Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Li, Jiayan; Chen, Yingna; Ye, Wanli; Zhang, Mengjiao; Zhu, Jingtao; Zhi, Wenxiang; Cheng, Qian

doi:10.1016/j.pacs.2023.100483

Cited by 15 publications

(7 citation statements)

References 64 publications

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“… 41 Interestingly, the spatial distribution and spectral analysis of the PA signal may also help distinguish subtypes of breast cancer. 45 , 46 Luminal breast cancers (both luminal A and luminal B) have exhibited higher external PA signals and lower internal PA signals than those of human epidermal growth factor receptor 2-positive and triple-negative cancers. 45 …”

Section: Tumormentioning

confidence: 99%

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Liu,

Wang,

et al. 2024

J. Biomed. Opt.

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. Significance Photoacoustic (PA) imaging is an imaging modality that integrates anatomical, functional, metabolic, and histologic insights. It has been a hot topic of medical research and draws extensive attention. Aim This review aims to explore the applications of PA clinical imaging in human diseases, highlighting recent advancements. Approach A systemic survey of the literature concerning the clinical utility of PA imaging was conducted, with a particular focus on its application in tumors, autoimmune diseases, inflammatory conditions, and endocrine disorders. Results PA imaging is emerging as a valuable tool for human disease investigation. Information provided by PA imaging can be used for diagnosis, grading, and prognosis in multiple types of tumors including breast tumors, ovarian neoplasms, thyroid nodules, and cutaneous malignancies. PA imaging facilitates the monitoring of disease activity in autoimmune and inflammatory diseases such as rheumatoid arthritis, systemic sclerosis, arteritis, and inflammatory bowel disease by capturing dynamic functional alterations. Furthermore, its unique capability of visualizing vascular structure and oxygenation levels aids in assessing diabetes mellitus comorbidities and thyroid function. Conclusions Despite extant challenges, PA imaging offers a promising noninvasive tool for precision disease diagnosis, long-term evaluation, and prognosis anticipation, making it a potentially significant imaging modality for clinical practice.

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Section: Tumormentioning

confidence: 99%

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Liu,

Wang,

et al. 2024

J. Biomed. Opt.

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“…Photoacoustic imaging (PAI) is a novel approach that has been used primarily to study tumor vascularization (9). Recent improvements in resolution and penetration depth have made imaging tissues like the placenta possible (10)(11)(12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Use of Photoacoustic Imaging to Study the Effects of Anemia on Placental Oxygen Saturation in Normoxic and Hypoxic Conditions

Noble,

Kirschenman,

Wiedemeyer

et al. 2023

Preprint

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We aimed to evaluate fetal and placental oxygen saturation (sO2) in anemic and non-anemic pregnant rats throughout gestation using photoacoustic imaging (PAI). Female Sprague Dawley rats were fed an iron-restricted or iron-replete diet before and during pregnancy. On gestational days 13, 18, and 21, PAI was coupled with high resolution ultrasound to measure oxygenation of the fetus, whole placenta, mesometrial triangle, as well as the maternal and fetal faces of the placenta. PAI was performed in 3D, which allowed sO2 to be measured within an entire region, as well as in 2D, which enabled SO2 measurements in response to a hypoxic event in real time. Both 3D and 2D PAI were performed at varying levels of FiO2 (fraction of inspired oxygen). Iron restriction caused anemia in dams and fetuses, a reduction in fetal body weight, and an increase in placental weight, but overall had minimal effects on sO2. Reductions in FiO2 caused corresponding reductions in sO2 which correlated to the severity of the hypoxic challenge. Regional differences in sO2 were evident within the placenta, and between the placenta and fetus. In conclusion, PAI enables non-invasive measurement of sO2 both rapidly and with a high degree of sensitivity. The lack of overt changes in SO2 levels between control and anemic fetuses may suggest reduced oxygen extraction and utilization in the latter group, which could be attributed to compensatory changes in growth and developmental trajectories.

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“… 45 Our group combined the PASA with machine leaning to better mine the data information and achieved a high diagnostic accuracy of prostate cancer, 46 – 49 osteoporosis, 50 and breast cancer. 51 , 52 PASA has shown considerable potential in evaluating the endogenous chromophore in biological tissues for tumor diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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.SignificanceCollagen 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.AimWe 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.ApproachEx 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.ResultsThe 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).ConclusionsWe 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.

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Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Cited by 15 publications

References 64 publications

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Mini review of photoacoustic clinical imaging: a noninvasive tool for disease diagnosis and treatment evaluation

Use of Photoacoustic Imaging to Study the Effects of Anemia on Placental Oxygen Saturation in Normoxic and Hypoxic Conditions

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

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