Journal Article

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo

Prasad, Keerthana, Ray, Satadru, Chandra, Subhash, Nayak, Subramanya G., Joshi, Manjunath B., Rai, Sharada, Mahato, Krishna Kishore, Raghushaker, Chandavalli Ramappa, Rodrigues, Jackson, Amin, Ashwini

2021

Overview

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology. The current study reports the assessment of tumor progression in athymic nude mice by Photoacoustic spectroscopy-based machine learning tools. The progressive tumors were classified using multi-class Support Vector Machine (SVM) algorithms with 99.5% accuracy. The serum metabolomic levels during tumor progression complemented photoacoustic spectral features, depicting breast cancer pathophysiology.

• Publisher: Elsevier Inc,Nature Publishing Group US,Nature Publishing Group
• Subject: 13/106 / 64/60 / 692/308/2778 / 692/308/575 / 9/10 / Algorithms / Animals / Breast cancer / Breast Neoplasms - diagnostic imaging / Breast Neoplasms - pathology / Computer programs / Feature extraction / Female / Histopathology / Humans / Image analysis / Image Interpretation, Computer-Assisted - methods / Image processing / Laboratory Medicine / Learning algorithms / Machine Learning / Mammary Neoplasms, Experimental - diagnostic imaging / Mammary Neoplasms, Experimental - pathology / MCF-7 Cells / Medicine / Medicine & Public Health / Metabolomics / Metabolomics - methods / Mice / Mice, Nude / Pathology / Pathophysiology / Photoacoustic spectroscopy / Photoacoustic Techniques - methods / Polynomials / Pulsed lasers / Radial basis function / Software / Spectra / Spectroscopy / Spectrum analysis / Spectrum Analysis - methods / Support vector machines / Tissue analysis / Tumors / Xenografts / Xenotransplantation