AIMC Topic: Tumor Burden
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Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Comparable Performance Between Automatic and Manual Laryngeal and Hypopharyngeal Gross Tumor Volume Delineations Validated With Pathology.
International journal of radiation oncology, biology, physics
Jan 7, 2025
PURPOSE: Deep learning is a promising approach to increase reproducibility and time-efficiency of gross tumor volume (GTV) delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, ...
Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases.
Scientific reports
Nov 19, 2024
The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for ...
Generalizable Magnetic Resonance Imaging-based Nasopharyngeal Carcinoma Delineation: Bridging Gaps Across Multiple Centers and Raters With Active Learning.
International journal of radiation oncology, biology, physics
Nov 16, 2024
PURPOSE: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasopharyngeal carcinoma (NPC), addressing the variability and inaccuracy when deploying segmentation models...
Development and validation of a machine-learning model for preoperative risk of gastric gastrointestinal stromal tumors.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Oct 22, 2024
BACKGROUND: Gastrointestinal stromal tumors (GISTs) have malignant potential, and treatment varies according to risk. However, no specific protocols exist for preoperative assessment of the malignant potential of gastric GISTs (gGISTs). This study ai...
Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).
Journal of imaging informatics in medicine
Sep 12, 2024
The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented...
Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning.
Academic radiology
Sep 10, 2024
RATIONALE AND OBJECTIVES: Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiom...
Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sep 3, 2024
BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.
Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection.
Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
Aug 26, 2024
PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrence (ER) after curative-intent resection of neuroendocrine liver metastases (NELMs).
A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.
Cancer science
Aug 9, 2024
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...
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