We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

Department of Cell and Developmental Biology Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel

Department of Clinical Hematology Institute of Hematology and Blood Transfusion Prague Czech Republic

Department of Haematology Aberdeen Royal Infirmary Aberdeen United Kingdom

Department of Haematology Oncology and Internal Medicine Warsaw Medical University Warsaw Poland

Department of Haematology Worcestershire Acute Hospitals National Health Service Trust and University of Birmingham Birmingham United Kingdom

Department of Hematology Aarhus University Hospital Aarhus Denmark

Department of Hematology Democritus University of Thrace Medical School University Hospital of Alexandroupolis Alexandroupolis Greece

Department of Hematology Oncology and Clinical Immunology Universitätsklinik Düsseldorf Düsseldorf Germany

Department of Hematology Radboudumc Nijmegen The Netherlands

Department of Internal Medicine 5 Innsbruck Medical University Innsbruck Austria

Department of Medicine Tel Aviv Sourasky Medical Center Tel Aviv Israel

Department of Molecular Medicine and Hematology Oncology Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo University of Pavia Pavia Italy

Department of Pathology Tel Aviv Sourasky Medical Center Tel Aviv Israel

Department of Tumor Immunology Nijmegen Center for Molecular Life Sciences Radboudumc Nijmegen The Netherlands

Division Hematology Department of Internal Medicine University of Patras Medical School Patras Greece

Division of Hematology Department of Medicine Karolinska Institutet Stockholm Sweden

Epidemiology and Cancer Statistics Group Department of Health Sciences University of York York United Kingdom

Hematology Department Hospital Universitario y Politécnico La Fe Valencia Spain

Sackler Faculty of Medicine Tel Aviv University Tel Aviv Israel

Service d'Hématologie Centre Hospitalier de Perpignan Perpignan France

Service d'Hématologie Centre Hospitalier Universitaire Brabois Vandoeuvre Nancy France

Service d'Hématologie Séniors Hôpital Saint Louis Assistance Publique des Hôpitaux de Paris and Université Paris 7 Paris France

St James's Institute of Oncology The Leeds Teaching Hospitals NHS Trust Leeds United Kingdom; and

Zobrazit více v PubMed

Newby DE, Adamson PD, Berry C, et al. . SCOT-HEART Investigators . Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med. 2018;379(10):924-933. PubMed

Smith LN, Smith ML, Fletcher ME, Henderson AJ. A 3D machine vision method for non-invasive assessment of respiratory function. Int J Med Robot. 2016;12(2):179-188. PubMed

Rotenstein LS, Huckman RS, Wagle NW. Making patients and doctors happier - the potential of patient-reported outcomes. N Engl J Med. 2017;377(14):1309-1312. PubMed

Greene JA, Lea AS. Digital futures past - the long arc of big data in medicine. N Engl J Med. 2019;381(5):480-485. PubMed PMC

de Swart L, Crouch S, Hoeks M, et al. . EUMDS Registry Participants . Impact of red blood cell transfusion dose density on progression-free survival in patients with lower-risk myelodysplastic syndromes. Haematologica. 2020;105(3):632-639. PubMed PMC

de Swart L, Smith A, Johnston TW, et al. . Validation of the revised international prognostic scoring system (IPSS-R) in patients with lower-risk myelodysplastic syndromes: a report from the prospective European LeukaemiaNet MDS (EUMDS) registry. Br J Haematol. 2015;170(3):372-383. PubMed

DeAngelo DJ, Stone RM. Myelodysplastic syndromes: biology and treatment. In: Hoffman R, Benz EJ Jr, Silberstein LE, Heslop H, Anastasi J, Weitz J, eds. Hematology: Basic Principles and Practice. Philadelphia, PA: Elsevier Health Sciences; 2013:882-903.

Malcovati L, Hellström-Lindberg E, Bowen D, et al. . European Leukemia Net . Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet. Blood. 2013;122(17):2943-2964. PubMed PMC

Mangi MH, Mufti GJ. Primary myelodysplastic syndromes: diagnostic and prognostic significance of immunohistochemical assessment of bone marrow biopsies. Blood. 1992;79(1):198-205. PubMed

Saad ST, Vassallo J, Arruda VA, Lorand-Metze I. The role of bone marrow study in diagnosis and prognosis of myelodysplastic syndrome. Pathologica. 1994;86(1):47-51. PubMed

Ríos A, Cañizo MC, Sanz MA, et al. . Bone marrow biopsy in myelodysplastic syndromes: morphological characteristics and contribution to the study of prognostic factors. Br J Haematol. 1990;75(1):26-33. PubMed

Tricot G, De Wolf-Peeters C, Vlietinck R, Verwilghen RL. Bone marrow histology in myelodysplastic syndromes. II. Prognostic value of abnormal localization of immature precursors in MDS. Br J Haematol. 1984;58(2):217-225. PubMed

Social Security Administration. Disability Evaluation Under Social Security. 7.10 Disorders Of Bone Marrow Failure. Accessed 29 August 2019.

Israel National Insurance Agency. Disability Level Establishment: Hematologic Disorders [in Hebrew]. Accessed 29 August 2019.

Oster HS, Carmi G, Kolomansky A, et al. . Is bone marrow examination always necessary to establish the diagnosis of myelodysplastic syndromes? A proposed non-invasive diagnostic model. Leuk Lymphoma. 2018;59(9):2227-2232. PubMed

Oster HS, Abu Shrkihe B, Crouch S, et al. . Can we diagnose MDS without bone marrow examination? a proposed EUMDS-based non-invasive diagnostic model [abstract]. Blood. 2017;130(suppl 1):2975.

Oster HS, Crouch S, Smith A, et al. . MDS diagnosis: many patients may not require bone marrow examination [abstract]. Blood. 2018; 132(suppl 1):4357.

Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):1189-1232.

Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38(4):367-378.

Greenwell B, Boehmke B, Cunningham J, Developers G. GBM: Generalized Boosted Regression Models. R package version 214. Accessed 16 June 2021.

Kuhn M, Wing J, Weston S, et al. . Caret: Classification and Regression Training. R package version 60-81. Accessed 16 June 2021.

Buckstein R, Jang K, Friedlich J, et al. . Estimating the prevalence of myelodysplastic syndromes in patients with unexplained cytopenias: a retrospective study of 322 bone marrows. Leuk Res. 2009;33(10):1313-1318. PubMed

Guralnik JM, Eisenstaedt RS, Ferrucci L, Klein HG, Woodman RC. Prevalence of anemia in persons 65 years and older in the United States: evidence for a high rate of unexplained anemia. Blood. 2004;104(8):2263-2268. PubMed

R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.

Davis BJ. The application of computers to clinical medical data (including machine demonstration). In: Proceedings of the 1st IBM Medical Symposium; 15-17 June 1959; Poughkeepsie, NY. 1959;179-185.

Banerjee A, Mathew D, Rouane K. Using patient data for patients’ benefit [editorial]. BMJ. 2017;358:j4413. PubMed

Lewis SJ, Gandomkar Z, Brennan PC. Artificial intelligence in medical imaging practice: looking to the future. J Med Radiat Sci. 2019;66(4): 292-295. PubMed PMC

Sakai A, Onishi Y, Matsui M, et al. . A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features. Radiol Phys Technol. 2019;13(1):27-36. PubMed

Aktolun C. Artificial intelligence and radiomics in nuclear medicine: potentials and challenges. Eur J Nucl Med Mol Imaging. 2019;46(13): 2731-2736. PubMed

Serag A, Ion-Margineanu A, Qureshi H, et al. . Translational AI and deep learning in diagnostic pathology. Front Med (Lausanne). 2019;6:185. PubMed PMC

Oster HS, Taccardi B, Lux RL, Ershler PR, Rudy Y. Noninvasive electrocardiographic imaging: reconstruction of epicardial potentials, electrograms, and isochrones and localization of single and multiple electrocardiac events. Circulation. 1997;96(3):1012-1024. PubMed

Oster HS, Taccardi B, Lux RL, Ershler PR, Rudy Y. Electrocardiographic imaging: noninvasive characterization of intramural myocardial activation from inverse-reconstructed epicardial potentials and electrograms. Circulation. 1998;97(15):1496-1507. PubMed

Rudy Y. Noninvasive electrocardiographic imaging of arrhythmogenic substrates in humans. Circ Res. 2013;112(5):863-874. PubMed PMC

Perez MV, Mahaffey KW, Hedlin H, et al. . Apple Heart Study Investigators . Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381(20):1909-1917. PubMed PMC

Yee CR, Narain NR, Akmaev VR, Vemulapalli V. A data-driven approach to predicting septic shock in the intensive care unit. Biomed Inform Insights. 2019;11:1178222619885147. PubMed PMC

Milani RV, Lavie CJ, Bober RM, Milani AR, Ventura HO. Improving hypertension control and patient engagement using digital tools. Am J Med. 2017;130(1):14-20. PubMed

Alpert JS. Digital medicine: “O Brave New World”. Am J Med. 2017;130(3):243-244. PubMed

Basch E, Deal AM, Kris MG, et al. . Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial [published corrections appear in J Clin Oncol. 2016;34(18):2198 and J Clin Oncol. 2019;37(6):528]. J Clin Oncol. 2016;34(6):557-565. PubMed PMC

Donovan K, Sanson-Fisher RW, Redman S. Measuring quality of life in cancer patients. J Clin Oncol. 1989;7(7):959-968. PubMed

Hsiao CJ, Dymek C, Kim B, Russell B. Advancing the use of patient-reported outcomes in practice: understanding challenges, opportunities, and the potential of health information technology. Qual Life Res. 2019;28(6):1575-1583. PubMed

Nelson EC, Eftimovska E, Lind C, Hager A, Wasson JH, Lindblad S. Patient reported outcome measures in practice. BMJ. 2015;350:g7818. PubMed

Schnipper LE, Davidson NE, Wollins DS, et al. . Updating the American Society of Clinical Oncology value framework: revisions and reflections in response to comments received. J Clin Oncol. 2016;34(24):2925-2934. PubMed

Stauder R, Lambert J, Desruol-Allardin S, et al. . Patient-reported outcome measures in studies of myelodysplastic syndromes and acute myeloid leukemia: literature review and landscape analysis. Eur J Haematol. 2020;104(5):476-487. PubMed PMC

Stauder R, Yu G, Koinig KA, et al. . Health-related quality of life in lower-risk MDS patients compared with age- and sex-matched reference populations: a European LeukemiaNet study. Leukemia. 2018;32(6):1380-1392. PubMed PMC

Cetto GL, Vettore L, De Matteis MC, Piga A, Perona G. Erythrocyte cation content, globin chain synthesis and glucose metabolism in dysmyelopoietic syndromes. Acta Haematol. 1982;68(2):124-130. PubMed

Basiorka AA, McGraw KL, Abbas-Aghababazadeh F, et al. . Assessment of ASC specks as a putative biomarker of pyroptosis in myelodysplastic syndromes: an observational cohort study. Lancet Haematol. 2018;5(9):e393-e402. PubMed PMC

Bouronikou E, Georgoulias P, Giannakoulas N, et al. . Metabolism-related cytokine and hormone levels in the serum of patients with myelodysplastic syndromes. Acta Haematol. 2013;130(1):27-33. PubMed

Hamoudeh E, Zeidan AM, Barbarotta L, Rosano N. The interactions between diabetes mellitus and myelodysplastic syndromes: current state of evidence and future directions. Curr Diabetes Rev. 2016;12(3):231-239. PubMed

Kachekouche Y, Dali-Sahi M, Benmansour D, Dennouni-Medjati N. Hematological profile associated with type 2 diabetes mellitus. Diabetes Metab Syndr. 2018;12(3):309-312. PubMed

Wu TJ, Chuang LM, Tai TY. Erythrocyte deformability in diabetes mellitus. Taiwan Yi Xue Hui Za Zhi. 1989;88(3):240-243. PubMed

Cintra LT, da Silva Facundo AC, Prieto AK, et al. . Blood profile and histology in oral infections associated with diabetes. J Endod. 2014;40(8):1139-1144. PubMed

Calvo X, Arenillas L, Luño E, et al. . Enumerating bone marrow blasts from nonerythroid cellularity improves outcome prediction in myelodysplastic syndromes and permits a better definition of the intermediate risk category of the Revised International Prognostic Scoring System (IPSS-R). Am J Hematol. 2017;92(7):614-621. PubMed

Greenbaum U, Joffe E, Filanovsky K, et al. . Can bone marrow cellularity help in predicting prognosis in myelodysplastic syndromes? Eur J Haematol. 2018;101(4):502-507. PubMed

Hallek M. Chronic lymphocytic leukemia: 2020 update on diagnosis, risk stratification and treatment. Am J Hematol. 2019;94(11):1266-1287. PubMed

Spivak JL. How I treat polycythemia vera. Blood. 2019;134(4):341-352. PubMed

Bejar R, Papaemmanuil E, Haferlach T, et al. . Somatic mutations in MDS patients are associated with clinical features and predict prognosis independent of the IPSS-R: analysis of combined datasets from the International Working Group for Prognosis in MDS-Molecular Committee [abstract]. Blood. 2015;126(23):907.

Papaemmanuil E, Gerstung M, Malcovati L, et al. . Chronic Myeloid Disorders Working Group of the International Cancer Genome Consortium . Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616-3627. PubMed PMC

Duetz C, Westers TM, van de Loosdrecht AA. Clinical implication of multi-parameter flow cytometry in myelodysplastic syndromes. Pathobiology. 2019;86(1):14-23. PubMed PMC

Agool A, Schot BW, Jager PL, Vellenga E. 18F-FLT PET in hematologic disorders: a novel technique to analyze the bone marrow compartment. J Nucl Med. 2006;47(10):1592-1598. PubMed

Depaoli L, Davini O, Foggetti MD, et al. . Evaluation of bone marrow cellularity by magnetic resonance imaging in patients with myelodysplastic syndrome. Eur J Haematol. 1992;49(2):105-107. PubMed

Nagata Y, Zhao R, Awada H, et al. . Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes. Blood. 2020;136(20):2249-2262. PubMed PMC

Baer C, Stengel A, Kern W, Haferlach C, Haferlach T. The potential of molecular genetic analysis for diagnostic and prognostic decision making in clonal cytopenia of undetermined significance (CCUS) and MDS - a study on 576 patients [abstract]. Blood. 2020;136(suppl):30-31.

Goll JB, Jensen TL, Lindsley RC, et al. . Targeted sequencing of 7 genes can help reduce pathologic misclassification of MDS [abstract]. Blood. 2020;136(suppl 1):32-33.

Radakovich N, Meggendorfer M, Malcovati L, et al. . A personalized clinical-decision tool to improve the diagnostic accuracy of myelodysplastic syndromes [abstract]. Blood. 2020;136(suppl 1):33-35.

Genovese G, Kähler AK, Handsaker RE, et al. . Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371(26):2477-2487. PubMed PMC

Jaiswal S, Fontanillas P, Flannick J, et al. . Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371(26): 2488-2498. PubMed PMC

Malcovati L, Gallì A, Travaglino E, et al. . Clinical significance of somatic mutation in unexplained blood cytopenia. Blood. 2017;129(25):3371-3378. PubMed PMC

Girelli D, Marchi G, Camaschella C. Anemia in the elderly. HemaSphere. 2018;2(3):e40. PubMed PMC

Goodnough LT, Schrier SL. Evaluation and management of anemia in the elderly. Am J Hematol. 2014;89(1):88-96. PubMed PMC

Pang WW, Schrier SL. Anemia in the elderly. Curr Opin Hematol. 2012;19(3):133-140. PubMed

Rauw J, Wells RA, Chesney A, Reis M, Zhang L, Buckstein R. Validation of a scoring system to establish the probability of myelodysplastic syndrome in patients with unexplained cytopenias or macrocytosis. Leuk Res. 2011;35(10):1335-1338. PubMed

Abelson S, Collord G, Ng SWK, et al. . Prediction of acute myeloid leukaemia risk in healthy individuals. Nature. 2018;559(7714):400-404. PubMed PMC