AIMC Topic: Pelvic Neoplasms

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Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.

Cancer imaging : the official publication of the International Cancer Imaging Society Mar 13, 2025
BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning.
Retrospective Studies Female Aged Adult Humans Magnetic Resonance Imaging Spinal Neoplasms Pelvic Neoplasms Male Sacrum Middle Aged Deep Learning
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Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.

Radiation oncology (London, England) Feb 4, 2025
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Deep Learning Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Female Male Magnetic Resonance Imaging Pelvis Radiotherapy Planning, Computer-Assisted Pelvic Neoplasms
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Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets.

Clinical oncology (Royal College of Radiologists (Great Britain)) Jan 20, 2025
AIM: Artificial intelligence (AI) based auto-segmentation aids radiation therapy (RT) workflows and is being adopted in clinical environments facilitated by the increased availability of commercial solutions for organs at risk (OARs). In addition, op...
Humans Artificial Intelligence Algorithms Female Pelvic Neoplasms Male Organs at Risk Radiotherapy Planning, Computer-Assisted
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Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Journal of applied clinical medical physics Nov 14, 2024
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Pelvic Neoplasms Radiotherapy Dosage Radiotherapy, Intensity-Modulated Phantoms, Imaging Radiotherapy Planning, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio Abdominal Neoplasms Radiotherapy, Image-Guided Radiographic Image Enhancement Female Male Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Organs at Risk Humans Algorithms Prospective Studies Tomography, X-Ray Computed
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Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

Medical physics Jul 27, 2024
BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic ...
Pelvis Radiotherapy Planning, Computer-Assisted Retrospective Studies Pelvic Neoplasms Tomography, X-Ray Computed Humans Female Male Deep Learning Image Processing, Computer-Assisted Lymph Nodes
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Artificial intelligence in female pelvic oncology: tailoring applications to clinical needs.

European radiology Nov 22, 2023
Female Genital Neoplasms, Female Pelvic Neoplasms Humans Artificial Intelligence
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Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Journal of applied clinical medical physics May 12, 2023
Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extracti...
Humans Male Deep Learning Image Processing, Computer-Assisted Pelvis Organs at Risk Pelvic Neoplasms Urinary Bladder
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Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Scientific reports Nov 16, 2022
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-b...
Soft Tissue Neoplasms Humans Uterine Neoplasms Female Pelvic Neoplasms Deep Learning Sensitivity and Specificity Leiomyoma Diagnosis, Differential Sarcoma
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A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

BMC cancer Mar 17, 2020
BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly ...
Adult Female Pelvic Neoplasms Humans Thoracic Neoplasms Male Aged Neural Networks, Computer Head and Neck Neoplasms Abdominal Neoplasms Middle Aged Aged, 80 and over Artificial Intelligence Positron Emission Tomography Computed Tomography Young Adult Fluorodeoxyglucose F18
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Robot-assisted tumorectomy for an unusual pelvic retroperitoneal leiomyoma: A case report.

Medicine Aug 5, 2022
RATIONALE: Extrauterine leiomyoma occasionally occurs in rare locations with unusual growth patterns, especially pelvic retroperitoneal leiomyoma, which brings great challenges for surgeons to make a diagnosis. It is essential to distinguish benign f...
Female Adult Robotics Pelvis Humans Leiomyoma Retroperitoneal Space Pelvic Neoplasms Retroperitoneal Neoplasms
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