Kerner Nadav, Hershkovitz Dov, Trestman Svetlana, Shragai Tamir, Nachmias Hila Lederman, Cohen Yael C, Ziv-Baran Tomer, Avivi Irit
Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
Pathology Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel.
Cancers (Basel). 2025 May 22;17(11):1733. doi: 10.3390/cancers17111733.
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.
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).
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 ( < 0.001), active versus smoldering MM ( = 0.009), the presence of lytic lesions ( = 0.023), and lower hemoglobin levels ( = 0.025).
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.
11号和14号染色体易位[t(11;14)(q13;32)]是多发性骨髓瘤（MM）最常见的原发性易位。携带t(11;14)的患者对BCL-2抑制剂表现出高反应率，这突出了快速检测以指导治疗决策的重要性。虽然荧光原位杂交（FISH）仍然是检测这种异常的金标准，但其应用受到与速度、可及性和成本相关的挑战的限制。
本研究评估了一种基于深度学习的方法，该方法使用来自268例未经治疗的MM患者（147例男性和121例女性）的苏木精-伊红（H&E）染色骨髓活检扫描来检测t(11;14)。
在这些患者中，47例（17.5%）被诊断为冒烟型MM，而218例（81.4%）患有活动性MM，其中22例（8.2%）伴有淀粉样变性。FISH分析在191例（71%）中检测到细胞遗传学异常，73例（27%）中鉴定出t(11;14)，t(11;14)阳性病例中阳性细胞的中位数为26%。在确定性结果中，人工智能算法的灵敏度为88%，特异性为83.1%，准确率为84.3%，受试者操作特征曲线下面积（AUROC）为0.85。骨髓中浆细胞百分比更高（<0.001）、活动性MM与冒烟型MM（=0.009）、存在溶骨性病变（=0.023）以及血红蛋白水平较低（=0.025）对该算法的性能有积极影响。
这些发现表明，这种人工智能方法有助于快速筛查FISH分析，但在MM管理中的临床应用还需要进一步改进。
