AIMC Topic: Liver Neoplasms

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Deciphering tumour tissue organization by 3D electron microscopy and machine learning.

Communications biology
Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organizatio...

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images.

Journal of applied clinical medical physics
Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low ...

Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography.

Medical physics
PURPOSE: Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict hu...

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions.

European radiology
OBJECTIVES: To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using mode...

Novel deep learning-based solution for identification of prognostic subgroups in liver cancer (Hepatocellular carcinoma).

BMC bioinformatics
BACKGROUND: Liver cancer (Hepatocellular carcinoma; HCC) prevalence is increasing and with poor clinical outcome expected it means greater understanding of HCC aetiology is urgently required. This study explored a deep learning solution to detect bio...

A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets.

BMC medical imaging
BACKGROUND: Most existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented datasets with unconventional liver...

A Single Institution's Experience With Robotic Minor and Major Hepatectomy.

The American surgeon
BACKGROUND: Minimally invasive liver resection is gradually becoming the preferred technique to treat liver tumors due its salutary benefits when compared with traditional "open" method. While robotic technology improves surgeon dexterity to better p...

Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
OBJECTIVE: To evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall sur...

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).

Medical physics
PURPOSE: Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board...

Supervised learning based on tumor imaging and biopsy transcriptomics predicts response of hepatocellular carcinoma to transarterial chemoembolization.

Cell reports. Medicine
Although transarterial chemoembolization (TACE) is the most widely used treatment for intermediate-stage, unresectable hepatocellular carcinoma (HCC), it is only effective in a subset of patients. In this study, we combine clinical, radiological, and...