Other Cancers

LILRB3 inhibition reverses immunosuppression in glioma: a nanoparticle-based therapeutic strategy.

Glioma is a highly aggressive brain tumor characterized by a profoundly immunosuppressive tumor microenvironment dominated by M2-polarized tumor-associated macrophages (TAMs). This study utilized machine learning and single-cell multi-omics technologies to investigate the role of LILRB3 in glioma immunosuppression and evaluated the therapeutic potential of BM@nano-siLILRB3. In a tumor-bearing mous...

Mapping intratumoral heterogeneity through PET-derived washout and deep learning after proton therapy.

The distribution of produced isotopes during proton therapy can be imaged with Positron Emission Tomography (PET) to verify dose delivery. However, biological washout, driven by tissue-dependent processes such as perfusion and cellular metabolism, reduces PET signal-to-noise ratio (SNR) and limits quantitative analysis. In this work, we propose an uncertainty-aware deep learning framework to impro...

FDG PET in pulmonary sarcoidosis: An update and future aspects.

Pulmonary sarcoidosis is a heterogeneous granulomatous disease with an unpredictable clinical course, in which accurate assessment of disease activity...

Mapping whole-organism genetic comorbidities across model Species using unified ontologies.

Understanding how genetic variation contributes to organism-wide phenotypes is critical for identifying mechanisms of disease. Here, we present a comp...

Circulating Immune Profiles in ACTH-Dependent Cushing's Syndrome.

OBJECTIVE: ACTH-dependent Cushing's syndrome (CS) causes profound immune dysfunction and severe infections. This study aimed to characterize immune ce...

A deep learning-based multimodal model with automated body composition analysis predicts prognosis in advanced clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with a high risk of postoperative recurrence. Body composition has emerged as a pr...

Integration of deep learning and radiomic features from multiplex immunohistochemistry images for reproducible Multi-Outcome prediction in a Multi-Center study of colorectal cancer.

OBJECTIVE: To develop and validate a robust, multimodal machine learning framework integrating radiomic and deep learning features from multiplex immu...

Co-repression of Yap1 and Sox9 Abrogates Established Cholangiocarcinoma by Eliminating Transcriptional Compensation.

BACKGROUND/AIMS: Intrahepatic cholangiocarcinoma (iCCA) represents an unmet clinical need due to its increasing incidence, aggressive biology, and lim...

Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid carcinoma.

RATIONALE AND OBJECTIVE: The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan...

Construction of an interpretable multimodal image model for differentiating T1-stage nasopharyngeal carcinoma from benign hyperplasia.

BACKGROUND AND OBJECTIVE: Differentiating T1-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) is challenging. This study aims to cons...

Predicting neoadjuvant therapy response in breast cancer from preoperative biopsy via spatial-semantic-differential learning and interpretable clinicopathological-guided fusion.

Predicting pathological complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer remains challenging due to high tumor heterogeneity and ...

Prediction of post-stroke brain swelling using biomechanical modelling and deep neural networks.

Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. Brain swelling volume...

Constructing a corpus of hematologic pathology notes for the fine-tuning of BERT models for named entity recognition.

BACKGROUND: Microscopic, immunologic, and chemical testing play a major role in the diagnostic process of hematologic cancer patients. Pathologists re...

Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.

Lung cancer (LC) is one of the leading causes of death globally. Early detection is essential for saving lives and ensuring effective treatment for pa...

Clinical and immunological significance of FOXO1 as a biomarker of improved response to immune checkpoint plus tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma.

BACKGROUND: Predicting response to immune checkpoint inhibitor plus tyrosine kinase inhibitor (IO+TKI) therapy in metastatic renal cell carcinoma (mRC...

Gut microbial extracellular vesicles modulate the development of metabolic dysfunction-associated steatohepatitis through the gut-liver axis.

Metabolic dysfunction-associated steatohepatitis (MASH) represents a growing global health challenge due to its propensity to progress to irreversible...

Artificial Intelligence-Assisted reflectance confocal microscopy for Real-Time intraoperative margin assessment in oral squamous cell carcinoma.

BACKGROUND: Oral cavity squamous cell carcinoma (OSCC) is a global health burden, where negative margins are essential for reducing recurrence and imp...

Reconstructing tumor tissues in 3D: From organoids to bioengineered niches.

Tumor tissue engineering, integrating organoid, microfluidic, and biofabrication technologies, has opened new avenues for cancer research. Leveraging ...

Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.

UNLABELLED: Metastasis is the leading cause of cancer deaths. To develop strategies for intercepting metastatic progression, a better understanding of...

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise...