Identification of different types of tumors based on photoacoustic spectral analysis - preclinical feasibility studies on skin tumors

Q: What is the goal of the photoacoustic spectral analysis (PASA) method in this paper?

To characterize tumor-related features by providing content and structural distribution of endogenous chromophores in biological tissues, enabling identification of different tumor types.

Q: How were tumor types classified and what performance was reported?

An SVM was applied for automatic skin cancer type detection, achieving diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).

Sep 24, 2026

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Tumor microenvironments (TME) rely on collagen and lipid, which serve as hallmarks for distinguishing tumor types and supporting diagnosis. This study presents a photoacoustic spectral analysis (PASA) approach that captures both the content and spatial distribution of endogenous chromophores in biological tissues to extract tumor-related features. Ex vivo human tissues with suspected SCC, suspected BCC, and normal samples are analyzed using PASA parameters and compared with histology. A support vector machine (SVM) classifies skin cancer types automatically.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Tumor microenvironments and collagen/lipid as biomarkers

Existing imaging and spectroscopy methods

Photoacoustics and endogenous chromophores

RESEARCH PAPER Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors Mengjiao Zhang ,a,† Long Wen ,b,† Chu Zhou,b Jing Pan,a Shiying Wu,a Peiru Wang,b Haonan Zhang,a,b Panpan Chen,a Qi Chen,b Xiuli Wang,b,* and Qian Chenga,c,d,* aTongji University, Institute of Acoustics, School of Physics Science and Engineering, Shanghai, China bTongji University, Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Shanghai, China cNational Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai, China dFrontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, China ABSTRACT. Significance: Collagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors. Aim: We aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors. Approach: Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection. Results: The PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7%(normal), 93 .3%(SCC), and 91 .7%(BCC) . Conclusions: We verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.28.6.065004] Keywords: photoacoustic spectral analysis; tumor-related biomarkers; endogenous chromophores; support vector machine Paper 230082GR received Mar. 28, 2023; revised May 19, 2023; accepted May 22, 2023; published Jun. 13, 2023 . *Address all correspondence to Qian Cheng, [q.cheng@tongji.edu.cn](q.cheng@tongji.edu.cn); Xiuli Wang, [wangxiuli_1400023@tongji.edu.cn](wangxiuli_1400023@tongji.edu.cn)[ ](wangxiuli_1400023@tongji.edu.cn)†These authors contributed equally to this paper. Journal of Biomedical Optics 065004-1 June 2023 • Vol. 28(6) Zhang et al.: Identification of different types of tumors based on photoacoustic. . . 1 Introduction In recent years, an increasing number of studies have shown that tumors are closely related to tumor microenvironments (TMEs) .1–3 The TME is a complex system mainly composed of blood vessels, collagen, lipid, etc.3–5 Lipid can provide energy for tumors and collagen mainly constitutes the scaffold of the tumor.6,7 Collagen and lipid content can affect the proliferation and differentiation of tumor cells.8–11 Alterations in the content of lipid and collagen are hallmarks of many diseases, including breast cancer, 12 prostate cancer, 13, 14 renal cell carcinoma, 15 skin cancers, 16 and others. Therefore, the collagen and lipid content in the TME can be used for diagnosis and differentiation of tumors. Currently, several methods are available for detecting the

FAQs

What is the goal of the photoacoustic spectral analysis (PASA) method in this paper?

To characterize tumor-related features by providing content and structural distribution of endogenous chromophores in biological tissues, enabling identification of different tumor types.

Which tissue groups were used for the ex vivo study?

Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used.

How were tumor types classified and what performance was reported?

An SVM was applied for automatic skin cancer type detection, achieving diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).

Sep 24, 2026

Upload by Margaret