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Neoplasm Recurrence, Local

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Multimodal deep learning improves recurrence risk prediction in pediatric low-grade gliomas.

Neuro-oncology
BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning (DL) of magnetic resonance imaging (MRI) tumor f...

Machine learning prediction of early recurrence after surgery for gallbladder cancer.

The British journal of surgery
BACKGROUND: Gallbladder cancer is often associated with poor prognosis, especially when patients experience early recurrence after surgery. Machine learning may improve prediction accuracy by analysing complex non-linear relationships. The aim of thi...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

The journal of pathology. Clinical research
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. W...

[A lightweight recurrence prediction model for high grade serous ovarian cancer based on hierarchical transformer fusion metadata].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether ...

On Entity Embeddings for Ordinal Features as Representation Learning in Recurrence Prediction of Urothelial Bladder Cancer.

Studies in health technology and informatics
BACKGROUND: Urothelial Bladder Cancer (UBC) is a common cancer with a high risk of recurrence, which is influenced by the TNM classification, grading, age, and other factors. Recent studies demonstrate reliable and accurate recurrence prediction usin...

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

Cancer medicine
PURPOSE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining c...

Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.

Cancer research communications
UNLABELLED: Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (M...

[Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tai...