Background: Accurate identification and classification of white blood cell (WBC) subtypes in peripheral blood smears (PBS) is essential for the diagnosis and monitoring of hematological malignancies, including leukemia. Conventional manual microscopy, although clinically established, is labor-intensive and subject to inter- and intra-observer variability. Deep learning-based object detection offers a route to automation, yet most prior studies are limited by small datasets, restricted cell taxonomies, or single-microscope acquisition. This study evaluates a YOLOv11-large (YOLOv11L) detector for simultaneous localization and classification of 13 leukemia-relevant WBC subtypes plus an artifact class (14 classes total), trained on the large-scale, multi-domain, open-source LeukemiaAttri dataset. Methods: From the LeukemiaAttri dataset, 18,664 annotated images (67,347 objects) acquired at 40× and 100× magnification were partitioned by stratified sampling into training (70%), validation (15%), and test (15%) sets. The training set was expanded to 65,785 images through extensive geometric, photometric, and AugMix augmentation. A YOLOv11L model pretrained on MS COCO was fine-tuned for 250 epochs (640 × 640 input) on a single NVIDIA H200 SXM GPU, using an auto-selected optimizer (momentum 0.9; weight decay 5 × 10⁻⁴), automatic mixed precision (AMP), and mosaic augmentation for the first 240 epochs. Results: On an internal held-out test set, the model achieved an mAP50 of 93.9%, mAP50-95 of 77.9%, precision of 94.1%, recall of 88.8%, and an F1 score of 0.913, with similar performance in the validation and test sets. Class-wise average precision (AP) ranged from 89.3% (monocyte) to 98.2% (monoblast), confirming consistent detection across morphologically diverse subtypes. Conclusions: The YOLOv11L detector achieved high performance across all 14 categories on the internal test set, with metrics exceeding those previously reported for subset-specific baselines. These findings support further evaluation of the model as a decision-support tool for peripheral blood smear analysis. External validation is required to determine its clinical utility and generalizability.

Background: Synovial sarcoma (SS) is a rare, aggressive malignant neoplasm with a high rate of metastasis despite its indolent nature. A distinct SS18::SSX fusion oncogene characterizes SS. The monophasic subtype is the most common and consists of spindle cells. Due to its rarity and subtle clinical presentation, SS can be challenging to identify, leading to misdiagnosis or delays in treatment. The present case highlights the importance of awareness and vigilance among healthcare providers when encountering slow-growing, seemingly benign soft-tissue tumors. Case presentation: A 43-year-old Haitian man experiencing homelessness with a history of psychiatric disorder, tuberculosis, and substance use presented to the emergency department with right leg pain and intermittent chest pain. Physical examination revealed a large, tender mass on his calf, initially considered benign in past encounters. However, further imaging (CT and MRI scans) showed a large 14.3 cm, lobulated, heterogeneous soleus intramuscular mass with multiple peripheral and internal calcifications suggestive of soft-tissue sarcoma. In addition, a CT chest revealed multifocal pulmonary nodules highly suspicious for metastatic disease. Histologic examination of lung biopsies showed cells positive for vimentin, BCL-2, and TLE-1, consistent with monophasic SS; molecular confirmation of the SS18::SSX fusion was not obtained. Systemic AIM chemotherapy (doxorubicin, ifosfamide, and mesna) was planned. However, the patient declined further care and was lost to follow-up. He was subsequently reported to have died from pulmonary complications of metastatic disease approximately 6 months later. Conclusions: This case highlights the complexity of diagnosing SS, given its subtle clinical findings, slow growth, and atypical presentation. Multidisciplinary care is essential for optimizing diagnosis and treatment outcomes. Prompt recognition and comprehensive care can improve patients’ prognosis.