AIMC Topic: Radiometry

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Machine learning-enhanced normal tissue complication probability modeling for late sciatic nerve toxicity prediction in carbon-ion radiotherapy: model development and clinical validation.

Physics in medicine and biology
To develop a machine learning-enhanced normal tissue complication probability (NTCP) model for predicting late sciatic nerve toxicity (LSNT) in sacrococcygeal chordoma (SC) and locally recurrent rectal cancer (LRRC) patients undergoing carbon-ion rad...

Predicting hematologic toxicity in advanced cervical cancer patients using interpretable machine learning models based on radiomics and dosimetrics.

BMC cancer
BACKGROUND AND OBJECTIVES: Hematologic toxicity (HT) is a common and serious side effect for advanced cervical cancer patients undergoing chemoradiotherapy. Accurately predicting HT can significantly improve patient management and treatment outcomes....

Research on error classification in gamma analysis on the basis of dosimetric feature engineering and deep learning.

Biomedical physics & engineering express
. Gamma analysis serves as a critical safety assurance tool in radiotherapy, yet its broader clinical implementation remains constrained by insufficient error cause determination. To address this limitation, this study proposes a gamma passing rate (...

The dosimetric impacts of ct-based deep learning autocontouring algorithm for prostate cancer radiotherapy planning dosimetric accuracy of DirectORGANS.

BMC urology
PURPOSE: In study, we aimed to dosimetrically evaluate the usability of a new generation autocontouring algorithm (DirectORGANS) that automatically identifies organs and contours them directly in the computed tomography (CT) simulator before creating...

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

Scientific reports
This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point kernels (DPKs) alongside the integration of artificial intelligence (AI) for accurate dose prediction w...

Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared with conventional multiple-time-point image-based dosimetry. Despite numerous advancements, current ST...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

Biomedical physics & engineering express
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

Biomedical physics & engineering express
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...

Uncertainty quantification for CT dosimetry based on 10 281 subjects using automatic image segmentation and fast Monte Carlo calculations.

Medical physics
BACKGROUND: Computed tomography (CT) scans are a major source of medical radiation exposure worldwide. In countries like China, the frequency of CT scans has grown rapidly, thus making available a large volume of organ dose information. With modern c...