Modern radiotherapy linacs often use carbon fibre for their couch tops due to its radio translucent properties. Beam attenuation by the couches is often ignored during planning and MU calculation. This work examines beam attenuation and loss of "skin sparing" (dose build up region) when various photon beams transit either the MedTec (Siemens) or Medical Intelligence (Elekta) couches. Additionally, measured doses were compared to CMS treatment planning system (XiO version 4.33.02) predictions. We found the two couches to have different structures, resulting in different attenuation signatures as a function of gantry angle. For normal beam incidence the Siemens and Elekta couches had radiological thicknesses of 4.5 mm and 6.0 mm, respectively. For a normal incidence 10×10 cm 6MV beam the surface dose after couch transmission was 93% vs. 83% for Elekta and Siemens, respectively. Conversely, the increased mass on the lateral edge of the Siemens couch resulted in a maximum attenuation (6 MV 5×5 cm beams) of 8% compared to 5% by the Elekta couch. Incorporating the treatment couch as part of the patient planning CT allowed the CMS TPS model to calculate couch attenuation within 1% of measurement, except at the very edge of the Siemens couch, where the attenuation is strongly gantry angle dependent. The CMS beam model was also able to predict the loss of skin sparing within 1%. In conclusion, the two patient couches are different, but both can significantly affect patient dose which can be accounted for in the CMS TPS.
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http://dx.doi.org/10.1118/1.2965951 | DOI Listing |
Clin Transl Radiat Oncol
March 2025
University Medical Center Utrecht, Department of Radiation Oncology, Utrecht, the Netherlands.
Background And Purpose: This study assessed the treatment time of online adaptive (i.e. Adapt-to-Shape, ATS) and virtual couch shift (i.
View Article and Find Full Text PDFJCO Oncol Pract
January 2025
Mayo Clinic, Department of Internal Medicine, Division of Oncology, Rochester, MN.
Purpose: Over 50% of households in the United States have at least one musician-many musicians are also breast cancer survivors. This group has not been well studied, and given the level of fine sensory-motor skill required for musicianship, we hypothesized that musicians experience unique manifestations of breast cancer treatment toxicities.
Methods: A nine-item Musical Toxicity Questionnaire (MTQ) was distributed to patients who had consented to participate in the Mayo Clinic Breast Cancer Registry.
Cancer Sci
January 2025
Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
To report clinical outcomes following highly hypofractionated biaxially rotational dynamic radiation therapy (BROAD-RT), a unique radiation therapy method that facilitates non-coplanar volumetric-modulated arc therapy (VMAT) without the need to rotate the couch or reposition the patient, for high-risk prostate cancer (PCa) with simultaneous integrated boost (SIB) for intra-prostatic dominant lesions (IPDLs), we performed a single-center prospective pilot study. In this study, patients with high-risk PCa according to the D'Amico classification or those with cT3aN0M0 PCa were eligible. VMAT was performed using BROAD-RT, and a dose of 54 Gy in 15 fractions was prescribed for the prostate in combination with SIB for IPDLs at a dose of 57 Gy in 15 fractions.
View Article and Find Full Text PDFBreast Cancer Res Treat
January 2025
Center for Discovery and Innovation (CDI), Hackensack Meridian Health, Nutley, NJ, USA.
Purpose: To study the association between clinicopathologic characteristics of ductal carcinoma in situ (DCIS) and risk of subsequent invasive breast cancer (IBC).
Methods: We conducted a case-control study nested in a multicenter, population-based cohort of 8175 women aged ≥ 18 years with DCIS diagnosed between 1987 and 2016 and followed for a median duration of 83 months. Cases (n = 497) were women with a first diagnosis of DCIS who developed a subsequent IBC ≥ 6 months later; controls (2/case; n = 959) were matched to cases on age at and calendar year of DCIS diagnosis.
ArXiv
July 2024
Department of Pathology and the Division of Clinical Informatics, Department of Medicine, BIDMC and with Harvard Medical School, Boston, MA 02215.
In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice-maximizing dataset size and class balance-does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly.
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