Oncology/Hematology

Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification

Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and m...

An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer

One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer using sophisticated machine learning techniques like Random Forest (RF) and Support Vector Machine (SVM). The Chi-squared test is one feature selection strategy ...

A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction

Longitudinal MRI analysis is crucial for predicting disease outcomes, particularly in chronic conditions like hepatocellular carcinoma (HCC), where ...

AI-Driven Hybrid Ecological Model for Predicting Oncolytic Viral Therapy Dynamics

Oncolytic viral therapy (OVT) is an emerging precision therapy for aggressive and recurrent cancers. However, its clinical efficacy is hindered by t...

Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China.

BACKGROUND: Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognos...

Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction

Measurement quality assurance (QA) practices play a key role in the safe use of Intensity Modulated Radiation Therapies (IMRT) for cancer treatment....

Neuroblastoma: nutritional strategies as supportive care in pediatric oncology

Neuroblastoma, is a highly heterogeneous pediatric tumour and is responsible for 15% of pediatric cancer-related deaths. The clinical outcomes can v...

Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.

OBJECTIVE: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies fo...

Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for...

Efficient Deep Learning-based Forward Solvers for Brain Tumor Growth Models

Glioblastoma, a highly aggressive brain tumor, poses major challenges due to its poor prognosis and high morbidity rates. Partial differential equat...

Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features

Hepatocellular carcinoma is the most spread primary liver cancer across the world ($\sim$80\% of the liver tumors). The gold standard for HCC diagno...

CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classificatio...

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma

Ewing's sarcoma (ES), characterized by a high density of small round blue cells without structural organization, presents a significant health conce...

Lung Cancer detection using Deep Learning

In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in orde...

A Multi-Modal Deep Learning Framework for Pan-Cancer Prognosis

Prognostic task is of great importance as it closely related to the survival analysis of patients, the optimization of treatment plans and the alloc...

Kolmogorov-Arnold Networks and Evolutionary Game Theory for More Personalized Cancer Treatment

Personalized cancer treatment is revolutionizing oncology by leveraging precision medicine and advanced computational techniques to tailor therapies...

A Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier

Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensi...

Artificial intelligence-enabled histology exhibits comparable accuracy to pathologists in assessing histological remission in ulcerative colitis: a systematic review, meta-analysis, and meta-regression.

BACKGROUND AND AIMS: Achieving histological remission is a desirable emerging treatment target in ulcerative colitis (UC), yet its assessment is chall...

Combination of white-light imaging-based and narrow-band imaging-based artificial intelligence models during colonoscopy in patients with ulcerative colitis.

BACKGROUND AND AIMS: The long-term treat-to-target (T2T) approach in ulcerative colitis (UC) aims for endoscopic remission, but variability among endo...

From Images to Insights: Transforming Brain Cancer Diagnosis with Explainable AI

Brain cancer represents a major challenge in medical diagnostics, requisite precise and timely detection for effective treatment. Diagnosis initiall...