Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Mirniaharikandehei, Seyedehnafiseh; VanOsdol, Joshua; Heidari, Morteza; Danala, Gopichandh; Sethuraman, Sri Nandhini; Ranjan, Ashish

doi:10.1038/s41598-019-43847-7

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Recently, we have been exploring and developing several new CADsupported machine learning models. These models can be applied to radiographic images of human bodies (i.e., presented in this paper) or animal models [52] and to digital histopathology images [28]. In this study, we reviewed several prediction models trained using global or non-tumor-related image features computed from a variety of medical images (FFDM, MRI, and CT) for breast, lung, and ovarian cancers with new experimental results.…”

Section: Discussionmentioning

confidence: 99%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

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In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

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Section: Discussionmentioning

confidence: 99%

Developing global image feature analysis models to predict cancer risk and prognosis

Qiu

Aghaei

Mirniaharikandehei

et al. 2019

Vis. Comput. Ind. Biomed. Art

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“…The locations, number as well as the size of the tumor and metastasis were recorded. Tumor volumes were calculated as previously reported [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

Establishment of an Endoscopy-Guided Minimally Invasive Orthotopic Mouse Model of Colorectal Cancer

Chen

Neumann

Kühn

et al. 2020

Cancers

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Open orthotopic mouse models of colorectal cancer have disadvantages such as the requirement for advanced surgical skills or the trauma caused by laparotomy. To overcome these drawbacks, this study aimed to evaluate the establishment of a minimally invasive model using murine colonoscopy. CT26 and MC38 CRC cells of different concentrations were injected into BALB/C and C57BL/6J mice, respectively. Follow-up endoscopies were performed to assign an endoscopic score to tumor growth. Gross autopsy, histologic and immuno-histochemical evaluation, and immune scoring were performed. To describe the learning curve of the procedures, a performance score was given. Local tumor growth with colorectal wall infiltration, luminal ulceration, the presence of tumor-infiltrating lymphocytes, lympho-vascular invasion, and early spontaneous lymph node, peritoneal, and hepatic metastases were observed. The tumors showed cytoplasmic immuno-staining for CK20. Compared to the MC38/C57BL/6J model, tumorigenicity and immunogenicity of the CT26/BALB/C model were higher. Tumor volume correlated with the endoscopic score. This endoscopy-guided orthotopic mouse model is easy to learn and quick to establish. It features early metastasis and enables the study of interactions with the immune system. When specific cell concentrations and cell lines are applied, controlled local tumor growth and metastasis can be achieved within short observation periods.

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“…Ultrasound imaging is extensively used for diagnosis and efficacy evaluation in almost every medical discipline worldwide. The popularity of ultrasound imaging mainly derives from its relatively low cost, portability, and the lack of exposure to ionizing radiation (Akbari and Fei, 2012;Mirniaharikandehei et al, 2019). Typically, the ultrasound probe emits plane or diverging ultrasound waves, which can get reflected or scattered when passing through nonhomogeneous tissues and then detected by the same probe (Kiessling et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Global Research Trends on Ultrasound Microbubble: A Quickly Developing Field

Tong

Wang

et al. 2021

Front. Pharmacol.

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Background: Microbubbles are widely used as highly effective contrast agents to improve the diagnostic capability of ultrasound imaging. Mounting evidence suggests that ultrasound coupled with microbubbles has promising therapeutic applications in cancer, cardiovascular, and neurological disorders by acting as gene or drug carriers. The aim of this study was to identify the scientific output and activity related to ultrasound microbubble through bibliometric approaches.Methods: The literature related to ultrasound microbubble published between 1998 and 2019 was identified and selected from the Science Citation Index Expanded of Web of Science Core Collection on February 21, 2021. The Scopus database was also searched to validate the results and provided as supplementary material. Quantitative variables including number of publications and citations, H-index, and journal citation reports were analyzed by using Microsoft Excel 2019 and GraphPad Prism 8.0 software. VOS viewer and CiteSpace V were used to perform coauthorship, citation, co-citation, and co-occurrence analysis for countries/regions, institutions, authors, and keywords.Results: A total of 6088 publications from the WoSCC were included. The United States has made the largest contribution in this field, with the majority of publications (2090, 34.3%), citations (90,741, 46.6%), the highest H-index (138), and close collaborations with China and Canada. The most contributive institution was the University of Toronto. Professors De Jong N and Dayton P A have made great achievements in this field. However, the research cooperation between institutions and authors was relatively weak. All the studies could be divided into four clusters: “ultrasound diagnosis study,” “microbubbles’ characteristics study,” “gene therapy study,” and “drug delivery study.” The average appearing years (AAY) of keywords in the cluster “drug delivery study” was more recent than other clusters. For promising hot spots, “doxorubicin” showed a relatively latest AAY of 2015.49, followed by “nanoparticles” and “breast cancer.”Conclusion: There has been an increasing amount of scientific output on ultrasound microbubble according to the global trends, and the United States is staying ahead in this field. Collaboration between research teams still needs to be strengthened. The focus gradually shifts from “ultrasound diagnosis study” to “drug delivery study.” It is recommended to pay attention to the latest hot spots, such as “doxorubicin,” “nanoparticles,” and “breast cancer.”

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Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Cited by 11 publications

References 24 publications

Developing global image feature analysis models to predict cancer risk and prognosis

Developing global image feature analysis models to predict cancer risk and prognosis

Establishment of an Endoscopy-Guided Minimally Invasive Orthotopic Mouse Model of Colorectal Cancer

Bibliometric Analysis of Global Research Trends on Ultrasound Microbubble: A Quickly Developing Field

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