{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:38:48Z","timestamp":1740184728359,"version":"3.37.3"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Cancer Prevention Research Institute of Health","award":["RR170068"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,7]]},"abstract":"Abstract<\/jats:title>\n \n Motivation<\/jats:title>\n Algorithms for classifying chromosomes, like convolutional deep neural networks (CNNs), show promise to augment cytogeneticists\u2019 workflows; however, a critical limitation is their inability to accurately classify various structural chromosomal abnormalities. In hematopathology, recurrent structural cytogenetic abnormalities herald diagnostic, prognostic and therapeutic implications, but are laborious for expert cytogeneticists to identify. Non-recurrent cytogenetic abnormalities also occur frequently cancerous cells. Here, we demonstrate the feasibility of using CNNs to accurately classify many recurrent cytogenetic abnormalities while being able to reliably detect non-recurrent, spurious abnormal chromosomes, as well as provide insights into dataset assembly, model selection and training methodology that improve overall generalizability and performance for chromosome classification.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Our top-performing model achieved a mean weighted F1 score of 96.86% on the validation set and 94.03% on the test set. Gradient class activation maps indicated that our model learned biologically meaningful feature maps, reinforcing the clinical utility of our proposed approach. Altogether, this work: proposes a new dataset framework for training chromosome classifiers for use in a clinical environment, reveals that residual CNNs and cyclical learning rates confer superior performance, and demonstrates the feasibility of using this approach to automatically screen for many recurrent cytogenetic abnormalities while adeptly classifying non-recurrent abnormal chromosomes.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n Software is freely available at https:\/\/github.com\/DaehwanKimLab\/Chromosome-ReAd. The data underlying this article cannot be shared publicly due to it being protected patient information.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab822","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T20:22:18Z","timestamp":1638390138000},"page":"1420-1426","source":"Crossref","is-referenced-by-count":8,"title":["Automated classification of cytogenetic abnormalities in hematolymphoid neoplasms"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8563-8059","authenticated-orcid":false,"given":"Andrew","family":"Cox","sequence":"first","affiliation":[{"name":"Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Chanhee","family":"Park","sequence":"additional","affiliation":[{"name":"Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Prasad","family":"Koduru","sequence":"additional","affiliation":[{"name":"Department of Pathology, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Kathleen","family":"Wilson","sequence":"additional","affiliation":[{"name":"Department of Pathology, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Olga","family":"Weinberg","sequence":"additional","affiliation":[{"name":"Department of Pathology, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5638-4371","authenticated-orcid":false,"given":"Weina","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pathology, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Rolando","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Pathology, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]},{"given":"Daehwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center , Dallas, TX 75235, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"2023020108550188200_btab822-B1","doi-asserted-by":"crossref","first-page":"157727","DOI":"10.1109\/ACCESS.2020.3019937","article-title":"Automated system for chromosome karyotyping to recognize the most common numerical abnormalities using deep learning","volume":"8","author":"Al-Kharraz","year":"2020","journal-title":"IEEE Access"},{"key":"2023020108550188200_btab822-B2","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1182\/blood-2016-03-643544","article-title":"The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia","volume":"127","author":"Arber","year":"2016","journal-title":"Blood"},{"first-page":"1","year":"2019","author":"Ding","key":"2023020108550188200_btab822-B3"},{"key":"2023020108550188200_btab822-B4","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1038\/2121437a0","article-title":"A computer programme for the analysis of human chromosomes","volume":"212","author":"Gilbert","year":"1966","journal-title":"Nature"},{"key":"2023020108550188200_btab822-B5","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1002\/ajh.25595","article-title":"Chronic lymphocytic leukemia: 2020 update on diagnosis, risk stratification and treatment","volume":"94","author":"Hallek","year":"2019","journal-title":"Am. 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