Purpose: To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).
Materials And Methods: For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.
Results: High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).
Conclusions: Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors.
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http://dx.doi.org/10.1016/j.radonc.2012.09.023 | DOI Listing |
Phys Imaging Radiat Oncol
October 2024
Université Paris-Saclay, Gustave Roussy, Inserm, Molecular Radiotherapy and Therapeutic Innovation, U1030, 94800 Villejuif, France.
Background And Purpose: Deep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT.
View Article and Find Full Text PDFBio Protoc
January 2025
Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark.
Magnetic resonance imaging (MRI) is an invaluable method of choice for anatomical and functional in vivo imaging of the brain. Still, accurate delineation of the brain structures remains a crucial task of MR image evaluation. This study presents a novel analytical algorithm developed in MATLAB for the automatic segmentation of cerebrospinal fluid (CSF) spaces in preclinical non-contrast MR images of the mouse brain.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
January 2025
Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Background And Purpose: A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.
Materials And Methods: Twenty prostate cancer patients were enrolled in this prospective clinical study.
Otol Neurotol
January 2025
Department of Otolaryngology-Head and Neck Surgery.
Objective: This study aims to identify 18F-FDG-PET imaging features for improving treatment response evaluation in patients with necrotizing otitis externa (NOE), aiding in the difficult differentiation between sterile inflammation and active infection.
Study Design: Retrospective cohort study.
Setting: Tertiary hospital.
J Man Manip Ther
January 2025
Department of Physical Therapy, Baylor University, Waco, TX, USA.
Objective: To investigate physical therapist adherence to the Academy of Orthopaedic Physical Therapy's (AOPT) clinical practice guidelines (CPGs) for the management of neck and low back pain (LBP) and to compare adherence among varying clinical specializations.
Design: Electronic cross-sectional survey.
Methods: The survey was sent to 17,348 AOPT members and 7,000 American Academy of Orthopaedic Manual Physical Therapists (AAOMPT) members.
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