Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.

A woman in her early 20s presented with cough and fever. She had undergone an abdominal myomectomy 3 years ago for uterine leiomyoma. Chest CT and positron emission tomography-CT revealed multiple round nodules in both lungs, suggesting metastatic lesions. A CT-guided lung biopsy was performed, and the tumour was diagnosed as pulmonary benign metastasising leiomyoma (PBML) based on pathological analyses. Whole exon capture sequencing of uterine leiomyoma and PBML sections revealed that 13 genes (MCM10, SLC16A9, RAG1, BAZ1A, NLRP2, TRMT61B, CPXM1, NGLY1, SUCLG2, FAM13A, CAGE1, PHTF2 and ZDHHC2) were concurrently present in the two tumours. The patient was prescribed goserelin injections every 4 weeks. The symptoms improved 2 weeks after starting the treatment. The lung nodules considerably decreased in size after three courses of goserelin treatment. The nodular size continues to decrease with the treatment.