrasound visualization.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Novel Multifunctional Nanoagent for Visual Chemo/Photothermal Therapy of Metastatic Lymph Nodes via Lymphatic Delivery

Cheng

Liu

He³

et al. 2020

ACS Omega

View full text Add to dashboard Cite

show abstract

“…Diffuse optical spectroscopy has been researched as clinical tool for monitoring chemotherapy treatment and even for predicting therapy response. 24,[39][40][41][42] However, the main focus of these published papers is on comparing pre-and post-chemotherapy measurements of individual patients, whereas we are interested in comparing groups of patients with and without neoadjuvant chemotherapy. The results presented in this paper show no evidence for a profound effect of chemotherapy on the optical measurements.…”

Section: Discussionmentioning

confidence: 99%

Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens

et al. 2019

View full text Add to dashboard Cite

Diffuse reflectance spectroscopy (DRS) can discriminate different tissue types based on optical characteristics. Since this technology has the ability to detect tumor tissue, several groups have proposed to use DRS for margin assessment during breast-conserving surgery for breast cancer. Nowadays, an increasing number of patients with breast cancer are being treated by neoadjuvant chemotherapy. Limited research has been published on the influence of neoadjuvant chemotherapy on the optical characteristics of the tissue. Hence, it is unclear whether margin assessment based on DRS is feasible in this specific group of patients. We investigate whether there is an effect of neoadjuvant chemotherapy on optical measurements of breast tissue. To this end, DRS measurements were performed on 92 ex-vivo breast specimens from 92 patients, treated with neoadjuvant chemotherapy and without neoadjuvant chemotherapy. Generalized estimating equation (GEE) models were generated, comparing the measurements of patients with and without neoadjuvant chemotherapy in datasets of different tissue types using a significance level of 5%. As input for the GEE models, either the intensity at a specific wavelength or a fit parameter, derived from the spectrum, was used. In the evaluation of the intensity, no influence of neoadjuvant chemotherapy was found, since none of the wavelengths were significantly different between the measurements with and the measurements without neoadjuvant chemotherapy in any of the datasets. These results were confirmed by the analysis of the fit parameters, which showed a significant difference for the amount of collagen in only one dataset. All other fit parameters were not significant for any of the datasets. These findings may indicate that assessment of the resection margin with DRS is also feasible in the growing population of breast cancer patients who receive neoadjuvant chemotherapy. However, it is possible that we did not detect neoadjuvant chemotherapy effect in the some of the datasets due to the small number of measurements in those datasets.

show abstract

“…This term α i j corresponds to the random effect, accounting for variability in each subject (subject i ) within each group (group j ). The random component can also be understood as modeling some "noise" in the response, but that does not arise from the sampling error term ε i jt from Equations (1) through (3).…”

Section: The Linear Mixed Model (Lmem) Casementioning

confidence: 99%

“…Tumor response [1][2][3][4], antibody expression [5,6], and cell metabolism [7,8] are examples of the different situations where researchers have used longitudinal designs to study some physiological response. Because the frequency of the measurements in a longitudinal study is dependent on the biological phenomena of interest and the experimental design of the study, the frequency of such measurements can range from minute intervals to study a short-term response such as anesthesia effects in animals [9], to weekly measurements would be necessary to enroll more subjects.…”

Section: Introductionmentioning

confidence: 99%

Generalized additive models to analyze non-linear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and Linear Mixed Models

Mundo

Tipton

Muldoon

2021

Preprint

View full text Add to dashboard Cite

In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies share an assumption of linearity in the measured response, which results in biased estimates and unreliable inference when they are used to analyze data where the trends are non-linear, which is a common occurrence in biomedical research. In contrast, generalized additive models (GAMs) relax the linearity assumption, and allow the data to determine the fit of the model while permitting missing observations and different correlation structures. Therefore, GAMs present an excellent choice to analyze non-linear longitudinal data in the context of biomedical research. This paper summarizes the limitations of rm-ANOVA and LMEMs and uses simulated data to visually show how both methods produce biased estimates when used on non-linear data. We also present the basic theory of GAMs, and using trends of oxygen saturation in tumors reported in the biomedical literature, we simulate example longitudinal data (2 treatment groups, 10 subjects per group, 6 repeated measures for each group) to demonstrate how these models can be computationally implemented. We show that GAMs are able to produce estimates that are consistent with the trends of biomedical non-linear data even in the case when missing observations exist (with 40% of the observations missing), allowing reliable inference from the data. To make this work reproducible, the code and data used in this paper are available at: https://github.com/aimundo/GAMs-biomedical-research.

show abstract

Multimodal approach in assessment of the response of breast cancer to neoadjuvant chemotherapy

Cited by 14 publications

References 55 publications

Novel Multifunctional Nanoagent for Visual Chemo/Photothermal Therapy of Metastatic Lymph Nodes via Lymphatic Delivery

Novel Multifunctional Nanoagent for Visual Chemo/Photothermal Therapy of Metastatic Lymph Nodes via Lymphatic Delivery

Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens

Generalized additive models to analyze non-linear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and Linear Mixed Models

Contact Info

Product

Resources

About