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Proliferation, as one of the [[Hallmarks of cancer|hallmarks]] and most fundamental biological processes in tumors,<ref>{{Cite journal|last1=Hanahan|first1=Douglas|last2=Weinberg|first2=Robert A.|title=Hallmarks of Cancer: The Next Generation|journal=Cell|volume=144|issue=5|pages=646–674|doi=10.1016/j.cell.2011.02.013|pmid=21376230|year=2011|doi-access=free}}</ref> is associated with tumor progression, response to therapy, and cancer patient survival.<ref>{{Cite journal|last1=Yerushalmi|first1=Rinat|last2=Woods|first2=Ryan|last3=Ravdin|first3=Peter M|last4=Hayes|first4=Malcolm M|last5=Gelmon|first5=Karen A|title=Ki67 in breast cancer: prognostic and predictive potential|journal=The Lancet Oncology|volume=11|issue=2|pages=174–183|doi=10.1016/s1470-2045(09)70262-1|pmid=20152769|date=February 2010}}</ref> Consequently, the evaluation of a tumor '''proliferative index''' (or growth fraction) has clinical significance in characterizing many solid tumors and hematologic malignancies.<ref>{{Cite journal|last1=Loo|first1=Suet Kee|last2=Ch'ng|first2=Ewe Seng|last3=Lawrie|first3=Charles H.|last4=Muruzabal|first4=María Arestin|last5=Gaafar|first5=Ayman|last6=Pomposo|first6=María Puente|last7=Husin|first7=Azlan|last8=Salleh|first8=Md. Salzihan Md.|last9=Banham|first9=Alison H.|title=DNMT1 is predictive of survival and associated with Ki-67 expression in R-CHOP-treated diffuse large B-cell lymphomas|journal=Pathology|volume=49|issue=7|pages=731–739|doi=10.1016/j.pathol.2017.08.009|pmid=29074044|date=December 2017|url=https://ora.ox.ac.uk/objects/uuid:f5b74a28-1c4d-45f8-9d2f-64cb670d8fc8}}</ref> This has led investigators to develop different technologies to evaluate the proliferation index in tumor samples. The most commonly used methods in evaluating a proliferative index include [[mitotic index]]ing, [[thymidine]]-labeling index, [[bromodeoxyuridine]] assay, the determination of fraction of cells in various phases of cell cycle, and the [[Immunohistochemistry|immunohistochemical]] evaluation of cell cycle-associated proteins.
== Mitotic index (also called mitotic count) ==
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Mitotic indexing is the oldest method of assessing proliferation and is determined by counting the number of mitotic figures (cells undergoing mitosis) through a light microscope on [[H&E stain|H&E]] stained sections. It is usually expressed as the number of cells per microscopic field. Cells in the mitotic phase are identified by the typical appearance of their chromosomes in the cell during the mitotic phase of the cell cycle.<ref>{{Cite journal|last=Baak|first=J. P.|date=July 1990|title=Mitosis counting in tumors|journal=Human Pathology|volume=21|issue=7|pages=683–685|issn=0046-8177|pmid=2131787|doi=10.1016/0046-8177(90)90026-2}}</ref> Usually the number of mitotic figures is expressed as the total number in a defined number of high power fields, such as 10 mitoses in 10 high power fields. Since the field of vision area can vary considerably between different microscopes, the exact area of the high power fields should be defined in order to compare results from different studies. Accordingly, one of the main problems of counting mitosis has been the reproducibility. Thus, the need for standardized methodology and strict protocols is important to achieve reproducible results.<ref>{{Cite journal|last1=van Diest|first1=P. J.|last2=Baak|first2=J. P.|last3=Matze-Cok|first3=P.|last4=Wisse-Brekelmans|first4=E. C.|last5=van Galen|first5=C. M.|last6=Kurver|first6=P. H.|last7=Bellot|first7=S. M.|last8=Fijnheer|first8=J.|last9=van Gorp|first9=L. H.|date=June 1992|title=Reproducibility of mitosis counting in 2,469 breast cancer specimens: results from the Multicenter Morphometric Mammary Carcinoma Project|journal=Human Pathology|volume=23|issue=6|pages=603–607|issn=0046-8177|pmid=1592381|doi=10.1016/0046-8177(92)90313-r}}</ref> Automated image analysis using deep learning-based algorithms has been proposed as a promising tool to assist pathologists and thereby improve reproducibility and accuracy.<ref>{{cite journal |last1=Bertram |first1=Christof A |last2=Aubreville |first2=Marc |last3=Donovan |first3=Taryn A |last4=Bartel |first4=Alexander |last5=Wilm |first5=Frauke |last6=Marzahl |first6=Christian |last7=Assenmacher |first7=Charles-Antoine |last8=Becker |first8=Kathrin |last9=Bennett |first9=Mark |last10=Corner |first10=Sarah |last11=Cossic |first11=Brieuc |last12=Denk |first12=Daniela |last13=Dettwiler |first13=Martina |last14=Gonzalez |first14=Beatriz Garcia |last15=Gurtner |first15=Corinne |last16=Haverkamp |first16=Ann-Kathrin |last17=Heier |first17=Annabelle |last18=Lehmbecker |first18=Annika |last19=Merz |first19=Sophie |last20=Noland |first20=Erika L |last21=Plog |first21=Stephanie |last22=Schmidt |first22=Anja |last23=Sebastian |first23=Franziska |last24=Sledge |first24=Dodd G |last25=Smedley |first25=Rebecca C |last26=Tecilla |first26=Marco |last27=Thaiwong |first27=Tuddow |last28=Fuchs-Baumgartinger |first28=Andrea |last29=Meuten |first29=Donald J |last30=Breininger |first30=Katharina |last31=Kiupel |first31=Matti |last32=Maier |first32=Andreas |last33=Klopfleisch |first33=Robert |title=Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy |journal=Veterinary Pathology |year=2021 |volume=59 |issue=2 |pages=211–226 |doi=10.1177/03009858211067478 |pmid=34965805 |s2cid=245567911 |doi-access=free |pmc=8928234 }}</ref>
== Thymidine-labeling index ==
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