AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Response Evaluation Criteria in Solid Tumors

Showing 1 to 10 of 11 articles

Clear Filters

Efficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus.

JCO clinical cancer informatics
PURPOSE: Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are crude, heterogeneous, incomplete, and largely unstructured, presenting challenges to effective analyses...

Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.

Cancer discovery
Real-world evidence (RWE), conclusions derived from analysis of patients not treated in clinical trials, is increasingly recognized as an opportunity for discovery, to reduce disparities, and to contribute to regulatory approval. Maximal value of RWE...

Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria.

Thorax
BACKGROUND: In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neu...

Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence.

Korean journal of radiology
Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. T...

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria.

BMC cancer
BACKGROUND: Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and...

Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

European radiology
OBJECTIVES: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis.

Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.

Journal of imaging informatics in medicine
Accurate treatment outcome assessment is crucial in clinical trials. However, due to the image-reading subjectivity, there exist discrepancies among different radiologists. The situation is common in liver cancer due to the complexity of abdominal sc...

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

JCO clinical cancer informatics
PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information ...