• The Research Journal of the Costume Culture
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• v.14 no.5
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• pp.840-849
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• 2006

Breast volume has been approximately estimated under the assumption that the shape of breast is a corn. However, women's breast is more like a bulged bag in reality. In this paper, three methods of breast volume estimation were compared to find out the more accurate method. The shape of the breast is assumed as a hemisphere in the first estimation method and a corn in the second one. In the third method, arc along the cross sectional shape of breast was utilized in the calculation. Comparisons among the methods were made using the actual 3D volume measurement of thirty seven women's breast. As results, the third method was the best one for the normal breast type, especially for the lower part of the breast ($R^2=0.74$) which is the crucial design parameter of the brassiere. Assumption of the shape of breast as a corn was reasonably acceptable when the breast is sagged. It was expected that when women wore brassiere, the accuracy of the third method would increase more, since the shape of breast becomes more symmetrical.

• Archives of Plastic Surgery
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• v.40 no.3
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• pp.203-208
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• 2013

Background Preoperative volume assessment is useful in breast reconstruction. Magnetic resonance imaging (MRI) and mammography are commonly available to reconstructive surgeons in the care of a patient with breast cancer. This study aimed to verify the accuracy of breast volume measured by MRI, and to identify any factor affecting the relationship between measured breast volume and actual breast weight to derive a new model for accurate breast volume estimation. Methods From January 2012 to January 2013, a retrospective review was performed on a total of 101 breasts from 99 patients who had undergone total mastectomy. The mastectomy specimen weight was obtained for each breast. Mammographic and MRI data were used to estimate the volume and density. A standard statistical analysis was performed. Results The mean mastectomy specimen weight was 340.8 g (range, 95 to 795 g). The mean MRI-estimated volume was $322.2mL^3$. When divided into three groups by the "difference percentage value", the underestimated group showed a significantly higher fibroglandular volume, higher percent density, and included significantly more Breast Imaging, Reporting and Data System mammographic density grade 4 breasts than the other groups. We derived a new model considering both fibroglandular tissue volume and fat tissue volume for accurate breast volume estimation. Conclusions MRI-based breast volume assessment showed a significant correlation with actual breast weight; however, in the case of dense breasts, the reconstructive surgeon should note that the mastectomy specimen weight tends to overestimate the volume. We suggested a new model for accurate breast volume assessment considering fibroglandular and fat tissue volume.

• Archives of Plastic Surgery
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• v.45 no.4
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• pp.333-339
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• 2018

Background Flap volume is an important factor for obtaining satisfactory symmetry in breast reconstruction with a transverse rectus abdominis myocutaneous (TRAM) free flap. We aimed to develop an easy and simple method to estimate flap volume. Methods We performed a preoperative estimation of the TRAM flap volume in five patients with breast cancer who underwent 2-stage breast reconstruction following an immediate tissue expander operation after a simple mastectomy. We measured the height and width of each flap zone using a ruler and measured the tissue thickness by ultrasound. The volume of each zone, approximated as a triangular or square prism, was then calculated. The zone volumes were summed to obtain the total calculated volume of the TRAM flap. We then determined the width of zone II, so that the calculated flap volume was equal to the required flap volume ($1.2{\times}1.05{\times}$the weight of the resected mastectomy tissue). The TRAM flap was transferred vertically so that zone III was located on the upper side, and zone II was trimmed in the sitting position after vascular anastomosis. We compared the estimated flap width of zone II (=X) with the actual flap width of zone II. Results X was similar to the actual measured width. Accurate volume replacement with the TRAM flap resulted in good symmetry in all cases. Conclusions The volume of a free TRAM flap can be straightforwardly estimated preoperatively using the method presented here, with ultrasound, ruler, and simple calculations, and this technique may help reduced the time required for precise flap tailoring.

• Archives of Plastic Surgery
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• v.33 no.5
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• pp.676-679
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• 2006

Purpose: Breast volume is one of the crucial measurements in preoperative planning and postoperative evaluation of the results in mammoplasty. There are several methods suggested by different authors, but there is still no commonly accepted standard methods for measuring breast volume. To help the surgeons to base their estimation on an objective evaluation, we developed a simple method using Polyethylene bag and plaster molding. Methods: After Polyethylene bags were put in suitable size on both breasts of the patient in upright position, silk plaster was molded on the surface evenly. Then molds can be obtained after marking boundaries of breasts with a pen. Breast volume measurement can be done by filling water in the molds and measuring it. Moreover, postoperative design for natural skin brassier was possible using the molds. Results: This method was applied to 2 patients for reduction mammoplasty and the breast volume measurement was simple, hygienic and accurate, done within 10 minutes. Conclusion: This method using Polyethylene bag and plaster molding has several advantages. 1. It is comparatively accurate regardless of the size and shape of patient's breasts in upright position. 2. Measurement time is short and inconvenience and shame of patients can be reduced by making molds after putting on Polyethylene bags. 3. It is relatively economical and uses easily available hygienic materials. 4. The postoperative shape and volume of breasts can be predicted by using molds preoperatively.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7785-7788
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• 2015

Utilization of high energy photons (>10MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6MV photons and then these calculations were repeated ten times with incorporating 18MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV ($D_{max}$) and the volume of CTV which covered with 95% Isodose line ($V_{CTV,95%IDL}$) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18MV photons was defined as the intersection point of $D_{max}$ and $V_{CTV,95%IDL}$ graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.83-92
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• 2023

This study was established to investigate the site environment of mixed forests in Korea and to estimate the growth and yield of stands using national forest resources inventory data. The growth of mixed forests was derived by applying the Chapman-Richards model with diameter at breast height (DBH), height, and cross-sectional area at breast height (BA), and the yield of mixed forests was derived by applying stepwise regression analysis with factors such as cross-sectional area at breast height, site index (SI), age, and standing tree density per ha. Mixed forests were found to be growing in various locations. By climate zone, more than half of them were distributed in the temperate central region. By altitude, about 62% were distributed at 101-400 m. The fitness indexes (FI) for the growth model of mixed forests, which is the independent variable of stand age, were 0.32 for the DBH estimation, 0.22 for the height estimation, and 0.18 for the basal area at breast height estimation, which were somewhat low. However, considering the graph and residual between the estimated and measured values of the estimation equation, the use of this estimation model is not expected to cause any particular problems. The yield prediction model of mixed forests was derived as follows: Stand volume =-162.6859+6.3434 ∙ BA+9.9214 ∙ SI+0.7271 ∙ Age, which is a step- by-step input of basal area at breast height (BA), site index (SI), and age among several growth factors, and the determination coefficient (R²) of the equation was about 96%. Using our optimal growth and yield prediction model, a makeshift stand yield table was created. This table of mixed forests was also used to derive the rotation of the highest production in volume.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4913-4916
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• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9101-9105
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• 2014

Background: The purpose of this study was to compare the accuracy and effectiveness of automated breast volume scanning (ABVS) and hand-held ultrasound (HHUS) in the detection of breast cancer in a large population group with a long-term follow-up, and to investigate whether different ultrasound systems may influence the estimation of cancer detection. Materials and Methods: Institutional review board approval was obtained for this retrospective study, and informed consent was waived. From September 2010 to August 2011, a total of 1,866 ABVS and 3,700 HHUS participants, who underwent these procedures at our institute, were included in this study. Cancers occurring during the study and subsequent follow-up were evaluated. The reference standard was a combination of histology and follow-up imaging (${\geq}12months$). The recall rate, cancer detection yield, diagnostic accuracy, sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with exact 95% confidence intervals. Results: The recall rate was 2.57 per 1,000 (48/1,866) for ABVS and 3.57 per 1,000 (132/3,700) for HHUS, with a significant difference (p=0.048). The cancer detection yield was 3.8 per 1,000 for ABVS and 2.7 per 1,000 for HHUS. The diagnostic accuracy was 97.7% for ABVS and 96.5% for HHUS with statistical significance (p=0.018). The specificity of ABVS and HHUS were 97.8%, 96.7%, respectively (p=0.022). Conclusions: ABVS shows a comparable diagnostic performance to HHUS. ABVS is an effective supplemental tool for mammography in breast cancer detection in a large population.

• Journal of Korean Society of Forest Science
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• v.89 no.3
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• pp.414-421
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• 2000

This study was carried out for the artificial forest stand of 23 years old jack pine(Pinus banksiana Lamb.) in Soheul-myun, Pochun-kun, Kyunggi province of Korea. The objectives of this study were to investigate the stand volume increment and the rate of stand volume, and were to investigate present stand volume to determine annual cutting volume for keeping stand volume to an ideal level for investigated jack pine stand. For a reasonable calculation of stand volume increment, diameter of breast height(DBH), tree height, bark width, and core length for the last 10 years for respective sampling plots were measured. By using these measurements annual diameter increment in DBH class, stand volume increment of 95% confidence interval and tree height curve equation were calculated. The tree height value was derived from the tree height curve equation. Calculation of tree volume by using the tree volume table was made by conferring the tree height value. The summarized results for investigated jack pine trees were having 7.7% annual stand volume increment with 6.1% estimated error. The total stand volume per ha was $79.58m^3$, accordingly the annual stand volume increment was $6.13m^3$ per ha, and the 95% confidence intervals range from 5.77 to $6.51m^3$.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.485-491
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• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.