{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:12:16Z","timestamp":1791245536121,"version":"4.1.0"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Education and Research (BMBF)","award":["03LW0239K"],"award-info":[{"award-number":["03LW0239K"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Head and neck squamous cell carcinoma (HNSCC) is often diagnosed at advanced stages. Due to pronounced intratumoral heterogeneity (ITH), reliable risk stratification and prediction of treatment response remain challenging. This study aimed to identify peptide signatures in HNSCC tissue that are associated with treatment outcomes in HPV-negative, advanced-stage HNSCC patients undergoing 5-fluorouracil\/platinum-based chemoradiotherapy (CDDP-CRT). We integrated matrix-assisted laser desorption\/ionization mass spectrometry imaging (MALDI-MSI) of tryptic peptides with univariate statistics and machine learning approaches to uncover potential prognostic patterns. Formalin-fixed, paraffin-embedded whole tumor sections from 31 treatment-naive, HPV-negative HNSCC patients were digested in situ with trypsin, and the generated peptides were analyzed using MALDI-MSI. Clinical follow-up revealed recurrence or progression (RecPro) in 20 patients, while 11 patients showed no evidence of disease (NED). Classification models were developed based on the recorded peptide profiles using both unrestricted and feature-restricted approaches, employing either the full set of m\/z features or a subset of the most discriminatory m\/z features, respectively. The unrestricted model achieved a balanced accuracy of 71% at the patient level (75% sensitivity, 66% specificity), whereas the feature-restricted model reached a balanced accuracy of 72%, showing increased specificity (92%) but reduced sensitivity (52%) in the CDDP-CRT cohort. In order to assess treatment specificity, models trained on the CDDP-CRT cohort were tested on an independent patient cohort treated with mitomycin C-based CRT (MMC-CRT). Neither model demonstrated prognostic performance in the MMC-CRT patient cohort, suggesting specificity for platinum-based therapy. Presented findings highlight the potential of MALDI-MSI\u2013based proteomic profiling to identify patients at elevated risk of recurrence following CDDP-CRT. This approach may support more personalized risk assessment and treatment planning, ultimately contributing to improved therapeutic outcomes in HPV-negative HNSCC.<\/jats:p>","DOI":"10.3390\/ijms26189084","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T12:54:26Z","timestamp":1758200066000},"page":"9084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrating MALDI-MSI-Based Spatial Proteomics and Machine Learning to Predict Chemoradiotherapy Outcomes in Head and Neck Cancer"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7189-5617","authenticated-orcid":false,"given":"Marta","family":"Grzeski","sequence":"first","affiliation":[{"name":"Imaging Mass Spectrometry Unit, Berlin Institute of Health, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 13353 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-4885","authenticated-orcid":false,"given":"Patrick Moeller","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1998-4033","authenticated-orcid":false,"given":"Benjamin-Florian","family":"Hempel","sequence":"additional","affiliation":[{"name":"Imaging Mass Spectrometry Unit, Berlin Institute of Health, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 13353 Berlin, Germany"},{"name":"Veterinary Center for Resistance Research, Department of Veterinary Medicine, Freie Universit\u00e4t Berlin, 14163 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2913-8305","authenticated-orcid":false,"given":"Herbert","family":"Thiele","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Digital Medicine MEVIS, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5243-0331","authenticated-orcid":false,"given":"Jan","family":"Lellmann","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Image Computing, University of L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7897-7116","authenticated-orcid":false,"given":"Simon","family":"Schallenberg","sequence":"additional","affiliation":[{"name":"Institute of Pathology, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 10117 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volker","family":"Budach","sequence":"additional","affiliation":[{"name":"Department of Radiooncology and Radiotherapy, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 13353 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6773-9406","authenticated-orcid":false,"given":"Ulrich","family":"Keilholz","sequence":"additional","affiliation":[{"name":"Charit\u00e9 Comprehensive Cancer Center, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 10117 Berlin, Germany"},{"name":"German Cancer Consortium (DKTK), Partner Site Berlin, a Partnership Between DKFZ and Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 10115 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0512-549X","authenticated-orcid":false,"given":"Ingeborg","family":"Tinhofer","sequence":"additional","affiliation":[{"name":"Department of Radiooncology and Radiotherapy, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 13353 Berlin, Germany"},{"name":"German Cancer Consortium (DKTK), Partner Site Berlin, a Partnership Between DKFZ and Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 10115 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver","family":"Klein","sequence":"additional","affiliation":[{"name":"Imaging Mass Spectrometry Unit, Berlin Institute of Health, Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, 13353 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41415-022-5166-x","article-title":"Reviewing the epidemiology of head and neck cancer: Definitions, trends and risk factors","volume":"233","author":"Gormley","year":"2022","journal-title":"Br. Dent. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1586\/14737140.9.3.293","article-title":"Concurrent chemotherapy and radiotherapy for head and neck cancer","volume":"9","author":"Burri","year":"2009","journal-title":"Expert. Rev. Anticancer Ther."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.radonc.2009.04.014","article-title":"Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): An update on 93 randomised trials and 17,346 patients","volume":"92","author":"Pignon","year":"2009","journal-title":"Radiother. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.soc.2015.03.001","article-title":"Epidemiology of head and neck cancer","volume":"24","author":"Rettig","year":"2015","journal-title":"Surg. Oncol. Clin. N. Am."},{"key":"ref_7","first-page":"57","article-title":"Cisplatin based chemoradiation late toxicities in head and neck squamous cell carcinoma patients","volume":"20","author":"Rivelli","year":"2015","journal-title":"Discov. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e313","DOI":"10.1016\/S1470-2045(19)30177-9","article-title":"Novel prognostic clinical factors and biomarkers for outcome prediction in head and neck cancer: A systematic review","volume":"20","author":"Budach","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_9","first-page":"93","article-title":"The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \"personalized\" approach to cancer staging","volume":"67","author":"Amin","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_10","first-page":"122","article-title":"Head and Neck cancers-major changes in the American Joint Committee on cancer eighth edition cancer staging manual","volume":"67","author":"Lydiatt","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, T.C., Wu, C.T., Ko, J.Y., Yang, T.L., Lou, P.J., Wang, C.P., and Chang, Y.L. (2020). Clinical characteristics and treatment outcome of oropharyngeal squamous cell carcinoma in an endemic betel quid region. Sci. Rep., 10.","DOI":"10.1038\/s41598-019-57177-1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1002\/cncr.28150","article-title":"High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma","volume":"119","author":"Mroz","year":"2013","journal-title":"Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mroz, E.A., Tward, A.D., Hammon, R.J., Ren, Y., and Rocco, J.W. (2015). Intra-tumor genetic heterogeneity and mortality in head and neck cancer: Analysis of data from the Cancer Genome Atlas. PLoS Med., 12, Erratum in PLoS Med. 2015, 12, e1001844.","DOI":"10.1371\/journal.pmed.1001844"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Berghmans, E., Boonen, K., Maes, E., Mertens, I., Pauwels, P., and Baggerman, G. (2020). Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges. J. Pers. Med., 10.","DOI":"10.3390\/jpm10020054"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.clinbiochem.2013.01.018","article-title":"MALDI imaging mass spectrometry in cancer research: Combining proteomic profiling and histological evaluation","volume":"46","author":"Schone","year":"2013","journal-title":"Clin. Biochem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2300001","DOI":"10.1002\/pmic.202300001","article-title":"Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma","volume":"24","author":"Stillger","year":"2024","journal-title":"Proteomics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e1700181","DOI":"10.1002\/prca.201700181","article-title":"MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods","volume":"13","author":"Klein","year":"2019","journal-title":"Proteom. Clin. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pertzborn, D., Arolt, C., Ernst, G., Lechtenfeld, O.J., Kaesler, J., Pelzel, D., Guntinas-Lichius, O., von Eggeling, F., and Hoffmann, F. (2022). Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning. Cancers, 14.","DOI":"10.3390\/cancers14174342"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Patterson, N.H., Alabdulkarim, B., Lazaris, A., Thomas, A., Marcinkiewicz, M.M., Gao, Z.H., Vermeulen, P.B., Chaurand, P., and Metrakos, P. (2016). Assessment of pathological response to therapy using lipid mass spectrometry imaging. Sci. Rep., 6.","DOI":"10.1038\/srep36814"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.jprot.2011.04.013","article-title":"Protein signatures for survival and recurrence in metastatic melanoma","volume":"74","author":"Hardesty","year":"2011","journal-title":"J. Proteom."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e1700173","DOI":"10.1002\/prca.201700173","article-title":"Identification of Proteomic Markers in Head and Neck Cancer Using MALDI-MS Imaging, LC-MS\/MS, and Immunohistochemistry","volume":"13","author":"Hoffmann","year":"2019","journal-title":"Proteom. Clin. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kurczyk, A., Gawin, M., Paul, P., Chmielik, E., Rutkowski, T., Pietrowska, M., and Widlak, P. (2022). Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging. Molecules, 27.","DOI":"10.3390\/molecules27175458"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.bbapap.2016.11.003","article-title":"A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples","volume":"1865","author":"Boskamp","year":"2017","journal-title":"Biochim. Biophys. Acta Proteins Proteom."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1080\/14789450.2016.1200470","article-title":"Machine learning approaches in MALDI-MSI: Clinical applications","volume":"13","author":"Galli","year":"2016","journal-title":"Expert Rev. Proteom."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kanter, F., Lellmann, J., Thiele, H., Kalloger, S., Schaeffer, D.F., Wellmann, A., and Klein, O. (2023). Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers, 15.","DOI":"10.3390\/cancers15030686"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1056\/NEJMoa0912217","article-title":"Human papillomavirus and survival of patients with oropharyngeal cancer","volume":"363","author":"Ang","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1021\/pr400743a","article-title":"Matrix-assisted laser desorption ionization mass spectrometry imaging for peptide and protein analyses: A critical review of on-tissue digestion","volume":"13","author":"Heeren","year":"2014","journal-title":"J. Proteome Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, Z., Hundsdoerfer, P., Schulte, J.H., Astrahantseff, K., Boral, S., Schmelz, K., Eggert, A., and Klein, O. (2021). Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging. Cancers, 13.","DOI":"10.3390\/cancers13133184"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3914","DOI":"10.1021\/acs.jproteome.8b00580","article-title":"Individual Variability of Protein Expression in Human Tissues","volume":"17","author":"Kushner","year":"2018","journal-title":"J. Proteome Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109668","DOI":"10.1016\/j.ejrad.2021.109668","article-title":"Intratumor heterogeneity is biomarker specific and challenges the association with heterogeneity in multimodal functional imaging in head and neck squamous cell carcinoma","volume":"139","author":"Rasmussen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.cell.2017.01.018","article-title":"Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future","volume":"168","author":"McGranahan","year":"2017","journal-title":"Cell"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1186\/s12967-018-1495-6","article-title":"Limited utility of tissue micro-arrays in detecting intra-tumoral heterogeneity in stem cell characteristics and tumor progression markers in breast cancer","volume":"16","author":"Kundig","year":"2018","journal-title":"J. Transl. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1038\/bjc.2013.640","article-title":"Tumour-infiltrating lymphocytes predict response to definitive chemoradiotherapy in head and neck cancer","volume":"110","author":"Balermpas","year":"2014","journal-title":"Br. J. Cancer"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1073\/pnas.85.4.1114","article-title":"Sequence of the human 40-kDa keratin reveals an unusual structure with very high sequence identity to the corresponding bovine keratin","volume":"85","author":"Eckert","year":"1988","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1242\/jcs.111.17.2477","article-title":"Role of plectin in cytoskeleton organization and dynamics","volume":"111","author":"Wiche","year":"1998","journal-title":"J. Cell Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22607","DOI":"10.1074\/jbc.R900037199","article-title":"Integrins and actin filaments: Reciprocal regulation of cell adhesion and signaling","volume":"275","author":"Calderwood","year":"2000","journal-title":"J. Biol. Chem."},{"key":"ref_37","first-page":"1095","article-title":"Characterization of human myosin light chains 1sa and 3nm: Implications for isoform evolution and function","volume":"10","author":"Hailstones","year":"1990","journal-title":"Mol. Cell Biol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"78","DOI":"10.4161\/cam.20101","article-title":"Small heat shock proteins in cellular adhesion and migration: Evidence from Plasmodium genetics","volume":"6","author":"Montagna","year":"2012","journal-title":"Cell Adhes. Migr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"R792","DOI":"10.1016\/S0960-9822(01)00473-0","article-title":"Adhesion signaling: How beta-catenin interacts with its partners","volume":"11","author":"Gottardi","year":"2001","journal-title":"Curr. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1083\/jcb.134.2.559","article-title":"Beta4 integrin is required for hemidesmosome formation, cell adhesion and cell survival","volume":"134","author":"Dowling","year":"1996","journal-title":"J. Cell Biol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1177\/00220345990780061001","article-title":"Keratin 19 downregulation by oral squamous cell carcinoma lines increases invasive potential","volume":"78","author":"Crowe","year":"1999","journal-title":"J. Dent. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s13402-023-00898-3","article-title":"Silencing AHNAK promotes nasopharyngeal carcinoma progression by upregulating the ANXA2 protein","volume":"47","author":"Lu","year":"2024","journal-title":"Cell Oncol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1016\/j.jprot.2011.12.018","article-title":"Plectin promotes migration and invasion of cancer cells and is a novel prognostic marker for head and neck squamous cell carcinoma","volume":"75","author":"Katada","year":"2012","journal-title":"J. Proteom."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1038\/s41416-020-0892-9","article-title":"Adaptive ERK signalling activation in response to therapy and in silico prognostic evaluation of EGFR-MAPK in HNSCC","volume":"123","author":"Rong","year":"2020","journal-title":"Br. J. Cancer"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/7651_2017_114","article-title":"MALDI Imaging Combined with Laser Microdissection-Based Microproteomics for Protein Identification: Application to Intratumor Heterogeneity Studies","volume":"1788","author":"Longuespee","year":"2018","journal-title":"Methods Mol. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e16061","DOI":"10.15252\/emmm.202216061","article-title":"Quantitative protein biomarker panels: A path to improved clinical practice through proteomics","volume":"15","author":"Hartl","year":"2023","journal-title":"EMBO Mol. Med."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s00134-009-1641-y","article-title":"A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage","volume":"36","author":"Turck","year":"2010","journal-title":"Intensive Care Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1586\/epr.09.83","article-title":"Bioinformatics for protein biomarker panel classification: What is needed to bring biomarker panels into in vitro diagnostics?","volume":"6","author":"Robin","year":"2009","journal-title":"Expert Rev. Proteom."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1878-5085-4-7","article-title":"Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine","volume":"4","author":"Drucker","year":"2013","journal-title":"EPMA J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Hemphill, E., Lindsay, J., Lee, C., Mandoiu, I.I., and Nelson, C.E. (2014). Feature selection and classifier performance on diverse bio- logical datasets. BMC Bioinform., 15.","DOI":"10.1186\/1471-2105-15-S13-S4"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1093\/biostatistics\/kxy044","article-title":"The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models","volume":"21","author":"Zhang","year":"2020","journal-title":"Biostatistics"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Benkarim, O., Paquola, C., Park, B.Y., Kebets, V., Hong, S.J., Vos de Wael, R., Zhang, S., Yeo, B.T.T., Eickenberg, M., and Ge, T. (2022). Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol., 20.","DOI":"10.1371\/journal.pbio.3001627"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1002\/prca.201200046","article-title":"Proteomic workflow for analysis of archival formalin-fixed and paraffin-embedded clinical samples to a depth of 10 000 proteins","volume":"7","author":"Wisniewski","year":"2013","journal-title":"Proteom. Clin. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1093\/annonc\/mdw426","article-title":"Targeted next-generation sequencing identifies molecular subgroups in squamous cell carcinoma of the head and neck with distinct outcome after concurrent chemoradiation","volume":"27","author":"Tinhofer","year":"2016","journal-title":"Ann. Oncol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ejca.2017.02.018","article-title":"MiR-200b and miR-155 as predictive biomarkers for the efficacy of chemoradiation in locally advanced head and neck squamous cell carcinoma","volume":"77","author":"Hess","year":"2017","journal-title":"Eur. J. Cancer"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bankhead, P., Loughrey, M.B., Fernandez, J.A., Dombrowski, Y., McArt, D.G., Dunne, P.D., McQuaid, S., Gray, R.T., Murray, L.J., and Coleman, H.G. (2017). QuPath: Open source software for digital pathology image analysis. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-17204-5"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hosmer, D., and Lemeshow, S. (2000). Applied Logistic Regression, John Wiley and Sons. [2nd ed.]. Chapter 5.","DOI":"10.1002\/0471722146"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1093\/bib\/bbm064","article-title":"ROC analysis: Applications to the classification of biological sequences and 3D structures","volume":"9","author":"Sonego","year":"2008","journal-title":"Brief. Bioinform."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"M111.010587","DOI":"10.1074\/mcp.M111.010587","article-title":"PEAKS DB: De novo sequencing assisted database search for sensitive and accurate peptide identification","volume":"11","author":"Zhang","year":"2012","journal-title":"Mol. Cell Proteom."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/18\/9084\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:47:44Z","timestamp":1760035664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/18\/9084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,18]]},"references-count":60,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["ijms26189084"],"URL":"https:\/\/doi.org\/10.3390\/ijms26189084","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,18]]}}}