Oncology/Hematology

Liver tissue classification of en face images by fractal dimension-based support vector machine.

Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is proposed with en face tomographic images. Twenty samples (10 normal and 10 cancerous) were operated from humans and composed of 285 en face tomograph...

Modified facelift approach for posterior segmental mandibulectomy: a blend of oncology and cosmesis.

BACKGROUND: The approach to the mandible for segmental resection in malignant pathologies entails a lip-split, angle-split or visor flap incision with extension of the incision into the neck for performing neck dissection. The modified facelift approach with robot-assisted neck dissection can be used to achieve oncologically safe resections with good cosmesis.

Upstaging to pT3a disease in patients undergoing robotic partial nephrectomy for cT1 kidney cancer: Outcomes and predictors from a multi-institutional dataset.

OBJECTIVES: Surgically treated clinical T1 (cT1) kidney cancer has in general a good prognosis, but there is a risk of upstaging that can potentially ...

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

BACKGROUND: The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant che...

Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis.

Imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. The development of these biomarkers requires advances in bo...

Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer.

BACKGROUND: Colorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy ...

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging.

PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor...

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence.

Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. C...

Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.

DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing...

Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.

BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic ...

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video).

BACKGROUND AND AIMS: The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-li...

Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video).

BACKGROUND AND AIMS: We assessed the preliminary diagnostic accuracy of a recently developed computer-aided detection (CAD) system for detection of Ba...

Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the po...

Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning.

OBJECTIVE: The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinom...

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.

This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multi...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the ...

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy.

PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response t...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patien...

A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional DenseNet.

PURPOSE: Prostate cancer (PCa) is a major health concern in aging males, and proper management of the disease depends on accurately interpreting patho...

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery. The existing workflow for intraoperative diagnosis ...