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January 14, 2026Journal of Health Sciences and Medicine0 citationsOpen Access

Prognostic value of the controlling nutritional status (CONUT) and the Prognostic Nutritional Index (PNI) scores in mycosis fungoides

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GKGökçe Işıl KurmuşEYErkin Berkay YılmazHKHanife Karataş

Key Points

  • To evaluate the prognostic significance of the CONUT and PNI scores in mycosis fungoides.
  • Retrospective study of 130 patients with histologically confirmed mycosis fungoides.
  • Assessment of demographic, clinical, and laboratory parameters at baseline.
  • Calculation of CONUT and PNI using serum albumin, total cholesterol, and lymphocyte count.
  • CONUT and PNI scores correlate with disease stage and clinical progression.
  • Identified potential biomarkers for predicting advanced disease in mycosis fungoides.

Abstract

Aims: Mycosis fungoides (MF) is the most prevalent cutaneous T-cell lymphoma, exhibiting an indolent but potentially progressive course. Although the tumor-node-metastasis-blood (TNMB) system is the standard prognostic tool, survival outcomes vary among patients with the same stage, underscoring the need for complementary biomarkers. The Controlling Nutritional Status (CONUT) score and the Prognostic Nutritional Index (PNI) are composite indicators reflecting systemic nutritional and immune status. Their prognostic value has been established in hematologic malignancies, but their role in MF remains unclear. To evaluate the prognostic significance of CONUT and PNI scores in MF and to examine their associations with disease stage, inflammatory markers, and clinical progression. Methods: This retrospective study included 130 patients with histologically confirmed MF. Demographic, clinical, and laboratory parameters were collected at baseline. CONUT and PNI were calculated using serum albumin, total cholesterol, and lymphocyte count. Patients were classified into early (IA-IIA) and advanced (≥IIB) stages per ISCL/EORTC criteria. Comparative analyses, Spearman correlations, ROC curve analyses, and multivariate logistic regressions were performed to determine predictive factors for advanced disease (p

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Cite This Study

Kurmuş et al. (2026) studied this question.

synapsesocial.com/papers/6966f31d13bf7a6f02c00b95https://doi.org/10.32322/jhsm.1814860
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