Abstract
BACKGROUND: Translocation of chromosomes 11 and 14 [t(11;14)(q13;32)] is the most common primary translocation in multiple myeloma (MM). Patients harboring t(11;14) exhibit high response rates to BCL-2 inhibitors, underscoring the importance of rapid detection to guide treatment decisions. While fluorescence in situ hybridization (FISH) remains the gold standard for detecting this abnormality, its application is limited by challenges related to speed, accessibility, and cost. OBJECTIVES AND METHODS: This study evaluated a deep-learning-based method for detecting t(11;14) using scans of H&E-stained bone marrow biopsies from 268 untreated MM patients (147 males and 121 females). RESULTS: Among these patients, 47 (17.5%) were diagnosed with smoldering MM, while 218 (81.4%) had active MM, including 22 (8.2%) that presented with concomitant amyloidosis. FISH analysis detected cytogenetic abnormalities in 191 cases (71%), with t(11;14) identified in 73 cases (27%) and a median of 26% positive cells in t(11;14)-positive cases. The AI algorithm achieved 88% sensitivity, 83.1% specificity, 84.3% accuracy, and an area under the receiver operating characteristic curve (AUROC) of 0.85 in conclusive results. The algorithm's performance was positively influenced by a higher percentage of plasma cells in the bone marrow (p < 0.001), active versus smoldering MM (p = 0.009), the presence of lytic lesions (p = 0.023), and lower hemoglobin levels (p = 0.025). CONCLUSIONS: These findings suggest that this AI approach could facilitate rapid screening for FISH analysis, although further enhancements are necessary for its clinical application in MM management.