Oncology/Hematology

Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs.

BACKGROUND: Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists.

Strategies for Testing Intervention Matching Schemes in Cancer.

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the m...

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and mo...

: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-craf...

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in th...

Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.

OBJECTIVE: To investigate the efficacy of contrast-enhanced computed tomography (CECT)-based radiomics signatures for preoperative prediction of patho...

Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.

OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) o...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic infor...

Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images.

Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical p...

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future.

In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Im...

Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation.

Nuclei segmentation is a vital step for pathological cancer research. It is still an open problem due to some difficulties, such as color inconsistenc...

Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks.

Bone scintigraphy is accepted as an effective diagnostic tool for whole-body examination of bone metastasis. However, the manual analysis of bone scin...

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning ha...

Radiomics and Artificial Intelligence for Renal Mass Characterization.

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quan...

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcino...

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemora...

Primary Robot-assisted Retroperitoneal Lymph Node Dissection for Men with Nonseminomatous Germ Cell Tumor: Experience from a Multi-institutional Cohort.

BACKGROUND: Primary robot-assisted retroperitoneal lymph node dissection (RA-RPLND) for men with nonseminomatous germ cell tumor (NSGCT) is an alterna...

Development and validation of an artificial neural network prognostic model after gastrectomy for gastric carcinoma: An international multicenter cohort study.

BACKGROUND: Recently, artificial neural network (ANN) methods have also been adopted to deal with the complex multidimensional nonlinear relationship ...

The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images.

Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for ...