In nine anesthetized dogs, accuracy of noninvasive measurements of lung weight (W) and gas volume in vivo was determined from volume and density determined by computed tomography (CT) and by rebreathing helium and the soluble gases dimethyl ether (WDME) and acetylene (WC2H2). Reference standards were obtained from the postmortem scale weight of the frozen lungs (Wscale) and compared with the CT lung weights measured in the living dog (WCT-38) and the frozen carcass (WCT-cold). WCT-cold did not significantly differ from Wscale [-2 +/- 9% (SD), P = 0.7]. WCT-cold was 10% greater than WCT-38 (0.10 greater than P greater than 0.05), suggesting an increase in lung weight despite immediately commencing freezing after death. WDME measured 64 +/- 6% and WC2H2 56 +/- 12% of WCT-38. Serial multiple measurements in three dogs over 14 wk showed a coefficient of variation (CV) of 10 +/- 2% for WDME, 18 +/- 2% for WC2H2, 4.1 +/- 0.9% for WCT, 2.6 +/- 0.8% for CT density, and 3.5 +/- 1.6% for functional residual capacity (FRC) by CT. FRC calculated from CT consistently underestimated FRC measured by rebreathing helium by 18 +/- 8% (P less than 0.005). This error, despite good agreement between WCT and Wscale, was explained by underestimation of CT total lung volume and overestimation of lung density by factors known to affect CT readings, such as partial volume effects, beam hardening, and limited number of input signals. These data show that CT scanning can provide serial measurement of the mass, density, and volume of the lungs with a CV in the order of 5%, but the rebreathing of soluble gases gives more than double this variability. Measurements of WDME performed on the same day had a CV of 3 +/- 1%, so that WDME provides a precise noninvasive means to measure lung weight in acute studies.
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http://dx.doi.org/10.1152/jappl.1989.67.1.166 | DOI Listing |
Forensic Sci Med Pathol
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Department of Surgical Sciences, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy.
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Department of Pharmacology-Physiology-Toxicology, Institute of Pharmaceutical and Biological Sciences of Lyon, University Claude Bernard Lyon 1, France.
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View Article and Find Full Text PDFMed Phys
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Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, California, USA.
Background: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement learning (DRL) has recently been introduced to the planning processes of intensity-modulated radiation therapy (IMRT) and brachytherapy for prostate, lung, and cervical cancers. However, existing DRL planning models are built upon the Q-learning framework and rely on weighted linear combinations of clinical metrics for reward calculation.
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BMJ Oncol
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