ArticleCell reports. Medicine2025
A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.PloS one · 2026Pooled it
- Response evaluation to systemic therapy in HCC: Current challenges and future perspectives.Hepatology (Baltimore, Md.) · 2026Review
- DTARNU-Net: Dense Tiered Attention Residual Nested U-Net for CT Liver Tumor Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- Large language model and Gd-EOB-DTPA-enhanced MRI-based risk stratification system for postoperative hepatocellular carcinoma: a multicenter study.European radiology · 2026Article
- Developing a fully automated imaging biomarker for HCC risk assessment via MRI-based tumor segmentation and EPM.Scientific reports · 2026Article
- Transformer-Driven Explainable Deep Learning with Quantitative Attribution Validation for Liver Tumor Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Global Advances in Hepatocellular Carcinoma Research and Therapy in 2025.Cancer innovation · 2026Review
- Deep Learning-Based Liver Tumor Segmentation from Computed Tomography Scans with a Gradient-Enhanced Network.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning in differentiating the colorectal cancer combined with hepatic enhancing nodules: liver metastases vs hemangiomas.Insights into imaging · 2026Article
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineation. Developed on 1,598 computed tomography (CT) scans and 4,908 liver tumors, SALSA demonstrates superior accuracy in tumor identification and volume quantification, outperforming state-of-the-art models and inter-reader agreement among expert radiologists. SALSA achieves a patient-wise detection precision of 99.65%, and 81.72% at lesion level, in the external validation cohorts. Additionally, it exhibits good overlap, achieving a dice similarity coefficient (DSC) of 0.760, outperforming both state-of-the-art and the inter-radiologist assessment. SALSA's automatic quantification of tumor volume proves to have prognostic value across various solid tumors (p = 0.028). SALSA's robust capabilities position it as a potential medical device for automatic cancer detection, staging, and response evaluation.
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