predicting-treatment-response-to-image-guided-therapies-using-machine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. The objective of this ...

Yale School of Medicine

To compare the ability of single- vs. multi-lesion assessment on baseline MRI using 1D- and 3D-based measurements to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC) before transarterial chemoembolization (TACE).

3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival.

Sorafenib in combination with transarterial chemoembolization for the treatment of hepatocellular carcinoma.

Intra-arterial therapies (IATs) play an integral role in the management of unresectable hepatocellular carcinoma and liver metastases. The ability to accurately assess tumor response to intra-arterial therapies is crucial for clinical management. Several one- and two-dimensional manual imaging-based response assessment techniques, based both on tumor size or enhancement, have shown to be highly subjective and merely surrogate for the actual tumor as a whole. Areas covered: Given the currently existing literature, we will discuss all available tumor assessment techniques and criteria for liver cancer with a strong emphasis on 3D quantitative imaging biomarkers of tumor response in this review. Expert commentary: The growing role of information technology in medicine has brought about the advent of software-assisted, segmentation-based assessment techniques that address the outstanding issues of a subjective reader and provide for more accurate assessment techniques for the locally treated lesions. Three-dimensional quantitative tumor assessment techniques are superior to one- and two-dimensional measurements. This allows for treatment alterations and more precise targeting, potentially resulting in improved patient outcome.

Intra-arterial therapies for liver cancer: assessing tumor response.

To investigate whether bevacizumab compromises early response assessment after Transarterial Chemoembolization (TACE) in patients with hepatocellular carcinoma by 3D quantitative European Association for the Study of the Liver (qEASL) criteria in comparison to other imaging-based criteria.

The impact of antiangiogenic therapy combined with Transarterial Chemoembolization on enhancement based quantitative tumor response assessment in patients with hepatocellular carcinoma.

Portal vein embolization (PVE) is applied in patients with extended oncologic liver disease to induce hyperplasia of the future liver remnant and make resection feasible. Ultrasound (US) guidance is the gold standard for percutaneous portal vein access. This study evaluated feasibility and safety of C-arm cone beam computed tomography (CBCT) for needle guidance.

C-Arm Cone Beam CT for Intraprocedural Image Fusion and 3D Guidance in Portal Vein Embolization (PVE).

The aim of this study was to evaluate the feasibility of cone-beam computed tomography (CBCT)-based real-time 3-D guidance of TIPSS placement and its positioning compared to standard guiding methods.

Clinical Experience with Real-Time 3-D Guidance Based on C-Arm-Acquired Cone-Beam CT (CBCT) in Transjugular Intrahepatic Portosystemic Stent Shunt (TIPSS) Placement.

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Julius Chapiro

Department of Radiology and Biomedical Imaging

Yale School of Medicine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Julius Chapiro

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Radiology and Biomedical Imaging

Yale School of Medicine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Department of Radiology and Biomedical Imaging