Oncology/Hematology

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assess...

Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These metho...

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue app...

HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images.

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric...

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Amon...

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis.

BACKGROUND: The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a han...

Fragmentomics features of ovarian cancer.

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the e...

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an e...

Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer.

BACKGROUND: Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standa...

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed mac...

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation...

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic...

Automated segmentation for early detection of uveal melanoma.

OBJECTIVE: Uveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumour...

A hybrid CNN with transfer learning for skin cancer disease detection.

The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the preci...

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutation...

Personalized prediction of postoperative complication and survival among Colorectal Liver Metastases Patients Receiving Simultaneous Resection using machine learning approaches: A multi-center study.

BACKGROUND: To predict clinical important outcomes for colorectal liver metastases (CRLM) patients receiving colorectal resection with simultaneous li...

Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify...

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment...

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which captu...

Role of artificial intelligence in brain tumour imaging.

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain ...