Oncology/Hematology

Using Deep Learning to Predict Treatment Response in Patients with Hepatocellular Carcinoma Treated with Y90 Radiation Segmentectomy.

Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose-response threshold, where dose estimates are highly dependent on accurate SPECT/CT acquisition, registration, and reconstruction. Any error can result in distorted absorbed dose distributions and inaccurate estimates of treatment success. This study improves upon the voxel-based ...

[Interdisciplinary case discussions].

BACKGROUND: Interdisciplinary case discussions, especially tumor conferences, represent a large part of the clinical radiologist's daily work. Radiology plays a key role in tumor conferences, since imaging findings have a direct influence on therapy decisions.

An AI-Aided Diagnostic Framework for Hematologic Neoplasms Based on Morphologic Features and Medical Expertise.

A morphologic examination is essential for the diagnosis of hematological diseases. However, its conventional manual operation is time-consuming and l...

Cervical cell classification with deep-learning algorithms.

Cervical cancer is a serious threat to the lives and health of women. The accurate analysis of cervical cell smear images is an important diagnostic b...

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images.

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a st...

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring syst...

Effectiveness of mobile robots collecting vital signs and radiation dose rate for patients receiving Iodine-131 radiotherapy: A randomized clinical trial.

OBJECTIVE: Patients receiving radionuclide 131I treatment expose radiation to others, and there was no clinical trial to verify the effectiveness and ...

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.

BACKGROUND: Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, whil...

Technological advancements in cancer diagnostics: Improvements and limitations.

BACKGROUND: Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal b...

Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients.

The goal of this study was to employ novel deep-learning convolutional-neural-network (CNN) to predict pathological complete response (PCR), residual ...

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been...

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed ...

A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation.

BACKGROUND: Artificial intelligence aided tumor segmentation has been applied in various medical scenarios and showed effectiveness in helping physici...

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.

BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essent...

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and w...

Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer.

The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aid...

Advances in artificial intelligence to predict cancer immunotherapy efficacy.

Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accu...

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images.

IMPORTANCE: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challengi...

Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.

Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation...

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer informat...