Oncology/Hematology

[Application and progress of artificial intelligence in endoscopic diagnosis of superficial esophageal cancer].

China is a country with high incidence of esophageal cancer. Advanced esophageal cancer not only brings serious threat to the health of patients, but also brings heavy economic burden to their families and society. Early diagnosis and treatment of esophageal cancer are always the hot spot in clinical research, and gastroscopy screening is the key point. The development of artificial intelligence i...

Regression on imperfect class labels derived by unsupervised clustering.

Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of the estimated effect parameters. Due to their generality we suggest to address the problem by use of regression calibration or the misclassification ...

Deep learning for biological age estimation.

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a com...

Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies.

OBJECTIVES: Clinical flow cytometry is laborious, time-consuming, and expensive given the need for data review by highly trained personnel such as tec...

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks.

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) i...

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently m...

Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variabil...

Radiomics and deep learning in liver diseases.

Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagno...

Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.

Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advan...

External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases.

PURPOSE: The Bone Metastases Ensemble Trees for Survival (BMETS) model uses a machine learning algorithm to estimate survival time following consultat...

Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases.

BACKGROUND/AIM: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called B...

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these...

Brain metastasis detection using machine learning: a systematic review and meta-analysis.

BACKGROUND: Accurate detection of brain metastasis (BM) is important for cancer patients. We aimed to systematically review the performance and qualit...

Generative adversarial network based data augmentation to improve cervical cell classification model.

The survival rate of cervical cancer can be improved by the early screening. However, the screening is a heavy task for pathologists. Thus, automatic ...

Deep learning for differentiation of benign and malignant solid liver lesions on ultrasonography.

PURPOSE: The ability to reliably distinguish benign from malignant solid liver lesions on ultrasonography can increase access, decrease costs, and hel...

[Evidence for robotics in oncological pancreatic surgery].

Robotic surgical procedures have been implemented and have become an important development in pancreatic surgery with an increasing acceptance worldwi...

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial...

Robot-assisted laparoscopic resection of a pelvic solitary fibrous tumor.

Solitary fibrous tumor (SFT) is a rare soft tissue neoplasm of mesenchymal origin. SFT is most commonly located in the thoracic cavity (in approximate...

Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.

Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of thi...

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neu...