Oncology/Hematology

Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.

PURPOSE: We developed a system to automate analysis of the clinical oncology scientific literature from bibliographic databases and match articles to specific patient cohorts to answer specific questions regarding the efficacy of a treatment. The approach attempts to replicate a clinician's mental processes when reviewing published literature in the context of a patient case. We describe the syste...

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictiv...

Artificial intelligence in upper GI endoscopy - current status, challenges and future promise.

White-light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, m...

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a l...

[A Case of Locally Advanced Rectal Cancer Treated by Robot Assisted Intersphincteric Resection after Neoadjuvant Chemotherapy].

We present a case of locally advanced rectal cancer(LARC)treated by robot assisted intersphincteric resection(ISR)and lateral lymph node dissection(LL...

Detection of Lung Cancer on Computed Tomography Using Artificial Intelligence Applications Developed by Deep Learning Methods and the Contribution of Deep Learning to the Classification of Lung Carcinoma.

BACKGROUND: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effectiv...

Overcoming Interpretability in Deep Learning Cancer Classification.

Since its inception, deep learning has revolutionized the field of machine learning and data-driven science. One such data-driven science to be transf...

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer.

About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite dif...

Decision support systems for the prediction of lymph node involvement in early breast cancer.

The prediction of lymph node involvement represents an important task which could reduce unnecessary surgery and improve the definition of oncological...

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient...

Clinical Databases for Breast Cancer Research.

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified i...

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinica...

The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of ...

Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images.

Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by p...

Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally hig...

[A Study on Radiation Dermatitis Grading Support System Based on Deep Learning by Hybrid Generation Method].

PURPOSE: Radiation dermatitis is one of the most common adverse events in patients undergoing radiotherapy. However, the objective evaluation of this ...

Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcino...

[Automation of Damage Detection and Damage Area Measurement of X-ray Protective Clothing Using Deep Learning].

PURPOSE: Damage to shielding sheets on X-ray protective clothing may be a cause of increased radiation exposure. To prevent increased radiation exposu...

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machi...

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data.

Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible com...