목 적 : 변형 근치적 유방절제술(modified radical mastectomy, MRM)후 흉벽에 전자선 치료를 받는 환자에게 3D-bolus와 step-bolus를 각각 적용하여 유용성을 비교 평가하였다. 대상 및 방법 : 본 연구는 광자선과 전자선을 이용한 역하키스틱법 방식으로 치료계획이 수립된 총 6명의 유방암 환자를 대상으로 하였다. 전방흉벽에 대한 전자선 처방선량은 회당 180 cGy로 3D 프린터(CubeX, 3D systems, USA)로 제작된 3D-bolus와 본원에서 자체 제작한 기존의 stepbolus를 적용하였다. 3D-bolus와 step-bolus에 대한 표면선량은 GAFCHROMIC EBT3 film (International specialty products, USA)을 이용하여, bolus의 다섯 측정지점(iso-center, lateral, medial, superior, and inferior)에 대한 선량 값을 통해 비교 분석하였다. 또한 3D-bolus와 step-bolus 적용에 따른 치료계획을 각각 수립하여 그 결과를 비교하였다. 결 과 : 표면선량은 3D-bolus 적용 시 평균 179.17 cGy이고 step-bolus는 172.02 cGy였다. 처방선량 180 cGy에 대한 평균 값의 오차율은 3D-bolus 적용 시 -0.47%이고 step-bolus는 -4.43%였다. 측정지점 iso-center에서의 오차율은 3D-bolus 적용 시 최대 2.69%의 차이를 보였고, step-bolus는 5.54%였다. 치료의 오차범위는 step-bolus에서 약 6%이고, 3D-bolus는 약 3%였다. 치료계획을 통해 비교한 흉벽의 평균 표적선량은 0.3%로 큰 차이를 나타내지 않았다. 그러나 폐와 심장의 평균 표적선량은 step-bolus에 비해 3D-bolus에서 -11%와 -8%로 감소하였다. 결 론 : 본 연구 결과로 볼 때 흉벽에 대한 피부표면의 접촉면이 고려된 3D-bolus는 step-bolus에 비하여 환자 피부에 잘 밀착되고, 정밀한 흉벽두께 보상이 가능하기 때문에 선량 균일성이 향상됨을 확인하였다. 또한 흉벽에 대한 선량은 동일하지만 인접장기의 선량을 감소시켜 정상조직을 더 많이 보호함으로써 3D-bolus가 임상적으로 유용한 보상체로 사용될 것으로 사료된다.
배경: 패실질 내에 위치한 소결절 및 간유리 병변은 깊이나 크기에 따라서 조직학적 진단이 기존의 방법으로는 어려운 경우가 있다. 이에 연구자는 흉부 전산화 단층 촬영 소견에서 흉강경을 통한 육안 확인이 어려울 것으로 예상되거나 경피 세침 흡인생검술이 부적절하였던 폐실질 내에 위치한 소결절 및 간유리 병변에서 수슬 전에 흉부컴퓨터단층촬영 유도하에 Hook wire를 이용한 위치 선정(CT-guided localization with hook wire)을 시행한 후 흉강경 폐절제술을 시행하였고, 그 결과를 보고 하고자 한다. 대상 및 방법: 2005년 8월부터 2008년 3월까지 흉부 전산화 단층 촬영 소견에서 폐실질내에 위치한 소결절 및 간유리 병변을 보인 18명 환자(남자 13명, 나이 중앙값 56세)를 대상으로 수술 흉부컴퓨터단층촬영 유도하에 Hook wire를 이용한 위치 선정을 시행한 후 흉강경 폐절제술을 시행하였다. Hook wire 위치의 정확도, 개흉술 전환 정도, 수술 시간, 수술 후 합병증, 폐병변의 조직학적 진단의 정확성 등을 분석하였다. 결과: 18명의 환자가 18개의 폐실질 내에 위치한 소결절 및 간유리 병변에 대해 흉강경 폐절제술을 받았다. 수술 전 흉부컴퓨터단층촬영 유도하에 Hook wire를 이용한 위치 선정은 전례에서 성공적으로 시행되었으나, 흉강경 소견에서 wire가 이탈된 경우가 1예 있었다. 수술 전 CT에서 폐 병변 크기의 중앙값은 8 mm (
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.