Oncology/Hematology

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification pe...

Vessel and Tension-Free Reconstruction During Robot-Assisted Partial Nephrectomy for Hilar Tumors: "Garland" Technique and Midterm Outcomes.

Robot-assisted partial nephrectomy (RAPN) is increasingly applied to renal hilar tumors. The present study aims to introduce our vessel and tension-free reconstruction technique and discuss the perioperative, functional, and midterm oncologic outcomes of RAPN for hilar tumors in a large cohort. We retrospectively reviewed clinical data of 286 consecutive patients with hilar tumors who underwent ...

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate a...

Artificial intelligence and convolution neural networks assessing mammographic images: a narrative literature review.

Studies have shown that the use of artificial intelligence can reduce errors in medical image assessment. The diagnosis of breast cancer is an essenti...

Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.

PURPOSE: Between 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for ...

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intellig...

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.

Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treat...

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional ne...

Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.

BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head ...

Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories.

BACKGROUND: Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients wit...

Achievability to Extract Specific Date Information for Cancer Research.

Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information as...

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of c...

A Residual Based Attention Model for EEG Based Sleep Staging.

Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and Rapid Eye Movement (REM). It plays an indispensabl...

Artificial intelligence as the next step towards precision pathology.

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-w...

Intracorporeal Studer Pouch Formation with Balbay's Technique Following Robotic Radical Cystectomy for Bladder Cancer: Experience with 22 Cases with Oncologic and Functional Outcomes.

Robot-assisted radical cystectomy (RARC) with intracorporeal Studer pouch formation (ICSPF) is increasingly being performed. Balbay's technique of IC...

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images.

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and...

Alpha Test of Intelligent Machine Learning in Staging Head and Neck Cancer.

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognos...

The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement.

Artificial intelligence (AI) and machine learning (ML) approaches have caught the attention of many in health care. Current literature suggests there ...