2026-10-14T13:12:34Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34050069 2026-10-01T18:00:09Z category_7 category_13 category_19 category_21 category_272 category_64 category_128 category_133 portal_5 item_type_1 month_year_10_2026

<p>Feature coefficients in the multivariable prediction model.</p> Tomoaki Ueda (1780057) Kentaro Fukushima (5227799) Sunggi Chi (25157827) Hiroshi Haeno (206672) Goichi Yoshimoto (25157830) Hironori Arai (10964553) Daisuke Ikeda (425740) Motoshi Ichikawa (25157833) Naoto Takahashi (385073) Naoko Hosono (9512304) Takahiro Yamauchi (534724) Takeshi Kondo (600866) Shigeru Kusumoto (785749) Junya Kuroda (81185) Yosuke Minami (464487) Medicine Genetics Pharmacology Biotechnology Environmental Sciences not elsewhere classified Cancer Hematology Plant Biology mutational patterns classified incomplete hematologic recovery improved overall survival aml ); however acute myeloid leukemia 8 %, 52 cri 33 %). type 1 – type 1 (< type 2 framework type 2 (< div >< p differential response patterns >) groups based treatment ngs data azacitidine plus venetoclax cri following aza among 53 first type 1 1 %, treatment response effective treatment ≥ third world cohort tet2 </ response heterogeneity patients treated nras </ line settings line setting line patients generation sequencing frequently observed cytogenetic analyses complete remission clinical variables clinical determinants 53 genes <p>A. ROC curves and AUCs for two classification models (LDA and SVM) predicting response to azacitidine plus venetoclax (Aza/Ven) using baseline clinical factors in 17 evaluable cases lacking dominant type 1 or type 2 mutations. Model performance was evaluated by leave-one-out cross-validation (LOOCV). The LDA model achieved an AUC of 0.914 (95% CI, 0.744–1.000). B. Features and coefficients of the LDA model are shown. AUC, area under the curve; BM, bone marrow; CI, confidence interval; LDA, linear discriminant analysis; NCC, nuclear cell count; PB, peripheral blood; ROC, receiver operating characteristic; SVM, support vector machine; WBC, white blood cell; <i>WT1</i>, Wilms tumor 1.</p> 2026-10-01T18:00:01Z Image Figure 10.1371/journal.pone.0357898.g004 https://figshare.com/articles/figure/_p_Feature_coefficients_in_the_multivariable_prediction_model_p_/34050069 CC BY 4.0