Model outperforms UISS/SSIGN, enabling precise ccRCC risk stratification and clinical decision support, and achieves excellent external C-index with robust multi-year RFS prediction.
Abstract
Highlights • Integrated ML framework fuses clinical features and DL-sign for ccRCC RFS prediction.• 3D-ViT outperforms ResNet variants, delivering high external test AUC of 0.846 for DL signature.• Combined nomogram achieves excellent external C-index of 0.910 with robust multi-year RFS prediction.• Model outperforms UISS/SSIGN, enabling precise ccRCC risk stratification and clinical decision support.
Pulmonary metastasis in high‐grade conventional osteosarcoma remains a leading cause of treatment failure and mortality. Traditional monitoring methods are insufficient for early detection. This study aims to develop and validate an interpretable machine learning (ML) model using multi‐dimensional data from electronic...
Jian-Hua Mu, Yi-Tian Wang, Han Liu et al.· Cancer Medicine· 0 citations
Predicting clear cell renal cell carcinoma (ccRCC) pathological grade preoperatively is critical for clinical management. This study aims to evaluate the diagnostic accuracy and clinical utility of machine learning (ML)‐based imaging models.
Yu-Chao Wang, Zhu-Wei Song, Zhaonan Hou et al.· Cancer Medicine· 0 citations
To investigate the clinical characteristics of newly diagnosed multiple myeloma (NDMM) patients with 1q21 gain/amplification (1q21+), construct and validate a progression‐free survival (PFS) prediction model, and explore the clinical implications of model‐based risk stratification.
Yue-Xuan Rao, Shi-You Li, Ao-Yang Yu et al.· Cancer Medicine· 0 citations
Liver cancer is a major life-threatening condition in the world, and early diagnosis is a key factor in increasing patients' survival rates and treatment efficiency. Conventional diagnosis methods involve extensive medical analysis and due to the complexity of clinical data may result in late diagnosis. This research a...
P. P, Sakthi Deepak A· International Journal of All...· 0 citations
Overall, the included studies demonstrated good discriminative performance, suggesting that machine learning-based models may support early identification of high-risk patients and facilitate personalised clinical decision-making.
Background Lymph node metastasis (LNM) is a prevalent feature in papillary thyroid carcinoma (PTC) that significantly influences surgical management and prognosis. Accurate identification of LNM risk is essential for optimizing adjuvant therapy and long-term surveillance strategies in clinical practice. Methods This re...
Xiao-Qin Yuan, Ya-Hui Wu, Yi-Xiang Xing et al.· Frontiers in Endocrinology· 0 citations
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