Oncology/Hematology

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks...

Machine Learning Methods Uncover Radiomorphologic Dose Patterns in Salivary Glands that Predict Xerostomia in Patients with Head and Neck Cancer.

PURPOSE: Patients with head-and-neck cancer (HNC) may experience xerostomia after radiation therapy (RT), which leads to compromised quality of life. ...

Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier.

Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervica...

Robot assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer.

Worldwide, the standard treatment for locally advanced esophageal cancer with curative intent is perioperative chemotherapy or preoperative chemoradio...

Central Nervous System Peripheral T Cell Lymphoma Manifesting as Lymphomatosis Cerebri That Was Misdiagnosed as Neuro-Behçet's Disease: A Case Report.

BACKGROUND: Lymphomatosis cerebri (LC) is a unique form of primary central nerves lymphoma (PCNSL), which presents as diffuse infiltration of lymphoma...

Dynamic changes in specific anti-L-asparaginase antibodies generation during acute lymphoblastic leukemia treatment.

BACKGROUND: L-asparaginase (L-asp) remains one of the key components of acute lymphoblastic leukemia therapy. Immune reactions to the drug are associa...

High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains.

PURPOSE: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventiona...

imaging of aminopeptidase N activity in hepatocellular carcinoma: a migration model for tumour using an activatable two-photon NIR fluorescent probe.

CD13/aminopeptidase N (APN), which is a zinc-dependent metalloproteinase, plays a vital role in the growth, migration, angiogenesis, and metastasis of...

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient infor...

A reliable method for colorectal cancer prediction based on feature selection and support vector machine.

Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the rel...

Increases in Serial Pretreatment F-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy.

PURPOSE: Quantitative changes in positron emission tomography with computed tomography imaging metrics over serial scans may be predictive biomarkers....

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) pat...

Dosing Oncology Therapeutics in Combination Therapy for Renal Dysfunction: The University of California San Diego Study of Personalized Cancer Therapy to Determine Response and Toxicity (UCSD-PREDICT) Experience.

Introduction Dose reductions are often required to avoid toxicity in combination therapy for advanced cancers, but information on appropriate dose red...

Precision immunoprofiling by image analysis and artificial intelligence.

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This re...

Disparities in the Treatment and Outcome of Stage I Non-Small-Cell Lung Cancer in the 21st Century.

BACKGROUND: African American (AA) individuals are less likely to receive treatment and more likely to die from cancer compared with Caucasian (C) indi...

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial int...

Deep learning in medical imaging and radiation therapy.

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b)...

Automated classification of benign and malignant lesions in F-NaF PET/CT images using machine learning.

PURPOSE: F-NaF PET/CT imaging of bone metastases is confounded by tracer uptake in benign diseases, such as osteoarthritis. The goal of this work was ...

Laplacian regularized low-rank representation for cancer samples clustering.

Cancer samples clustering based on biomolecular data has been becoming an important tool for cancer classification. The recognition of cancer types is...