{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:50:59Z","timestamp":1786978259545,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T00:00:00Z","timestamp":1666828800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Dual\u2010energy X\u2010ray absorptiometry (DXA) is an exam that measures areal bone mineral density (aBMD) and is regularly used to diagnose and monitor osteoporosis. Except for exam quality issues such as operator error, the quantitative results of an exam are not modified by a radiologist or other physician. DXA cross\u2010calibration errors can shift diagnoses, conceivably leading to alternate intervention decisions and patient outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>After identifying and correcting a cross\u2010calibration bias of 3.8% in our two DXA scanners\u2019 aBMD measurements, we investigated misdiagnosis rates for given cross\u2010calibration errors in a single patient cohort to determine the impact on patient care and the value of cross\u2010calibration quality control.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The studied cohort was 8012 patients of all ages and sexes with femoral neck exams that were scanned on a single DXA unit from October 1, 2018 to March 31, 2021. There were six subcohorts delineated by age and sex, three female groups and three male groups. Data reporting focused on the highest risk subcohort of 2840 females aged 65 or older. The DXA unit had no calibration changes during that time. Only one femoral neck\u2014left or right\u2014was randomly chosen for analysis. Patients with multiple qualifying exams within the time interval had one exam randomly chosen. The proof\u2010of\u2010principle simulation shifted the aBMD values within a range of \u00b110%, \u00b18%, \u00b16%, \u00b14%, \u00b13.5%, \u00b13%, \u00b12.5%, \u00b12%, \u00b11.5%, \u00b11%, \u00b10.5%, and 0 (no shift); the cross\u2010calibration shifts were informed by published results and institutional experience. Measurement precision was modeled by randomly sampling a Gaussian distribution characterized by the worst acceptable least significant change (LSC) of 6.9%, with 100\u00a0000 samplings for each patient. T\u2010scores were recalculated from the shifted aBMD values, followed by reassigned diagnoses from the World Health Organization's T\u2010score\u2010based scheme.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The unshifted original subcohort of women aged 65 and older had 599 normal diagnoses (21.1% of the cohort), 1784 osteopenia diagnoses (62.8%), and 455 osteoporosis diagnoses (16.1%). Osteoporosis diagnosis rates were highly sensitive to aBMD shifts. At the extrema, a \u221210% aBMD shift led to +161% osteoporosis cases, and a +10% aBMD shift led to \u221264.5% osteoporosis cases. Within the more plausible \u00b14% aBMD error range, the osteoporosis diagnosis rate changed \u221210.5% per +1% aBMD shift as indicated by linear regression (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u00a0=\u00a00.98). Except for the men aged 49 years and younger subcohort, the total cohort and five subcohorts had fit line slopes ranging between \u22129.7% and \u221212.1% with\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2265 0.98. Cross\u2010calibration bias had greater influence for diagnosis count rates compared to measurement precision, that is, LSC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results quantify the degree of misdiagnosis that can occur in a clinically relevant cohort due to cross\u2010calibration bias. In medical practices where patients may be scanned on more than one DXA unit, ensuring cross\u2010calibration quality is a critical and high\u2010value quality control task with direct impact on patient diagnosis and treatment course. The clinical impact and incidence of poor DXA quality control practices, and cross\u2010calibration in particular, should be studied further.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16057","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T23:31:48Z","timestamp":1666135908000},"page":"1623-1634","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quantifying misdiagnosis rates from cross\u2010calibration biases and precision errors in dual\u2010energy X\u2010ray absorptiometry of the femoral neck"],"prefix":"10.1002","volume":"50","author":[{"given":"Samuel J.","family":"Fahrenholtz","sequence":"first","affiliation":[{"name":"Section of Diagnostic Physics Department of Radiology Mayo Clinic Arizona  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremiah R.","family":"Long","sequence":"additional","affiliation":[{"name":"Section of Musculoskeletal Radiology Department of Radiology Mayo Clinic Arizona  Phoenix Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Whitaker","sequence":"additional","affiliation":[{"name":"Section of Endocrinology Department of Internal Medicine Mayo Clinic Arizona  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William F.","family":"Sensakovic","sequence":"additional","affiliation":[{"name":"Section of Diagnostic Physics Department of Radiology Mayo Clinic Arizona  Scottsdale Arizona USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(18)32112\u20103"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00198\u2010017\u20103996\u20101"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11657\u2010013\u20100136\u20101"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00198\u2010017\u20104110\u20104"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mayocp.2014.09.011"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01622200"},{"key":"e_1_2_10_8_1","unstructured":"KanisJA.World Health Organization. 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