Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis
2021; Elsevier BV; Volume: 191; Issue: 8 Linguagem: Inglês
10.1016/j.ajpath.2021.01.014
ISSN1525-2191
Autores Tópico(s)Cancer Genomics and Diagnostics
ResumoA critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes. A critical knowledge gap has been noted in breast cancer detection, prognosis, and evaluation between tumor microenvironment and associated neoplasm. Artificial intelligence (AI) has multiple subsets or methods for data extraction and evaluation, including artificial neural networking, which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training. Deep machine learning and AI hold great potential to accurately assess tumor microenvironment models employing vast data management techniques. Despite the significant potential AI holds, there is still much debate surrounding the appropriate and ethical curation of medical data from picture archiving and communication systems. AI output's clinical significance depends on its human predecessor's data training sets. Integration between biomarkers, risk factors, and imaging data will allow the best predictor models for patient-based outcomes. The tumor microenvironment (TME) or surrounding stroma contains various vital components such as immune cells and extracellular matrix (ECM), which act against antitumor immune cells (https://www.eurekalert.org/pub_releases/2018-10/c-mdf101618.php, last accessed July 12, 2021; https://www.sciencedaily.com/releases/2019/12/191226134100.htm, last accessed July 12, 2021).1Web P. Machine learning-assisted prognostication based on genomic expression in the tumor microenvironment.in: Cision AI powered analysis provides quantitative measurements of human interpretable features in tumor microenvironment. PRWEB, Boston, MA2020Google Scholar, 2Ehteshami Bejnordi B. Mullooly M. Pfeiffer R.M. Fan S. Vacek P.M. Weaver D.L. Herschorn S. Brinton L.A. van Ginneken B. Karssemeijer N. Beck A.H. Gierach G.L. van der Laak J. Sherman M.E. 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Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.Gut. 2020; 69: 681-690Crossref PubMed Scopus (28) Google Scholar which allows computational foundations, similar to neurons, to make connections and new neural pathways during data set training (Figure 1). One such method used for quantitative biology is massive parallel reporter assay, which assesses DNA.4Behravan H. Hartikainen J.M. Tengstrom M. Kosma V.M. Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.Sci Rep. 2020; 10: 11044Crossref PubMed Scopus (6) Google Scholar This allows biologists the ability to predict molecular and gene interactions. The mechanistic framework of gene regulation allows the possibility of new therapies to be developed.2Ehteshami Bejnordi B. Mullooly M. Pfeiffer R.M. Fan S. Vacek P.M. Weaver D.L. Herschorn S. Brinton L.A. van Ginneken B. Karssemeijer N. Beck A.H. Gierach G.L. van der Laak J. Sherman M.E. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.Mod Pathol. 2018; 31: 1502-1512Crossref PubMed Scopus (73) Google Scholar,5Allinen M. Beroukhim R. Cai L. Brennan C. Lahti-Domenici J. Huang H. Porter D. Hu M. Chin L. Richardson A. Schnitt S. Sellers W.R. Polyak K. Molecular characterization of the tumor microenvironment in breast cancer.Cancer Cell. 2004; 6: 17-32Abstract Full Text Full Text PDF PubMed Scopus (990) Google Scholar,6Tsai M.J. Chang W.A. Huang M.S. Kuo P.L. Tumor microenvironment: a new treatment target for cancer.ISRN Biochem. 2014; 2014: 351959Crossref PubMed Google Scholar,11Segovia-Mendoza M. Morales-Montor J. Immune tumor microenvironment in breast cancer and the participation of estrogen and its receptors in cancer physiopathology.Front Immunol. 2019; 10: 348Crossref PubMed Scopus (39) Google Scholar There is a lack of congruence between biologists and artificial neural networking (ANN) systems; the latest custom ANNs allow mathematical assumptions of common biological concepts so that the output reflects how a biologist would interpret results.6Tsai M.J. Chang W.A. Huang M.S. Kuo P.L. Tumor microenvironment: a new treatment target for cancer.ISRN Biochem. 2014; 2014: 351959Crossref PubMed Google Scholar,10Wang S. Yang D.M. Rong R. Zhan X. Fujimoto J. Liu H. Minna J. Wistuba I.I. Xie Y. Xiao G. Artificial intelligence in lung cancer pathology image analysis.Cancers (Basel). 2019; 11: 1673Crossref Scopus (42) Google Scholar,12Doukas C.N. Maglogiannis I. Chatziioannou A. Papapetropoulos A. Automated angiogenesis quantification through advanced image processing techniques.Conf Proc IEEE Eng Med Biol Soc. 2006; 2006: 2345-2348Crossref PubMed Scopus (17) Google Scholar, 13Bi W.L. Hosny A. Schabath M.B. Giger M.L. Birkbak N.J. Mehrtash A. Allison T. Arnaout O. Abbosh C. Dunn I.F. Mak R.H. Tamimi R.M. Tempany C.M. Swanton C. Hoffmann U. Schwartz L.H. Gillies R.J. Huang R.Y. Aerts H.J.W.L. Artificial intelligence in cancer imaging: clinical challenges and applications.CA Cancer J Clin. 2019; 69: 127-157PubMed Google Scholar, 14Reichling C. Taieb J. Derangere V. Klopfenstein Q. Le Malicot K. Gornet J.M. Becheur H. Fein F. Cojocarasu O. Kaminsky M.C. Lagasse J.P. Luet D. Nguyen S. Etienne P.L. Gasmi M. Vanoli A. Perrier H. Puig P.L. Emile J.F. Lepage C. Ghiringhelli F. 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Role of sonography in diagnosing and staging invasive lobular carcinoma.J Clin Ultrasound. 2004; 32: 323-332Crossref PubMed Scopus (79) Google Scholar A prospective study using CAD software during diagnosis has shown a 74% increase in cancer detection.19Selinko V.L. Middleton L.P. Dempsey P.J. Role of sonography in diagnosing and staging invasive lobular carcinoma.J Clin Ultrasound. 2004; 32: 323-332Crossref PubMed Scopus (79) Google Scholar Certain technical advances in breast imaging—such as harmonic tissue imaging, compound imaging, and an extended field of view—have made its use integral during a breast cancer diagnosis. Standardized CAD techniques used in conjunction with ultrasound reduce the interobserver variation.18Muralidhar G.S. Haygood T.M. Stephens T.W. Whitman G.J. Bovik A.C. Markey M.K. Computer-aided detection of breast cancer - have all bases been covered?.Breast Cancer (Auckl). 2008; 2: 5-9PubMed Google Scholar The detection rate of invasive cancers measuring <1 cm increases with the use of CAD systems. It can reduce false-negative rates from 31% to 19%,20Berg W.A. Gilbreath P.L. Multicentric and multifocal cancer: Whole-breast US in preoperative evaluation.Radiology. 2000; 214: 59-66Crossref PubMed Scopus (157) Google Scholar,21Berg W.A. Gutierrez L. NessAiver M.S. Carter W.B. Bhargavan M. Lewis R.S. Ioffe O.B. Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer.Radiology. 2004; 233: 830-849Crossref PubMed Scopus (1066) Google Scholar in conjunction with dedicated breast imagers. The system assigns various sensitivity and specificity rates to cancers based on the lesion type. The sensitivity for malignant calcifications is 86% to 99% with CAD, with only 57% marked as amorphous calcifications.22Hogg P. Kelly J. Mercer C. Digital Mammography: A Holistic Approach. Springer International Publishing Switzerland, Cham, Switzerland2015Crossref Scopus (17) Google Scholar The sensitivity for masses is estimated at 43% to 85%.23Berg W.A. Leung K. Diagnostic Imaging: Breast. ed 3. Elsevier, Philadelphia, PA2019Google Scholar Further research is required to recognize suspicious asymmetries as they develop over time during serial imaging follow-up and to assess the medico-legal implication of retained CAD-marked image information. A more extensive explanation of the various AI subtypes is discussed below. ANN is the process of nonlinear mapping between set inputs and outputs. It achieves physical performance using dense processing elements similar to biological neurons. The ANN can learn and generalize from the examples given. Success is measured if complex linear functions govern the relationship between variables. Evolutionary computing consists of a collection of algorithms based on population evolution toward the solution of a problem. It is subdivided into genetic algorithms and genetic programming, as well as evolutionary algorithms. Using select features for classification of mammogram calcifications is a measure of success.4Behravan H. Hartikainen J.M. Tengstrom M. Kosma V.M. Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.Sci Rep. 2020; 10: 11044Crossref PubMed Scopus (6) Google Scholar,8Mittal S. Stoean C. Kajdacsy-Balla A. Bhargava R. Digital assessment of stained breast tissue images for comprehensive tumor and microenvironment analysis.Front Bioeng Biotechnol. 2019; 7: 246Crossref PubMed Scopus (10) Google Scholar,24Gruszauskas N.P. Drukker K. Giger M.L. Sennett C.A. Pesce L.L. 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The quality of a feature depends on its contribution to detection, cancer classification, and the preprocessing steps and classification methods.15Chen D.R. Chien C.L. Kuo Y.F. Computer-aided assessment of tumor grade for breast cancer in ultrasound images.Comput Math Methods Med. 2015; 2015: 914091Crossref PubMed Scopus (10) Google Scholar,25Shan J. Alam S.K. Garra B. Zhang Y. Ahmed T. Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods.Ultrasound Med Biol. 2016; 42: 980-988Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar,27Brkljačić B. Divjak E. Tomasović-Lončarić C. Tešić V. Ivanac G. Shear-wave sonoelastographic features of invasive lobular breast cancers.Croat Med J. 2016; 57: 42-50Crossref PubMed Scopus (6) Google Scholar The quality of features cannot be categorized because the quality of a feature depends on its contribution to detection, classification, prognosis, and features dependent on its preprocessing steps and the classification measures. There are various types of features, such as geometric, which refer to factors such as size and shape. The boundary is the starting point of extracting an object using AI. Various boundary methods are used, such as binary sets, which refers to the sets of pixels in a grayscale image, and edge detection, which defines an object by its edges. Other geometric features include area, volume, contrast, counting pixels inside an object boundary, and perimeters, as well as shape (no single shape descriptor can be used on its own to define an object).13Bi W.L. Hosny A. Schabath M.B. Giger M.L. Birkbak N.J. Mehrtash A. Allison T. Arnaout O. Abbosh C. Dunn I.F. Mak R.H. Tamimi R.M. Tempany C.M. Swanton C. Hoffmann U. Schwartz L.H. Gillies R.J. Huang R.Y. Aerts H.J.W.L. Artificial intelligence in cancer imaging: clinical challenges and applications.CA Cancer J Clin. 2019; 69: 127-157PubMed Google Scholar,14Reichling C. Taieb J. Derangere V. Klopfenstein Q. Le Malicot K. Gornet J.M. Becheur H. Fein F. Cojocarasu O. Kaminsky M.C. Lagasse J.P. Luet D. Nguyen S. Etienne P.L. Gasmi M. Vanoli A. Perrier H. Puig P.L. Emile J.F. Lepage C. Ghiringhelli F. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.Gut. 2020; 69: 681-690Crossref PubMed Scopus (28) Google Scholar,26Acs B. Rantalainen M. Hartman J. Artificial intelligence as the next step towards precision pathology.J Intern Med. 2020; 288: 62-81Crossref PubMed Scopus (50) Google Scholar,28Wang S. Liu J.-B. Zhu Z. Eisenbrey J. 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Molecular characterization of the tumor microenvironment in breast cancer.Cancer Cell. 2004; 6: 17-32Abstract Full Text Full Text PDF PubMed Scopus (990) Google Scholar described in detail below as the various cell type subsets related to breast cancer diagnosis and prognosis. As Dvorak34Dvorak H.F. Tumors: wounds that do not heal-redux.Cancer Immunol Res. 2015; 3: 1-11Crossref PubMed Scopus (273) Google Scholar stated, tumors are much more than wounds that do not heal. Tumor cells undergo significant changes causing release from regulatory signals, promoting proliferation and invasion. The most crucial factor thereof is the overexpression of vascular endothelial growth factor, allowing surrounding stroma to be incorporated in its progression process. The use of AI technology to improve diagnostic detection rates and remote disease monitoring can reduce the overall time required for overall patient treatment planning. Anti-vascular endothelial growth factor agents and AI-generated prognoses have been studied using vision loss, which could promote the prevention of vision loss before its occurrence.35Adamis A.P. Brittain C.J. Dandekar A. Hopkins J.J. Building on the success of anti-vascular endothelial growth factor therapy: a vision for the next decade.Eye (Lond). 2020; 34: 1966-1972Crossref PubMed Scopus (11) Google Scholar The angiogenesis process includes a complex interplay between tumor, endothelial, and stromal cells, promoting tumor growth. A study in 2006 found a novel method of assessing angiogenesis employing chick embryo and its chorioallantoic membrane. An automated image analysis method was developed to quantify the microvessel density and growth potential in images. This shows the potential to be used for tumor growth detection in breast cancer imaging,6Tsai M.J. Chang W.A. Huang M.S. Kuo P.L. 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Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion.Cell. 2005; 121: 335-348Abstract Full Text Full Text PDF PubMed Scopus (2718) Google Scholar; however, it lacks efficacy for extensive tumor series analysis of TME. Other methods proposed for TME composition analysis are Gene Set Enrichment Analysis (San Diego), xCell (California), and TIminer (Russia), which allows immunogenic analysis and quantification of the immune infiltrate.5Allinen M. Beroukhim R. Cai L. Brennan C. Lahti-Domenici J. Huang H. Porter D. Hu M. Chin L. Richardson A. Schnitt S. Sellers W.R. Polyak K. Molecular characterization of the tumor microenvironment in breast cancer.Cancer Cell. 2004; 6: 17-32Abstract Full Text Full Text PDF PubMed Scopus (990) Google Scholar,6Tsai M.J. Chang W.A. Huang M.S. Kuo P.L. 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Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (21539) Google Scholar is a computational method able to define concordant differences between two biological states as a statistical output (Figure 2).39Subramanian E. Tamayo A. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (21539) Google Scholar xCell40Aran D. Hu Z. Butte A.J. xCell: digitally portraying the tissue cellular heterogeneity landscape.Genome Biol. 2017; 18: 220Crossref PubMed Scopus (715) Google Scholar is a novel signature-based method used for 64 immune and stromal cell types. Utilizing in silico analyses and cross-comparison to cytometry immunophenotyping, xCell shows excellent promise when compared with other methods.40Aran D. Hu Z. Butte A.J. xCell: digitally portraying the tissue cellular heterogeneity landscape.Genome Biol. 2017; 18: 220Crossref PubMed Scopus (715) Google Scholar TIminer41Tappeiner E. Finotello F. Charoentong P. Mayer C. Rieder D. Trajanoski Z. TIminer:
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