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339 Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Program: Oral and Poster Abstracts
Type: Oral
Session: 634. Myeloproliferative Syndromes: Clinical and Epidemiological: Avant-garde and Traditional Prognostic and Response Assessment in MPNs
Hematology Disease Topics & Pathways:
Research, MPN, Clinical Research, Chronic Myeloid Malignancies, Diseases, registries, survivorship, Myeloid Malignancies
Saturday, December 10, 2022: 4:30 PM

Adrian Mosquera Orgueira1*, Manuel Perez Encinas, MD2*, Alberto Hernández Sánchez, MD3*, Teresa González, PhD4*, Eduardo Arellano-Rodrigo, MD5*, Javier Martinez Martínez Elicegui6*, Angela Villaverde Ramiro7*, Jose Maria Raya Sanchez, MD, PhD8*, Rosa Ayala, MD, PhD9*, Francisca Ferrer Marin, MD, PhD10, Maria Laura Fox, MD11*, Patricia Velez Tenza12*, Elvira Mora Casterá, MD13*, Blanca Xicoy, MD14*, Maria Isabel Mata Vazquez, MD15*, María García Fortes16*, Anna Angona, MD17*, Beatriz Cuevas, MD18*, María Alicia Senin, MD19*, Angel Ramírez Payer, MD20*, María José Ramírez21*, Raul Perez Lopez, MD, PhD22*, Sonia González de Villambrosia, MD23*, Clara Martínez24*, María Teresa Gómez-Casares, MD, PhD25*, Carmen Garcia-Hernandez, MD26*, Mercedes Gasior Kabat, MD27*, Beatriz Bellosillo28*, Juan Luis Steegmann29, Alberto Alvarez-Larran, MD, PhD30*, Jesús María Hernández-Rivas, MD, PhD31 and Juan Carlos Hernandez Boluda, MD, PhD32*

1Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), SERGAS, SANTIAGO DE COMPOSTELA, Spain
2Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain
3Hematology Department, University Hospital of Salamanca, Salamanca, Spain
4University Hospital of Salamanca, Salamanca, Spain
5Hospital Clinic de Barcelona- GEMFIN, Barcelona, Spain
6Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
7Molecular Genetics in Oncohematology, Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
8Department of Hematology, Hospital Universitario de Canarias, Tenerife, Spain, La Laguna, Spain
9Hematology Department, Hospital Universitario 12 de Octubre, MADRID, Spain
10Servicio de Hematología y Oncología Médica. H.U. Morales Meseguer. Centro Regional de Hemodonación. Universidad de Murcia. IMIB-Arrixaca, Murcia, Spain
11Hematology Department, Hospital Universitario Vall d'Hebron, Barcelona, Spain
12Hospital Del Mar, Barcelona, Spain
13Dept. of Hematology, Hospital Universitario y Politécnico La Fe, Valencia, Spain
14HU German Trias i Pujol - Institut Català d' Oncologia, Barcelona, Spain
15Hospital Costa del Sol, Malaga, ESP
16Hospital Virgen de la Victoira, Malaga, Spain
17HU Dr. J Trueta - Institut Català d'Oncologia, Girona, Spain
18Hematology, Hospital Universitario de Burgos, BURGOS, ESP
19Hospital Duran i Reynals, Institut Catalá dÓncologia, L´Hospitalet de LLobregat., ESP
20Hospital Universitario Central de Asturias, Oviedo, Spain
21Hematology Department, Hospital de Jerez de la Frontera, Jerez, Spain
22HU Virgen de la Arrixaca, Murcia, Spain
23Hematology Department, Hospital Universitario Marqués de Valdecilla, Santander, Spain
24Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
25Servicio de Hematología, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
26Hospital General de Alicante, Alicante, Spain
27Hospital Universitario La Paz, Madrid, ESP
28Hospital del Mar, Barcelona, Spain
29University Hospital of La Princesa, Madrid, Spain
30Hematology Department, Hospital Clínic, Barcelona, Spain
31HARMONY, Salamanca, Salamanca, Spain
32Department of Hematology, Hospital Clínico Universitario de Valencia, Instituto de Investigación Sanitaria INCLIVA, Valencia, Spain

Purpose

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with a heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its significant morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision making.

Patient and Methods

Registry data were retrieved from patients diagnosed with MF between January 2000 and October 2021 in 59 Spanish institutions. A total of 1,386 patients were randomly divided into a training set (80% of the cohort) and a test set (20% of the cohort). A machine learning (ML) technique (random forests) was used to model overall survival in the training set and validate the results in the test set.

Results

The ML model, based on eight clinical variables determined at diagnosis, achieved high accuracy in predicting overall survival (training set c-index, 0.750; test set c-index, 0.744) and leukemia-free survival (training set c-index, 0.697; test set c-index, 0.703). We confirmed the superiority of the model compared with the IPSS in the prediction of survival at all time points evaluated (Figure 1A). Importantly, the model was superior to the IPSS regardless of age range (<60 or >60 years). We derived 4 equally distributed groups of patients from the model predictions, comprising 25% of patients each. By comparing this distribution with the IPSS risk groups, we observed that circa 50% of patients were reassigned to a different risk group by the ML algorithm (Figure 1B). Additionally, we did not observe any benefit from including MPN driver mutations or high-risk somatic mutations in the model. Furthermore, the ML model outperformed the prognostic accuracy of the MIPSS70 in primary MF and the MYSEC-PM in secondary MF.

Conclusion

The AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) is based exclusively on clinical variables that are readily available in any healthcare facility. This simple model has demonstrated high prognostic accuracy for predicting survival in patients with primary and secondary MF, outperforming other well-established risk scoring systems. An interactive web calculator of this model can be accessed in the following link: https://geneticsoncohematology.com/MF/

Disclosures: Mosquera Orgueira: Roche, Amgen, AstraZeneca, Janssen, Abbvie, Takeda, Brystol, GSK, Pfizer,: Consultancy, Honoraria, Research Funding. Ferrer Marin: Incyte: Research Funding. González de Villambrosia: Incyte: Honoraria; Janssen: Honoraria; Takeda: Honoraria; EusaPharma: Honoraria. Gómez-Casares: Novartis: Speakers Bureau; Pfizer: Speakers Bureau; BMS: Speakers Bureau. Gasior Kabat: Brystol Myers Squibb: Other: Advisory Board ; Eusa Pharma: Speakers Bureau; Novartis: Other: Advisory Board , Speakers Bureau. Hernández-Rivas: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Research Support, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Research support, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Research Support; GSK: Consultancy, Honoraria; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Beigene: Membership on an entity's Board of Directors or advisory committees; Lilly: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Rovi: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees.

*signifies non-member of ASH