• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br The aforementioned sub region segmentation is built


    The aforementioned sub-region segmentation is built upon the results of our preliminary study on 21
    patients15, where the Y-27632 bone into three sub-region: iliac bone (ilium, iliac crest), lower pelvis (pubic, ischium, femoral head, and femoral neck), and lumbosacrum. We found that metabolic uptake of iliac and lower pelvic bones similar (p=0.589) yet significantly different from lumbosacral regions (p=0.034, p=0.011, respectively). To simplify the analysis in this study, we integrated the lower pelvic and iliac regions into lower-pelvis-iliac region, as described.
    2.3 Metabolic activity of pelvic bone
    The bone marrow metabolic activity represented by SUV of PET image fused on the CT images. The average uptake of pelvic regions, SUVmean, was measured in whole pelvic bone marrow, as well as LSBM and LPBM. To reduce intra-patient image variations, pelvic SUVmean was normalized by subtracting mean uptake of un-irradiated extra-pelvic bone marrow. Changes in metabolic activity pre-and post-CRT were calculated as follows ∆SUVmean= SUVmean_post-CRT – SUVmean_pre-CRT and correlated with treatment response.
    2.4 Active bone marrow
    Pelvic ABM was characterized in all PET images as the volume having SUV larger than threshold. Where threshold was defined as the mean uptake in the extra-pelvic bone marrow. Sub-regional ABM was also measured for LSBM and LPBM, referred to LS-ABM, and LP-ABM, respectively. We calculated ABM
    volumes and determined the proportion of ABM within each structure: ABM ratio = volume of ABM/ volume of BM. The ABM volumes and their ratios to BM volume were calculated in both pre- and post-CRT, respectively. The CRT-induced ABM changes were calculated as the changes of ABM ratio between pre-CRT and post-CRT, such as ∆ABM= ABMpost-CRT/BMpost-CRT – ABMpre-CRT/BMpre-CRT. Correspondingly, ∆LS-
    ABM and ∆LP-ABM represent the change of ABM ratio between pre-CRT and post-CRT in the lumbosacral and iliac-pubic region, respectively.
    2.5 Dose evaluations:
    The radiation dose was defined as the cumulative dose to bone marrow during the course of RT treatment. We used dose-volume histograms to generate multiple dose-volume point metrics at dose levels 10, 20, 30, 40, 50 Gy. This analysis was performed on multiple volumes, including pelvic TBM, LSBM, LPBM, ABM, and individual structure LS-ABM, and LP-ABM.
    2.6 Hematologic toxicity and blood cell count nadirs:
    All patients were monitored weekly during and after CRT for acute toxicities including but not limited to fatigue, erythema, bloating, urinary urgency, urinary frequency, fecal incontinence, bleeding, dermatitis, nausea, vomiting, constipation, diarrhea, and proctitis and HT graded using CTCAE v. 4. Acute HT is
    represented by the difference of blood cell counts between nadir and baseline: acute HT= Cell counts at nadir – Cell counts at pre-CRT, where cell counts can be WBC, ANC, PLT, LC and Hg. We also used the
    post-CRT HT as an alternative endpoint, post-treatment HT = Cell counts at post-CRT– Cell counts at pre-CRT.
    2.7 Statistical analysis
    An independent-sample T-test was used to analyze baseline differences in age, clinical stages, and Karnofsky Performance Scale (KPS), between patients from both institutions. The Fisher exact test was used to compare categorical variables. We used Shapiro–Wilk statistic to test for normality of variables. The primary image-based response was defined as the change of ratio of the ABM% from pre- to post-treatment. A generalized linear modeling was used to investigate the eventual correlation between dosimetric variables and blood cells nadirs. The post-treatment hematologic toxicity was used as the clinical endpoint for evaluating the dosimetric results. Significant covariates on univariable linear regression analysis were included in the multivariable linear regression model. The Pearson coefficient and multicollinearities index variance inflation factor (VIF) are used to evaluate the effectiveness of input data in the multivariable analysis. The standard error of the fit and Hosmer-Lemeshow (HL) test was employed to evaluate goodness of model fit. The threshold of dosimetric parameter was validated by AUC of ROC analysis. MATLAB Statistical Toolbox Software (version 11.1) and GraphPad Prism (version 7) were employed for analysis.