Structural basis for the in vitro efficacy of nirmatrelvir against SARS-CoV-2 variants
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Engineering topics
Publications and source records attributed to Craig, Tim.
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The magnitude of bladder filling variation during bladder cancer radiation therapy varies considerably between patients. Population-based approaches for planning target volume (PTV) margin calculation may be suboptimal for this disease site, and a strategy for personalizing PTV margins for each patient may be particularly beneficial. The purpose of this study was to identify the optimal number and sampling pattern of cone beam CT image data sets that are required when generating personalized PTV margins for whole bladder (WB) and partial bladder (PB) radiation therapy.
Purpose: To identify risk factors associated with a decline in liver function after stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma. Methods and Materials: Data were analyzed from patients with hepatocellular carcinoma treated on clinical trials of 6-fraction SBRT. Liver toxicity was defined as an increase in Child-Pugh (CP) score ≥2 three months after SBRT. Clinical factors, SBRT details, and liver dose-volume histogram (DVH) parameters were tested for association with toxicity using logistic regression. CP class B patients were analyzed separately. Results: Among CP class A patients, 101 were evaluable, with a baseline score of A5 (72%) or A6 (28%). Fifty-three percent had portal vein thrombus. The median liver volume was 1286 cc (range, 766-3967 cc), and the median prescribed dose was 36 Gy (range, 27-54 Gy). Toxicity was seen in 26 patients (26%). Thrombus, baseline CP of A6, and lower platelet count were associated with toxicity on univariate analysis, as were several liver DVH-based parameters. Absolute and spared liver volumes were not significant. On multivariate analysis for CP class A patients, significant associations were found for baseline CP score of A6 (odds ratio [OR], 4.85), lower platelet count (OR, 0.90; median, 108 × 10{sup 9}/L vs 150 × 10{sup 9}/L), higher mean liver dose (OR, 1.33; median, 16.9 Gy vs 14.7 Gy), and higher dose to 800 cc of liver (OR, 1.11; median, 14.3 Gy vs 6.0 Gy). With 13 CP-B7 patients included or when dose to 800 cc of liver was replaced with other DVH parameters (eg, dose to 700 or 900 cc of liver) in the multivariate analysis, effective volume and portal vein thrombus were associated with an increased risk. Conclusions: Baseline CP scores and higher liver doses (eg, mean dose, effective volume, doses to 700-900 cc) were strongly associated with liver function decline 3 months after SBRT. A lower baseline platelet count and portal vein thrombus were also associated with an increased risk.
Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.