{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:22:59Z","timestamp":1777555379422,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T00:00:00Z","timestamp":1598832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2016\/23\/B\/NZ4\/03901"],"award-info":[{"award-number":["2016\/23\/B\/NZ4\/03901"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies\u2014papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer\u2014and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.<\/jats:p>","DOI":"10.3390\/ijms21176289","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T08:11:19Z","timestamp":1598861479000},"page":"6289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-9385","authenticated-orcid":false,"given":"Agata","family":"Kurczyk","sequence":"first","affiliation":[{"name":"Maria Sk\u0142odowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-5129","authenticated-orcid":false,"given":"Marta","family":"Gawin","sequence":"additional","affiliation":[{"name":"Maria Sk\u0142odowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mykola","family":"Chekan","sequence":"additional","affiliation":[{"name":"Maria Sk\u0142odowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7554-1803","authenticated-orcid":false,"given":"Agata","family":"Wilk","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3567-2811","authenticated-orcid":false,"given":"Krzysztof","family":"\u0141akomiec","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4158-1265","authenticated-orcid":false,"given":"Grzegorz","family":"Mrukwa","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1322-9977","authenticated-orcid":false,"given":"Katarzyna","family":"Fr\u0105tczak","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8004-9864","authenticated-orcid":false,"given":"Joanna","family":"Polanska","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krzysztof","family":"Fujarewicz","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9317-7822","authenticated-orcid":false,"given":"Monika","family":"Pietrowska","sequence":"additional","affiliation":[{"name":"Maria Sk\u0142odowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5099-4726","authenticated-orcid":false,"given":"Piotr","family":"Widlak","sequence":"additional","affiliation":[{"name":"Maria Sk\u0142odowska-Curie National Research Institute of Oncology, Gliwice Branch, 44-102 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3322\/caac.21254","article-title":"Cancer statistics, 2015","volume":"65","author":"Siegel","year":"2015","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"965212","DOI":"10.1155\/2013\/965212","article-title":"Worldwide increasing incidence of thyroid cancer: Update on epidemiology and risk factors","volume":"2013","author":"Pellegriti","year":"2013","journal-title":"J. Cancer Epidemiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/thy.2015.0020","article-title":"2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer","volume":"26","author":"Haugen","year":"2016","journal-title":"Thyroid"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.suronc.2010.06.003","article-title":"Thyroid nodules; interpretation and importance of fine-needle aspiration (FNA) for the clinician-practical considerations","volume":"19","author":"Sakorafas","year":"2010","journal-title":"Surg. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1507\/endocrj.EJ13-0494","article-title":"Introducing the reporting system for thyroid fine-needle aspiration cytology according to the new guidelines of the Japan Thyroid Association","volume":"61","author":"Kakudo","year":"2014","journal-title":"Endocr. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"622","DOI":"10.5858\/2008-132-622-TTGRPI","article-title":"The thyroid gland: Recurring problems in histologic and cytologic evaluation","volume":"132","author":"Faquin","year":"2008","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.mce.2010.01.010","article-title":"Molecular fine-needle aspiration biopsy diagnosis of thyroid nodules by tumor specific mutations and gene expression patterns","volume":"322","author":"Eszlinger","year":"2010","journal-title":"Mol. Cell. Endocrinol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1245\/s10434-014-3508-x","article-title":"The impact of molecular testing on the surgical management of patients with thyroid nodules","volume":"21","author":"Olson","year":"2014","journal-title":"Ann. Surg. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1586\/14789450.2015.1062369","article-title":"Proteome analysis in thyroid pathology","volume":"12","author":"Pagni","year":"2015","journal-title":"Expert Rev. Proteomics."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1038\/nmeth1094","article-title":"MALDI imaging mass spectrometry: Molecular snapshots of biochemical systems","volume":"4","author":"Cornett","year":"2007","journal-title":"Nat. Methods"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/nrc2917","article-title":"Molecular imaging by mass spectrometry-looking beyond classical histology","volume":"10","author":"Schwamborn","year":"2010","journal-title":"Nat. Rev. Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.tibtech.2010.12.002","article-title":"MALDI imaging mass spectrometry of human tissue: Method challenges and clinical perspectives","volume":"29","author":"Seeley","year":"2011","journal-title":"Trends Biotechnol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/labinvest.2014.156","article-title":"MALDI Imaging mass spectrometry: Current frontiers and perspectives in pathology research and practice","volume":"95","author":"Aichler","year":"2015","journal-title":"Lab. Investig."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1007\/s12022-013-9273-8","article-title":"An alternative approach in endocrine pathology research: MALDI-IMS in papillary thyroid carcinoma","volume":"24","author":"Mainini","year":"2013","journal-title":"Endocr. Pathol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"934","DOI":"10.3346\/jkms.2014.29.7.934","article-title":"Imaging mass spectrometry in papillary thyroid carcinoma for the identification and validation of biomarker proteins","volume":"29","author":"Min","year":"2014","journal-title":"J. Korean Med. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.bbapap.2016.10.006","article-title":"Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging","volume":"1865","author":"Pietrowska","year":"2017","journal-title":"Biochim. Biophys. Acta"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1002\/pmic.201500448","article-title":"Proteomics in thyroid cytopathology: Relevance of MALDI-imaging in distinguishing malignant from benign lesions","volume":"16","author":"Pagni","year":"2016","journal-title":"Proteomics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-1-4939-7051-3_5","article-title":"MALDI-MSI Analysis of Cytological Smears: The Study of Thyroid Cancer","volume":"1618","author":"Mosele","year":"2017","journal-title":"Methods Mol. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Capitoli, G., Piga, I., Galimberti, S., Leni, D., Pincelli, A.I., Garancini, M., Clerici, F., Mahajneh, A., Brambilla, V., and Smith, A. (2019). MALDI-MSI as a Complementary Diagnostic Tool in Cytopathology: A Pilot Study for the Characterization of Thyroid Nodules. Cancers, 11.","DOI":"10.3390\/cancers11091377"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21401","DOI":"10.1073\/pnas.1911333116","article-title":"Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies","volume":"116","author":"DeHoog","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5007","DOI":"10.1007\/s00216-019-01908-w","article-title":"The management of haemoglobin interference for the MALDI-MSI proteomics analysis of thyroid fine needle aspiration biopsies","volume":"411","author":"Piga","year":"2019","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e1700170","DOI":"10.1002\/prca.201700170","article-title":"Feasibility Study for the MALDI-MSI Analysis of Thyroid Fine Needle Aspiration Biopsies: Evaluating the Morphological and Proteomic Stability Over Time","volume":"13","author":"Piga","year":"2019","journal-title":"Proteom. Clin. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3791214","DOI":"10.1155\/2016\/3791214","article-title":"A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data","volume":"2016","author":"Galli","year":"2016","journal-title":"Adv. Bioinform."},{"key":"ref_24","first-page":"82","article-title":"On Stability of Feature Selection Based on MALDI Mass Spectrometry Imaging Data and Simulated Biopsy","volume":"Volume 1033","author":"Korbicz","year":"2020","journal-title":"Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.bbapap.2016.11.020","article-title":"Proteomic profiles of thyroid tumors by mass spectrometry-imaging on tissue microarrays","volume":"1865","author":"Galli","year":"2017","journal-title":"Biochim. Biophys. Acta Proteins Proteom."},{"key":"ref_26","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates. [2nd ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.mce.2017.11.020","article-title":"Proteome profiles of different types of thyroid cancers","volume":"474","author":"Gawin","year":"2018","journal-title":"Mol. Cell. Endocrinol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/S0753-3322(00)00010-X","article-title":"Pathogenesis of thyroid nodules: Histological classification?","volume":"55","year":"2001","journal-title":"Biomed. Pharmacother."},{"key":"ref_29","unstructured":"DeLellis, R.A., Lloyd, R.V., Heitz, P.U., and Eng, C. (2004). Pathology and Genetics of Tumours of Endocrine Organs. WHO Classification of Tumours, IARC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ando.2007.04.004","article-title":"Papillary and follicular thyroid carcinoma","volume":"68","author":"Schlumberger","year":"2007","journal-title":"Ann. Endocrinol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12022-014-9301-3","article-title":"Follicular Variant of Papillary Thyroid Carcinoma: Accuracy of FNA Diagnosis and Implications for Patient Management","volume":"25","author":"Ustun","year":"2014","journal-title":"Endocr. Pathol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S1044-0305(01)00327-0","article-title":"Comparing Similar Spectra: From Similarity Index to Spectral Contrast Angle","volume":"13","author":"Wan","year":"2002","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1021\/ac503952q","article-title":"Comprehensive Analysis of the Mouse Brain Proteome Sampled in Mass Spectrometry Imaging","volume":"87","author":"Heijs","year":"2015","journal-title":"Anal. Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10735-018-9802-3","article-title":"Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids","volume":"50","author":"Bednarczyk","year":"2019","journal-title":"J. Mol. Histol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.spl.2014.08.016","article-title":"A generalized boxplot for skewed and heavy-tailed distributions","volume":"95","author":"Bruffaerts","year":"2014","journal-title":"Stat. Probab. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1021\/ac050619p","article-title":"Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets","volume":"77","author":"Wong","year":"2005","journal-title":"Anal. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Polanski, A., Marczyk, M., Pietrowska, M., Widlak, P., and Polanska, J. (2015). Signal partitioning algorithm for highly efficient gaussian mixture modeling in mass spectrometry. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0134256"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1850012","DOI":"10.1142\/S0219876218500123","article-title":"Initializing EM algorithm for univariate Gaussian, multi-component, heteroscedastic mixture models by dynamic programming partitions","volume":"15","author":"Polanski","year":"2018","journal-title":"Int. J. Comput. Methods."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1021\/pr070361e","article-title":"Clustering millions of tandem mass spectra research articles","volume":"7","author":"Frank","year":"2008","journal-title":"J. Proteome Res."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/17\/6289\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:04:55Z","timestamp":1760177095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/21\/17\/6289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,31]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["ijms21176289"],"URL":"https:\/\/doi.org\/10.3390\/ijms21176289","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,31]]}}}