An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via ¹⁸F-FDG PET/CT: a multicenter study.

ABSTRACT:

Purpose

Accurate identification of bone marrow invasion (BMI) is critical for determining the prognosis of and treatment strategies for lymphoma. Although bone marrow biopsy (BMB) is the current gold standard, its invasive nature and sampling errors highlight the necessity for noninvasive alternatives. We aimed to develop and validate an interpretable machine learning model that integrates clinical data, ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) parameters, radiomic features, and deep learning features to predict BMI in lymphoma patients.

Methods

We included 159 newly diagnosed lymphoma patients (118 from Center I and 41 from Center II), excluding those with prior treatments, incomplete data, or under 18 ye

SUBMITTER: Zhu X 

PROVIDER: S-EPMC12261613 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature